Cryptocurrency Concepts, Technology, and Applications [1 ed.] 1032324414, 9781032324418

Table of contents :
Cover
Half Title
Title
Copyright
Dedication
Contents
List of Figures and Tables
Preface
List of Contributors
About the Editor
Chapter 1: Cryptocurrency Industry: A Review of Current and Future Trends
Different Cryptocurrencies: Timeline of Development
Cryptocurrencies: Market Development
Cryptocurrencies, Financial Markets, and Monetary Policies
The Development of the Cryptocurrency Goods and Services Market and Factors Market
The Development of the Cryptocurrency: Summary of Outlook
References
Chapter 2: Investor Attention in Cryptocurrency Markets
Introduction
Measures of Investor Attention
Google Search Volume
Other Direct Measures
Investor Attention in Stock Markets
Investor Attention in Cryptocurrency Markets
Returns
Volatility and Crash Risk
Liquidity
Concluding Remarks
References
Chapter 3: How Much to Invest, If Any, in Bitcoin?
Introduction
A Look at the Literature
Portfolio Analysis and the Black-Litterman Model
Data
Results
Additional Considerations
Conclusion
References
Chapter 4: Global Central Bank Digital Currency Research and Developments: Implication for Cryptocurrency
4.1 Introduction
4.2 Conceptual Background
4.2.1 CBDC Definitions
4.2.2 Similarities and Differences between CBDCs and Cryptocurrencies
4.3 CBDC Research and Developments around the World
4.3.1 Recent CBDC Research in the Literature
4.3.2 CBDC Developments in Africa
4.3.3 CBDC Developments in Europe
4.3.4 CBDC Developments in the Region of the Americas
4.3.5 CBDC Developments in Asia
4.3.6 CBDC Developments in Oceania
4.3.7 Global Interest in Information about CBDC
4.4 CBDC Adoption: Implications for Cryptocurrency
4.5 Conclusion
References
Chapter 5: Blockchain Governance: To Govern, or Not to Govern?
Theoretical Approaches to Blockchain Governance
Micro-Level Governance
Infrastructure Architecture
Application Architecture
Interoperability
Meso-Level Governance
Decision-Making Mechanism
Incentive Mechanism
Consensus Mechanism
Macro-Level Governance
Power Distribution
Accountability Mechanism
Control Mechanism
Conclusion
References
Chapter 6: Cryptocurrency Crime
Introduction
Crypto-Enabled and Crypto-Dependent Crimes
Characteristics of Cryptocurrencies That Facilitate Crime
Types of Cryptocurrency Crimes
Fraud
Other Cybercrimes
Cryptocurrency Mining Crimes
Money Laundering
Terrorism Financing
Sanctions Evasion
Tax Evasion
Darknet Marketplaces
Bribery and Corruption
Cryptocurrency-Adjacent Crimes
Crime Risks Associated with Specific Types of Cryptocurrency Technology
DeFi Crime Risks
NFT Crime Risks
Conclusions
References
Chapter 7: Following the Virtual Money: Investigating Crypto-Based Money Laundering and Confiscating Virtual Assets
The Crypto Bombshell
See No Evil, Hear No Evil, Investigate No Evil
Moving under the Radar
Basic Elements That All Investigators Need to Know
Analyzing the Blockchain
How Heuristics Help
Linking Clusters to the Real World
The Regulation Dilemma
How to Harmonize Regulations in a Borderless Industry
International Standards in Practice
Implementation—Patchy but Positive
Law Enforcement: Staying One Step Ahead
From 0 to 100 in Just a Few Years
New Investigative Possibilities
Sidebar: “Welcome to Video”: Taking Down the Biggest Child Abuse Website
Cooperation Across Borders and with VASPs
Hosted vs Unhosted Wallets
Emerging Criminal Strategies
Catching the Wave
A Snapshot of Criminal Strategies
Confiscation: Deterring Crypto-Enabled Crime
Seeking the Key to the Crypto Treasure
Crypto before the Court
Chapter 8: Regulatory and Legal Issues in Cryptocurrencies
Introduction
Major Legal Concerns in Cryptocurrency
Hacks and Thefts
Frauds and Scams
Money Laundering and Tax Evasion
Terrorist Financing and Rogue Actors
A Spectrum of Regulatory Responses
International Harmonization Efforts
Concluding Remarks
References
Chapter 9: Cryptocurrencies, Blockchain, and Public Choice
Introduction
Cryptocurrency, Blockchain, and Decentralized Autonomous Organizations (DAO)
Smart Contracts
Illustrative Case 1: Questions of Voting: Unanimity, Parties, Coalitions/Interest Groups
Public Choice Issue 1: Unanimity
Public Choice Issue 2: Parties and Vote-Trading
Public Choice Issue 3: Special Interest Groups
The Impact of Innovative Crypto-Linked Technology on Voting and Unanimity Rules
Understanding Political Parties and Their Influence
Illustrative Case 2: Questions of Law and Contracts
Public Choice
Smart Contracts and the Law
Illustrative Case 3: Regulation and Governance
Examples from Crypto Regulation
Conclusion
References
Chapter 10: Bitcoin Is King
I. Introduction
II. Function
III. Fit
Lack of Banking
High Inflation
Transaction Costs
Capital Controls
IV. Founding
Bootstrapping Money
Leaderless Money
From Founding to Now
V. Layer Zero
VI. Implications
Policy-Making
Journalism
Research
VII. Conclusion
References
Chapter 11: Cryptocurrency Options Strategy, Analysis, and Valuation
Equity and Cryptocurrency Option Markets: Overview
Uncovered (Naked) Option Trading Strategy Contracts
Buying Call Options
Buying Put Options
Selling Call and Put Options
Buying and Selling Straddles
Covered Option Trading Strategy Contracts
Protective Put Strategy
Covered Call Strategy
Collars
Advanced Option Trading Strategies
Bull Call Vertical Spreads
Bear Put Vertical Spreads
Bull Put Vertical Spreads
Bear Call Vertical Spreads
Option Valuation Methods
Binomial Option Pricing Model: Single-Period Probability Method
Binomial Option Pricing Model: Two-Period Probability Method
Black-Scholes-Merton (BSM) Model
Other Statistical Concepts
1. Compounding Using Exponential (e)
2. Natural Logarithm (ln)
3. Normal Probability Distribution (N)
Put Call Parity
Hedge Ratio (h) and Leverage (Borrowing) Methods: Using BOPM
Summary
Chapter 12: The Role of Blockchain and Smart Contracts in International Relations
12.1 Introduction
12.2 Models of International Relations
12.2.1 The Rational Actor Model
12.2.2 The Organizational Behavior Model
12.2.3 The Governmental Model
12.3 Conclusion
References
Glossary
Index

Citation preview

CRYPTOCURRENCY CONCEPTS, TECHNOLOGY, AND APPLICATIONS

CRYPTOCURRENCY CONCEPTS, TECHNOLOGY, AND APPLICATIONS Edited by Jay Liebowitz

Boca Raton and London

First edition published 2023 by CRC Press 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742 and by CRC Press 4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN CRC Press is an imprint of Taylor & Francis Group, LLC © 2023 selection and editorial matter, Jay Liebowitz Reasonable eforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. Te authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microflming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, access www. copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. For works that are not available on CCC please contact [email protected] Trademark notice: Product or corporate names may be trademarks or registered trademarks and are used only for identifcation and explanation without intent to infringe. Library of Congress Cataloging-in-Publication Data A catalog record for this title has been requested ISBN: 978-1-032-32441-8 (hbk) ISBN: 978-1-032-32437-1 (pbk) ISBN: 978-1-003-31504-9 (ebk) DOI: 10.1201/9781003315049 Typeset in Adobe Garamond Pro, Avenir LT Pro by DerryField Publishers Services

Trademarks Used in This Book Abbvie is a registered trademark of AbbVie Inc. Accenture is a registered trademark of Accenture Global Services Limited. Alcoa is a registered trademark of Alcoa USA Corp. Allergan is a registered trademark of Allergan, Inc. Allstate is a registered trademark of Allstate Insurance Company. AMC is a registered trademark of American Multi-Cinema, Inc. Avalanche is a registered trademark of Ava Labs, Inc. AWS is a registered trademark of Amazon Technologies, Inc. Berkshire Hathaway Group is a registered trademark of Columbia Insurance Company. Binance is a registered trademark of Binance Holdings Limited. Biogen is a registered trademark of Biogen Ma Inc. Bitfnex is a registered trademark of iFinex Inc. Bitmex is a registered trademark of HDR SG Pte. Ltd. Bitmain is a registered trademark of Bitmain Technologies Limited. Bloomberg is a registered trademark of Bloomberg Finance One L.P. Bristol Myers Squibb is a registered trademark of Bristol-Myers Squibb Company. BZX is a registered trademark of Cboe Exchange, Inc. Cardano is a registered trademark of Cardano Foundation. Calgene is a registered trademark of Celgene Corporation. Chainalysis is a registered trademark of Chainalysis, Inc. CME is a registered trademark of Chicago Mercantile Exchange Inc. Coinbase is a registered trademark of Coinbase, Inc. Coinfip is a registered trademark of GPD Holdings, LLC. Comcast is a registered trademark of Comcast Corporation. Cryptokitties is a registered trademark of Dapper Labs Inc. Cryptowall is a registered trademark of Radguard Ltd. Danaher is a registered trademark of Danaher Corporation. Decentraland is a registered trademark of Decentraland Foundation. Discord is a registered trademark of Discord Inc. Ebay is a registered trademark of eBay Inc. Egifter is a registered trademark of GroupGifting.com, Inc. Electrum is a registered trademark of Electrum Technologies GmbH. Ethereum is a registered trademark of Stiftung Ethereum (Foundation Ethereum). Excel is a registered trademark of Microsoft, Inc. Exodus is a registered trademark of Exodus Movement, Inc. Facebook is a registered trademark of Meta Platforms, Inc. Fedwire is a registered trademark of Federal Reserve Bank of New York, Federal Reserve Bank of Boston, Federal Reserve Bank of Chicago, etc. Gilead is a registered trademark of Gilead Sciences, Inc. Goldman Sachs is a registered trademark of Goldman Sachs & Co. LLC. Google is a registered trademark of Google LLC. Hyperledger is a registered trademark of Digital Asset Holdings, LLC. Intel is a registered trademark of Intel Corporation. Kerrygold is a registered trademark of Ornua Co-operative Limited. Kraken is a registered trademark of Payward, Inc. Ledger is a registered trademark of Ledger SAS.

Trademarks, cont. Litecoin is a registered trademark of Litecoin Foundation Ltd. Makerdao is a registered trademark of Dai Fonden. Mastercard is a registered trademark of MasterCard International Incorporated. Medtronic is a registered trademark of Medtronic, Inc. Merck is a registered trademark of Merck Sharp & Dohme LLC. Mycelium is a registered trademark of Mycelium Ventures Pty Ltd. Microsoft is a registered trademark of Microsoft, Inc. Microstrategy is a registered trademark of MicroStrategy Incorporated. Netfix is a registered trademark of Netfix, Inc. Nextera Energy is a registered trademark of Nextera Energy, Inc. Nvidia is a registered trademark of Nvidia Corporation. OKEx is a registered trademark of Okex Malta Ltd. Opensea is a registered trademark of Ozone Networks, Inc. PayPal is a registered trademark of PayPal, Inc. Rarible is a registered trademark of Rarible, Inc. Reuters is a registered trademark of Tomson Reuters Canada Limited. Ripple is a registered trademark of Ripple Labs Inc. Robinhood is a registered trademark of Robinhood Markets, Inc. Rube Goldberg is a registered trademark of Rube Goldberg, Inc. Sheetz is a registered trademark of Sheetz of Delaware, Inc. Solana is a registered trademark of Solana Foundation. Starbucks is a registered trademark of Starbucks Corporation. Telegram is a registered trademark of Telegram FZ-LLC. Terra is a registered trademark of Zooterra Inc. Tesla is a registered trademark of Tesla, Inc. Tether is a registered trademark of Tether Operations Limited. Trezor is a registered trademark of SatoshiLabs Group A.S. Twitter is a registered trademark of Twitter, Inc. Union Pacifc is a registered trademark of Union Pacifc Railroad Company. Uniswap is a registered trademark of Universal Navigation Inc. Upbit is a registered trademark of Dunamu Inc. U.S. Bancorp is a registered trademark of U. S. Bancorp. Vanguard is a registered trademark of Te Vanguard Group, Inc. Venmo is a registered trademark of PayPal, Inc. Visa is a registered trademark of Visa International Service Association. Walmart is a registered trademark of Walmart Apollo, LLC. Wikipedia is a registered trademark of Wikimedia Foundation, Inc. Yahoo is a registered trademark of Yahoo Inc. Xilinx is a registered trademark of Xilinx, Inc.

Dedication I dedicate this book to my brother-in-law, Dr. Michael Zeide, who encouraged me to write about cryptocurrency as an emerging hot topic.

vii

Contents Dedication

vii

Contents

ix

List of Figures and Tables

xvii

Preface

xxi

List of Contributors

xxiii

About the Editor

xxv

Chapter 1: Cryptocurrency Industry: A Review of Current and Future Trends

1

Huijian Dong Different Cryptocurrencies: Timeline of Development

2

Cryptocurrencies: Market Development

4

Cryptocurrencies, Financial Markets, and Monetary Policies

11

The Development of the Cryptocurrency Goods and Services Market and Factors Market

14

The Development of the Cryptocurrency: Summary of Outlook References

Chapter 2: Investor Attention in Cryptocurrency Markets

16 19

21

Lee Smales Introduction

21

Measures of Investor Attention

23

Google Search Volume

24

Other Direct Measures

25

Investor Attention in Stock Markets

26

ix

x Cryptocurrency Concepts, Technology, and Applications

Investor Attention in Cryptocurrency Markets

28

Returns

29

Volatility and Crash Risk

30

Liquidity

31

Concluding Remarks

31

References

32

Chapter 3: How Much to Invest, If Any, in Bitcoin?

37

Anthony L. Loviscek Introduction

37

A Look at the Literature

39

Portfolio Analysis and the Black-Litterman Model

42

Data

46

Results

46

Additional Considerations

52

Conclusion

54

References

55

Chapter 4: Global Central Bank Digital Currency Research and Developments: Implication for Cryptocurrency

59

Peterson K. Ozili 4.1 Introduction

59

4.2 Conceptual Background

62

4.2.1 CBDC Defnitions 4.2.2 Similarities and Differences between CBDCs and Cryptocurrencies 4.3 CBDC Research and Developments around the World

62 62 63

4.3.1 Recent CBDC Research in the Literature

63

4.3.2 CBDC Developments in Africa

64

4.3.3 CBDC Developments in Europe

65

4.3.4 CBDC Developments in the Region of the Americas

65

4.3.5 CBDC Developments in Asia

66

4.3.6 CBDC Developments in Oceania

66

4.3.7 Global Interest in Information about CBDC

67

4.4 CBDC Adoption: Implications for Cryptocurrency

68

4.5 Conclusion

70

References

71

Contents

Chapter 5: Blockchain Governance: To Govern, or Not to Govern?

xi

75

Evrim Tan Theoretical Approaches to Blockchain Governance

77

Micro-Level Governance

78

Infrastructure Architecture

78

Application Architecture

79

Interoperability

80

Meso-Level Governance

81

Decision-Making Mechanism

81

Incentive Mechanism

82

Consensus Mechanism

83

Macro-Level Governance

85

Power Distribution

85

Accountability Mechanism

86

Control Mechanism

87

Conclusion

89

References

90

Chapter 6: Cryptocurrency Crime

93

Arianna Trozze Introduction

93

Crypto-Enabled and Crypto-Dependent Crimes

94

Characteristics of Cryptocurrencies That Facilitate Crime

94 94

Types of Cryptocurrency Crimes Fraud

96

Other Cybercrimes

99

Cryptocurrency Mining Crimes

100

Money Laundering

101

Terrorism Financing

103

Sanctions Evasion

103

Tax Evasion

105

Darknet Marketplaces

105

Bribery and Corruption

106

Cryptocurrency-Adjacent Crimes

106

Crime Risks Associated with Specifc Types of Cryptocurrency Technology DeFi Crime Risks

106 106

xii

Cryptocurrency Concepts, Technology, and Applications

NFT Crime Risks

109

Conclusions

110

References

111

Chapter 7: Following the Virtual Money: Investigating Crypto-Based Money Laundering and Confscating Virtual Assets

119

Federico Paesano The Crypto Bombshell

119

See No Evil, Hear No Evil, Investigate No Evil

121

Moving under the Radar

121

Basic Elements That All Investigators Need to Know

122

Analyzing the Blockchain

124

How Heuristics Help

124

Linking Clusters to the Real World

125

The Regulation Dilemma

126

How to Harmonize Regulations in a Borderless Industry

126

International Standards in Practice

127

Implementation—Patchy but Positive

128

Law Enforcement: Staying One Step Ahead

129

From 0 to 100 in Just a Few Years

129

New Investigative Possibilities

130

Sidebar: “Welcome to Video”: Taking Down the Biggest Child Abuse Website Cooperation Across Borders and with VASPs Hosted vs Unhosted Wallets Emerging Criminal Strategies

131 132 134 134

Catching the Wave

134

A Snapshot of Criminal Strategies

135

Confscation: Deterring Crypto-Enabled Crime

136

Seeking the Key to the Crypto Treasure

136

Crypto before the Court

138

Chapter 8: Regulatory and Legal Issues in Cryptocurrencies

141

Usman W. Chohan Introduction Major Legal Concerns in Cryptocurrency Hacks and Thefts

141 142 143

Contents

xiii

Frauds and Scams

145

Money Laundering and Tax Evasion

146

Terrorist Financing and Rogue Actors A Spectrum of Regulatory Responses

147 148

International Harmonization Efforts

151

Concluding Remarks

153

References

153

Chapter 9: Cryptocurrencies, Blockchain, and Public Choice

159

Ryan M. Yonk and David Waugh Introduction

159

Cryptocurrency, Blockchain, and Decentralized Autonomous Organizations (DAO)

161

Smart Contracts

162

Illustrative Case 1: Questions of Voting: Unanimity, Parties, Coalitions/Interest Groups Public Choice Issue 1: Unanimity

163 164

Public Choice Issue 2: Parties and Vote-Trading

164

Public Choice Issue 3: Special Interest Groups

165

The Impact of Innovative Crypto-Linked Technology on Voting and Unanimity Rules Understanding Political Parties and Their Infuence

165 166

Illustrative Case 2: Questions of Law and Contracts

167

Public Choice

167

Smart Contracts and the Law

168

Illustrative Case 3: Regulation and Governance

169

Examples from Crypto Regulation

170

Conclusion

171

References

172

Chapter 10: Bitcoin Is King

175

Andrew M. Bailey and Craig Warmke I. Introduction

175

II. Function

176

III. Fit

178 Lack of Banking

180

High Infation

181

Transaction Costs

181

xiv

Cryptocurrency Concepts, Technology, and Applications

Capital Controls IV. Founding

182 183

Bootstrapping Money

183

Leaderless Money

185

From Founding to Now

186

V. Layer Zero

189

VI. Implications

193

Policy-Making

194

Journalism

194

Research

195

VII. Conclusion

195

References

196

Chapter 11: Cryptocurrency Options Strategy, Analysis, and Valuation

199

Christopher Droussiotis Equity and Cryptocurrency Option Markets: Overview

199

Uncovered (Naked) Option Trading Strategy Contracts

200

Buying Call Options

201

Buying Put Options

203

Selling Call and Put Options

205

Buying and Selling Straddles

206

Covered Option Trading Strategy Contracts

208

Protective Put Strategy

208

Covered Call Strategy

209

Collars

211

Advanced Option Trading Strategies

211

Bull Call Vertical Spreads

214

Bear Put Vertical Spreads

214

Bull Put Vertical Spreads

217

Bear Call Vertical Spreads

220

Option Valuation Methods

220

Binomial Option Pricing Model: Single-Period Probability Method 223 Binomial Option Pricing Model: Two-Period Probability Method 225 Black-Scholes-Merton (BSM) Model Other Statistical Concepts

228 230

1. Compounding Using Exponential (e)

230

2. Natural Logarithm ( ln)

231

Contents

xv

3. Normal Probability Distribution (N)

231

Put Call Parity

234

Hedge Ratio (h) and Leverage (Borrowing) Methods: Using BOPM Summary

Chapter 12: The Role of Blockchain and Smart Contracts in International Relations

234 236

239

Stephan Unger and Hossein Hassani 12.1 Introduction

239

12.2 Models of International Relations

243

12.2.1 The Rational Actor Model

243

12.2.2 The Organizational Behavior Model

244

12.2.3 The Governmental Model

245

12.3 Conclusion

246

References

246

Glossary

249

Index

251

List of Figures and Tables Cryptocurrency Interactions with Key Indicators of the Financial Market

12

Figure 2.1

Academic Articles Mentioning Bitcoin and Cryptocurrency

22

Figure 2.2

Media Articles Mentioning Bitcoin and Cryptocurrency

23

Figure 2.3

Google Search Volume and Bitcoin Prices

26

Figure 3.1

Illustration of the Application of Portfolio Optimization Based on the Black-Litterman Model for the S&P 100, 2011–2021, in Which the Return Is on the Vertical Axis and the Volatility, or Risk, Is on the Horizontal Axis.

51

Figure 4.1

Global Interest in Information About CBDC on the Internet

67

Figure 4.2

Global Interest in Internet Information About CBDC and Cryptocurrency

68

Figure 4.3

Countries with the Highest Interest in Information About CBDC

69

Figure 6.1

Types of Cryptocurrency Crime

95

Figure 6.2

Relationships Among DeFi, NFT, and Cryptocurrency Crime Types

107

Figure 7.1

A Bitcoin Address and Its Corresponding Private Key

123

Figure 7.2

A Bitcoin Transaction

123

Figure 8.1

Legal Concerns Regarding Cryptocurrencies

143

Figure 1.1

Figure 10.1 Global Crypto Adoption Index

180

Figure 10.2 Of Just over Four Billion OP Tokens at Launch, 5–19% Go to Everyday Users

185

Figure 10.3 Bitcoin Supply Equality Ratio

187

Figure 10.4 Bitcoin Network Distribution Factor

188

Figure 10.5 Global Map of Bitcoin Node Distribution

188

xvii

xviii

Cryptocurrency Concepts, Technology, and Applications

Bitcoin’s Hashrate Distribution across Mining Pools, for Mid-May Through Mid-June, 2022

191

A History of Changes to Ethereum’s and Bitcoin’s Monetary Policies

192

Figure 11.1

Uncovered Option Strategies—Buying Call Options

202

Figure 11.2

Uncovered Option Strategies—Buying Put Options

204

Figure 11.3

Uncovered Option Strategies—Buying Straddles

207

Figure 11.4

Covered Option Strategy—Protective Puts

210

Figure 11.5

Covered Option Strategies—Covered Call

212

Figure 11.6

Covered Option Strategies—Collars

213

Figure 11.7

Vertical and Horizontal Spreads

215

Figure 11.8

Advanced Option Strategies—Bull Call Spreads

216

Figure 11.9

Advanced Option Strategies—Put Bear Spreads

218

Figure 11.10

Advanced Option Strategies—Bull Put Spreads

219

Figure 11.11

Advanced Option Strategies—Bear Call Spreads

221

Figure 10.6 Figure 10.7

Figure 11.12a Calculating the Fair Bet Based on Probability of Winning on Coin Toss

222

Figure 11.12b Calculating the Fair Bet Based on Probability of Getting a “6” on Die Toss

222

Figure 11.13 Figure 11.14 Figure 11.15

One-Period Binomial Option Pricing Model—Calculating Call Option Premium

224

One-Period Binomial Option Pricing Model—Calculating Put Option Premium

226

Two-Period Binomial Option Pricing Model—Calculating Call and Put Option Premiums

227

Figure 11.16

Calculating the Compound Interest Using the Exponential “e” 231

Figure 11.17

Normal Distribution Table Showing –3.2 To +3.2 Diviations Normalized from 0 to 1

232

Figure 11.18

Calculating Call Option Premiums Using Black-Scholes

235

Figure 11.19

Calculating Put Option Premiums Using Black-Scholes

235

Figure 11.20 Binomial Option Pricing Model–Leveraged: Six-Step Method

237

Table 1.1

Cryptocurrencies with the Largest Market Capitalizations

3

Table 1.2

The Evolution of Total Market Capitalization of Cryptocurrencies (in U.S. Dollars)

5

List of Figures and Tables xix

Table 1.3

Different Market Analyses Foci When Viewing Cryptocurrencies as Foreign Currencies

6

Table 1.4

The Evolution of Number of Crypto Wallets

7

Table 1.5

The Evolution of Cryptocurrency Trading Volumes (The Block Legitimate Index)

8

Table 1.6

Spot to Futures Trading Volume Ratios of Bitcoin and Ethereum

9

Table 1.7

Monthly Options Trading Volume of Bitcoin and Ethereum (in billion U.S. Dollars)

10

Correlations between Cryptocurrencies and Key Indicators of the Financial Market

13

Milestones for Cryptocurrencies in the Next Decades: A Projection

18

Annual Summary Statistics, Including Mean Returns, Volatilities, as Measured by Annual Standard Deviations, Betas, and Sharpe Reward-to-Variability Ratios, Respectively, for the Black-Litterman Portfolio (PORT), the Vanguard 500 Index (VAN), and Bitcoin (BIT), January 2011–December 2021

47

Composition of the PORT Based on Rates of Return from 2011–2021, Where the “Weight” Is the Percentage Allocated to Each Stock of Each Company and to Bitcoin

49

Respective Correlation Coeffcients Per Year between the Rates of Return on BIT, the Rates of Return on the PORT, and the Rates of Return on the VAN, 2011–2021

50

Rates of Return, Volatilities, as Measured by Annual Standard Deviations and Sharpe Reward-to-Variability Ratios for the PORT, the VAN, and BIT for Each of the Portfolio Construction Periods, Including the Weight Assigned to BIT Per Period.

53

Table 1.8 Table 1.9 Table 3.1

Table 3.2

Table 3.3

Table 3.4

Table 7.1

Travel Rule

128

Table 8.1

A Spectrum of Cryptocurrency Legality

149

Preface In 2012, Forbes had a cover story that the Data Scientist is the “Sexiest Job of the 21st Century.” Even though data science has certainly grown in stature over the years, and the demand for data scientists still outstrips the supply of data science talent, a new “hot” player has recently entered the market to vie for this new title. Te new player is the “Cryptocurrency” or Digital Currency Analyst. Whatever I read, as of late, has something about Bitcoin, crypto zone, cryptocurrency, blockchain, digital assets, and the like. Whether the source is more industry-based or academic research, there certainly appears to be a growing interest in the feld of cryptocurrency. Te New York Times had a cover story on March 24, 2022, titled “Time to Enter the Crypto Zone?,” and they talked about institutional investors pouring billions into digital tokens, salaries being taken in Bitcoins, and even Bitcoin ATMs in grocery stores. Certainly, we have seen ups and downs in crypto, but it has a kind of alluring presence that tempts one to include crypto as part of one’s portfolio. Tose who are “prime crypto-curious” investors are usually familiar with the tech/pop culture and feel they want to diversify a bit in this fast-moving market. Even universities are beginning to ofer more courses and create “Centers on Cryptocurrency.” Some universities are even requiring their students who take a crypto course to pay the course tuition via cryptocurrency. With this growing interest and fascination about the crypto industry and cryptocurrency in general, I felt it was appropriate timing to assemble a book with many leading worldwide contributors to discuss the concepts, technologies, and issues associated with cryptocurrency. As such, we are covering a wide array of crypto-related topics, including blockchain, NFTs, data analytics and AI, crypto crime, crypto industry, crypto regulation, options, crypto and public choice, consumer confdence, Bitcoin et al., crypto research challenges and opportunities, xxi

xxii Cryptocurrency Concepts, Technology, and Applications

and others. We present various viewpoints on where this cryptoindustry is heading and point out both the advantages and limitations of this emerging feld. Further evidence of the importance of crypto was demonstrated by the White House’s moving toward developing a policy approach on crypto. On March 9, 2022, President Biden signed an executive order on “Ensuring Responsible Development of Digital Assets.” Te European Union, as of March 25, 2022, is also well on its way to passing the landmark regulatory framework Markets in Crypto Assets (MiCA) for governing crypto assets. Tus, the time is ripe for publishing this book! I would like to thank the wonderful contributors to this book, as well as the reviewers and those at Taylor & Francis for all their helpful assistance and to Teron Shreve and Susan Culligan of DerryField Publishing Services for their help in production. In addition, I would like to thank both Seton Hall University and Columbia University (my new home) for allowing me to think creatively in exploring this emerging topic. And, of course, my wife and family continue to give me the energy and love to make it all worthwhile! Jay Liebowitz, D.Sc. Executive-in-Residence for Public Service Columbia University Data Science Institute

List of Contributors Andrew M. Bailey Yale-NUS College, Singapore Usman Waqqas Chohan Centre for Aerospace and Security Studies, Pakistan Huijian Dong School of Business, New Jersey City University, USA; Teacher’s College, Columbia University, USA Christopher Droussiotis Seton Hall University, USA Hossein Hassani University of Tehran, Iran Anthony Loviscek Seton Hall University, USA Peterson K. Ozili Central Bank of Nigeria, Nigeria Federico Paesano Basel Institute on Governance, Italy

Lee Smales University of Western Australia, Australia Evrim Tan KU-Leuven, Belgium Arianna Trozze University College London, United Kingdom Stephan Unger Saint Anselm College, USA Craig Warmke Northern Illinois University, USA David Waugh American Institute for Economic, Research, USA Ryan Yonk American Institute for Economic Research, USA

xxiii

About the Editor Dr. Jay Liebowitz is the Executive-in-Residence for Public Service at Columbia University’s Data Science Institute. He was previously a Visiting Professor in the Stillman School of Business and the MS-Business Analytics Capstone & Co-Program Director (External Relations) at Seton Hall University. He also served as the Distinguished Chair of Applied Business and Finance at Harrisburg University of Science and Technology (HU). Before HU, he was the Orkand Endowed Chair of Management and Technology in the Graduate School at the University of Maryland University College (UMUC). Dr. Liebowitz served as a Full Professor in the Carey Business School at Johns Hopkins University. He was ranked one of the top 10 knowledge management researchers/practitioners out of 11,000 worldwide, and was ranked #2 in KM Strategy worldwide according to the January 2010 Journal of Knowledge Manage­ ment. At Johns Hopkins University, he was the founding Program Director for the Graduate Certifcate in Competitive Intelligence and the Capstone Director of the MS-Information and Telecommunications Systems for Business Program, where he engaged over 30 organizations in industry, government, and not-forproft in capstone projects. Prior to joining Hopkins, Dr. Liebowitz was the frst Knowledge Management Ofcer at NASA Goddard Space Flight Center. Before NASA, Dr. Liebowitz was the Robert W. Deutsch Distinguished Professor of Information Systems at the University of Maryland-Baltimore County, Professor of Management Science at George Washington University, and Chair of Artifcial Intelligence at the U.S. Army War College. Dr. Liebowitz is the Founding Editor-in-Chief of Expert Systems With Appli­ cations: An International Journal (published by Elsevier, ranked as a top-tier journal; Tomson Impact Factor from June 2021 is 8.665). He is a Fulbright Scholar, IEEE-USA Federal Communications Commission Executive Fellow, and xxv

xxvi Cryptocurrency Concepts, Technology, and Applications

Computer Educator of the Year (International Association for Computer Information Systems). He has published over 45 books and myriad journal articles on knowledge management, analytics, fnancial literacy, intelligent systems, and IT management. Dr. Liebowitz served as the Editor-in-Chief of Procedia­CS (Elsevier). He is also the Series Book Editor of the Data Analytics Applications book series (Taylor & Francis), as well as the Series Book Editor of the new Digital Transformation: Accelerating Organizational Intelligence book series (World Scientifc Publishing). In October 2011, the International Association for Computer Information Systems named the “Jay Liebowitz Outstanding Student Research Award” for the best student research paper at the IACIS Annual Conference. Dr. Liebowitz was the Fulbright Visiting Research Chair in Business at Queen’s University for the Summer 2017 and a Fulbright Specialist at Dalarna University in Sweden in May 2019. He is in the top two percent of the top scientists in the world, according to a 2019 Stanford study. As of 2021, he is the Visiting Distinguished Professor at the International School for Social and Business Studies in Slovenia. His recent books are Data Analytics and AI (Taylor & Francis, 2021), A Research Agenda for Knowledge Management and Analytics (Elgar Publishers, 2021), Te Business of Pandemics: Te COVID­19 Story (Taylor & Francis, 2021), and Digital Transformation for the University of the Future (World Scientifc, 2023). He has lectured and consulted worldwide.

Chapter 1 Cryptocurrency Industry: A Review of Current and Future Trends Huijian Dong School of Business, New Jersey City University Teacher’s College, Columbia University

Tis chapter summarizes the current state of cryptocurrencies and expands their development trajectories to shed light on the industry outlook. Te topics introduce the nature of each cryptocurrency, evaluate cryptocurrencies as fnancial assets, and assess cryptocurrencies as money. Each topic in this chapter starts from the current trend review and concludes with future trends in the specifc area. Te frst topic, diferent cryptocurrencies: timeline of development, reviews the development path of the major cryptocurrencies and provides the prospect of their status in the coming decade. Tis topic also sets the groundwork by articulating multiple concepts that are interchangeably used by stakeholders. Such groundwork avoids obfuscating the issue under discussion by identifying the misinterpretation oftentimes seen. Te second topic, cryptocurrencies: market development, illustrates the players at the marketplace and explains the related fnancial instruments. Te key elements are the primary market, the secondary market, the exchanges, and transaction service 1

2 Cryptocurrency Concepts, Technology, and Applications

providers such as wallets. Te related fnancial instruments are cryptocurrencies and their derivatives, including futures contracts, options, exchange-traded funds (ETF), and cryptocurrency tokens. Tis topic regards cryptocurrencies as a fnancial asset rather than as money. Te third topic, cryptocurrencies, fnancial markets, and monetary policies, reviews the interaction of the price of cryptocurrency with other fnancial assets and monetary policies. Tese reviews help provide a clear outlook of cryptocurrency prices and transactions. Tis topic approaches cryptocurrencies from the unit of account function as money. Te fourth topic, development of cryptocurrency goods and services market and factors market, makes an inventory of the existing use of cryptocurrencies in the conventional goods and services market. Tis market is often referred to as the real economy. Te primary focus of this topic is to observe the acceptance of cryptocurrencies in the transactions that provide nonmonetary utility among individuals and organizations around the world. Te secondary focus is to point out the issues with cryptocurrencies that prevent them from being widely accepted as a quasi-fat money. Furthermore, the view of cryptocurrency’s being an element of the factors market initiates a new angle by examining the current and future status of cryptocurrencies with respect to three factors: human capital, fnancial capital, and resource capital (Myers and Czarnezki 2021). Unlike the second topic, which views cryptocurrencies as fnancial assets, this topic explores the future trend that cryptocurrencies serve as the infrastructure to incubate fnancial assets. Tis topic approaches cryptocurrencies from the medium of exchange function and the store of value function as money. Te last and ffth topic, the outlook of the development of cryptocurrency, provides the projected path of cryptocurrencies in the next decade. It starts from the number of diferent types of crypto, the market capitalization, and the trading volumes and forecasts the fnancial market development of cryptocurrencies. Tis topic also marks several important milestones that cryptocurrencies need to conquer to be recognized as quasi-fat money. Tis topic concludes by depicting the ideal characters of the cryptocurrencies.

Different Cryptocurrencies: Timeline of Development Although currency has been developed and utilized by humans for thousands of years, there are only two types of currency: physical money and script money. Te former refers to shells, precious metals, or even cigarettes; the latter refers to notes that have trivial value but are trusted to have physical value empowered

Cryptocurrency Industry: A Review of Current and Future Trends 3

by the issuers. Cryptocurrencies are script moneys whose credits are established on decentralized technological outputs and the expectation that such outputs would be widely embraced, especially by trusted authorities (e.g., in September 2021, Salvadoran President Nayib Bukele announced that El Salvador would become the frst country in the world to accept Bitcoin as legal tender, alongside the U.S. dollar) and large institutions (e.g., J.P. Morgan Chase announced its development of JPM coin on February 14, 2019, and Meta (then Facebook®) announced its development of Libra on June 18, 2019). According to Howarth (2022), by the end of 2013, there were over 50 diferent cryptocurrencies. And by the end of 2014, this fgure had increased by approximately 10 times to more than 500. As of March 2022, there are 18,465 total cryptocurrencies in circulation, though two-thirds of them do not have signifcant values that are monetarily infuential. Tey are mainly traded in North America, Europe, and Asia. According to Howarth (2022), as a continent, Asia has more than four times more cryptocurrency users than any other continent. As of April 2022, the cryptocurrencies with the largest market capitalizations are presented in Table 1.1. Table 1.1 Cryptocurrencies with the Largest Market Capitalizations Cryptocurrency

Symbol

Rank

April 2022 Market Capitalization in Billion U.S. Dollars

Bitcoin

BTC

1

735.83

Ethereum®

ETH

2

340.29

Tether ®

USDT

3

83.23

Binance Coin

BNB

4

66.15

USD Coin

USDC

5

49.29

Ripple®

XRP

6

31.46

SOL

7

29.49

ADA

8

29.44

Solana® Cardono

®

®

LUNA

9

25.94

Avalanche®

AVAX

10

17.86

Terra

Percentage of top 10 cryptocurrencies market cap in the overall cap: 81%.

Vantage Market Research (2022) anticipates that the compound annual growth rate of cryptocurrencies will be 6.9% in the 2020s, with the top market players Bitmain® (China), Nvidia® (United States), Xilinx® (United States), and Intel® (United States).

4 Cryptocurrency Concepts, Technology, and Applications

Te global cryptocurrency market can be categorized by technology focus, market phase, coin type, and region. By technology focus, the market is classifed as hardware and software. By market phase, the market is classifed as mining (primary market) and transaction (secondary market). By coin type, the market includes the coins highlighted in Table 1.1 and many more. By region, the market is participated by three major hubs: North America, Europe, and the Asia-Pacifc. Cryptocurrency is based on decentralization of transaction monitoring. Miners record and validate transactions performed by all users as part of the transaction monitoring process. To authenticate the transactions in this procedure, many computer resources are required when encrypting the transactions through producing of hash codes. As a result, the hardware segment has gained a signifcant portion of the cryptocurrency market (Cai and Gomaa 2019), though the software segment is believed to develop at the fastest rate, as it helps to handle the vast volume of data being generated, according to the Vantage Market Research (2022). Te next topic focuses on the development of the market and provides details regarding the primary and secondary markets. Other chapters of this book focus on the technical nuances of the diferent cryptocurrencies and the regional progress.

Cryptocurrencies: Market Development Tis topic illustrates the players in the marketplace and explains the related fnancial instruments. Te key elements are the primary market, the secondary market, the exchanges, and transaction service providers such as wallets. Te related fnancial instruments are cryptocurrencies and their derivatives, including futures contracts, options, ETF, and cryptocurrency tokens. Tis topic regards cryptocurrencies as a fnancial asset rather than as money. Te primary market emits cryptocurrencies. Tis chapter uses the most representative cryptocurrency, Bitcoin, as an example. Its issuance is highly decentralized through the execution of computer protocols. Anyone can participate in the Bitcoin network as a miner and be rewarded newly created Bitcoins by providing computing resources that help to secure the network, a mechanism called Proof-of-Work (PoW). Other cryptocurrencies use mining as their consensus mechanism to conduct money supply. In addition to mining, alternative consensus mechanisms include Proof-of-Stake (PoS), Proof-of-Authority (PoA), and Byzantine Fault Tolerance (BFT). In addition, cryptocurrencies have built-in policies to prevent oversupply of money and value dilution. For example, Bitcoins are created at a decreasing and

Cryptocurrency Industry: A Review of Current and Future Trends 5

predictable rate. Te number of Bitcoins generated per block is set to decrease geometrically, with a 50% reduction every 210,000 blocks, or approximately four years, until Bitcoin issuance halts completely with a total of 21 million Bitcoins in existence. Te secondary market conducts the exchanges of cryptocurrencies. Table 1.2 records the historical total market capitalizations of the overall cryptocurrency market value. It refects the growing size of diferent cryptocurrency types, the growing supply of each type of cryptocurrency, and the growing fat money supply by the central banks and governments across the world. On November 8, 2021, the total cap reached its all-time high of $3.08 trillion. Table 1.2 The Evolution of Total Market Capitalization of Cryptocurrencies (in U.S. Dollars) Timeline

Total Market Capitalization

Sunday, April 28, 2013

$1,661,441,770

Growth Rate

Monday, April 28, 2014

$6,245,216,902

276%

Tuesday, April 28, 2015

$3,629,975,807

–42%

Thursday, April 28, 2016

$8,493,068,640

134%

$36,218,911,230

326%

Saturday, April 28, 2018

$425,767,902,838

1076%

Sunday, April 28, 2019

$171,280,493,716

–60%

Wednesday, April 29, 2020

$250,867,781,548

46%

Tuesday, April 27, 2021

$2,137,859,254,115

752%

Wednesday, April 27, 2022

$1,900,988,713,740

–11%

Friday, April 28, 2017

Average cap from 2013 to 2022

$494,301,276,031

Projected* 2025 total market capitalization

$666,927,408,200

Projected 2035 total market capitalization

$1,810,094,904,673

* Projected market cap based on the average annual return, 10.5%, of the S&P 500 Index.

Te investments at the secondary market are guided by fundamental analyses, statistical analyses, and modeling, as well as technical analyses conducted by market participants and the media. Tis is similar to the analytical framework used by the foreign exchange market (FOREX), regarding cryptocurrencies as foreign fat money. However, there are some vital diferences. Table 1.3 provides some examples of such diferences.

6 Cryptocurrency Concepts, Technology, and Applications

Table 1.3 Different Market Analyses Foci When Viewing Cryptocurrencies as Foreign Currencies Foreign Exchange Market Analysis

Cryptocurrency Market Analysis

Changes in monetary policy matter.

Only monetary policies that are related to cash infows and outfows matter.

Changes in fscal policy are of interest.

Government spending is less relevant.

Changes in economic data are important.

Economic data from large economies are only relevant when they affect cash infows and outfows.

Changes in geopolitical conditions matter.

Geopolitical events are less relevant.

Celebrities are less relevant.

Celebrity tweets and attitudes signifcantly affect the market.

Government acceptance is not an issue.

Governments’ acceptance affects the market signifcantly.

Social media and individual investors are less infuential.

Social networks and individual investors are very infuential.

Te secondary market transaction is facilitated by the fnancial service providers. Te two most important groups of providers are the wallet and the exchange. A cryptocurrency wallet allows an investor to store their cryptocurrencies and manage transactions. It implies the direct and active users of cryptocurrencies for transaction purposes. On the other hand, a cryptocurrency exchange refers to a website or service where one can sell or buy digital currency or convert fat currency into digital currency. Some widely-used wallets include Coinbase®, Mycelium®, Exodus®, Electrum®, Ledger®, and Trezor®, along with traditional investment service providers such as PayPal®, Venmo®, and Robinhood®. On the other hand, the number of cryptocurrency users skyrocketed between 2017 and 2022. Te digital asset custody platform Blockchain.com records the number of unique crypto wallets, presented in Table 1.4. Tis table also provides a forecast of the future number of unique crypto wallets. Te count of wallet users is not to be confused with the population who own a share of cryptocurrencies. Te latter is larger because it includes the investors who hold cryptocurrencies as fnancial assets instead of using them as a medium of exchange in real economy transactions. A more comprehensive review related to the wallet users is the cryptocurrency exchanges, which serve both purposes of investments and transactions.

Cryptocurrency Industry: A Review of Current and Future Trends 7

Table 1.4 The Evolution of Number of Crypto Wallets Timeline

Number of Unique Crypto Wallets (in millions)

Growth Rate

November 2017

18.36

November 2018

29.80

62.31%

November 2019

43.15

44.80%

November 2020

55.59

28.83% 41.64%

November 2021

78.74

Projected* 2022

100.23

Projected 2025

201.67

Projected 2035

862.43

* Projected annual growth rate based on November 2020, diminish rate = 1.54%

Cryptocurrency exchanges are organized as an over-the-counter (OTC) market among brokers and dealers, according to Chu (2018). Tey come in the forms of centralized exchanges (managed by one organization) and decentralized exchanges (organized with members). Some widely-used cryptocurrency exchanges include Coinbase, Upbit®, Kraken®, and Bitfnex®. According to Tepper (2022), as of May 2022, there were nearly 600 cryptocurrency exchanges worldwide inviting investors to trade Bitcoin, Ethereum, and other digital assets. From the user’s perspective, these exchanges compete to provide better results in various metrics, such as beginner friendly, mobile friendly, security, low fee, and currency compatibility. From the business perspective, these exchanges compete to engage more cryptocurrency types, token types, and fat money choices. From the fnancial service perspective, these exchanges compete to ofer tight spread, slippage avoidance, faster execution speed, greater liquidity, and versatile fund transfer options. Table 1.5 presents the evolution of cryptocurrency trading volumes and the projections of future trends. Te trading volume is the aggregate of more than 6,000 diferent types of cryptocurrencies and more than 250,000 trading pairs formed between cryptocurrencies and fat money. Tere are numerous derivatives developed based on cryptocurrencies. One of the most signifcant is the token. Ethereum blockchain infrastructure projects execute smart contract projects to create tokens on top of the blockchain. Tese tokens enable applications in diferent kinds: utility tokens enable digital access to an application; payment tokens enable purpose-specifc currencies for a designated service; and security tokens enable the contractual relations for certain claims of assets.

8 Cryptocurrency Concepts, Technology, and Applications

Table 1.5 The Evolution of Cryptocurrency Trading Volumes (The Block Legitimate Index)a Monthly Trading Volume (Apr, Jul, Oct)

Timeline

Monthly Trading Volume (May, Aug, Nov)

2018Q2

66.92

115.18

70.59

252.69

69.72

66.61

67.18

203.51

2018Q4

44.62

56.31

49.66

150.59

2019Q1

34.85

33.91

37.64

106.40

2019Q2

59.18

115.19

277.17

2019Q3

97.12

102.8 58.13

49.82

205.07

2019Q4

52.17

44.87

39.55

136.59

2020Q1

67.12

93.76

115.66

276.54

2020Q2

85.83

103.4

71.36

260.59

2020Q3

108.22

190.62

168.89

467.73

2020Q4

125.55

294.52

385.53

805.60

2021Q1

923.40

1,610.00

1,070.00

3,603.40

2021Q2

1,610.00

2,230.00

979.32

4,819.32

2021Q3

662.66

1,100.00

1,250.00

3,012.66

2021Q4

1,290.00

1,400.00

1,040.00

3,730.00

833.64

683.07

739.40

2,256.11

Projected 2025Q2–2026Q1 Year-round

3,505.79

Projected 2035Q2–2036Q1 Year-round

34,100.38

b

b

Quarterly Trading Volume (in billion dollars)

2018Q3

2022Q1

a

Monthly Trading Volume (Jun, Sep, Dec)

Data source: CRYPTOCOMPARE Projected annual growth rate based on 2019Q2–2020Q1 aggregate volume and assumed 25.54% annual growth rate.

A second signifcant kind of derivatives is the futures contracts of the cryptocurrencies. Crypto futures are contracts between the buy and sell sides to trade a standard amount of cryptocurrency at a specifc future price on a previously determined date and time. Tis contract gains exposure and hedges risks to a wide range of cryptocurrencies (Matkovskyy and Jalan, 2021). Table 1.6 records the spot to futures trading volume ratios of the two most signifcantly traded cryptocurrencies: Bitcoin and Ethereum. On average, the size of the futures market is four times larger than that of the spot market. Te latter is more utilized when the cryptocurrency price is higher, implying more

Cryptocurrency Industry: A Review of Current and Future Trends 9

Table 1.6 Spot to Futures Trading Volume Ratios of Bitcoin and Ethereum BTC Spot to Futures Volume

ETH Spot to Futures Volume

6/30/2021

0.17

0.22

7/31/2021

0.16

0.19

8/31/2021

0.16

0.21

9/30/2021

0.19

0.25

10/31/2021

0.2

0.23

11/30/2021

0.2

0.22

12/31/2021

0.18

0.23

1/31/2022

0.14

0.18

2/28/2022

0.12

0.16

3/31/2022

0.13

0.18

4/30/2022

0.14

0.18

5/31/2022

0.15

0.21

Average

0.16

0.21

Time

active speculative day tradings. Te outlook of the futures trading volume is proportionate (80%) to the total trade volume presented in Table 1.5. A third signifcant kind of derivatives is the options of cryptocurrencies. Crypto options are a certifcate of privilege, but there is no obligation for the buyer to purchase a standard amount of cryptocurrency at a specifc future price on a previously determined date and time. Similar to the futures, this instrument gains exposure and hedges risks to a wide range of cryptocurrencies. A limited number of cryptocurrencies have active options markets and contracts organized; two of the most signifcant are organized for Bitcoin and Ethereum. Its volume is smaller than the futures market and the spot market. Table 1.7 records the option volumes for Bitcoin and Ethereum and presents the outlook for market development. Some fast-growing types of derivatives developed for the cryptocurrencies are the ETF, exchange-traded notes (ETNs), and other structured products such as the Grayscale Bitcoin Investment Trust. A cryptocurrency ETF is a fund that tracks the price of one or more digital tokens. It provides access to cryptocurrencies with lower investment and transaction costs (Cohney et al. 2019), the beneft of diversifcation, and a transaction process that is user-friendly for new

Table 1.7 Monthly Options Trading Volume of Bitcoin and Ethereum (in billion U.S. Dollars) Timeline Jul 2020

Volume of BTC Options

Volume of ETH Options

4.17

0.5

Jul 2021

Timeline

Volume of BTC Options

Volume of ETH Options

10.99

4.18

Aug 2020

4.57

1.19

Aug 2021

18.1

7.39

Sep 2020

4.26

1.05

Sep 2021

19.98

8.49

Oct 2020

5.81

0.7

Oct 2021

31.21

10.25

Nov 2020

14.27

1.83

Nov 2021

26.27

13.69

Dec 2020

16.87

2.15

Dec 2021

22.53

15.81

Jan 2021

31.03

4.4

Jan 2022

21.89

15.81

Feb 2021

26.93

4.63

Feb 2022

17.14

12.06

Mar 2021

30.55

4

Mar 2022

20.78

11.45

Apr 2021

35.13

8.12

Apr 2022

15.81

12.27

May 2021

29.1

17.24

May 2022

21.22

15.12

14.93

4.99

Average monthly volume

19.28

7.71

26.13

10.45

72.03

28.80

Jun 2021 Projected May 2025 a

a

*Projected May 2035

Projection based on rolling twelve-month volume growth rate of 10.67%.

Cryptocurrency Industry: A Review of Current and Future Trends 11

players. Tere are two kinds of cryptocurrency ETFs: the frst type is backed by physical cryptocurrencies, and the second is a synthetic variant that tracks cryptocurrency derivatives like futures contracts and cryptocurrency exchange traded products (ETPs). However, Todorov (2021) points out that futures-based ETFs can exacerbate price movements and create additional volatility when they have a large footprint on the underlying asset. Te frst cryptocurrency ETF started trading in October 2021. By April 2022, there were more than 20 types of cryptocurrency ETFs. Te crypto ETFs have lower Asset Under Management (AUM) values than the above-mentioned derivatives. At the peak price of cryptocurrencies in November 2021, the largest crypto ETF worldwide was Amplify Transformational Data Sharing ETF (BLOK), with an AUM of 1.7 billion U.S. dollars. Only three crypto ETFs had AUM exceeding one billion U.S. dollars. However, crypto ETFs are anticipated to be the fnancial instrument that grows rapidly and to be the dominating tool that provides market access.

Cryptocurrencies, Financial Markets, and Monetary Policies Te prices and trading volumes of cryptocurrencies are signifcantly afected by regulations and government recognition, as Warren (2020) suggests. As this is one of the dominating factors for the entire industry, this chapter leaves it for a separate chapter of elaborations by simply reminding the audience regarding its signifcance. Te prices and trading volumes of cryptocurrencies are also highly cointegrated with the global money supply. Tis implies that cryptocurrencies are still widely considered investment objectives rather than foreign currencies. Typically speaking, fat moneys are only regarded as investment objectives engaged in high-leverage transactions. In the case that the supply of a fat money increases signifcantly, other fat moneys’ values appreciate simultaneously, ceteris paribus. However, in the case of cryptocurrencies, the oversupply of fat money brings incremental cash infows to cryptocurrencies. Figure 1.1 reports the cryptocurrencies’ prices and the evolution of other major indices, using Bitcoin and Ethereum as the representatives of cryptocurrencies. Table 1.8 records the correlation coefcients between cryptocurrencies and the key indicators of the fnancial market. Te interactions of asset prices described in Table 1.8 also prove the conclusion that cryptocurrencies are regarded as investment objectives rather than foreign currencies at the current stage. Tey are highly correlated with the money supply (0.86 and 0.84), similar to the Standard and Poor’s 500 Index (0.98). Tey are

Figure 1.1 Cryptocurrency Interactions with Key Indicators of the Financial Market

Table 1.8 Correlations Between Cryptocurrencies and Key Indicators of the Financial Market

Bitcoin ETH WTI SP500 Gold Price USD Index 10-Year Yield M2

Bitcoin

ETH

WTI

SP500

Gold Price

1.00

0.93

–0.06

0.87

0.58

1.00

0.63

0.92

1.00

–0.37 1.00

USD Index

10-Year Yield

M2

0.30

–0.34

0.86

0.58

–0.19

–0.12

0.84

0.20

–0.81

0.46

–0.40

0.42

0.63

–0.40

0.98

1.00

–0.18

–0.57

0.49

1.00

–0.31

0.63

1.00

–0.50 1.00

Bitcoin is the Bitcoin price in USD, ETH is the Ethereum price in USD, WTI is the crude oil price in USD, gold price is the spot market gold price per ounce, USD index is an index of the value of the United States dollar relative to a basket of foreign currencies, 10-Year Yield is the interest rate of the debt obligation note by the United States Treasury, and M2 is the liquid money supply of the United States. These correlations are computed with monthly data from August 2010 to April 2022.

14 Cryptocurrency Concepts, Technology, and Applications

not regarded as assets that store value and provide utility as crude oil (–0.40) in an infationary environment; nor are they regarded as safe-haven assets such as gold (0.49). Te outlook of the relationships is diferent. As cryptocurrencies are more accepted in the circulation of goods and services market, and as central banks resume the trajectory of normalized monetary policies after the pandemic years, the stabilized value of cryptocurrencies may assist in enhancing their roles as store of value. Te prices of cryptocurrencies may also be less afected by the money supply and be more independent from other risky assets, proving the investors with diversifcation beneft.

The Development of the Cryptocurrency Goods and Services Market and Factors Market As of the summer 2022, the use of cryptocurrencies in the goods and services market and factors market is still in the process of discussion by senior administrations and celebrities (such as Elon Musk) around the world. Such discussions often send mixed signals from diferent lenses and are received and interpreted with various motivations. One major consensus is to embrace cryptocurrencies with enhanced regulations and bold attempts; the other consensus focuses on condemning its nature as evil. As of this writing, Federal Reserve Chair Jerome Powell claimed that he had no intention to ban cryptocurrencies like Ethereum in the U.S., while Securities and Exchange Commission Chairman Gary Gensler advocated the SEC’s and Commodity Futures Trading Commission (CFTC)’s roles in regulating the industry. Gensler believed that investors were disadvantaged if stricter regulation was absent. Furthermore, the Internal Revenue Service (IRS) emphasized the tax fling requirements for cryptocurrency income. Some view the anticipated regulations as the removal of a signifcant roadblock for cryptocurrency; for example, according to Haar (2022), Jefery Wang (Amber Group), Shehan Chandrasekera (CoinTracker.io), and Ben Weiss (CoinFlip®). Investing in cryptocurrencies and the use of cryptocurrencies in the goods and services market and the factor market are not overwhelmingly advocated. According to Prater (2022), leading investments legacy Warren Bufett had a renowned comment “. . . if you told me you own all of the Bitcoin in the world and you ofered it to me for $25, I wouldn’t take it because what would I do with it?” Bufett’s question about what to do with Bitcoins refects the fact of limited use of cryptocurrencies in the goods and services market and factors market.

Cryptocurrency Industry: A Review of Current and Future Trends 15

As of summer 2022, the circulations are slowly growing, with approximately 2,300 U.S. businesses accepting cryptocurrencies. Tere are two primary paths for using crypto: the hands-of path with which the business uses crypto just to facilitate payments, and the hands-on path with which the business broadens crypto adoption within operations and the treasury functions. Some examples of the hands-of path are limited online retailers like Overstock. com accepting Bitcoin; AMC® accepts Bitcoin, Ethereum, Bitcoin Cash, and Litecoin® as online payments; Tesla® has unstable acceptance to Bitcoin; Sheetz®, a major Mid-Atlantic restaurant and convenience chain, accepts digital currency payments. Some examples of the hands-on path are at eGifter®, Bitcoins can exchange for gift cards for popular merchandise; a debit card named BitPay card can convert crypto assets into dollars for daily purchases. Luther (2016) records more examples. Overall, the use of cryptocurrencies in global merchandise is still extremely limited. According to the International Monetary Fund (IMF), the global GDP was 94 trillion U.S. dollars in 2021, and the total market capitalization of all cryptocurrencies was 0.494 trillion U.S. dollars on average from 2013 to 2021. Tis implies a value ratio of 0.53%. However, assuming a 10.5% growth rate of cryptocurrency market capitalization, as presented in Table 1.2 (page 3), the cap will be 1.81 trillion U.S. dollars by 2035. Te annual average growth rate of the world GDP was 3.43% from 1961 to 2020, according to the World Bank. Tis implies a global GDP of 145.7 trillion U.S. dollars and a cryptocurrency value ratio of 1.24%. As a reference, the Russian ruble, the South African rand, the Turkish lira, and the Brazilian real each had value ratios of approximately 1.1% as of 2019 and were the 17th to the 20th most traded currencies (Bank for International Settlements, 2019). Tis chapter also views cryptocurrency as an element in the factor market—a new angle that examines the current and future status of cryptocurrencies with respect to three factors: human capital, resource capital, and fnancial capital. Unlike the second topic, which views cryptocurrencies as fnancial assets, this topic explores the future trend that cryptocurrencies serve as the infrastructure to incubate fnancial assets. Cryptocurrencies have yet to engage with human capital from a deeper level than as a research and investment area that provides employment opportunities. A future milestone of cryptocurrencies would be their wide application in human capital developments, from the education of its functioning areas to the adoption of cryptocurrencies as a component of incentive plans. Cryptocurrencies, as of the early 2020s, are not regarded as a typical type of resource capital that is comparable to land, commodities (Tiwari 2018), and strategic reserves. Tis is partly due to the volatility of the cryptocurrencies’ prices and also because of the longer time needed for transformative cognitive

16 Cryptocurrency Concepts, Technology, and Applications

recognitions. Another milestone of cryptocurrencies would be the inclusion of leading digital currencies by large economies as strategic state reserves and the recognition of generally accepted accounting principles as cash and cash equivalents, rather than as investments subject to amortization. Cryptocurrencies are developing as fnancial capital. Tis should be distinguished from being recognized as fnancial assets. Te former refers to being an infrastructure for the development of fnancial assets. Although multiple types of derivatives have grown from cryptocurrencies as introduced in topic two, freestanding cryptocurrencies as an asset class are premature.

The Development of the Cryptocurrency: Summary of Outlook Te dotcom bubble between 1995 and 2000 was enabled by concept-based investment, story-driven business, low interest rate environment, and sufcient liquidity. Te NASDAQ Composite Index rose from under 1,000 to more than 5,000 during the period, and the bubble burst in 2001 with a 77% drop in the NASDAQ Index. Te bubble was not sustainable due to the technological limit of the bandwidth of the internet connection, the bleak outlook of the proftability of the internet companies, the tightening liquidity, and the hike in interest rates. Much like the dotcom bubble, the surge of cryptocurrency prices from March 2020 to the end of 2022 was enabled by concept-based investment, celebrity efect, low interest rate environment, and sufcient liquidity. By May 2022, only a limited amount of goods and services in the real economy could be purchased with any type of cryptocurrency. Te Federal Reserve started its interest rate hike in March 2022 and planned to shrink its massive portfolio in June 2022. With higher fnancing cost and lower liquidity, the cryptocurrency price bubble burst will head toward the pre-pandemic price level. Tis level pertains until two vital obstacles of cryptocurrencies are cleared: currency values become stable and currency circulations are widely accepted by government authorities, according to Davoodalhosseini and Rivadeneyra (2020). As the frst topic mentioned, cryptocurrencies are script moneys. Te accountability of script currencies needs to be interpreted in an innovative way with cryptocurrencies, which includes value accountability, volatility accountability, and technology accountability. Value accountability refers to issuance control and circulation management. A cryptocurrency is accountable when its users can reasonably be assured that the money supply will increase stably and will not lead toward hyperinfation.

Cryptocurrency Industry: A Review of Current and Future Trends 17

Te fash crash of Tether (USDT) around early May 2022 proved that even the so-called Stablecoin may not be comparable to currencies whose values are anchored with the gold standard. In addition, cryptocurrency needs to give its user confdence that the circulation will not be blocked and that the issuance will not be forfeited. Tis concern is closely related to the regulations for this industry. Volatility accountability refers to purchasing power stability and exchange rate stability relative to other major fat moneys in the global fnancial and commercial markets. Consistent with Harwick (2016), cryptocurrency needs to exist with a sizable market capitalization to be value resilient in response to signifcant cash infows and outfows. Any fat money, even issued by smaller economies, is backed by its economic productions as well as its political, military, cultural, and diplomatic supports. On the other hand, cryptocurrencies are not backed by these resources but by decentralized technology protocols and unorganized expectations of their acceptance. Terefore, the volatility accountability is one of the most signifcant obstacles to be conquered by the industry. Te technology accountability refers to the ease of use in transactions, storage, and circulations. Compared to the fat moneys, cryptocurrencies require a much higher threshold of knowledge, which limits its use within a smaller technologically savvy population as of the early 2020s. Te infrastructure of the transaction network and the banking system requires signifcant development before it is widely adopted by the indiscriminate population and organizations. To be clear, while media and researchers frequently list wide business acceptance and broad user base as milestones of cryptocurrencies’ being quasi-fat money, this chapter regards such outcomes as the efect of the maturity of the industry, not the cause of such maturity. Te causes are the aforementioned accountabilities from the perspectives of value, volatility, and technology. According to a report from Fireblocks (2022), the milestones for the crypto industry in the near term are the following: more institutions will embrace cryptocurrency; tech giants will invest in crypto solutions; developing economies will experiment with central bank digital currencies (CBDCs); crypto will spread through the metaverse; NFTs will usher in Web 3.0; Europe will lead the way in regulation, with the U.S. as a close second; and private key management will focus on scalability. Specifcally, regarding the milestones that cryptocurrencies need to conquer to be recognized as quasi-fat money, Table 1.9 provides a projected timeline and path. Te number of diferent cryptocurrency types, the market capitalization, and the trading volumes evolve to a dynamic equilibrium status and interact with the maturity of the fnancial market for cryptocurrencies. Tis industry is reasonably believed to gradually carry the ideal characteristics of cryptocurrencies: stabilized unit of account, wide acceptance in the goods and services market, absorption by institutional investors as store of value, volatility resilience from

18 Cryptocurrency Concepts, Technology, and Applications

the impact of monetary policies, beneft of diversifcation, and safe-haven asset. As these factors are realized around the year 2035, some leading cryptocurrencies may be able to challenge the status of major fat moneys. Table 1.9 Milestones for Cryptocurrencies in the Next Decades: A Projection More Recognition from the Authorities Recognition of government and public institutions as vouchers of fat money. Governments’ acceptance as foreign currency reserve and strategic reserve. More governments and countries regard it as fat money and as an overtake on a bend opportunity to be a social innovation. More recognition from business and industry More enterprise acceptance as goods and services settlement and human capital compensation. More development of new cryptocurrencies and tokens by enterprise for specifc industrial use. More Mature Financial Market The total number of different types of cryptocurrencies greatly decrease from the summer 2022 count of 18,465. The total market capitalization of the top 10 cryptocurrencies is even greater than the 81% of the entire cryptocurrencies market cap in summer 2022. The total market capitalization of the entire cryptocurrencies market is 1.8 trillion U.S. dollars. The count of cryptocurrencies wallets is around 860 million. The count of cryptocurrencies exchanges grows from 600 in 2022 to almost all the commercial banks around the world. The quarterly trading volume of cryptocurrencies grows to 34 trillion U.S. dollars. The size of the cryptocurrency futures market remains four times larger than that of the spot market. The trading volume of the cryptocurrencies option market grows to be 1 trillion U.S. dollars. The count of cryptocurrency ETFs grows tremendously from the 2022 level of 20. Diverse functions of the ETF emerge, such as the cryptocurrency bond ETF, the cryptocurrency volatility ETF, and the leveraged cryptocurrency ETF. Cryptocurrency prices are less related with money supply and other risky assets. More Recognition from the Community Cryptocurrency mechanisms and usages introduced in the K12 education phase. The average profle of cryptocurrency users is consistent with the average profle of the global population, especially from a level of education perspective.

Cryptocurrency Industry: A Review of Current and Future Trends 19

References Bank for International Settlements (2019, September 16). Triennial central bank survey foreign exchange turnover in April 2019. Bank for International Settle­ ments Report 10. Cai, J., and Gomaa, A. (2019). Initial coin ofering to fnance venture capital: A behavioral perspective. Te Journal of Private Equity 22(3): 93–101. Chu, D. (2018). Broker-dealers for virtual currency: Regulating cryptocurrency wallets and exchanges. Columbia Law Review, 118(8): 2323–2360. Cohney, S., Hofman, D., Sklarof, J., and Wishnick, D. (2019). Coin-operated capitalism. Columbia Law Review, 119(3): 591–676. Davoodalhosseini, S. M., and Rivadeneyra, F. (2020). A policy framework for e-money. Canadian Public Policy / Analyse de Politiques 46(1): 94–106. Haar, R. (2022, May 3). Te future of cryptocurrency: 5 experts’ predictions after a ‘breakthrough’ 2021. NextAdvisor. https://time.com/nextadvisor/investing /cryptocurrency/future-of-cryptocurrency/ Harwick, C. (2016). Cryptocurrency and the problem of intermediation. Te Indepen­ dent Review, 20(4): 569–588. Howarth, J. (2022, March 25). How many cryptocurrencies are there in 2022? Explod­ ing Topics. https://explodingtopics.com/blog/number-of-cryptocurrencies/ Industry Insights (2022, January 4). Fireblocks forecast: 7 predictions for 2022. Fire­ blocks. https://www.freblocks.com/blog/freblocks-forecast-7-predictions -for-2022/ Luther, W. J. (2016). Bitcoin and the future of digital payments. Te Independent Review, 20(3): 397–404. Matkovskyy, R., and Jalan, A. (2021). Can Bitcoin be an infation hedge? Evidence from a quantile-on-quantile model. Revue Économique 72(5): 785–798. Myers, C., and Czarnezki, J. J. (2021). Sustainable business law? Te key role of corporate governance and fnance. Environmental Law, 51(4): 991–1040. Prater, E. (2022, April 30). Bitcoin ‘stupid and evil,’ Berkshire Hathaway vice chair Munger says. Fortune. https://fortune.com/2022/04/30/bitcoin-stupid-evil -berkshire-hathaway-vice-chair-munger-says-warren-bufett-cryptocurrency/ Tepper, T. (2022, May 1). Te best crypto exchanges of May 2022.Forbes Advisor.https:// www.forbes.com/advisor/investing/cryptocurrency/best-crypto-exchanges/ Tiwari, N. (2018). Te commodifcation of cryptocurrency. Michigan Law Review, 117(3): 611–634. Todorov, K. (2021, December 6). Launch of the frst US bitcoin ETF: Mechanics, impact, and risks. BIS Quarterly Review. https://www.bis.org/publ/qtrpdf/r _qt2112t.htm Vantage Market Research. (2022, April 11). $23+ million global cryptocurrency market size is expected to grow at a CAGR of over 6.9% during 2022–2028.

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Vantage. https://www.globenewswire.com/en/news-release/2022/04/11/241 9956/0/en/23-Million-Global-Cryptocurrency-Market-Size-is-Expected-to-Grow -at-a-CAGR-of-over-6-9-During-2022-2028-Vantage-Market-Research.html Warren, J. M. (2020). A too convenient transaction: Bitcoin and its further regulation. Journal of Law and Cyber Warfare, 8(1): 5–29.

Chapter 2 Investor Attention in Cryptocurrency Markets Lee Smales University of Western Australia

Introduction Human beings face cognitive constraints when performing tasks and making decisions. Attention to one task diverts attention from another, and while it may be easy to focus attention on a single task, it is more difcult to divide attention among several tasks (Kahneman 1973). Choosing an optimal portfolio is a particularly complex task faced by investors. In making this decision, they face myriad choices, and this is as true in cryptocurrency markets—where more than 10,000 cryptocurrencies are currently listed—as it is in traditional markets for stocks and bonds. Teoretically, a rational investor will calculate the cost and beneft of every possible combination of assets before making an investment decision. In reality, the virtually limitless number of potential combinations means that investors adopt mental shortcuts, or heuristics, to simplify the decision-making process. Focusing on assets that attract investor attention is an example of one such shortcut, and one that potentially introduces bias into the investment decision. Because the terms are sometimes (incorrectly) used interchangeably in the literature, it is 21

22 Cryptocurrency Concepts, Technology, and Applications

important to diferentiate the concept of investor attention from that of investor sentiment. Smales (2021) explains that attention occurs when investors are aware of the existence of information, whereas sentiment is the mood-biased interpretation of that information and its likely impact on asset prices. Lim and Teoh (2010) note that the infuence of sentiment on prices can be intensifed in the presence of greater investor attention. Because investors have limited attention, they will pay more attention to information that is salient, easy to access, and straightforward to process. Nonsalient information and difcult-to-process information is ignored. Unless there is important news, such as that triggered by large price changes, then investors will not usually pay attention to a particular asset. In the case of cryptocurrencies, the substantial price volatility, alongside explosive market growth, and the occasional market plunge, has garnered a signifcant amount of attention. Indeed, cryptocurrencies have captured the attention of academics (Figure 2.1) and global media (Figure 2.2), in addition to that of investors. Examining the efect of behavioural factors, such as investor attention, on cryptocurrency markets is particularly important since, in contrast to traditional assets, they often have little intrinsic value, and so prices cannot be explained by fundamentals. Te aim of this chapter is to provide an overview of research on the topic of investor attention, with a particular focus on cryptocurrency markets.

Figure 2.1 Academic Articles Mentioning Bitcoin and Cryptocurrency. A line chart showing the number of academic articles that mention Bitcoin or cryptocurrency, and the Bitcoin price, in each year from 2010 to 2021. From 2016, the number of articles increases quickly, peaking near to 25,000, as the Bitcoin price surges.

Investor Attention in Cryptocurrency Markets 23

Figure 2.2 Media Articles Mentioning Bitcoin and Cryptocurrency. A line chart showing the number of media articles that mention Bitcoin or cryptocurrency, and the Bitcoin price, in each year from 2010 to 2021. From 2016, the number of articles increases quickly, peaking near to 129,000, as the Bitcoin price surges.

Te chapter proceeds as follows: Te next section presents several of the most commonly used proxies for investor attention, incorporating both indirect and direct measures. Te following section then briefy introduces the research regarding the efects of investor attention in the context of stock markets. Tis provides a framework from which it is possible to understand the mechanism by which investor attention may infuence cryptocurrencies. Te subsequent section then discusses the emerging research that specifcally relates to investor attention in cryptocurrency markets, including various measures of attention and the impact on returns, liquidity, volatility, and crash risk. Finally, some concluding remarks are provided.

Measures of Investor Attention Many diferent metrics have been used to proxy for investor attention. Initially, indirect measures related to market activity and attention-grabbing events were used. More recently, technological innovations have permitted the use of direct, or revealed, measures of investor sentiment.

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Turning to indirect measures frst, because investors are more likely to trade when paying attention, abnormal trading volume ofers one proxy (Gervais et al. 2001; Barber and Odean 2008; Hou et al. 2009). Similarly, investors are more likely to pay attention to stocks that have important news releases, produce extreme returns (Barber and Odean 2008), or hit upper price limits (Seasholes and Wu 2007). Grullon et al. (2004) ofer another indirect proxy of investor attention when relating a frm’s overall visibility, measured by advertising spend, to stock market liquidity. Specifc to cryptocurrencies, Sabah (2020) argues that the number of new venues accepting cryptocurrencies as a form of payment is a novel proxy of attention and can be linked to return volatility. However, market activity can be driven by a multitude of other factors that are unrelated to investor attention. It is also the case that investors are not necessarily conscious of new information unless they read it. In recent years, the most commonly used measures of investor attention in cryptocurrency markets have been those that utilize web-based tools such as Google® search volume (e.g., Philippas et al. 2019; Liu and Tsyvinski 2021; Liu et al. 2022; Smales 2022a), Twitter® frequency (e.g., Shen et al. 2019; Al Guindy 2021; Choi 2021), and Wikipedia® page visits (Kristoufek 2013). Since Googling, tweeting, or accessing a Wikipedia page all require an action to be taken, these proxies may be thought of as direct measures of investor attention. News disseminated via social media could be considered more important for cryptocurrency markets owing to the similar audience attributes and because traditional media has a perceived lack of coverage of these new markets (Kraaijeveld and De Smedt 2020).

Google Search Volume Da et al. (2011) were among the frst to utilize Google search volume as a measure of attention for retail investors, and it has been widely used since. Tis metric is based on the frequency of Google searches for particular keywords. It is argued that Google search volume provides a direct measure of attention, since investors who are Googling a particular keyword are undeniably paying attention to that item, or in the case of searching for an asset name, are paying attention to that asset. Since Google accounts for approximately 90% of global internet search volume (Smales 2022a), searches should be indicative of the search behavior of the broad population. Google search volume may be obtained from Google Trends (https://trends .google.com/), and it is currently available from January 2004. Data on search queries is normalized so that numbers are in the range zero to 100 (inclusive) and are based on the frequency of searches for that topic in proportion to all searches for all topics within a particular geographical region. Tis means that a lower Google search volume score does not always means that there are fewer

Investor Attention in Cryptocurrency Markets 25

searches for that topic, just that the proportion of searches for that topic relative to all others has fallen. A value of 100 indicates that the search query is extremely active for that particular time and location. In some cases, it makes sense to focus on specifc countries or continents, but it is also possible to consider global searches. Google Trends applies flters to remove duplicate searches, searches with special characters, and searches made by very few people. Two issues should be considered when using Google search volume. First, the data frequency provided by Google Trends decreases as the sample period increases. Daily data is available for periods up to three months, weekly data for periods up to fve years, and monthly data for longer intervals. Since Google search volume is standardised according to the specifc time interval selected, it may be necessary to adjust the data when using diferent intervals. For instance, normalization would be required for daily data over a two-year sample period or weekly data over a 10-year sample period. Bleher and Dimpf (2019a,b) discuss this process in detail and provide an algorithm as one solution. Second, the choice of search term or key word is important. Da et al. (2011) note possible ambiguity arises since an internet user may search a company name for reasons unrelated to investing (e.g., online shopping), and this is more problematic if the name has multiple meanings (e.g., “Apple”). It’s also possible that investors will access information on a specifc frm using diferent variations of its name. Da et al. (2011) overcome this ambiguity by using stock tickers. Tis is an equally problematic issue for cryptocurrencies which have taken ambiguous names such as Avalanche, Stellar, and Tether, and where names can have a tendency to be similar (Cahill and Liu 2021). Figure 2.3 (on next page) illustrates the generally positive relationship between the price of Bitcoin (BTC) and Google search volume for the “Bitcoin” and “Cryptocurrency” search terms. Te implications for this relationship are discussed in more detail later in the chapter.

Other Direct Measures Twitter ofers another internet-based measure of investor attention that has been used to explain cryptocurrency market activity (e.g., Philippas et al. 2019; Shen et al. 2019; Al Guindy 2021; Choi 2021; Liu and Tsyvinski 2021). Al Guindy (2021) suggests that Twitter is a good measure because, in addition to demonstrating attention is paid when tweets are liked, it reveals the extent to which investors spread information when tweets are retweeted. Shen et al. (2019) argue that, since their tweets involve commentary and market predictions, Twitter is more likely to be used by well-informed investors with cryptocurrency knowledge. Twitter is also used by (some) investment professionals and news organisations to advertise their other services.

26 Cryptocurrency Concepts, Technology, and Applications

Figure 2.3 Google Search Volume and Bitcoin Prices. A line chart showing the Bitcoin price and Google search volume for Bitcoin and cryptocurrency monthly from 2011 to 2021. Search volume peaks are in late 2017 and early 2021, which coincides with Bitcoin price highs.

Since institutional investors can access professional news services, such as Bloomberg® and Reuters®, it is likely that Google search volume is most relevant as a proxy for attention by retail investors. Ben-Rephael et al. (2017) ofers an alternate measure that focuses on institutional investors. Using news searching and reading activity on Bloomberg terminals, they construct a measure of abnormal institutional investor attention. Tey report that institutional attention leads retail attention and responds more promptly to news events in stock markets.

Investor Attention in Stock Markets Investor attention has been shown to have a substantial and widespread infuence on stock markets, with two competing theories attempting to explain the market response: choice asymmetry and information discovery. Choice asymmetry (Odean 1999; Barber and Odean 2008) contends that investors face the problem of choosing among a large investment universe when buying stocks and so only buy attention-grabbing stocks—stocks that have recently experienced extreme returns or abnormally high trading volume. In contrast, when looking to sell stocks, investors typically only sell stocks they own

Investor Attention in Cryptocurrency Markets 27

and so face a less signifcant problem. Tis theory is not necessarily applicable to institutional investors because they have additional resources to cope with the issue of limited attention, and an ability to short-sell means they may also face a signifcant search problem when selling stocks. Information discovery ofers an alternate explanation. Andrei and Hasler (2014) developed a theoretical model that explains the efect of investor attention on stock return volatility and risk premia. Teir model suggests that when investors are paying attention to news, this new information is quickly incorporated into prices, resulting in higher volatility and a larger risk premium. In this case, greater attention helps to improve market efciency in making returns less predictable (Vozlyublennaia 2014). Tis theory is also consistent with markets responding to news only when investors pay attention to it (Huberman and Regev 2001). Relatedly, the market response may also depend on the quantity of competing information (or stimuli). For instance, Peress and Schmidt (2020) fnd that days on which retail investors are distracted by unrelated news events (e.g., the O. J. Simpson trial) are associated with lower volatility, while the earnings drift is stronger for announcements on days with many other announcements (Hirshleifer et al. 2009) and when institutional investors are not paying attention (Ben-Rephael et al. 2017). Te empirical results are not defnitive. On the one hand, several articles identify a positive relationship between investor attention and individual stock returns. Barber and Odean (2008) note that buying of attention-grabbing stocks stimulates higher trading volume, leading to higher price pressure and magnifying the response to news events, which is consistent with Gervais et al. (2001) and Jiang et al. (2021). Da et al. (2011) show that higher investor attention is related to positive stock returns and frst-day IPO returns. In contrast, there is evidence that global stock index returns (Chen 2017; Smales 2021) and U.S. stock sector returns (Smales 2020a) are negatively related to investor attention. Smales (2020a; 2021) suggests this is because retail investors are searching for information to resolve uncertainty about their household concerns rather than specifcally for stock purchases, and so is consistent with information discovery reasoning. Prior to focusing on cryptocurrency, it is worthwhile discussing two additional concepts that provide a link between investor attention in stock and cryptocurrency markets. First, Peng and Xiong (2006) note that, owing to attention capacity constraints, investors tend to focus on market- and sector-wide news, rather than frm-specifc information. One potential implication for the cryptocurrency market is that investors focus their attention on market-wide news or use a proxy for the overall market, such as Bitcoin. Tis may then explain why Bitcoin is so important for explaining returns across the cryptocurrency market (Smales 2020b).

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Second, it is possible that investors make a conscious attempt to focus on particular information. Peng (2005) generates a model that shows investors allocate greater attention toward more volatile assets since they generate greater potential reward. Given the much higher level of return volatility exhibited by cryptocurrencies, this may ofer one explanation as to why so much attention is focused on cryptocurrency markets.

Investor Attention in Cryptocurrency Markets As mentioned in the prior sections, there are several characteristics of cryptocurrency markets that make them particularly prone to behavioral factors, and specifcally limited investor attention. First, cryptocurrencies ofer little intrinsic value, and so returns are more likely to be determined by behavioral infuences. Second, until cryptocurrencies started to attain legitimacy among institutional investors, likely alongside the introduction of Bitcoin futures in 2017, the speculative nature of the market primarily appealed to retail investors. Lucey at al. (2022) suggest these investors may interpret information in a unique way compared to institutional investors. Since Google search volume is often attributed as a measure of retail investor attention, it may be particularly relevant to cryptocurrency markets. Tird, the large number of cryptocurrencies, many of which disappear after a short time only to be replaced by new oferings, creates a predicament when having to choose among them. Finally, investors (and media) pay attention to the cryptocurrency market because it experiences high levels of volatility and so regularly generates a signifcant amount of news. Just as with stock markets, several diferent proxies for investor attention are considered for cryptocurrency markets. Although Wikipedia page visits and accepting venues are also found within the literature, the most used proxies are Google search volume and Twitter tweets and re-tweets. Before considering the efects of investor attention on cryptocurrency markets, it is worthwhile contemplating the causes of investor attention in these novel instruments. Urquhart (2016) proposes that at least some of the attention on Bitcoin is a result of its innovative features, simplicity, and transparency. Consistent with the stock market literature, there is evidence that investor attention is related to positive past returns (Lin 2021) and realized volatility (Urquhart 2018) and tends to be concentrated on those cryptocurrencies that generate the most news (Subramaniam and Chakraborty 2020). Tere appears to be mixed evidence as to whether the relationship is uni- or bi-directional. Urquhart (2018) shows a uni-directional causal relationship from

Investor Attention in Cryptocurrency Markets 29

returns to attention, whereas Dastgir et al. (2019) and Smales (2022a) note bi-direction causality, with the former noting this is concentrated in the tails of the distribution. Earlier, it was noted that cryptocurrency names are often similar. Launched in 2008). Bitcoin was the original cryptocurrency and still accounts for around 40% of market capitalisation. Cahill and Liu (2021) note a preponderance of “copycat” cryptocurrencies that have a name similar to Bitcoin (e.g., Wrapped Bitcoin, Bitcoin Cash, Bitcoin SV ), and a search today would reveal at least 100 oferings. Tese copycats have high initial returns but low survival rates. Cahill and Liu (2021) suggest that investors pay attention to these cryptocurrencies because the names are less complex (more familiar) and so information is easier to process. Since cryptocurrency is a novel technology, valuation is more uncertain than in mature markets, and so the name may be a signalling tool for investors. Tis is not dissimilar to the “dotcom” efect documented by Cooper et al. (2001), which found a substantial stock price response around the announcement of corporate name changes. Tey argue that investors have a fear of missing out in new technologies and so buy shares in frms that may be only loosely connected the technology. In a case of “history rhyming,” Cahill et al. (2020) note positive abnormal returns for frms acknowledging interest in blockchain. As a pertinent side note, after observing frequent bubbles and crashes in cryptocurrencies, Harris (2003) suggests that bubbles often start when buyers become overly optimistic about the growth of new markets and the potential of new technologies. We now consider the infuence of investor attention on specifc aspects of cryptocurrency markets. Owing to the early launch and market dominance, many of the early studies focused on Bitcoin, but more recently it has become common to evaluate a wider range of cryptocurrencies.

Returns Much research is devoted to understanding the relationship between investor attention and cryptocurrency returns. Tis is not surprising given the potential opportunity for substantial returns should a proftable trading strategy be determined. Unfortunately, the empirical evidence is mixed so far, with estimated results dependent on the sample period chosen, methodology used, and set of cryptocurrencies considered. Focusing on Bitcoin, Urquhart (2018), Bleher and Dimpf (2019a), and Smales (2022a) fnd that returns are not caused by investor attention, whereas Kristoufek (2013) and Philippas et al. (2019) fnd that investor attention has a strong causal relationship with prices, particularly during periods of higher uncertainty.

30 Cryptocurrency Concepts, Technology, and Applications

Expanding the consideration set to a broader range of cryptocurrencies seems to provide greater confrmation of a statistically signifcant relationship between investor attention and returns. Subramaniam and Chakraborty (2020) fnd that high-quantile spikes in investor attention led to increased returns; Li et al. (2021) note interdependence between investor attention and returns using wavelet-based quantile Granger causality; and Lin (2021) fnds bi-directional causality when using Granger Causality tests, but not when using vector autoregression models. Smales (2022a) considers a set of 20 leading cryptocurrencies and fnds a positive relationship between investor attention and returns, with the efect heightened during the COVID-19 pandemic. Using proxies generated from Google search volume and Twitter, Liu and Tsyvinski (2021) fnd that high investor attention predicts high future returns up to six weeks ahead. In a similar vein, investor attention is shown to generate momentum in the cryptocurrency market (Sockin and Xiong 2020). Liu et al. (2022) fnd that momentum occurs at times of high investor attention and is greater for the large and well-known coins that receive more attention. In general, this positive attention–return relationship is consistent with the “choice-asymmetry” stock market literature and the purchase of attention-grabbing assets (Barber and Odean 2008). On a related note, Ibikunle et al. (2020) investigate the infuence of investor attention on the price discovery process for Bitcoin. Tey fnd that, whereas informed trading is not related to attention, more uninformed trading activity tends to occur when intention levels are high. As a result, price efciency tends to drop during periods of heightened attention.

Volatility and Crash Risk Identifying the relationship between investor attention and cryptocurrency returns may help to formulate proftable trading strategies; however, it is also important to understand the risk associated with such investments. Understanding the efect of investor attention on volatility (and related crash risk) can aid investors in grasping inherent risk and is also useful for regulators (and other policy makers) in identifying circumstances of heightened risk. Aside from Urquhart (2018), the general empirical fnding is that investor attention is positively related to cryptocurrency volatility. Bleher and Dimpf (2019a) suggest that retail investors fnd information via Google searches that leads to trading activity and an associated increase in Bitcoin return volatility. Similarly, Shen et al. (2019) discover that the frequency of tweets drives Bitcoin trading volume and realized volatility. Al Guindy (2021) and Smales (2022a) show that investor attention is positively related to volatility for the 20 largest cryptocurrencies, with greater distractions associated with lower volatility (Al Guindy 2021). Likewise, Sabah (2020)

Investor Attention in Cryptocurrency Markets 31

fnds that a one-standard deviation increase in new crypto-accepting business venues increases volatility by nearly 20%. Smales (2022b) investigates whether investor attention has a connection with cryptocurrency crash risk, fnding that there is a signifcant relationship but that it is concentrated in the tails of the crash risk distribution. Te attention–crash risk relationship is positive when crash risk is low and negative when crash risk is high. Te positive attention–volatility relationship is consistent with the theoretical stock market model of Andrei and Hasler (2014). Tat is, an increased level of investor attention results in a higher volatility because more attention allows information to be refected into prices more quickly.

Liquidity Liquidity is another important component of market activity. Without sufcient liquidity, it is not possible to buy and sell cryptocurrencies easily or quickly, and implementation of any trading strategies would be prohibitively expensive. Smales (2022a) fnds that higher levels of investor attention can reduce liquidity, largely because of the greater volatility associated with this attention. Conversely, Choi (2021) fnds an increase in Twitter activity leads to a short-term improvement in liquidity, while Yao et al. (2021) fnd that investor attention reduces idiosyncratic risk for cryptocurrencies by increasing liquidity. Tis efect is more noticeable for newer and smaller cryptocurrencies, which are absent from the sample used by Smales (2022a).

Concluding Remarks Since its 2009 inception, the cryptocurrency market has grown tremendously, and with a market capitalisation approaching $1.2 trillion (having peaked near $3 trillion), it is a non-trivial market. Similar to the novel technologies that have come before (e.g., railroads, telephones, and the internet), cryptocurrencies ofer great potential but are difcult to understand. Teir unique characteristics might reshape the entire fnancial ecosystem, democratising the system in the process, but there is great uncertainty as to the precise mechanism by which this will occur. Since cryptocurrencies are essentially a piece of code that does not typically generate cashfows, it is argued that they lack intrinsic value, and so any value that is ascribed to them is likely to be driven by behavioral factors. Te substantial price gains and extreme return volatility exhibited by cryptocurrencies has grabbed the attention of a range of investors, suggesting that investor attention is a particularly important behavioral factor to consider.

32 Cryptocurrency Concepts, Technology, and Applications

Initially, cryptocurrency investors were primarily individuals—retail investors who are perhaps less aware of behavioral biases but more afected by them. More recently, institutional investors have started to take a larger role in the cryptocurrency market, and so it is possible that the infuence of behavioral factors will evolve over time. Terefore, it is important to gain a better understanding of investor behavior in cryptocurrency markets. Tis will help investors to determine the best way in which to allocate capital and aid policymakers as they consider how to avoid an accumulation of systemic risk as the market continues to grow. It seems that the measures and theories concerning investor attention in stock markets can also help to explain the infuence of investor attention in cryptocurrency markets. A range of indirect (extreme returns, abnormal trading volume) and direct (Google search volume, Twitter frequency, Wikipedia page visits) proxies are utilised. Recent empirical studies for a wide range of cryptocurrencies have found a positive attention–return relationship that is consistent with the purchase of attention-grabbing stocks (Barber and Odean 2008) and a positive attention–volatility relationship consistent with an information discovery model (Andrei and Hasler 2014).

References Al Guindy, M. (2021). Cryptocurrency price volatility and investor attention. International Review of Economics and Finance, 76: 556–570. Andrei, D., and Hasler, M. (2014). Investor attention and stock market volatility. Review of Financial Studies, 28: 33–72. Barber, B. M., and Odean, T. (2008). All that glitters: Te efect of attention and news on the buying behavior of individual and institutional investors. Review of Financial Studies, 21: 785–818. Ben-Rephael, A., Da, Z., and Israelsen, R. D. (2017). It depends on where you search: Institutional investor attention and underreaction to news. Review of Financial Studies, 30: 3009–3047. Bijl, L., Kringhaug, G., Molnar, P., and Sandvik, E. (2016). Google searches and stock returns. International Review of Financial Analysis, 45: 150–156. Bleher, J., and Dimpf, T. (2019a). Today I got a million, tomorrow, I don’t know: On the predictability of cryptocurrencies by means of Google search volume. International Review of Financial Analysis, 63: 147. Bleher, J., and Dimpf, T. (2019b). Recovering Google’s lost frequencies: An algorithm to knit multi-annual high-frequency search volume time series. SSRN Working Paper. https://ssrn.com/abstract=3357424

Investor Attention in Cryptocurrency Markets 33

Cahill, D., Baur, D. G., Liu, Z., and Yang, J. W. (2020). I am a blockchain too: How does the market respond to companies’ interest in blockchain? Journal of Banking and Finance, 113: 105740. Cahill, D., and Liu, Z. (2021). Limitation of imitation: Lessons from another Bitcoin copycat. Journal of Corporate Finance, 68: 101992. Chen, T. (2017). Investor attention and global stock returns. Journal of Behavioral Finance, 18: 358–372. Choi, H. (2021). Investor attention and Bitcoin liquidity: Evidence from Bitcoin tweets. Finance Research Letters, 38: 101555. Cooper, M. J., Dimitrov, O., and Rau, P. R. (2001). A rose.com by any other name. Journal of Finance, 56: 2371–2388. Da, Z., Engelberg, J., and Gao, P. (2011). In search of attention. Journal of Finance, 66: 1461–1499. Dastgir, S., Demir, E., Downing, G., Gozgor, G., and Lau, C. K. M. (2019). Te causal relationship between Bitcoin attention and Bitcoin returns: Evidence from the copula-based Granger causality test. Finance Research Letters, 28: 160–164. Gervais, S., Kaniel, R., and Mingelgrin, D. H. (2001). Te high-volume return premium. Journal of Finance, 56: 877–919. Grullon, G., Kanatas, G., and Weston, J. P. (2004). Advertising, breadth of ownership, and liquidity. Review of Financial Studies, 17: 439–461. Harris, L. (2003). Trading and Exchanges: Market Microstructure for Practitioners. New York: Oxford University Press. Hirshleifer, D., Lim, S. S., and Teoh, S. H. (2009). Driven to distraction: Extraneous events and underreaction to earnings news. Journal of Finance, 64: 2287–2323. Hou, K., Xiong, W., and Peng, L. (2009). A tale of two anomalies: Te implications of investor attention for price and earnings momentum. Working Paper, Princeton University. Huberman, G., and Regev, T. (2001). Contagious speculation and a cure for cancer: A non-event that made stock prices soar. Journal of Finance, 56: 387–306. Ibikunle, G., McGroarty, F., and Rzayev, K. (2020). More heat than light: Investor attention and bitcoin price discovery. International Review of Financial Analy­ sis, 68: 101459. Jiang, L., Liu, J., Peng, L., and Wang, B. (2021). Investor attention and asset pricing anomalies. Review of Finance, 26: 563–593. Kahneman, D. (1973). Attention and Efort. Englewood Clifs, NJ: Prentice-Hall. Kraaijeveld, O., and De Smedt, J. (2020). Te predictive power of public Twitter sentiment for forecasting cryptocurrency prices. Journal of International Financial Markets, Institutions and Money, 65: 101188.

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Kristoufek, L. (2013). Bitcoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the internet era. Scientifc Reports, 3: 1–7. Li, R., Li, S., Yuan, D., and Zhu, H. (2021). Investor attention and cryptocurrency: Evidence from wavelet-based quantile Granger causality analysis. Research in International Business and Finance, 56: 101389. Lim, S. S., and Teoh, S. H. (2010). Limited attention. In: H. K. Baker and J. R. Nofsinger (Eds.), Behavioral Finance, 295–312. New Jersey: Wiley & Sons. Lin, Z. Y. (2021). Investor attention and cryptocurrency performance. Finance Research Letters, 40: 101702. Liu, Y., and Tsyvinski, A. (2021). Risk and returns of cryptocurrency. Review of Financial Studies, 34: 2689–2727. Liu, Y., Tsyvinski, A., and Wu, X. (2022). Common risk factors in cryptocurrency. Journal of Finance, 76: 1133–1177. Lucey, B. M., Vigne, S. A., Yarovaya, L., and Wang, Y. (2022). Te cryptocurrency uncertainty index. Finance Research Letters, 45: 102147. Odean, T. (1998). Do investors trade too much? American Economic Review, 88: 1279–1298. Peng, L. (2005). Learning with information capacity constraints. Journal of Finan­ cial and Quantitative Analysis, 40: 307–329. Peng, L., and Xiong, W. (2006). Investor attention, overconfdence and category learning. Journal of Financial Economics, 80: 563–602. Peress, J., and Schmidt, D. (2020). Glued to the TV: Distracted noise traders and stock market liquidity. Journal of Finance, 75: 1083–1133. Philippas, D., Rjiba, H., Guesmi, K., and Goutte, S. (2018). Media attention and Bitcoin prices. Finance Research Letters, 30: 37–43. Sabah, N. (2020). Cryptocurrency accepting venues, investor attention, and volatility. Finance Research Letters, 36: 101339. Seasholes, M. S., and Wu, G. (2007). Predictable behavior, profts, and attention. Journal of Empirical Finance, 14: 590–610. Shen, D., Urquhart, A., and Wang, P. (2018). Does Twitter predict Bitcoin? Eco­ nomics Letters, 174: 118–122. Smales, L. A. (2020a). Investor attention and the response of US stock market sectors to the COVID-19 crisis. Review of Behavioral Finance, 13: 20–39. Smales, L. A. (2020b). One cryptocurrency to explain them all? Understanding the importance of Bitcoin in cryptocurrency returns. Economic Papers: A Journal of Applied Economics and Policy, 38: 118–132. Smales, L. A. (2021). Investor attention and global market returns during the COVID-19 crisis. International Review of Financial Analysis, 73: 101616. Smales, L. A. (2022a). Investor attention in cryptocurrency markets. International Review of Financial Analysis, 78: 101972.

Investor Attention in Cryptocurrency Markets 35

Smales, L. A. (2022b). Investor attention and cryptocurrency price crash risk: A quantile regression approach. Studies in Economics and Finance, 38: 490–505. Sockin, M., and Xiong, W. (2020). A model of cryptocurrencies. NBER Working Paper, 26816. Subramaniam, S., and Chakraborty, M. (2020). Investor attention and cryptocurrency returns: Evidence from quantile causality approach. Journal of Behavioral Finance, 21: 103–115. Urquhart, A. (2016). Te inefciency of Bitcoin. Economics Letters, 148: 80–82. Urquhart, A. (2018). What causes the attention of Bitcoin? Economics Letters, 166: 40–44. Vozlyublennaia, N. (2014). Investor attention, index performance, and return predictability. Journal of Banking & Finance, 41: 17–35. Yao, S., Kong, X., Sensoy, A., Akyildirim, E., and Cheng, F. (2021). Investor attention and idiosyncratic risk in cryptocurrency markets. European Journal of Finance. https://doi.org/10.1080/1351847X.2021.1989008

Chapter 3 How Much to Invest, If Any, in Bitcoin? Anthony L. Loviscek Seton Hall University

Introduction With trust in fnancial institutions compromised by the turmoil of the global fnancial crisis of 2008–2009, Satoshi Nakamoto, a pseudonym for a person or a group, created Bitcoin on January 3, 2009, begetting the cryptocurrency movement. A medium of exchange not pegged to any fnancial intermediary or controlled by any organization, it relies on “blockchain” technology that resembles multilateral contractual obligations among its cooperative participants. Te outcome has been a peer-to-peer network of transaction activity that, in turn, has fostered over 18,000 currently circulating cryptocurrencies. As the most widely used cryptocurrency, at 19 million units, Bitcoin comprises over 40% of the market. However revolutionary and sustained Bitcoin may be as a currency, one that is free from infationary pressures and monetary stimulus, its stronger draw has been as a “store of value,” or as an investment play. For instance, its meteoric rise from $0.20 on 264,000 transactions on December 2010 to $42,587 and 37 billion transactions by December 2021 has led to a market capitalization cresting at $1.3 trillion in late 2021, registering an extraordinary compound annual 37

38 Cryptocurrency Concepts, Technology, and Applications

return of 205.1%. Its return is nearly seven times that of the high-performing, high-profle stocks of Amazon® and Apple® for the same period, and nearly 14 times that of the benchmark S&P 500.* Its high-level performance has led to bullishly upbeat forecasts of $100,000 by 2022–2023 (DeMatteo, 2022; Ashford and Curry, 2022), $500,000 by decade’s end, and up to $1,000,000 in the long term (Kharpal, 2021). Tis outsized higher return, however, has not come without a signifcant price: outsized volatility. For example, closing at $455 in December 2013, Bitcoin leaped to $991 by February before plummeting to $263 by March, followed by jumping to $564 in July before settling at $303 by December 2014, a one-year volatility of 123.61%. By comparison, the Vanguard 500 Index, an accurate approximation for the S&P 500, registered a volatility of 8.18%, or 93% lower. Even the relatively high 24% volatilities each for Apple and Amazon during the period were 80% lower. More recently, investors saw a price spring from $29,361 in July 2021 to $68,789 by November 2021 only to watch it plummet to $26,350 by May 2022. Such volatility presents a formidable challenge for accurate return forecasting. Zweig (2022) describes it as “get(ting) crypto right but still play(ing) it wrong” when attempting to proft from its movements. With the classic investment return-to-risk paradigm in focus, these observations lead to the following question: to what degree, if any, should Bitcoin be part of an asset allocation strategy? Tis question motivates this study. As impressive as Bitcoin’s return performance has been to date, given shares traded and gains earned, the price of overlooking the volatility of Bitcoin—or any security, for that matter—can be signifcant: in the case of Bitcoin, the high probability of loss, or risk, especially in light of the documented loss-aversion efect. Studies show that investors feel a loss in utility associated with negative returns that exceeds the gain in satisfaction from positive returns, often leading to selling winning positions too early but holding losing positions too long (Kahneman, Knetsch, and Taler 1991; Gal and Rucker 2018; Merkle 2020). Translated, the feeling concerning the loss of money can be quite painful. Supporting this point, Glumov (2013) shows tolerance toward risk dropped to a record low following the global fnancial crisis of 2007–2009, in which fewer than 10% of individual investors surveyed could be classifed as having aboveaverage tolerance for risk. Although the volatility of Bitcoin and the potential loss-aversion efect weigh on its asset allocation, views on a percentage allocation are buoyant, ranging from 2%–10% (Kassar 2022; Krueger 2022), with a reported weight of as high *

During the same period, Amazon and Apple registered annual returns each of approximately 30%, while the S&P 500 recorded a gain of approximately 16%.

How Much to Invest, If Any, in Bitcoin? 39

as 20% (Stankiewicz 2022). An objective assessment of these allocations, however, is not apparent. Such an assessment, or gathering of empirical evidence, would include, for example, results based on an optimization strategy, such as the minimization of risk given an expected rate of return. Without such evidence, investors are left with little more than scattered subjective views, which do not inspire confdence. Furthermore, recourse to the academic and professional investment literatures for cogent and compelling guidance is found largely wanting. Te most relevant studies look at the diversifcation potential of cryptocurrencies (e.g., Wu and Pandy 2014; Guesmi, Saadi, Abid, and Ftiti 2019; Liu 2019; Li, Jiang, Wei, and Wang 2021). Although these studies are constructive in demonstrating the potential diversifcation benefts of cryptocurrencies, they do not ofer a prescription on the percentage allocation. To address the question, this study applies modern portfolio theory to construct a series of stock portfolios from the Standard & Poor’s 100—a broad equity market index—that also include Bitcoin. Te application is based on mean-variance optimization. It is unique in at least two respects: First, it uses the model of Black and Litterman (1992), well known in applications of portfolio analysis, to construct a series of portfolios to determine if an allocation to Bitcoin exists. Second, given the results in the frst stage, it compares the performance of the portfolios with that of Bitcoin alone and with that of a broad market index to determine a percentage allocation strategy, if any, that can guide investment decisions in light of the varied asset allocation recommendations expressed in the literature. Te focus is on active individual investors—namely, those who construct portfolios with the expectation of market-beating performance. In this study, performance is assessed by applying the well-known “reward-to-variability” portfolio performance ratio of Sharpe (1994).* Accordingly, the focus is not only on return but also on risk, as approximated by the volatility of rates of return. Te central issue is whether the return-to-risk of Bitcoin is high enough to enhance portfolio performance, as displayed in the weight assigned to it. A higher weight signifes the potential for a high added value, while a lower weight points to a low added value.

A Look at the Literature Because Bitcoin has only existed since 2009, the literature is still in an early stage; however, research is accelerating. Sharma, Jain, Mahendru, and Bansal *

Passive investing, the counterpart to active investing, relies on index investing, such as found in funds, for example, of the Vanguard® family (e.g., Vanguard 500 Index).

40 Cryptocurrency Concepts, Technology, and Applications

(2019) ofer a worthy starting point in their overview of research on Bitcoin and cryptocurrencies. Tey examine 121 articles and fnd that topics on prices, rates of return, volatility, fnancial indicators, and regulation dominate the literature. Although Bitcoin does serve as a (limited) medium of exchange similar to conventional currencies, Glaser et al. (2014) conclude that the larger volume of Bitcoin use is for speculative investment purposes. Tis observation, which evidence to date continues to support, serves to focus this literature review on Bitcoin either as a market-beating asset or as an addition to a portfolio. In a seminal paper, Gandal and Halaburda (2014) examine the co-movement of cryptocurrencies for the purpose of identifying opportunities for triangular arbitrage, or the search for anomalous performance that exceeds that of “the market.” Although they fnd little evidence of arbitrage, they uncover signs that such potential exists. Urquhart (2016) employs fve diferent time series tests, concluding that Bitcoin is inefciently priced, which points to proftable opportunities based on historical data, a result inconsistent with the concept of an informationally efcient market. Nadarajah and Chu (2017), however, counter this conclusion in their fndings based on a power transformation of Bitcoin returns. Still, Hong (2017) fnds evidence of time series momentum in Bitcoin trading, discovering persistence in prices up to eight weeks before a mean reversion downturn, suggesting market inefciency and thus the opportunity for aboveaverage returns. Bariviera (2017) draws the same conclusion, although the move toward efcient pricing is apparent over the longer term. Caporale, Gli-Alana, and Plastun (2018) uncover evidence of past prices being correlated with future prices, another sign of inefcient pricing. Cheah, Mishra, Parhi, and Zhang (2018) fnd evidence of long-term memory in Bitcoin pricing, another outcome that is inconsistent with market information efciency. Hattori and Ishida (2020) investigate how investors seek arbitrage in the Bitcoin spot and futures markets through the Chicago Board of Exchange. As expected, under normal conditions, arbitrage opportunities are difcult to uncover, but not during “crashes,” in which large proftable opportunities arise. In addition to Bitcoin’s trading behaviors on exchanges, Holub and Johnson (2019) examine bid–ask spreads and closing prices on local platforms. Tey fnd signifcant deviations from exchange prices, pointing to market inefciencies and signaling opportunities for abnormal returns. Using high-frequency data, Nazir and Kumar (2019) come to the same conclusion as frequency increases. Vidal-Tomas (2020), however, fnds that inefciency occurs only at frequencies of one minute and weekly, concluding that daily data ofer the most efcient frequency. Beyond the time series tests for market efciency, Hu, Hwang, Jain, and Washam (2020) uncover evidence of price manipulation in Bitcoin across 519 million orders, representing additional

How Much to Invest, If Any, in Bitcoin? 41

evidence counter to efcient pricing. Other studies pointing to mispricing include those by Day et al. (2021) on the proftability of using Bollinger bands, a method based on charting stock prices; Hung, Liu, and Yang (2021) on the destabilizing behavior of retail Bitcoin traders; and Nepp and Karpeko (2022) on the impact of “collective hysteria” on Bitcoin pricing. A related strand of research deals with a characteristic of investor behavior referred to as “herding.” Classifed under a feld called “behavioral fnance,” the study of the impact of psychology on fnancial decision-making, herding occurs when investors tend to trade either consciously or unconsciously on one side of the market rather than performing their own analyses. Such behavior can lead to market mispricing. As evidence in equity markets, Dorn, Huberman, and Sengmueller (2008), Barber, Odean, and Zhu (2009), and Merli and Roger (2013) each uncover herding behavior among retail investors, a tendency that appears more salient than among professional investors. Regardless, the evidence points to investors mimicking their own past activities, however inaccurate such activities are for estimating expected return. Te same observation applies to cryptocurrency markets. Ballis and Drakos (2020) uncover clear signals of herding, attributing it to irrational investing that is unrelated to individual investor’s views. Teir conclusions align with those of Nepp and Karpeko (2022) on “collective hysteria” among Bitcoin traders. Junior, Palazzi, Taveras, and Klotzle (2020) obtain similar evidence, fnding that herding occurs across market conditions, as revealed in both price and volatility. Yarovaya, Matkovskyy, and Jalan (2021) acknowledge the presence of herding in cryptocurrency markets but caution on its prevalence, as they are unable to fnd evidence of a “black swan” during the global pandemic. Gemayal and Preda (2022) provide evidence of herding behavior during both bull and bear markets but fnd that herding is signifcantly more prevalent during bearish periods. Tey also show that herding is almost natural when information is sporadic and hard to fnd. Tey also fnd evidence of investors frst acting on fundamental information before being overwhelmed by emotional reaction based on the feelings of “missing out.” Te collective evidence points to inefcient pricing—a fnding that aligns with exceptions to “weak form” efciency, which states that past price and volume data, no matter how compelling, cannot systematically be used to achieve above-market performance. Tis signals an incentive for Bitcoin to be incorporated into portfolios. As compelling as the evidence may be, however, studies to date concentrate on the behavior of Bitcoin but without providing guidance to investors on the percentage allocation. As a cryptocurrency, Bitcoin is a unique asset. As such, it may have diversifcation potential. In an early study, Wu and Pandy (2014) advance the analysis by using optimization techniques to demonstrate efciency gains from Bitcoin

42 Cryptocurrency Concepts, Technology, and Applications

in terms of higher returns and lower risks across portfolios consisting of currencies, commodities, stocks, bonds, and real estate. Briere, Oosterlinck, and Szafarz (2017) examine correlations between Bitcoin and other asset classes and find evidence that supports diversification benefits. Kajtazi and Moro (2018) use time series data and optimization methods applied to U.S., European, and Chinese portfolios and conclude that Bitcoin has return-enhancement potential but not risk-reduction potential. They acknowledge that Bitcoin’s volatility calls into question its risk-reduction efficacy. Using time series modeling, Guesmi et al. (2019) find that Bitcoin reduces the risk in a portfolio of stocks, gold, and Bitcoin. Moreover, selling Bitcoin short can also be an effective hedge. Implementing a new portfolio optimization method, Li, Jiang, Wei, and Wang (2021) show the efficacy of Bitcoin to enhance portfolio return while reducing risk, leading to higher Sharpe ratios. Collectively, the studies support the efficacy of Bitcoin’s diversification potential, signaling opportunities to enhance return–risk performance. When aligned with the evidence supporting return-enhancement potential, especially in instances of inefficient pricing, the motivation for including Bitcoin into investment portfolios is compelling. Again, the issue of the right allocation, however, remains unaddressed.

Portfolio Analysis and the Black-Litterman Model Portfolio analysis rests on three fundamental equations, as follows*: (1) 𝑛𝑛𝑛𝑛

𝑛𝑛𝑛𝑛

σ𝑝𝑝𝑝𝑝 2 = E E 𝑤𝑤𝑤𝑤𝑖𝑖𝑖𝑖 𝑤𝑤𝑤𝑤𝑗𝑗𝑗𝑗 σ𝑖𝑖𝑖𝑖 σ𝑗𝑗𝑗𝑗 ρ𝑖𝑖𝑖𝑖𝑗𝑗𝑗𝑗 𝑖𝑖𝑖𝑖=1 𝑗𝑗𝑗𝑗=1

(2)

(3)

*

Although the equations are in mean-variance form, convention has led to the use of standard deviation in place of variance as a measure of risk, which is frequently referred to as volatility.

How Much to Invest, If Any, in Bitcoin? 43

where, E (Rp )

= expected rate of return on a portfolio of securities;

σp2

= variance of the portfolio as a measure of risk;

σi σj ρij

= covariance of the returns of the securities comprised by the portfolio; and

wi

= weight assigned to each security.

Because the analysis in this study is restricted to “long-only” portfolios, wi ≥ 0. Tis is to state that “shorting” of securities does not apply. Te objective is to minimize portfolio risk subject to an expected return—namely, minimizing equation (2) subject to equation (1). It leads to the positive relationship between expected return and risk. Although this quadratic is relatively easy to optimize, the results are often out of step with reality, as Benninga (2008, 349) expresses: It is possible to come away from a standard textbook discussion of portfolio optimization with the impression that a fxed set of mechanical optimization rules, combined with a bit of knowledge about personal preferences, sufces to defne an investor’s optimal portfolio. Anyone who has tried to implement portfolio optimization using market data knows that the results are often a nightmare.

Why is this so? Although historical data are useful for estimating portfolio risks, the same does not apply for expected returns. In other words, past results are often not an accurate indicator of future returns, as cogently expressed by Elton (1999). Tis is the motivation for the model of Black and Litterman (1992). Rather than relying strictly on historical returns, it estimates expected returns from the capital asset pricing model (CAPM), which models expected returns, as follows:

E (Ri ) = βi [E (Rm ) – Rf ],

(4)

where, E (R i )

= expected rate of return on security i ;

E (R m )

= expected rate of return on “the” market;

[E (Rm ) – R f ]

= the reward for assuming additional risk, a risk premium;

Rf

= a “risk-free” rate of return; and

βi

= index of systematic, or market, risk normalized around 1.0, defned as the covariance of the rates of return on security i relative to the variance of the rates of return on “the” market.

44 Cryptocurrency Concepts, Technology, and Applications

Te model, which relies on one factor only, is simple, stating that the only variable driving expected return is the beta, with the risk premium measuring the incremental gain per increase in beta. Tere is precedent in the literature for the Black-Litterman application, as seen, for example, in Wu and Pandy (2014); Bessler, Opfer, and Wolf (2014); and Loviscek (2021). It has a two-step Bayesian foundation. Te frst one is the estimation of the CAPM to determine the expected returns. Tis begins with the estimation of the beta for each security from a simple time series model of volatility, or risk, as follows:

(R i,t – R f ) = βi (R m,t – R f ) + ei,t

(5)

where all terms are defned as before and ei is an error term. In this equation, beta is the coefcient, whereas it is the variable in the CAPM. Again, the beta is the index of systematic risk, and as such, the model is one of risk, where the variance of ei measures idiosyncratic, or unpredictable, risk—that is, risk that is unique to the security (e.g., unexpected death of a CEO, loss of a key patent, underperformance relative to expectations, failed product, etc.). Tis risk is diversifable; however, its counterpart, systematic risk, is not diversifable because it is linked to market conditions. Although multi-factor modeling is often a preferred choice, Frazzini and Pederson (2014), Cederberg and O’Doherty (2015), and Kolari, Liu, and Huang (2021) support the CAPM when applied within a portfolio framework. Te estimated returns align with equilibrium expected returns that replace the expected returns based exclusively on historical data. After the estimation of the CAPM returns, a “reverse optimization” determines the market-capitalized weights (Idzorek 2011), which lead to the implied returns consistent with portfolio optimization. To explain, after the estimation of the CAPM expected returns, along with the risks and covariances, the model maximizes a reward-to-variability ratio attributed to Sharpe (1994), leading to the respective weights for each security. To get to the fnal step—the CAPM equilibrium returns—an investor has two choices: either use CAPM returns in the optimization process or supplement the CAPM returns with additional information in the optimization, such as private information, mean reversion, momentum, or mispricing estimates. Tis study uses the frst choice, efectively assuming that markets are informationally efcient. Tese returns are considered to be the “prior” estimates within the Bayesian framework because they do not include the additional information that occurs in the second choice (i.e., the posterior estimates), in which an investor assumes that markets are inefcient by “tilting” the expected return on the portfolio to capture the additional information.

How Much to Invest, If Any, in Bitcoin? 45

Either way, the optimization process includes an estimate of a risk-aversion coefcient (λ). It can be estimated by the CAPM risk premium, [E (R m ) – R f ], relative to the variance of the portfolio. It represents a return–risk tradeof that is central to portfolio analysis. During the reverse optimization, it scales the estimates of the variance–covariance matrix. Combined with the capitalized market weights, the result is the implied vector of equilibrium returns, as follows:

Π = λΣwm

(6)

where, Π is the implied excess equilibrium return vector (N × 1 column vector); λ is the risk-aversion coefficient, which incorporates the expected return-risk tradeoff; Σ is the variance–covariance matrix of excess returns (N × N matrix); and, wm is the market capitalization weights (N × 1 column vector) of the assets.* Te optimization procedure used here is standard, as opposed to those that rely on “shrinkage” of the data or other means of data arrangement (Jorion 1991). Incorporated into the optimization, the estimates from equation (4) embody an exponentially weighted moving average (EWMA) coefcient of 0.97, as reportedly used by major fnancial institutions (Hoadley 2017). It weighs more recent observations more heavily than older observations, helping to capture any possible momentum efects. It is defned as follows: EWMA i,t = γ Ri,t + (1 – γ)EWMA i,t – 1

(7)

where, EWMA i ,0

= arithmetic average of the historical rates of return;

R i ,t

= rate of return on security i at time t ;

n

= number of observations, including EWMA i ,0 ; and

γ

= 0 < γ ≤1, which determines the length of the memory, in this case, 0.97.

*

Formally, N assets make up the market, with MP representing the market portfolio and Σ representing the variance–covariance matrix. Te equilibrium expected returns on the securities, in the form of a vector, have a mean of µ, with λ refecting risk tolerance. Te equilibrium CAPM expected returns are Π = λΣPM. Te optimal portfolio MP* = Σ–1 µ/λ.

46 Cryptocurrency Concepts, Technology, and Applications

Data Te stocks come from the Standard & Poor’s 100, a subset of the S&P 500. It records the prices of large-capitalization companies, including the largest, such as Apple, Amazon, ExxonMobil, Merck®, and Walmart®. With the focus on individual investors, the prices originate from Yahoo® Finance and are monthly, running from December 2010 through December 2021. Compared to the current literature, the period represents one of the longest to date, increasing confdence compared to studies based on as few as three years. In addition, it incorporates the full recovery from the global fnancial crisis of 2008–2009, the fnancial market downturn from the global pandemic of 2020, and the recovery from it. To estimate equation (4), the Vanguard 500 Index represents the market rate of return, or R m . Te three-month U.S. Treasury bill rate approximates the riskfree rate of return, or R f , which come from the Federal Reserve Bank of St. Louis. Tree years of monthly data were used to construct each of the Black-Litterman portfolios. For example, the frst portfolio consists of rates of return across the companies from January 2011 through December 2013. Te next portfolio is based on the rates of return from January 2012 through December 2014, and so up through January 2019 through December 2021. Although most frms listed in 2010 are also listed in 2011, such as Apple, Amazon, and Merck, this is not the case for all frms, such as Meta Platforms (i.e., previously Facebook®), which was not listed until 2015, and Tesla®, which was not listed until 2021. In other instances, frms listed at the beginning were later removed, such as Alcoa®, Allstate®, and Baker Hughes Holdings. As a result, the data had to be adjusted accordingly from one year to the next. Moreover, there was the occurrence of mergers, such as AbbVie®’s purchase of Allergan®, which was a member of the S&P 100 from 2015 to the frst quarter of 2019, or the buyout of Celgene®, another one-time member of the S&P 100, by Bristol Myers Squibb® in 2019. In these cases, the risk-free rate of return replaced the months remaining in the year of purchase. For example, in the case of Celgene, its fnal stock price was in November 2019. As a result, its return for December was the monthly U.S. Treasury bill rate for December 2019.

Results Table 3.1 provides annual conventional summary statistics generated by the Black-Litterman model (PORT), the Vanguard 500 Index (VAN), and Bitcoin

How Much to Invest, If Any, in Bitcoin? 47

(BIT) from January 2011 through December 2021, 11 individual years. For the entire period, in terms of (geometric) mean returns, BIT stands out, with an exceptional 205.06%—a major segment generated by the phenomenal maximum return of 3568.55% in 2013—ten times higher than that of PORT at 20.42% and well over 12 times greater than that of VAN at 16.21%. As pointed out, however, these outsized returns come with the price of higher volatility, or risk, as measured by the annual standard deviation. At 152.46%, it is over 11 times higher than those of PORT and VAN. Furthermore, its maximum volatility is 387.76%, nearly 15 times higher than the maximum volatilities of PORT and VAN. Its minimum return of –66.28% is over 14 times lower than that of VAN and 66 times lower than that of PORT. Tese much higher volatilities are also seen in BIT’s beta of 2.22, which is over twice the systematic risk index of its counterparts. As perspective, every 10% change in the rate return on VAN, on average, led to a 22.2% change in the rate of returns on BIT, which is additional evidence of large price swings in BIT. Tese results should serve as a strong word of caution to risk-averse investors, especially to those with strong risk aversion, that an accurate expected return on Bitcoin is difcult to estimate, signaling the potential for signifcant loss aversion for investors (Kahneman, Knetsch, and Taler 1991). Taken together, the returns and volatilities lead to Sharpe ratios that are revealing. At 1.46, PORT has the highest ratio, despite its much lower return than BIT,

Table 3.1 Annual Summary Statistics, Including Mean Returns, Volatilities, as Measured by Annual Standard Deviations, Betas, and Sharpe Reward-to-Variability Ratios, Respectively, for the Black-Litterman Portfolio (PORT), the Vanguard 500 Index (VAN), and Bitcoin (BIT), January 2011–December 2021 PORT

VAN

BIT

Mean Return

20.42%

16.21%

Max Return

38.42

32.38

3,568.55

Min Return

–1.01

–4.60

–66.28

Mean Vol

13.57

13.66

152.46

205.06%

Max Vol

26.54

26.55

387.76

Min Vol

4.22

4.57

58.00

Beta

1.01

1.00

2.22

Sharpe Ratio

1.46

1.15

1.34

48 Cryptocurrency Concepts, Technology, and Applications

followed by BIT at 1.34 and VAN at 1.15.* Based on the results at this level, particularly between PORT and BIT, the extent to which Bitcoin can add value to an optimized portfolio—that is, to contribute more to the rate of return than to the risk—appears to be quite limited. As a result, risk-averse investors may wish to look elsewhere for market-beating performance. Additional evidence, however, is in order before drawing a frm conclusion. Table 3.2 provides insight on the performance of BIT at a more micro level. It displays the composition of PORT for the entire period, 2011–2021. It also resembles the portfolio composition for each of the three-year periods. Tese companies are comprised by PORT when applying the Black-Litterman model to all frms in the S&P 100 as of December 2021. Te sample size is 132 months. Te application generates a portfolio of 45 stocks, 15 of which dominate, plus Bitcoin. Microsoft® (MSFT) registers the highest weight at 6.96%. Accenture® (ACN), Alphabet (GOOG), Apple (AAPL), Goldman Sachs® (GS), NextEra® (NEE), Union Pacifc® (UNP), and U.S. Bancorp® (USB) follow with weights in excess of 4%. Amazon (AMZN), Berkshire Hathaway® (BRK-A), Comcast® (CMCSA), Danaher® (DHR), Gilead® (GLD), MasterCard® (MA), and Medtronic® (MDT) represent those with weights at least at 3%. In contrast, BIT has a weight of only 0.19%. Although its annual return is extraordinary, its high volatility weighs on its contribution to the portfolio. As Table 3.1 illustrates, its average volatility sits at 152.46%. Of this, however, more than 84% is idiosyncratic, or unpredictable. Te higher the degree of unpredictability, the more the weight of a security will be reduced by means of diversifcation. Only predictable, or market, risk is priced; all other risk is diversifed away. Tis is also seen, for example, in Netfix® (NFLX), another high-risk investment. Although it registers a return of 33.50% for the period, 92% of its volatility of 55.4%, more than double that of Amazon and Apple, is idiosyncratic, leading to a weight of only 0.26%. Te correlation of BIT’s returns with those of the stocks also weighs on BIT’s asset allocation. Table 3.3 shows the correlation coefcients between BIT and PORT and between BIT and VAN per year. Previous studies show low correlations between Bitcoin and other asset classes, with some instances displaying negative correlations. As the results in Table 3.3 display, however, the extent to which this is true is not without reservation. Although there are fve instances of negative correlation coefcients between BIT and PORT, registering a low *

Although not a focus in this study, the higher Sharpe ratio for PORT compared to that of VAN indicates the efcacy of an optimization application in portfolio analysis. Te risks are approximately the same, but the return on PORT exceeds that of VAN by 413 basis points.

How Much to Invest, If Any, in Bitcoin? 49

Table 3.2 Composition of the PORT Based on Rates of Return from 2011–2021, Where the “Weight” Is the Percentage Allocated to Each Stock of Each Company and to Bitcoin Company

Weight

Company

Weight

Abbot Labs (ABT)

0.53%

General Motors (GM)

0.99%

Accenture (ACN)

4.44%

Gilead (GLD)

3.98%

Adobe (ADBE)

1.67%

Goldman Sachs (GS)

4.18%

Alphabet (GOOG)

4.53%

Home Depot (HD)

2.09%

Amazon (AMZN)

3.03%

Honeywell (HON)

1.44%

Apple (AAPL)

4.49%

Johnson & Johnson (JNJ)

0.71%

Berkshire Hathaway (BRK-A)

3.67%

MasterCard (MA)

3.44%

Biogen (BIIB)

1.75%

Medtronic (MDT)

3.63%

BlackRock (BLK)

1.66%

Williams (WMB)

2.11%

Bookings Holdings (BOOK)

0.09%

Microsoft (MSFT)

6.96%

Charter Communications (CHTR)

1.07%

Netfix (NFLX)

0.26%

Chevron (CVX)

2.80%

NextEra (NEE)

4.41%

Comcast (CMCSA)

3.17%

Nike (NKE)

0.44%

Costco (COST)

1.59%

Nvidia (NVDA)

1.43%

CVS (CVS)

1.65%

Pfzer (PFE)

2.97%

Danaher (DHR)

3.59%

Qualcomm (QCOM)

0.81%

Dupont de Nemours (DD)

0.81%

Raytheon (RTX)

0.70%

Emerson Electric (EMR)

1.52%

Union Pacifc (UNP)

4.84%

Excelon (EXC)

0.60%

United Healthcare (UNH)

1.09%

ExxonMobil (XOM)

0.62%

U.S. Bancorp (USB)

4.77%

FedEx (FDX)

0.88%

Verizon (VZ)

0.16%

Ford (F)

0.33%

Haliburton (HAL)

2.34%

General Dynamics (GD)

1.57%

Bitcoin (BTC-USD)

0.19%

of –0.18 in 2013, and six instances between BIT and VAN, registering a low of –0.30 in 2013, two instances show strong positive correlations, 0.76 and 0.78, respectively. Tey indicate high correlations, which lean against the diversifcation efcacy of BIT. Te variation from year to year, such as –0.10 to 0.50 and then to –0.18 in the case of PORT from 2011 through 2013, or –0.09 to 0.78 to –0.10 in the case of VAN from 2018 through 2021, only adds to the reservation.

50 Cryptocurrency Concepts, Technology, and Applications

Although not shown in the table, the average correlation between BIT and the securities comprised by PORT is 0.22, which is below 0.35 of the securities comprised by PORT. However, it points to positive movement between BIT and the securities in PORT, limiting the diversifcation efcacy of BIT. In addition, and at a more micro level, 0.22 is not the lowest correlation coefcient. Biogen® (BIIB), Gilead (GLD), Netfix (NFLX), and NextEra (NEE), for example, each register lower correlations, with BIIB being the lowest at 0.07. Table 3.3 Respective Correlation Coeffcients Per Year Between the Rates of Return on BIT, the Rates of Return on the PORT, and the Rates of Return on the VAN, 2011–2021 Correlation Coeffcients BIT w/PORT

BIT w/VAN

2011

2012

2013

2014

2015

2016

–0.10

0.50

–0.18

0.07

0.41

0.22

2017

2018

2019

2020

2021

–0.01

0.21

–0.06

0.76

–0.12

2011

2012

2013

2014

2015

2016

–0.15

0.36

–0.30

–0.16

0.39

0.18

2017

2018

2019

2020

2021

–0.23

0.16

–0.09

0.78

–0.10

Te results in Tables 3.1, 3.2, and 3.3 suggest that investors should be especially cautious about a signifcant allocation in Bitcoin. Te high-level risks and the variation in correlation coefcients point to an investment that is largely speculative. As a perspective, Figure 3.1 displays the location of the companies within the graphical depiction of the investment frontier, which is the relationship between the portfolio return and volatility (i.e., the application of equations [1], [2], and [3]). Te closer the position to the frontier, the more likely the company will be part of the “optimal” portfolio based on the results from the application of the capital asset pricing within the Black-Litterman model. Although the tight cluster makes precise location for most companies indiscernible, Bitcoin is positioned the farthest from the frontier compared to that of all companies. Tis reduces the likelihood, relative to the positions of the other securities, of Bitcoin receiving a signifcant weight, such as in the range of 2%–10%, as listed by the fnancial media.

Figure 3.1 Illustration of the Application of Portfolio Optimization Based on the Black-Litterman Model for the S&P 100, 2011–2021, in Which the Return Is on the Vertical Axis and the Volatility, or Risk, Is on the Horizontal Axis.

52 Cryptocurrency Concepts, Technology, and Applications

However, these results apply only to the entire period. An examination of the results from the portfolio construction periods is in order before drawing a frmer conclusion. Table 3.4 displays portfolio results per portfolio construction period—that is, 2011–2013, 2012–2014, and so on through 2019–2021. As expected, BIT records the highest mean return, 120.65%—displaying a maximum return of 1,281.56% from 2011 through 2013—seven times higher than the 16.99% of PORT and eight times higher than the 14.94% of VAN. It also, however, has the highest volatility at 193.22%, approximately 15 times higher than those of PORT and VAN, results that align more with those of Kajtazi and Moro (2018) than with, for example, Wu and Pandy (2014) on the limited risk-reduction potential of Bitcoin. Tis is also seen in the variation in BIT’s returns, a range from 6.12% to 1,281.56%. Te outcome is an average Sharpe reward-to-variability ratio of 0.62, the lowest among the group by a wide margin, with PORT and VAN registering 1.28 and 1.09, respectively. Furthermore, a period-by-period comparison shows that BIT’s Sharpe ratio exceeds that of PORT only in 2011-2013, and exceeds that of VAN only twice, 2011-2013 and 2016-2018. Te underperformance of BIT explains its low percentage allocations, ranging from 0% in three periods to a high of 1.95% in 2018-2020. When aligned with the results in Tables 3.1, 3.2, and 3.3 and in Figure 3.1, the results in Table 3.4 point to a low portfolio allocation, or weight, to BIT. Te average is 0.37%, which is not meaningfully diferent from the 0.19% weight in Table 3.2, each well below the 2%-10% range suggested in the fnancial media.

Additional Considerations Te results imply the classic “buy-and-hold” strategy. Although the results point only to a very small allocation to Bitcoin, its high volatility could provide shortterm investors and traders with proftable opportunities, given the cited price inefciencies in Bitcoin (e.g., Urquhart 2016; Hong 2017; Bariviera 2017). Tis implies possibly a higher weight than those found in this study. Short-term trading, however, carries with it tax implications, with realized gains being taxed at ordinary income rates, often much higher than the 15% U.S. rate applied to realized long-term capital gains. As a result, the before-tax return could be reduced by over 40% when also incorporating state and local income taxes. Te tax efect only further diminishes a Sharpe ratio that is already low. Te results in this study are “long only.” Given its volatility, Bitcoin might be used in short-term trading strategies that involve “going long” blended with

How Much to Invest, If Any, in Bitcoin? 53

Table 3.4 Rates of Return, Volatilities, as Measured by Annual Standard Deviations and Sharpe Reward-to-Variability Ratios for the PORT, the VAN, and BIT for Each of the Portfolio Construction Periods, Including the Weight Assigned to BIT Per Period Portfolio Construction Period

PORT Return

2011–2013

18.95%

15.96%

2012–2014

20.28

20.24

304.51

0.00

2013–2015

16.01

15.00

216.87

0.21

2014–2016

10.34

8.69

6.12

0.00

2015–2017

15.64

11.29

9.15

1.04

2016–2018

16.97

9.12

105.10

0.79

2017–2019

15.93

15.16

95.49

0.00

2018–2020

11.47

14.03

27.85

1.95

2019–2021

28.25

25.96

17.91

0.43

Mean

16.99

14.94

120.65

0.37

PORT Volatility

VAN Volatility

BIT Volatility

PORT Sharpe

VAN Sharpe

2011–2013

11.97%

12.56%

355.53%

1.58

1.27

3.60

2012–2014

8.40

9.24

295.58

2.41

2.19

1.03

2013–2015

10.55

11.08

297.47

1.52

1.35

0.73

2014–2016

10.86

10.84

63.08

0.95

0.80

0.10

2015–2017

9.43

10.28

17.77

1.66

1.10

0.51

2016–2018

11.60

11.10

88.61

1.46

0.82

1.19

2017–2019

10.94

12.44

94.83

1.46

1.22

1.01

2018–2020

19.86

19.23

81.14

0.58

0.73

0.34

2019–2021

16.73

17.78

77.71

1.69

1.46

0.23

Mean

12.74

13.14

193.22

1.28

1.09

0.62

Portfolio Construction Period

VAN Return

BIT Return 1,281.56%

BIT Weight 0.51%

BIT Sharpe

“selling short.” Tis form of active investing, however, applies only to a small percentage of investors, with short selling comprising only about 2.5% of total market capitalization (Pisani 2021).

54 Cryptocurrency Concepts, Technology, and Applications

Investors should also be aware of an unsystematic risk in Bitcoin that is not refected in the numbers: non-regulatory risk. Because Bitcoin does not trade on established exchanges, such as those found on stocks, bonds, and commodities, and its activities are not regulated by a central authority that promotes smooth and transparent operations, it is subject to theft. For example, on February 8, 2020, the Department of Justice, uncovered a conspiracy to launder $4.5 billion in stolen cryptocurrency. Such an instance is not unique because cryptocurrency exchanges are essentially (unregulated) platforms that are easy targets for criminals. Non-regulatory risk, however, is not limited to safety issues. Bitcoin is subject, for example, to the threat of price manipulation given its concentrated ownership. As of 2022, the top 10% of Bitcoin miners control an estimated 90% of the mining, with 50 miners dominating 50% of the capacity. Such concentration raises the specter not only of price inefciency but also of price “unfairness,” should manipulation—even its perception—occur, which is rarely a major issue on established exchanges. As one more concern, because Bitcoin trades on platforms without a central governing body, an investor is unable to recover a lost password (which is recoverable in a regulated exchange). Te unfortunate result is lost access forever to investment gains.

Conclusion Bitcoin has captured the attention of investors unlike any asset in recent times, compellingly so by registering a decade-long rate of return of over 200%, with daily transaction activity well into the billions. Tis gain, however, has come with the price of high volatility, from an average of up to 15 times higher than that of the Vanguard 500 Index. As a result, accurate estimates of expected return can be especially difcult, which raises the central question in this study: how much to invest, if any, in Bitcoin? Interviews from the fnancial media answer this question with an asset allocation range of 2% to 10%. Tis range, however, is largely subjective, lacking in objective evidence, such as from an optimization method in which risk is minimized for an expected return. Furthermore, recourse from the academic and professional literatures is limited at best. Tis study addresses this question by applying the Black-Litterman model of portfolio analysis to the stocks comprised by the S&P 100 with Bitcoin. Te analysis runs from January 2011 through December 2021, one of the longest to date in an examination of the cryptocurrency. A series of three-year optimized portfolios, in which risk is minimized for an expected rate of return, reveals only a very small allocation: an average of 0.37%, much lower than published views

How Much to Invest, If Any, in Bitcoin? 55

suggest. Furthermore, three of the nine periods register a weight of 0%, signaling an investment that does not carry a sufcient added value for inclusion in portfolios, as also expressed in the relatively low Sharpe reward-to-variability ratios. Te impressive returns are largely undermined by high volatilities, indicating an asset that is largely speculative, especially in light of non-regulatory risks. As a result, Bitcoin is a suitable investment only for individuals who have a high tolerance for risk, an attribute possessed only by a small percentage of investors.

References Ashford, K., and Curry, B. (2022). Bitcoin could hit $100,00 within a year, crypto frm’s CEO predicts. https://www.cnbc.com/2022/04/14/Bitcoin-btc-price -could-hit-100000-within-a-year-crypto-ceo.html Ballis, A., and Drakos, K. (2020). Testing for herding in the cryptocurrency market. Finance Research Letters, 33: 101210. Barber, B. M., Odean, T., and Zhu, N. (2009). Systematic noise. Journal of Financial Markets, 1(4): 547–569. Bariviera, A. (2017). Te inefciency of Bitcoin revisited. Economics Letters, 161 (December): 1–4. Benninga, S. (2008). Financial Modeling, 3rd ed. Boston: MIT Press. Bessler, W., Opfer, H., and Wolf, D. (2014). Multi-asset portfolio optimization and out-of-sample performance: An evaluation of Black-Litterman, mean-variance, and naïve diversifcation. European Journal of Finance, 23(1): 1–30. Black, F., and Litterman, R. (1992). Global portfolio optimization. Financial Analysts Journal, 48 (September–October): 28–43. Briere, M., Ooserlinck, K., and Szafarz, A. (2015). Virtual currency, tangible return: Portfolio diversifcation with Bitcoin. Journal of Asset Management, 16(6): 365–373. Caporale, G. M., Gil-Alana, L., and Plastun, A. (2018). Persistence in the cryptocurrency market. Research in International Business and Finance, 46(C): 141–148. Cederberg, S., and O’Doherty, M. S. (2015). Asset-pricing anomalies at the frm level. Journal of Econometrics, 186(1): 113–128. Cheah, E.-T., Mishra, T., Parhi, M., and Zhang, Z. (2018). Long memory interdependency and inefciency in Bitcoin markets. Economic Letters, 167 (June): 18–25. Day, M.-Y., Huang, P., Cheng, Y., Lin, Y.-T., and Ni, Y. (2022). Proftable day trading Bitcoin futures following continuous bullish (bearish) candlesticks. Applied Economics Letters, 29(10): 947–954.

56 Cryptocurrency Concepts, Technology, and Applications

DeMatteo, M. (2022). Experts say Bitcoin could hit $100,000 in 2022. What investors should know. https://time.com/nextadvisor/investing/cryptocurrency /Bitcoin-price-predictions/ Dorn, D., Huberman, G., and Sengmueller, P. (2008). Correlated trading and return. Journal of Finance, 63(2): 885–920. Elton, E. J. (1999). Expected return, realized return, and asset pricing tests. Journal of Finance, 54(4): 1199–1220. Frazzini, A., and Pedersen, L. H. (2014). Betting against beta. Journal of Financial Economics. 111(1): 1–25. Gal, D., and Rucker, D. D. (2018). Te loss of loss aversion: Will it loom larger than its gain? Journal of Consumer Psychology, 28(3): 1–20. Gandal, N., and Halaburda, H. (2014). Competition in the cryptocurrency market. CESifo Working Paper Series, 4980. https://papers.ssrn.com/sol3/papers.cfm ?abstract_id=2506577 Gemayela, R., and Preda, A. (2022). Herding in the cryptocurrency market: A transaction level analysis. King’s College of London, London. (Presented at the meetings of the Academy of Finance, Chicago, IL, March 23–25.) Glaser, F., Zimmerman, K., Haferkorn, M., Weber, C., and Siering, M. (2014). Bitcoin—Asset or currency? Revealing users’ hidden intentions. Proceed­ ings of the 22nd European Conference on Information Systems, Tel Aviv, June 2014. Glumov, D. (2013). How did the great fnancial crisis afect portfolio allocations and attitudes towards risk? Evidence from the Survey of Consumer Finances. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2293189 Guesmi, K., Saadi, S., Ilyes, A., and Ftiti, Z. (2019). Portfolio diversifcation with virtual currency: Evidence from Bitcoin. International Review of Financial Analysis, 63(C): 431–437. Hattori, T., and Ishida, R. (2020). Te relationship between arbitrage in futures and spot markets and Bitcoin price movements: Evidence from the Bitcoin markets. Journal of Futures Markets, 41(1): 105–114. Hoadley, P. (2017). Hoadley trading and investment tools. https://www.hoadley.net /options/options.htm Hong, K. (2017). Bitcoin as an alternative investment vehicle. Information Technology Management, 18(4): 265–275. Holub, M., and Johnson, J. (2019). Bitcoin price anomalies: Peer-to-peer trading on LocalBitcoins. Journal of Alternative Investments, 22(1): 127–139. Hu, B., Hwang, J. H., Jain, C., and Washam, J. (2020). Applied Economics Letters, 29(2): 140–144. Hung, J.-C., Liu, H.-C., and Yang, J. (2021). Price discovery in Bitcoin futures markets. Journal of Empirical Finance, 62 (June): 107–120.

How Much to Invest, If Any, in Bitcoin? 57

Idzorek, T. M. (2011). A step by step guide to the Black-Litterman model: Incorporating user-specifed confdence levels. In: S. Satchell (Ed.). Forecasting Expected Returns in the Financial Markets. New York: Elsevier, 17–37. Jorion, P. (1991). Bayesian and CAPM estimators of the means: Implications for portfolio selection. Journal of Banking and Finance, 15(3): 717–727. Junior, G., Palazzi, R. B., Tavares, R., and Klotzle, M. C. (2020). Market stress and herding: A new approach to the cryptocurrency market. Journal of Behavioral Finance, 23(1): 43–57. Kahneman, D., Knetsch, J. L., and Taler, R. H. (1991). Anomalies: Te endowment efect, loss aversion, and status quo bias. Journal of Economic Perspectives, 5(1): 193–206. Kassar, N. (2022) How much crypto should be in your investment portfolio? Washington Post. https://www.washingtonpost.com/business/how-much -crypto-should-be-in-your-investment-portfolio/2022/02/18/4ce9b92a-90b7 -11ec-8ddd-52136988d263_story.html Kajtazi, A., and Moro, A. (2018). Te role of Bitcoin in well diversifed portfolios: A comparative global study. International Review of Financial Analysis, 61 (January): 143–157. Kharpal, A. (2021). Bitcoin at $1 million? Some analysts are bullish but others warn of risks ahead. https://www.cnbc.com/2021/02/18/Bitcoin-btc-gets-1-million -price-call-but-there-are-risks-ahead.html Krueger, P. (2022). Is there a right way to invest in Bitcoin in 2022? Yes. https:// www.kiplinger.com/investing/cryptocurrency/604070/is-there-a-right-way -to-invest-in-Bitcoin-in-2022-yes Kolari, J. W., Liu, W., and Huang, J. Z. (2021). A New Capital Asset Pricing Model: Teory and Evidence. New York: Palgrave Macmillan. Li, Y., Jiang, S., Wei, Y., and Wang, S. (2021). Take Bitcoin into your portfolio: A novel portfolio optimization framework for broad commodity assets. Financial Innovation, 7(1): 2–26. Liu, W. (2019). Portfolio diversifcation across cryptocurrencies. Finance Research Letters, 29 (June): 200–205. Loviscek, A. (2021). How much to invest, if any, in precious metals? A 21st century perspective. Journal of Investing, 20(4): 95–111. Merkle, C. (2020). Financial loss aversion illusion. Review of Finance, 24(2): 381–413. Merli, M., and Roger, T. (2013). What drives the herding behavior of individual investors? Finance, 34(3): 67–104. Nazir, F., and Kumar, Z. D. (2019). Informational inefciency of Bitcoin: A study based on high-frequency data. Research in International Business and Finance, 47(C): 344–353.

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Nepp, A., and Karpeko, F. (2022). Hype as a factor on the global market: Te case of Bitcoin. Journal of Behavioral Finance. DOI: 10.1080/15427560.2022.20 73593 Nadarajah, S., and Chu, J. (2017). On the inefciency of Bitcoin. Economics Letters, 150(C): 6–9. Pisani, B. (2021). Short sellers are down $91 billion in January as GameStop leads squeeze in stocks they bet against. https://www.cnbc.com/2021/01/26/short -sellers-are-down-91-billion-in-january-as-gamestop-leads-squeeze-in-stocks -they-bet-against.html Sharma, G. D., Jain, M., Mahendru, M., Bansal, S., and Kumar, G. (2019). Emergence of Bitcoin as an investment alternative: A systematic review and research agenda. Journal of Business and Information, 14(1): 47–84. Sharpe, W. F. (1994). Te  Sharpe  Ratio.  Journal  of  Portfolio  Management,  21(1): 49–58. Stankiewicz, K. (2022). Kevin O’Leary says he’s put 20% of his portfolio in crypto, including token and blockchain frms. https://www.cnbc.com/2022/03/11 /kevin-oleary-20percent-of-my-portfolio-is-in-crypto.html Urquhart, A. (2016). Te inefciency of Bitcoin. Economics Letters, 148 (November): 80–82. Vidal-Tomas, D. (2020). All the frequencies matter in the Bitcoin market: An efciency analysis. Applied Economics Letters, 29 (December): 212–218. Wu, C., and Pandy, V. K. (2014). Te value of Bitcoin enhancing the efciency of an investor’s portfolio. Journal of Financial Planning, 27(9): 44–52. Yarovaya, L., Matkovskyy, R., and Jalan, A. (2019). Te efects of a ‘black swan’ event (COVID-19) on herding behavior in cryptocurrency markets. Finance Research Letters, 29 (June): 200–205. Zweig, J. (2022). You can get crypto right and still play it wrong. Wall Street Journal. https://www.wsj.com/articles/Bitcoin-you-can-get-crypto-investing-rightand-still-play-it-wrong-11643990277

Chapter 4 Global Central Bank Digital Currency Research and Developments: Implication for Cryptocurrency Peterson K. Ozili Central Bank of Nigeria

4.1 Introduction Central bank digital currency (CBDC) is part of the ongoing change and disruptive fnancial innovation occurring in the modern fnancial system. Historically, fnancial innovation lies within the realm of private sector agents such as regulated fnancial institutions and fnancial technology companies. Tey develop innovative ideas and create innovative fnancial products which the central bank or the regulator may approve or disallow. Tis means that the originator of disruptive fnancial innovations are primarily private sector agents, not the central bank. Tis historical understanding is important because it helps to understand why issuing a CBDC is a signifcant venture for any central bank because issuing

59

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a CBDC presents an opportunity for central banks to innovate, even though central banks are not innovative by nature. Discussions about CBDC frst emerged in 2017. Policy and academic discussions about CBDC became popular in mid-2019 and during the 2020– 2022 COVID-19 pandemic. During this time, there was optimism that CBDC would present an opportunity for a central bank to innovate and expand the usefulness of money as a medium of exchange, a payment alternative, and a tool to improve the conduct of monetary policy. Other widely acknowledged benefts of issuing a CBDC include its potential to facilitate cross-border payments, decrease cash management costs, increase fnancial inclusion, and ofer a relatively low transaction cost on CBDC fnancial transactions (Mancini-Grifoli et al. 2018; Ozili 2022a). To fully understand why CBDC exists today, we need to understand the meteoric rise in cryptocurrencies. Te frst cryptocurrency was developed in 2008. Since then, cryptocurrency began to gain traction from 2012 to 2017 and rose to prominence globally in 2019. Large volumes of fnancial activities were carried out using cryptocurrencies, also known as private digital curren­ cies. Te uncontrollable rise of many cryptocurrencies, their widespread usage by members of society, and their ability to evade all forms of regulation became a major concern for fnancial system regulators in many countries (Ozili 2022b). Regulators and central banks had serious concerns that cryptocurrency activities might pose systemic risks to the fnancial system and threaten fnancial stability (Kim and Kwon 2022). Te perceived threat from cryptocurrency activities gave central banks an added motivation to take seriously the possibility of issuing a central bank digital currency as a counter-reaction to the rise of cryptocurrency (Berentsen and Schär 2018). Since then, many central banks have announced that they are researching CBDCs, while other central banks have stated that they will launch a pilot CBDC project sooner or later (Barontini and Holden 2019). Central banks’ push to issue and adopt CBDC have been met with mixed reactions. Some think that central banks do not have the technological sophistication that is needed to develop and operate a CBDC in a sustainable way (Panetta 2018). Others argue that since existing money alternatives, such as debit cards and credit cards, are working very well, a lot of people will not use CBDC if a central bank issues it as a payment alternative; therefore, there is no strong reason for a central bank to do so (Emery 2019). Others also argue that existing legal and political bottlenecks may frustrate the efort of central banks in developing a CBDC (Bossu et al. 2020). Despite the potential benefts and bottlenecks that may be encountered by central banks in issuing and adopting a CBDC, there is no doubt that CBDCs are fast becoming a necessary invention or innovation that cannot be resisted for too long. Central banks that are not interested in issuing a CBDC now will

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eventually become interested in issuing a CBDC later—it is only a matter of time. Te question that needs to be asked is whether CBDC can coexist with cryptocurrency or whether cryptocurrency either has to be banned in order to allow CBDC to thrive in society at the expense of cryptocurrency or should be regulated, which means it will lose some of its anonymity features. But to answer these questions, there is a need to frst understand what a CBDC is in terms of defnitions and in comparison to cryptocurrency. It is also important to identify the important CBDC research in the world as well as the factors infuencing CBDC issuance and adoption. Te purpose of this chapter is to present a discussion of central bank digital currency. Te chapter begins with the defnition of CBDC and compares CBDC with cryptocurrency. It also reviews the recent CBDC research in the literature and ofers a global perspective on the state of CBDC research in the world. Tereafter, it identifes the factors infuencing CBDC issuance and adoption. Finally, the chapter draws some implications of recent CBDC development for cryptocurrency. CBDC research is growing in the literature, although it still represents a small part of the fnancial innovation literature. Notable studies in the recent CBDC literature include Kim and Kwon (2022); Allen et al. (2020); Auer and Böhme (2020); Ward and Rochemont (2019); Ozili (2022a); and Agur, Ari, and Dell’Ariccia (2022). Much focus in the CBDC literature has been placed on CBDC design and how CBDC can be used for retail and wholesale purposes (see, for example, Allen et al 2020; Auer and Böhme 2020; Agur, Ari, and Dell’Ariccia 2022; Ward and Rochemont 2019). Policy studies in the literature have examined the benefts of using cash and CBDC together rather than using only CBDC or cash (see, for example, Davoodalhosseini 2021; Armelius, Claussen, and Hull 2021). Other studies identifed the risks that CBDC could pose to the stability of the fnancial system, such as a possible run on commercial bank deposits and cybersecurity risks (see, for example, Kim and Kwon 2022; Bindseil 2020). More importantly, several studies stress the need for collaboration among central banks in developing an efective central bank digital currency, which also provides an opportunity for central banks to learn from each other (Barontini and Holden 2019). Te discussion in this chapter contributes to the literature in the following ways: it ofers a perspective on the need for CBDC, it identifes and discusses the global CBDC research around the world, and it identifes CBDC as an innovation designed to either coexist with cryptocurrency or to counteract the growth of cryptocurrency. Te rest of the chapter is organized as follows: Section 2 presents the conceptual background, defnes CBDC, and describes the similarities and diferences between CBDC and cryptocurrencies. Section 3 presents a review of recent CBDC

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research and the research in various regions of the world. Section 4 draws some implications for cryptocurrency. Te conclusion is presented in Section 5.

4.2 Conceptual Background Tis section presents a simple defnition of central bank digital currency. It also presents some technical defnitions of CBDC in the literature. Te similarities and diferences between CBDC and cryptocurrencies are presented in this section.

4.2.1 CBDC Defnitions In simple terms, CBDC is the electronic form of fat money. Tere are technical defnitions of CBDC in the literature. For instance, Meaning et al. (2018, 4) defned CBDC “as an electronic, fat liability of a central bank that can be used to settle payments or as a store of value.” Kif et al. (2020, 9) defned CBDC as “a digital representation of a sovereign currency issued by a central bank and is a liability of a jurisdiction’s central bank or other monetary authority” (paraphrased). Ward and Rochemont (2019, 3) defned a CBDC as “a digital form of central bank money that is diferent from balances in traditional reserve or settlement accounts.” Kumhof & Noone (2018, 4) defned CBDC as “electronic central bank money that (i) can be accessed more broadly than reserves, (ii) potentially has much greater functionality for retail transactions than cash, (iii) has a separate operational structure to other forms of central bank money, allowing it to potentially serve a diferent core purpose, and (iv) can be interest bearing, under realistic assumptions paying a rate that would be diferent to the rate on reserves.”

4.2.2 Similarities and Differences Between CBDCs and Cryptocurrencies One similarity between CBDCs and cryptocurrencies is that they are are both electronic forms of money. Secondly, they are both delivered on blockchain and distributed ledger technology. Tirdly, they can both be used privately to facilitate transactions where both parties agree to do so. Te diferences between CBDCs and cryptocurrencies are as follows: • CBDCs are a broad range of digital currencies issued by central banks, while cryptocurrencies are a broad range of privately issued digital assets or privately issued digital currencies (Richards 2021; Ozili 2022a).

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• Te value of CBDC is always pegged to a ratio of 1:1 with the physical currency equivalent, while most cryptocurrencies are not pegged to a ratio of 1:1 with any physical currency, excluding stablecoins (Klein, Gross, and Sandner 2020). • CBDCs are denominated in the currency of the sovereign issuer, while cryptocurrencies are not. • CBDCs are backed by the issuing central bank and rely on trust in the central bank or the government, while cryptocurrencies are not backed by any issuer and they rely on users’ trust in the software protocol that controls the system (Kif et al. 2020). • All CBDC can be used as perfect money substitutes, while cryptocurrencies, including stablecoins, are not perfect money substitutes because they do not have the key attributes of money: they are rarely used or accepted as a means of payment for retail purchases in everyday life, they are not used as a unit of account, and their prices are often volatile (Richards 2021).

4.3 CBDC Research and Developments around the World Tis section presents a concise overview of recent central bank digital currency research and developments in several regions of the world. It also shows interest in information about CBDC relative to cryptocurrency.

4.3.1 Recent CBDC Research in the Literature Recent CBDC studies, such as Virtanen (2021), show that a major factor driving the global interest in CBDC is the decline in cash usage and the need for better settlement systems. Khiaonarong and Humphrey (2022) confrm this claim in their empirical analysis. Tey examined 25 countries from 2012 to 2019 and showed that, prior to CBDC adoption, the use of cash for payments had been declining over the years. Tey argue that the observed reduction in cash use across countries can speed up the adoption of retail CBDC if central banks issue them. Other recent studies in the literature identify the benefts of issuing CBDC, such as reduced transaction cost, decrease in tax evasion, decrease in fnancial crime, greater fnancial inclusion, greater protection against illegal activity, lower cost of migrants’ remittances, lower seigniorage cost, improved conduct of

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monetary policy, smoother cross-border transactions, and more efcient fnancing to support sustainable development goals such as circular economy goals (see, for example, Taskinsoy 2021; Jossey 2022; Ozili 2022a; Edwards 2021; Ozili 2022d). Regarding CBDC design, Taskinsoy (2021) and Sethaput and Innet (2021) argue that CBDCs should have the properties of cash or paper money, should be implemented using blockchain technology to execute and settle peer-to-peer transactions, should be minimally invasive, and the benefts should outweigh its cost. However, Edwards (2021) and Soderberg (2022) suggest that a smooth rollout of CBDCs in all countries will be difcult, but such difculty can be surmounted through international collaboration and coordination. Several recent policy studies in the literature have called for caution in issuing a CBDC. For instance, Jossey (2022) points out that CBDCs could become a powerful tool that governments can use to exert undue economic and social control on the life of citizens, and many central banks are pushing to adopt CBDC to seek self-preservation and relevance in the digital age and not necessarily because CBDC introduces any lasting benefts in the life of citizens. Rizk (2022) showed that issuing CBDC may present legal issues, especially if central bank laws do not authorize CBDC to be issued to the general population. Taskinsoy (2021) and Ozili (2022a) identifed other challenges and risks associated with CBDC adoption, such as CBDC design tradeofs, a possible run on bank deposits, data privacy issues, the reduction of private commercial bank intermediation, and the decline in the use of ATMs. From the review of recent studies above, it can be seen that despite the many benefts which CBDC can ofer, there are important risks that should be understood and mitigated for CBDCs to have a lasting positive impact on society.

4.3.2 CBDC Developments in Africa A review of publicly available information shows that few African countries have shown interest in issuing a CBDC. African countries such as Kenya, Ghana, Zimbabwe, Tanzania, Egypt, Morocco, Uganda, and Zambia have indicated interest in exploring the issuance of a CBDC. Meanwhile, Nigeria has already issued a CBDC. Te high level of cryptocurrency activities in Nigeria in 2021 was a major factor that infuenced Nigeria’s central bank to issue a CBDC. Te central bank of Nigeria barred Nigerian banks from facilitating cryptocurrency trading and activities; soon after, the central bank issued a CBDC known as the eNaira (Ozili 2022c). Other African countries such as Ghana, Mauritius, and South Africa have already launched a pilot CBDC project which is presently being tested. African countries that have not reached a decision on whether to issue a CBDC include Rwanda, Cameroon, Sao Tome and Principe, and Djibouti. Te factors

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responsible for the low interest in CBDC among African central banks include low smartphone penetration, lack of political support for central banks to issue a CBDC, the underdeveloped payment system, widespread digital illiteracy, refusal of merchants to accept digital payment, and increased cyber-attacks on existing technological infrastructure (Ozili 2022a).

4.3.3 CBDC Developments in Europe Policy discussions about CBDC in Europe have emerged recently. Existing policy discussion about CBDC in Europe show that there are plans to develop a digital euro for countries in the European Union. Boonstra (2022) argues that the creation of a well-developed digital euro will help to restore the international position of the euro, which has been stagnating in the last decade, and the digital euro can improve cross-border trade outside the Eurozone. Grunewald, Zellweger-Gutknecht, and Geva (2021) show that the European Central Bank (ECB) plans to issue a digital euro that will be used by all countries in the European Union, and the issuance of digital euro will help EU countries to fulfll their monetary policy mandate and role as lender of last resort (Panetta 2021). Te push to issue a digital euro is largely due to the potential threat from big tech frms, who are also issuing their own private digital currencies to compete with traditional money (Mayer 2019). Te ECB’s digital euro will be able to track users, which is in stark contrast with cryptocurrencies, which do not permit the tracking of users (Mayer 2019). European countries outside the European Union, such as the United Kingdom, Turkey, Iceland, Albania, Ukraine, and Russia, are also researching and exploring the issuance of a CBDC. Te purpose of researching and exploring the issuance of a CBDC by European countries is to develop advanced knowledge and expertise about CBDC to enable European countries to decide the exact use case of CBDC that is appropriate for their economies.

4.3.4 CBDC Developments in the Region of the Americas Observations from publicly available information show that only the Bahamas and the Eastern Caribbean Currency Union have issued a CBDC in this region, while other countries in the region are still exploring the possibility of issuing a CBDC. Te Bahamas issued the Sand Dollar CBDC, primarily to increase fnancial inclusion and to prevent illicit fnancial fows. Te Eastern Caribbean Currency Union issued a digital currency to improve the efciency of payments and to increase fnancial inclusion. Tese countries have a large number of unbanked adults, and there are expectations that the issuance of a CBDC can help fnancial services providers to reach more unbanked adults in these

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countries. Other countries such as the United States, Mexico, Peru, and Canada are still building expertise and knowledge to determine the appropriate and best use case of CBDC in their countries, while countries such as Brazil and Jamaica have already launched pilot CBDC tests.

4.3.5 CBDC Developments in Asia Compared to Europe and Africa, the most advanced CBDC in the world is found in Southeast Asia in China, which is the only country in the region that has issued an operational CBDC. Wang (2022) showed that China issued a two-tiered CBDC “e-CNY” that will become a substitute for cash in circulation and will coexist with physical money. However, Wang argued that the sustainability of the e-CNY would be infuenced by economic, political economy, legal, regulatory, and governance factors in China. Didenko and Buckley (2021) argue that even though China leads the way in issuing a CBDC in the region, the China CBDC bears some risks, which need to be properly understood. Tey state that now is not the time for countries in the region to issue a CBDC; rather, central banks in the region should begin laying the groundwork for building specifc knowledge and expertise about CBDC. Other Asian countries such as India, Cambodia, Singapore, the Philippines, South Korea, the United Arab Emirates, and Hong Kong are exploring the case for issuing and operationalizing a CBDC.

4.3.6 CBDC Developments in Oceania Several policy discussions in the Oceania region point to a general lack of interest in issuing a CBDC just yet, and central banks in the Oceania region have argued that there is no strong policy case to do so at this time. For instance, central bankers in Australia (according to Richards et al. 2020; Emery 2019; and Richards 2021) argue that there is a weak policy case for issuing a CBDC at the moment. Tis is because Australia’s existing electronic payments system, also known as the New Payments Platform (NPP), already provides households and businesses with a wide range of safe, fast, convenient, and low-cost payment services, which CBDC also promises to ofer. Terefore, issuing a CBDC to act as a payments alternative in Australia would be an unnecessary duplication of the existing payment systems. Meanwhile, in New Zealand, Hawkesby and Wadsworth (2020) assert that the central bank (or Reserve Bank) of New Zealand has no plans to issue a CBDC at the moment but it will closely monitor global CBDC development to identify the right time to do so. Other countries in the region have not shown a strong intention to issue a CBDC.

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4.3.7 Global Interest in Information about CBDC Having identifed some progress in CBDC development in the previous section, it is important to assess the worldwide interest in information or knowledge about CBDC. One way to undertake such assessment is to observe the trend in CBDC as a search keyword on a major search engine such as Google®. Data were collected from the Google Trends database for a fve-year period from January 2017 to March 2022. Figure 4.1 shows that global interest in internet information about CBDC gained momentum in 2017 and increased signifcantly between 2019 and 2022. Meanwhile, in comparison to cryptocurrency, Figure 4.2 (on next page) shows an opposite interest in information about CBDC and cryptocurrency, as higher global interest in information about cryptocurrency was associated with lower global interest in information about CBDC from 2018 to 2022. Tis suggests that people who were more interested in information about CBDC were less interested in information about cryptocurrency, and vice versa. Also, Figure 4.3 (on page 69) shows that Jamaica, Singapore, Kenya, the UAE, and Nigeria recorded the highest interest in internet information about CBDC. Overall, these data suggest that growing interest in CBDCs can lead to lower interest in cryptocurrencies.

Figure 4.1 Global Interest in Information About CBDC on the Internet

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Figure 4.2 Global Interest in Internet Information About CBDC and Cryptocurrency (Source: Google Trends)

4.4 CBDC Adoption: Implications for Cryptocurrency Te emergence of CBDCs has several implications for cryptocurrency’s survival. Te frst implication is the potential for CBDCs to replace cryptocurrencies (Allen, Gu, and Jagtiani 2022). A well-developed CBDC can completely displace cryptocurrency from societies if cryptocurrency developers do not ensure a responsible and orderly development of cryptocurrencies. Recently, cryptocurrencies, or crypto assets, have become more suitable for use as speculative assets rather than as money (Nguyen et al. 2019). Te high valuation of cryptocurrencies and their high volatility have made them unattractive to be used as money. Tis can lead to loss of confdence in using cryptocurrency for day-today retail transactions. Another implication of the emergence of CBDC for cryptocurrency relates to the need to regulate all types of digital currencies in the formal payment system. Te adoption of CBDC raises serious debate about whether a CBDC should coexist with cryptocurrencies in the formal payment system through cryptocurrency regulation (Bolt, Lubbersen, and Wierts 2022). Tis is a sensitive topic, because such regulation may require cryptocurrencies to lose some or all of their anonymity features or cryptographic properties, which is the essential feature that makes it a cryptocurrency. If regulation completely removes the

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Figure 4.3 Countries with the Highest Interest in Information About CBDC (Source: Google Trends)

anonymous features or cryptography of a cryptocurrency, then it ceases to be a cryptocurrency, and this could lead to its collapse. Another implication of the emergence of CBDC for cryptocurrency is the emergence of stablecoins, which have made cryptocurrency more appealing to governments and members of societies (Bullmann, Klemm, and Pinna 2019).

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Stablecoins are cryptocurrencies that are pegged to a hard currency in a ratio of 1:1. Stablecoins can address some problems of cryptocurrencies; for example, they can mitigate the volatility problem of cryptocurrency (Smales 2021). However, they also create inefciencies and liquidity risks in the fnancial system, which are undesirable to fnancial system regulators. For this reason, many CBDC advocates argue that CBDCs, not stablecoins, will be the operational currency in the modern fnancial system and in the future, simply because CBDC design ofers benefts that outweigh the benefts of stablecoins (Allen, Gu, and Jagtiani 2022). Finally, CBDCs also have their own challenges. For instance, interest-bearing CBDCs can lead to a run on bank deposits, which poses funding risks to commercial banks (Garratt and Zhu 2021). Also, many countries have a weak digital payment infrastructure that could lead to fnancial exclusion for many people if CBDCs are deployed using insufcient technological infrastructure. Tere is also the risk of cyber attacks if CBDCs are deployed using insufcient technological infrastructure, as well as the risk of unwarranted and inappropriate government surveillance of the CBDC transactions of individuals and corporations (Atako 2021). Despite these challenges, CBDCs appear to ofer more benefts that are very appealing and advantageous compared to its risks. For instance, they can make payments cheaper, safer, faster, and more reliable. In contrast, cryptocurrencies and stablecoins have not yet proven themselves to ofer cheaper or faster payments. Tis is making CBDC become more appealing than cryptocurrencies and stablecoins for day-to-day transactions.

4.5 Conclusion Tis chapter presented a discussion of central bank digital currency research and developments. It defned CBDC and highlighted the diferences and similarities between CBDC and cryptocurrency. Te review of recent CBDC research showed that CBDC can ofer numerous benefts and also pose some risks. Tere have been calls to exercise caution in developing and adopting CBDC. Central banks are being urged to seek international collaboration and coordination in developing a CBDC. Regarding CBDC developments, only a few countries have issued a CBDC. Many countries have shown interest in researching the possibility of issuing a CBDC, while other countries are building specifc knowledge and expertise about CBDCs before deciding to do so. Interest in information about CBDC is also growing among members of the public, and such interest may lead to a decline in interest about cryptocurrency.

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Te emergence of CBDC presents many implications for cryptocurrency. Firstly, it might lead to calls for the regulation of cryptocurrency; it can lead to the acceptance of stablecoins, even though the benefts of stablecoins do not outweigh the benefts of issuing a CBDC; and fnally, the general benefts of CBDC for society may outweigh the risks, thereby making it more attractive than cryptocurrency. In the future, central banks will seek to retain greater control of the fnancial and monetary systems, even as cash is being used less in society. Central banks’ desire to innovate by issuing their own digital currency paints a grim future for private digital currencies. Tis is because central banks have the legal authority to ban the use of private digital currency in the fnancial system. However, the power of central banks to ban cryptocurrencies can be diminished by political and legal bottlenecks, as legal rules may prevent central banks from banning private digital currencies that are used for privately negotiated transactions. Tese bottlenecks create an uncertain future for cryptocurrency but leave a more certain future for CBDC. Only the future can tell whether CBDC and cryptocurrency will coexist or whether CBDCs will bring an end to the era of private digital currencies, also known as cryptocurrencies.

References Agur, I., Ari, A., and Dell’Ariccia, G. (2022). Designing central bank digital currencies. Journal of Monetary Economics, 125: 62–79. Allen, F., Gu, X., and Jagtiani, J. (2022). Fintech, cryptocurrencies, and CBDC: Financial structural transformation in China. Journal of International Money and Finance, 124: 102625. Allen, S., et al. (2020). Design choices for central bank digital currency: Policy and technical considerations (No. w27634). National Bureau of Economic Research. Armelius, H., Claussen, C. A., and Hull, I. (2021). On the possibility of a cash-like CBDC. Sveriges Riksbank Staf memo. Atako, N. (2021). Privacy beyond possession: Solving the access conundrum in digital dollars. Vanderbilt Journal of Entertainment and Technology Law, 23(4). Auer, R., and Böhme, R. (2020, March). Te technology of retail central bank digital currency. BIS Quarterly Review. Barontini, C., and Holden, H. (2019). Proceeding with caution—A survey on central bank digital currency. BIS Paper, No. 101. Berentsen, A., and Schär, F. (2018). Te case for central bank electronic money and the non-case for central bank cryptocurrencies. Available at SSRN 3194981.

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Bindseil, U. (2020). Tiered CBDC and the fnancial system. Available at SSRN 3513422. Bolt, W., Lubbersen, V., and Wierts, P. (2022). Getting the balance right: Crypto, stablecoin and central bank digital currency. Journal of Payments Strategy & Systems, 16(1): 39–50. Boonstra, W. (2022, March) CBDC and the international position of the euro. SUERF Policy Note, Issue No. 269. Bossu, W., et al. (2020). Legal aspects of central bank digital currency: Central bank and monetary law considerations. Available at SSRN 3758088. Davoodalhosseini, S. M. (2021). Central bank digital currency and monetary policy. Journal of Economic Dynamics and Control, 104150. Didenko, A. N., and Buckley, R. P. (2021). Central bank digital currencies: A potential response to the fnancial inclusion challenges of the Pacifc. UNSW Law Research Paper, 21–63. Edwards, S. (2021). Central bank digital currencies and the emerging markets: Te currency substitution challenge. Challenge, 1–12. Emery, D. (2019). Fintech and central bank digital currency in Australia (No. 1028). ADBI Working Paper Series. Garratt, R., and Zhu, H. (2021). On interest-bearing central bank digital currency with heterogeneous banks. Available at SSRN 3802977. Grunewald, S., Zellweger-Gutknecht, C., and Geva, B. (2021). Digital euro and ECB powers. Common Market Law Review, 58(4): 1029–1055. Hawkesby, C., and Wadsworth, A. (2020). Working together to be ‘on the money’. A Reserve Bank of New Zealand Paper. A speech delivered to the Royal Numismatic Society of New Zealand, Annual Conference, Wellington. Jossey, P. H. (2022). Central bank digital currencies threaten global stability and fnancial privacy. Competitive Enterprise Institute. Issue Analysis, No 1. Khiaonarong, T., and Humphrey, D. (2022). Falling use of cash and demand for retail central bank digital currency. IMF Working Paper, No. 027. Kif, M. J., et al. (2020). A survey of research on retail central bank digital currency. IMF Working Paper. Kim, Y. S., and Kwon, O. (2022, January). Central bank digital currency, credit supply, and fnancial stability. Journal of Money, Credit and Banking. Klein, M., Gross, J., and Sandner, P. (2020). Te digital euro and the role of DLT for central bank digital currencies. Frankfurt School of Finance & Management GmbH. FSBC Working Paper. Kumhof, M., and Noone, C. (2018). Central bank digital currencies—Design principles and balance sheet implications. Bank of England Staf Working Paper, No. 605. Mancini-Grifoli, T., et al. (2018). Casting light on central bank digital currency. IMF Staf Discussion Notes, 18(08).

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Mayer, T. (2019). A digital euro to compete with Libra. Te Economists’ Voice, 16(1). Meaning, J., Dyson, B., Barker, J., and Clayton, E. (2018). Broadening narrow money: Monetary policy with a central bank digital currency. Staf Working Paper, No. 724. Nguyen, T. V. H., Nguyen, B. T., Nguyen, K. S., and Pham, H. (2019). Asymmetric monetary policy efects on cryptocurrency markets. Research in International Business and Finance, 48: 335–339. Ozili, P. K. (2022a, February). Central bank digital currency research around the world: A review of literature. Journal of Money Laundering Control. Ozili, P. K. (2022b). Central bank digital currency can lead to the collapse of cryptocurrency. Available at SSRN 3850826. Ozili, P. K. (2022c). Central bank digital currency in Nigeria: Opportunities and risks. Available at SSRN 3917936. Ozili, P. K. (2022d). Circular economy and central bank digital currency. Circular Economy and Sustainability, 1–16. Panetta, F. (2018). 21st century cash: Central banking, technological innovation and digital currencies. Do We Need Central Bank Digital Currency, 28–31. Panetta, F. (2021). A digital euro to meet the expectations of Europeans. SUERF, SUERF Policy Brief, 95. Richards, T., Tompson, C., and Dark, C. (2020). Retail central bank digital currency: Design considerations, rationales and implications. Reserve Bank of Australia. Richards, T. (2021). Te future of payments: Cryptocurrencies, stablecoins or central bank digital currencies? A speech presented to the Australian Corporate Treasury Association. Rizk, A. (2022). Central bank digital currency: Legal and regulatory issues. Available at SSRN 4073218. Sethaput, V., and Innet, S. (2021). Blockchain application for central bank digital currencies (CBDC). In: 2021 Tird International Conference on Blockchain Computing and Applications (BCCA), 3–10. IEEE. Smales, L. A. (2021). Volatility spillovers among cryptocurrencies. Journal of Risk and Financial Management, 14(10): 493. Soderberg, G., et al. (2022). Behind the scenes of central bank digital currency: Emerging trends, insights, and policy lessons. FinTech Notes, No. 004. Taskinsoy, J. (2021). Say good bye to physical cash and welcome to central bank digital currency. Available at SSRN 3972858. Virtanen, P. (2021). Central bank digital currency: Cases of Sweden and Great Britain. Jyväskylä University School of Business and Economics, Working Paper. Wang, H., (2022). China’s approach to central bank digital currency. Available at SSRN 4036466. Ward, O., and Rochemont, S. (2019). Understanding central bank digital currencies (CBDC). Institute and Faculty of Actuaries.

Chapter 5 Blockchain Governance: To Govern, or Not to Govern? Evrim Tan KU Leuven, Belgium

Disruptive innovation at the helm of digital technologies is the defning characteristic of our time. Blockchain, along with artifcial intelligence, big data, robotics, quantum computers, and many other up-and-coming technologies will be a key component of creating automated, decentralized, and human-machine hybrid systems, which will highly likely defne in a not-so-distant future our systems of transactions in business, societal, and government domains. But why is blockchain a key technology in this transformation? Te importance of this technology is that it provides a viable solution to the agency problem—the trade-of between the delegation of authority and the control of the agent’s act. Te agency problem occurs when a legitimate actor in governance delegates some part of its authority to a trusted party and in return relinquishes some control in governance. In a blockchain, the information is archived in a distributed ledger that is shared across a network of users where every participant (called node) has a copy of the ledger and records all transactions. By agreeing on a form of consensus mechanism, nodes validate the transactions, provide an immutable (or nearly immutable) record of transactions, and ensure traceability. In this way, blockchain ensures nearly unhackable decentralized systems 75

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of encrypted data, without a need for a centralized authority to ensure the continuity of the system. By creating an autonomous, transparent, secure distributed system, blockchainbased systems may enable the removal of intermediaries in any system of governance as trustees replacing them with an algorithmic confdence system (Tan and Rodriguez Müller 2021; De Filippi et al. 2020). Tis feature upholds a tremendous potential to redesign the global fnancial system, election systems, and government in a more transparent, efcient, and efective way (Tapscott and Tapscott 2016). Teoretically, blockchain can disrupt any system of transactions in which the legitimacy to control lies within a higher institutional authority or within the constituents. Tis technological feature of blockchain makes the question of governance a contradictory and convoluted concept, as not everyone agrees that governance without a centralized control is feasible or desirable. Te failure of the Libra association* or the low uptake of blockchain-based systems in the public sector (Lindman et al. 2020) are cases in point. Te global system is based on the supremacy of governments and big businesses that keep the control of monetary policies and regulations. Centralized power structures in government and business domains would like to reap the benefts of this technology, such as higher efciency due to reduced transaction costs and higher security through distributed ledgers, without necessarily losing control of the means and authority in governance. For the communitarian initiatives and crypto-anarchist communities, on the contrary, the true value of this technology is in creating a new global fnancial and social order without governments or big companies dictating the rules of the games. Te true challenge for these initiatives is designing a governance system around the code and ensuring decentralized action of communities in acting for the beneft of everyone. An additional level of complexity is the fact that that blockchain as a technology re-establishes the importance of trust and legitimacy in the initial policy and system design phase instead of the implementation. Te rules of transactions are set at the design phase, and once a decision is made, a change in the system requires the consent of authorized users. Furthermore, blockchains can confrm the genuineness of a transaction, but whether a given input in a transaction is genuine or the transaction rules are fairly established is beyond the technical scope of blockchains. Terefore, “who will be authorized to make changes in the system?” and “what are the rules/procedures to follow in the change of the *

https://www.thetimes.co.uk/article/facebooks-libra-cryptocurrency-project-ends-in -failure-cxvnnc3kx

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system?” are key questions for blockchain governance. Vili Lehdonvirta calls these inherent contradictions of blockchain governance a “governance paradox,” as once you address these questions of governance, blockchain loses its value over conventional data infrastructures because you are already trusting some organization or process to make the rules (Werbach 2018). As a technology whose most salient feature is to build trust in governance processes without a need for a trusted third party, understanding what to govern and how to govern is fundamental for the application of blockchain technology. Tis question will be the central focus of this chapter. First, various theoretical approaches to blockchain governance within diferent disciplines will be discussed. Ten, what does governance entail in blockchain-based systems will be examined, followed by what type of governance decisions need to be made by system designers.

Theoretical Approaches to Blockchain Governance Te conceptual scope of blockchain governance largely varies within diferent disciplines. For instance, in the information and computer-science literature, blockchain governance often focuses on the way decision rights, incentives, and accountabilities are arranged in a blockchain network to encourage desirable behavior in the use of resources (e.g., Beck et al. 2018; Rikken et al. 2019; Van Pelt et al. 2021). In business management literature, blockchain governance is conceptualized as the process by which individuals and groups with ongoing relationships bargain about how to adapt to changes within an institutional environment (e.g., Allen et al. 2020; Daluwathumullagamage and Sims 2020; Gruin 2020). In the economics literature, and more specifcally commons scholarship, blockchain governance is associated with polycentric systems operating simultaneously at many diferent levels of interaction, and governance decisions are perceived as similar to the governance of community-pool resources (e.g. Howell, Potgieter, and Sadowski 2019). In an earlier study, we found out that there are three categories of studies in the literature on the way they operationalize blockchain governance (Tan et al. 2022). Te frst category of studies focuses on the infrastructure of the blockchain (e.g., Ertz and Boily 2019; Ozdemir, Ar, and Erol 2020). In those studies, blockchain governance is embedded in the technical design of blockchain infrastructure, and the research focus is on elucidating how diferent choices by system designers about blockchain architecture, blockchain applications, and technical standards shape blockchain governance.

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Te second category is about the operational processes of blockchain governance and captures the technical design choices and algorithms that afect the information exchanges, transactions, and collective actions between users (e.g., De Filippi and Loveluck 2016; Reijers et al. 2018; Van Pelt et al. 2021). Decision-making through on-chain and of-chain methods, initiative, and consensus mechanisms are at the foci of this category. In the third category, blockchain is treated as a governance technology that allows decentralized, algorithmic, and automated forms of governance to implement policy decisions (e.g., Allen et al. 2020; Yeung and Galindo 2019). In those studies, blockchain governance focuses on the regulation, organization, and control of the blockchain-based system in a particular institutional framework. Based on the systematic literature published in Tan et al. (2022), we have developed a conceptual framework that embeds these diferent approaches into a holistic approach. Te framework identifes micro, meso, and macro levels and a total of nine types of governance decisions to address in blockchain-based systems. Te presumption is that governance decisions at each level are connected, and decisions at one level may dictate the choices at another level. In the remainder of the chapter, this framework will be used to elucidate the complexity of blockchain governance at each level of governance.

Micro-Level Governance Micro-level governance is about the choices of system designers regarding the infrastructure of a blockchain-based system. Te decisions concerning the infrastructure architecture of blockchain, modular applications of smart contracts and decentralized applications, and interoperability of the blockchain-based system with the existing IT infrastructure fall under this category.

Infrastructure Architecture Blockchains can have permissionless/permissioned and public/private forms (Hileman and Rauchs 2017). Te diference between public and private blockchains is about who owns the data infrastructure. Te diference between permissionless and permissioned systems is about the restrictions imposed on network participants in terms of read, write, and audit/commit functions. In the former, anyone can participate in the network and validate the transactions taking place on the platform; in the latter, only selected entities are authorized to validate the transactions.

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Tese classifcations are not exclusive to each other, and it is possible to have a public and private blockchain with varying degrees of permission models (see Hileman and Rauchs 2017). For instance, the two most well-known blockchain platforms, Bitcoin and Ethereum®, are public and permissionless blockchains. Hyperledger Fabric, R3Corda, and Quorum are some examples of technology developers that provide build-in tools for permissioned systems applicable to public and private blockchains. Te merits of diferent types of blockchains vary technically concerning decentralization, security, scalability, speed, throughput, privacy/confdentiality, trust, and fnality dimensions. On the one hand, public and permissionless blockchains engender better trust and security for data infrastructure while experiencing a lack of scalability and performance issues. On the other hand, private and permissioned blockchains are largely developed and preferred by private enterprises (e.g. IBM®, Microsoft®) as they allow a certain extent of control of the data privacy and the governance of the system. Te choices with the blockchain infrastructure call for value-laden governance decisions tied to several trade-of conditions. Tese trade-of conditions are related to the performance of blockchain infrastructure in delivering the expected values. According to Kannengießer et al. (2019), there are 23 endogenous trade-of conditions among seven DLT properties (usability, performance, fexibility, security, transparency, law and regulation, and community) that vary according to the design choice with infrastructure architecture. For example, if the purpose of the blockchain-based system is to enhance trust among users, the system architecture should prioritize transparency, which can come at the expense of other properties (e.g., usability, performance, fexibility). Te key takeaway is that the choice of blockchain infrastructure requires a self-assessment of the expected added value to the system of transactions.

Application Architecture Blockchain infrastructures are predisposed to particular limitations in blockchain governance. However, these limitations can be mitigated through the use of decentralized applications (DApps) and smart contracts. DApps are open-source coded digital applications or programs that exist and run on a blockchain or P2P network of computers. Trough DApps, users can access blockchain networks and engage with other users for diferent purposes (e.g., storage of data space). Each DApp comes with certain advantages and disadvantages (e.g., fnality, speed, scalability, energy consumption, etc.) based on their consensus and incentive mechanisms, which makes choosing a DApp inherently a governance decision. It is also possible through modular DApps to

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anchor values in a non-blockchain system or a private blockchain to a public blockchain. Terefore, it is possible to circumvent certain trade-ofs between DLT properties (e.g., performance vs. security) through the use of DApps. Smart contracts are mechanisms that contain digital assets of two or more involved parties, where assets are distributed automatically according to predefned response actions when trigger conditions are met (Governatori et al. 2018). DApps access the blockchain network through smart contracts, which enforce the term of the agreement between two parties. Trough the combination of smart contracts and DApps, it is possible to create decentralized autonomous organizations (DAO) where the operational rules are encoded on blockchain in the form of smart contracts, and DAOs can autonomously or semi-autonomously operate without centralized control or third-party intervention (Wang et al. 2019). Tese new forms of organizations can redefne the mechanisms of control and coordination in governance systems.

Interoperability Blockchain interoperability refers to the ability of a blockchain network to share, see, and access information across existing data management systems in the public and private domains without the need for an intermediary to do the exchange. Tere are three types of blockchain interoperability challenges: First is the lack of standardization from technical protocols to smart contracts that connect auxiliary technologies such as AI, verifable credentials, and wallet solutions in blockchain platforms (Janssen et al. 2020). Second, blockchains are by design not interoperable with each other (Lafourcade and Lombard-Platet 2020). Several solutions have been proposed to facilitate cross-chain transactions, such as facilitating messaging via a management chain, using cross-chain cryptocurrency for atomic asset transfers, or direct communication via hardware connections using TLS or smart contracts, each with its unique set of problems to solve (Johnson et al. 2019). Tird, interoperability challenges can arise from the specifc needs in the sectoral area of application. For example, Zhang et al. (2017) identify particular interoperability challenges in the healthcare sector concerning the evolvability, fexibility, extent, and continuity of data exchange across various data providers. Interoperability challenges can afect blockchain governance choices in two possible ways. First, they can preclude the scalability of blockchain-based solutions and thereby limit the use of blockchain only to specifc functions (e.g., data verifcation through verifable credentials). Te lack of scalability can further undermine the applicability of decentralized and permissionless systems. Second, the technology choices of stakeholder organizations in a blockchain ecosystem can elevate one blockchain infrastructure and associated governance choices over

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others and establish it de facto as the system architecture choice in sectoral and geographical areas. As such, the interoperability requirements across blockchain networks may delimit available governance choices at the micro level.

Meso-Level Governance Meso-level governance concerns the interactions among the network community upon which the blockchain-based system rests. A blockchain-based system relies on the interactions among diferent types of users such as miners, verifers (or node operators), core developers, token holders, content producers, and network users. Governance decisions at the meso level set up how decision-making among these actors is managed, what type of incentive mechanisms support system maintenance, and how consensus mechanisms afect the role of actors in blockchain governance.

Decision-Making Mechanism Te decision-making mechanism varies between on-chain and of-chain governance processes (Reijers et al. 2018). In on-chain governance, stakeholders participate in discussions and decisions through the protocol itself, and when a decision is reached through a voting procedure or surpasses a threshold user number, the protocol adapts automatically to the decision. In on-chain governance, the way that the engagement and decision-making take place is encoded directly in the underlying infrastructure. For example, during the introduction of soft-fork activation protocols to Bitcoin (i.e., BIP 8 or BIP 9), miners used on-chain means to signal their support for the proposed upgrade. When 95% of blocks signaled support for the upgrade, the changes are enforced automatically after a short period. Te strength of on-chain governance is its enforcement mechanism—when a decision is agreed upon, it is implemented by following the rules embedded in the code. Of-chain governance refers to the endogenous and exogenous rules and processes of deliberations around the protocol that contribute to the operations and development of blockchain-based systems (Reijers et al. 2018). For example, Bitcoin developers share their improvement proposals (BIPs) through a mailing list, and Ethereum collects improvement proposals (EIPs) on Github (Ehrsam 2017). Te miners can decide whether to adopt the improvement proposals in practice. Although of-chain governance presents a more democratic alternative and allows an incremental adaptation, it preserves an inherent security risk in permissionless systems. Te miners of the system are rent-seekers, and they do not

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necessarily preserve the technical expertise to evaluate a proposal. Terefore, an initially benign-looking software modifcation initiated by a malicious actor can create security risks in the future (Finck 2018). Both governance processes infer some trade-ofs for system designers. On-chain governance brings the trade-of between efcient decision-making and transition processes whilst risking destabilization due to political dissonance. Of-chain governance brings the trade-of between enhancing the political consensus in the decision-making and transition processes whilst making the system security vulnerable to the rent-seeking behavior of the miners. It is also possible to develop hybrid systems, where some decisions can be taken through on-chain processes, whereas others can be through of-chain processes. Or innovative solutions in weighting votes might be used to mitigate risks associated with voting mechanisms. For instance, quadratic voting—a voting mechanism that allows users to invest in additional votes to express their support for a given issue more strongly—can increase the legitimacy of decision-making processes in on-chain governance. In addition, depending on the blockchain protocol, some actors may hold veto rights over certain decisions, or some decisions can be open only to certain actors to vote on. Here the institutional framework, area of application, and country-specifc conditions may dictate diferent actors to take a more prominent role in the on-chain and of-chain governance processes. Since a core added value of blockchain is to establish confdence in processes where there is a lack of it, the decision-making mechanisms may need to prioritize the involvement of the relevant actors and increase the level of transparency in the design and operation of blockchain applications. Te challenge is establishing an inclusive decision-making mechanism that creates legitimacy in blockchain governance without deadlocking the system (e.g., Bitcoin block size debate). A democratic constitution of blockchain governance may be needed to establish the rules and roles of the verifers, developers, and users of the system.

Incentive Mechanism In the Bitcoin whitepaper, Nakamoto (2008) introduced an incentive mechanism to ensure the continuous engagement of Bitcoin users in network maintenance. Both in Bitcoin and Ethereum blockchains, this mechanism relies on monetary rewards in the form of blockchain cryptocurrency, which aligns the individual rent-seeking behavior with the overall beneft of the platform. Alternatively, allocations of tokens and reputation scores that grant the users enhanced access to platform functions and weighted voting rights in decision-making processes can be some non-monetized rewards.

Blockchain Governance: To Govern, or Not to Govern? 83

Not only the methods of incentivizing but also the way that consensus protocols incentivize the miners and verifers can have drastic implications for the overall blockchain governance. For instance, the PoW mechanism of Bitcoin, alongside the monetary incentive mechanism, has paved the way for the consolidation of mining. Consequently, big mining pools have emerged across the globe, and specialized hardware among miners has become widespread. Currently, it is estimated that four big mining pools on the Bitcoin network and two mining pools on the Ethereum network control over 50% of transactions (De Filippi and Wright 2018, 40). Tis consolidation of power undermines the distributed and decentralized features of blockchain networks, even for the big ones such as Bitcoin and Ethereum, and risks the legitimacy and trust invested in on-chain governance mechanisms. Another related governance decision is about the disincentivizing efect of transaction fees. Transaction fees are the cost of actions in a particular blockchain, such as transactions or executions of smart contracts (e.g., “gas” in Ethereum). Te disincentivizing efect of transaction fees and the incentivizing efect of rewards to miners create the conditions of trade-ofs and rational choice calculations for the users and miners to engage with blockchain. For instance, in May 2011, during the Dogecoin crisis and the rush in NFT projects, the transaction fees on the Ethereum network had increased seven-fold in a short period.* In case the cost outweighs the anticipated benefts of engagement, some blockchain applications can lose their attractiveness, or miners might fnd less value to support the blockchain altogether, adversely afecting the security of the blockchain (De Filippi and Wright 2018, 41).

Consensus Mechanism A consensus mechanism is at the core of blockchain-based systems to coordinate the decentralized actions of users in deciding which information can be added to the blockchain. Diferent consensus mechanisms exist, but most blockchains rely on either Nakamoto consensus (or PoW) that ties mining capability to computing power or Byzantine consensus that uses staking to assign miners, such as proof-of-stake (PoS) and delegated proof-of-stake (DPoS). Tere are also proof-of-authority (PoA) systems, where a lower number of nodes or masternodes take the role of transaction validators. Each of these consensus mechanisms has advantages and disadvantages over others, and their afnity toward decentralized or centralized governance structures varies. *

See https://ycharts.com/indicators/ethereum_average_transaction_fee

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PoW is mostly used in permissionless, public blockchains, such as Bitcoin and Ethereum,* where the users validate the transactions by solving complex mathematical puzzles through the computing power of the hardware. Te advantage of the PoW system is its stability to deter cyberattacks (e.g., denialof-service attacks) while maintaining a distributed system governance. However, its vulnerability to 51% attack, high energy cost, increasing centralization of mining operations, and low transaction throughput are some of the signifcant challenges for PoW-based systems. Especially, increased strain on the environment imposed by the high energy demand of PoW-based systems reduces their likelihood of preference for public sector projects.†10 In PoS, the mining power is attributed to nodes in the proportion of tokens (or coins) held by nodes instead of their computing powers. Node operators lock away a stake for the right to participate in block creation, and nodes with bigger stakes have higher chances to be selected to verify transactions. A transaction fee is paid to the node operator in return for the transaction verifcation. In the case of fraudulent transactions or any misbehavior, the node loses the right to participate in staking. Te diference in DPoS is that the token holders choose a small number of nodes as delegates by staking tokens with diferent candidates. If the delegate is chosen for block creation, a fraction of the reward is allocated to those who voted for the delegate. In some systems, decision-making power is associated with the delegation of tokens where delegates may have the capability to monitor and amend network parameters such as fees, block size, block rewards, and the length of the transaction cycle (Karjalainen 2020). Te advantage of PoS is that it is less susceptible to 51% attacks and more scalable with higher transaction throughput, but it is assumed as less secure than completely decentralized PoW systems (EdChain 2018). In PoA systems, unlike the PoS and PoW systems, the identity of the validator is known, and the assumption is that the reputation of the validator plays the role of stake. Te advantage of PoA systems is that they can achieve much higher throughput as a result of the lower number of validators, and they are suited for both private and public networks. However, the downside is that PoA is not imbued with the sense of security derived from decentralized consensus mechanisms, and the nodes, therefore, need to be kept uncompromised. Hence *

†

In 2020, Ethereum launched a series of upgrades called Ethereum 2.0, which includes a transition to PoS. For example, during the initial design process of European Blockchain Services Infrastructure (EBSI), the European Commission announced its intention to avoid PoW for the anticipated system.

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algorithmic trust typical of Bitcoin or Ethereum is not present in a PoA-based blockchain, rather an ex-ante trust is required in node selection.

Macro-Level Governance Macro-level governance focuses on the rules and norms that afect the power distribution, coordination, and control across the blockchain network. More specifcally, how the decision-making rights are distributed across network actors, what type of accountability mechanisms are in place, and who controls and oversees the implementation processes fall under the scope of macro-level governance.

Power Distribution Power distribution in a blockchain network takes shape in diferent degrees of decentralization. Centralized governance happens when a specifc group of people or organizations make the governance decisions, and the decision-making processes can be organized through of-chain or on-chain processes. A semi-centralized or hybrid governance happens when some governance decisions (e.g., confict resolution) are taken only by a centralized board of directors, and some other governance decisions (e.g., concerning the network of users or platform functions) can be taken with an additional on-chain voting procedure by the platform users. Polycentric governance occurs when diferent clusters of actors (e.g., miners, developers, nodes) hold diferent roles and responsibilities in blockchain governance, and governance necessitates taking into account what the others are doing (Stephan et al. 2019). Decentralized governance happens when decision-making processes are not dominated by a single actor or a group of actors; rather the decisions are taken by the majority of users operating in the blockchain network, either through on-chain voting processes or coordinated mining action. Here the choice architecture of voting processes and the openness of propositions to the public may infuence the decentralized nature of the governance structure. Furthermore, in open-source decentralized systems, improvement proposals and DApps can be introduced directly by network users without the interference of an intermediary actor (e.g., developers). It is important to underline that certain organizational structures may have diferent afnities for particular governance choices at micro or meso levels. For example, while centralized governance might be more suitable for permissioned blockchain-based systems, polycentric or decentralized governance may be more

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suitable for permissionless systems. Or, while PoA may be more suitable for semi-centralized systems, PoS and PoW may be more suitable for polycentric or decentralized systems. It is also possible that the power relations of actors may alter an initially diferent-looking governance structure into another. For example, if on-chain governance processes in PoW or PoS-based systems are dominated by a few large operators who control most of the mining resources and/or token holdings, an initial decentralized governance structure may act as a semi-centralized or polycentric governance structure. Or, if of-chain governance processes are dominated by a few specialized infuential players, a centralized governance structure may act like a semi-centralized or polycentric structure.

Accountability Mechanism Accountability is about how rules in governance (e.g., dispute resolution, change management) are regulated and enforced. Following Treib et al.’s typology (2007), four forms of accountability mechanisms can be identifed in blockchain governance: coercion, voluntarism, targeting, and framework regulation. Tese four types are distinguished along two dimensions: the type of instruments applied (legally binding legislation or soft law) and the approach to enforcement (fexible or rigid). Coercion is characterized by binding regulative instruments prescribing detailed and fxed standards in the implementation. In the blockchain context, coercion can be captured by the concept of lex cryptographica (de Filippi and Wright 2018). Lex cryptographica means that the rules of exchanges are inbuilt codes, and as such code becomes the law in blockchain governance. Trough the use of smart contracts, rewards and sanctions are executed automatically, creating a deterministic system of governance. Te challenge with transposing law into code is that codes are written ex-ante, in strict and formalized language, and therefore, code-based rules need to be predictable and leave no room for interpretation (de Filippi and Wright 2018). Tis inherently limits the applicability of code-based rules in areas where the contingencies and conditionalities cannot be determined a priori. Voluntarism is based on legally non-binding instruments and defnes broad goals in implementation. In the blockchain context, this mode of governance is captured by soft forks. A soft fork does not change the structure of blockchain, but it modifes the functions of blockchain. Te implementation of a soft fork relies on the coordinative action of the majority of users to implement the suggested changes. Te changes enter into force only if the majority of the network’s mining power adopts them. Otherwise, the soft fork fails, and the old chain remains unchanged. In the case of political dissonance among diferent groups

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of users in a blockchain network, a soft fork presents a fexible instrument to modify the system. For example, when no political consensus was achieved among the blockchain community to change the Bitcoin block size (i.e., the 1-megabyte rule), a segregated witness through a user-activated soft fork was used. Given that the consensus rules of Bitcoin are controlled by the economic majority, the economic majority was able to activate segregated witness on their own, bypassing the blocking miners. Targeting uses non-binding recommendations, but unlike voluntarism, it relies on detailed descriptions for regulations. In the blockchain context, targeting practices are often used for the introduction of improvement proposals and DApps in a blockchain network. Trough improvement proposals (e.g., BIPs), anyone can suggest software changes, which are subsequently evaluated and debated by the network community. If the proposal reaches community consensus, it is considered fnal. In the implementation of improvement proposals, users exercise agency in deciding whether or not to install new software. Finally, framework regulation creates binding rules for users, but unlike coercion, users have freedom of choice whether or not to accept the policy options. Tis accountability mechanism in the blockchain context is best captured by hard forks. A hard fork occurs when a rule change is adopted in blockchain protocol and the nodes of the newest version of a blockchain no longer accept the older version of the blockchain. In the case of a hard fork, all nodes are meant to work by the new rules to upgrade their software. Otherwise, a permanent split from the previous version of the blockchain occurs, and resulting in two diferent blockchains being created. A famous case of a hard fork occurred in the Ethereum blockchain following the DAO hack in 2016, where $50 million worth of funds held by the platform was hijacked by exploiting a bug in the system (Finck 2018). Following the hack, a philosophical debate broke out in the community about the right course of action. Te non-intervention faction argued that changing the code would undermine the immutability of the code and thereby the “code is law” notion of the governance system. Te others argued for rewriting the transaction history by creating a hard fork in the system to reduce the likelihood of judicial action. Eventually, a hard fork was accepted by the majority of the miners, creating efectively a new chain based on the older version of the transaction history before the hack occurred (Finck 2018). Yet the split resulted in the creation of two blockchains—namely, Ethereum Classic and Ethereum.

Control Mechanism Te last macro-level governance question is about what type of control mechanisms are placed in the implementation of decisions and to what extent the

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governance decisions are automated. Automation of decisions can pertain to systemic changes (e.g., hard and soft forks), rules of operations, or system functions (e.g., DApps). Tree possible forms of automation can take place in blockchain governance: fettered governance, semi-autonomous governance, and automated governance. In fettered governance, human agents hold all decision rights, and the decisions are implemented by the consent and collaborative actions of actors. Currently, most of the blockchain platforms operate on fettered governance, where the operations (e.g., verifcation, order of transactions, voting, etc.) are controlled by humans, be it centralized or decentralized. In semi­autonomous governance, automated agents supply certain governance functions. Here the access to of-chain data through oracles* and convergence of platform functions with other digital technologies such as AI and IoT can supplement the role of automated agents. For instance, through the injection of AI technologies in blockchain networks, it is possible to improve the capacities of smart contract management and introduce more efcient mining processes to reduce energy consumption (Hassani et al. 2018). A case in point is the QTUM blockchain, where in the case of problematic gas prices for certain operations (e.g., higher prices for processing blocks than creating them), smart contracts can temporarily increase the gas prices for the problematic operations to mitigate malign attacks on the network. Tese so-called decentralized governance protocols (DGP) provide an alternative to hard and soft forks for hotfxes without disrupting the user experience (Bosankic 2018). In automated governance, new digital technologies and advanced data analytics techniques supplemented by exogenous data sources can create complex automated governance mechanisms. For example, the terra0 project in the Netherlands aims to create self-governing ecosystems for the management of publicly owned natural resources such as forests by utilizing remote sensing, AI, token, and blockchain technologies.† Te project envisions a DAO, where the satellite imagery through smart contracts designates the trees that can be harvested before damaging the forest too much, and that automatically trades licenses to cut the trees to vendees. Te generated income is used to purchase shares of land from the actual landowners in the form of tokens, creating a non-human–owned property capable of utilizing its economic unit. A decision toward the removal of the human element is closely linked with the embedded values in the sectoral area of application. Te salience of efciency *

†

Oracles are third-party services that bridge external information in a blockchain network. For details, see https://terra0.org/

Blockchain Governance: To Govern, or Not to Govern? 89

and cost-efectiveness principles may insinuate the replacement of certain functions and organizations in public services with non-human–controlled DAOs or automated agents. Yet again, the cultural and behavioral reservations may suggest certain roles and decisions that can be automated to be left in the hands of actual people. Furthermore, the adaptation of automated solutions needs to comply with the existing capacities and practices at the user level. A core consideration here is assessing to what extent people can manage their digital identities and assets and whether they need custodian organizations in governance.

Conclusion Governance is a core challenge and yet one of the central value propositions of blockchain technology. In this chapter, I highlighted the key dimensions of how to approach the governance question in a blockchain-based system. Each type of governance decision is interlinked to each other at a techno-social level. System designers need to be aware of these interlinkages across various types of governance decisions. Tis does not mean that there is a hierarchy or a sequence of governance decisions that can be deduced from the presented model. Rather, the design of blockchain governance would require an iterative process of assessment where not only IT experts but also business and ethical experts may take part. Depending on the area of application, legislative, market, political-administrative, and socio-technological framework conditions may accentuate certain choices in blockchain governance. Te existence or absence of a regulative framework concerning data privacy, token technology, smart contracts, and DAOs can limit the available choices in governance design. Furthermore, trust dynamics among the blockchain network and public values connected to the area of application are also crucial to assessing the design choices in blockchain-based solutions. Tese social factors complement and are infuenced by the presence or absence of technological means and capabilities in the network of users. Lastly, the multifaceted and value-laden challenges connected with the governance question is leading to innovative and tech-driven approaches across blockchain communities. Bounded curves, quadratic voting, and token economic models are some evolving innovative solutions to regulate the behavior of users and communities in blockchain networks. Tese solutions are currently tested and developed in fringe networks outside of the mainstream enterprise applications. However, their innovative character to address certain conundrums in the “governance paradox” and challenges of the fnancial and institutional systems appear to be promising and interesting for the future applications of this technology.

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References Allen, D. W. E., and Berg, C. (2020). Blockchain governance: What we can learn from the economics of corporate governance. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3519564 Beck, R., Müller-Bloch, C., and King, J. L. (2018). Governance in the blockchain economy: A framework and research agenda. Journal of the Association for Information Systems, 19(10): 1020–1034. https://doi.org/10.17705/1jais.00518 Bosankic, L. (2018). Blockchain governance: Takeaways from nine projects. Medium. https://medium.com/@leo_pold_b/blockchain-governance-takeaways-from -nine-projects-8a80ad214d15 Daluwathumullagamage, D. J., and Sims, A. (2020). Blockchain-enabled corporate governance and regulation. International Journal of Financial Studies, 8(2): 1–41. https://doi.org/10.3390/ijfs8020036 De Filippi, P., Mannan, M., and Reijers, W. (2020). Blockchain as a confdence machine: Te problem of trust & challenges of governance. Technology in Society, 62: 101284. https://doi.org/10.1016/j.techsoc.2020.101284 De Filippi, P., and Wright, A. (2018). Blockchain and the Law: Te Rule of Code. Cambridge, MA: Harvard University Press. EdChain (2018). POW vs. PoS. A comparison of two blockchain consensus algorithms. Medium. PoS. Ehrsam, F. (2017). Blockchain governance: Programming our future. Medium. https:// medium.com/@FEhrsam/blockchain-governance-programming-our-future -c3bfe30f2d74 Finck, M. (2018). Blockchain Regulation and Governance in Europe. Cambridgeshire, UK: Cambridge University Press. Governatori, G., Idelberger, F., Milosevic, Z., Riveret, R., Sartor, G., and Xu, X. (2018). On legal contracts, imperative and declarative smart contracts, and blockchain systems. Artifcial Intelligence and Law, 26(4): 377–409. https:// doi.org/10.1007/s10506-018-9223-3 Gruin, J. (2020). Te epistemic evolution of market authority: Big data, blockchain and China’s neostatist challenge to neoliberalism. Competition and Change. https://doi.org/10.1177/1024529420965524 Hassani, H., Huang, X., and Silva, E. (2018). Big­crypto: Big data, blockchain and cryptocurrency, 2: 34. https://doi.org/10.3390/bdcc2040034 Hileman, G., and Rauchs, M. (2017). 2017 global blockchain benchmarking study. Available at SSRN 3040224. Howell, B. E., Potgieter, P. H., and Sadowski, B. M. (2019). Governance of blockchain and distributed ledger technology projects. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3365519

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Janssen, M., Weerakkody, V., Ismagilova, E., Sivarajah, U., and Irani, Z. (2020). A framework for analyzing blockchain technology adoption: Integrating institutional, market and technical factors. International Journal of Information Management, 50: 302–309. https://doi.org/10.1016/j.ijinfomgt.2019.08.012 Johnson, S., Robinson, P., and Brainard, J. (2019). Sidechains and interoperability. ArXiv. http://arxiv.org/abs/1903.04077 Kannengießer, N., Lins, S., Dehling, T., and Sunyaev, A. (2019). Mind the gap: Trade­ofs between distributed ledger technology characteristics. http://arxiv.org /abs/1906.00861 Karjalainen, R. (2020). Governance in decentralised networks. SSRN Electronic Journal. Available at SSRN 3551099. Lafourcade, P., and Lombard-Platet, M. (2020). About blockchain interoperability. Information Processing Letters, 161: 105976. https://doi.org/10.1016/j.ipl.2020 .105976 Lindman, J., et al. (2020). The uncertain promise of blockchain for government.  OECD Working Papers on Public Governance, No. 43. Paris: OECD Publishing. https://doi.org/10.1787/d031cd67-en. Nakamoto, S. (2008). Bitcoin: A peer­to­peer electronic cash system. https://bitcoin.org /bitcoin.pdf Reijers, W., et al. (2018). Now the code runs itself: On-chain and of-chain governance of blockchain technologies. Topoi, 37: 17. https://doi.org/10.1007 /s11245-018-9626-5 Rikken, O., Janssen, M., and Kwee, Z. (2019). Governance challenges of blockchain and decentralized autonomous organizations. Information Polity, 24(4): 397–417. https://doi.org/10.3233/ip-190154. Tan, E., Mahula, S., and Crompvoets, J. (2022). Blockchain governance in the public sector: A conceptual model for public management. Government Information Quarterly, 39(1). Tan, E., and Rodriguez Müller, A. P. (2020). Te use of blockchain technology in digital coproduction: Te case of Barcelona. Proceedings of Ongoing Research, Practitioners, Workshops, Posters, and Projects of the International Conference EGOV­CeDEM­ePart 2020, 125–134. CEUR. Tapscott, D., and Tapscott, A. (2016). Te Blockchain Revolution: How the Tech­ nology Behind Bitcoin Is Changing Money, Business, and the World. Penguin Books. Treib, O., Holger, B., and Falkner, G. (2007). Modes of governance: Towards a conceptual clarifcation. Journal of European Public Policy, 14(1): 1–20 Van Pelt, R., Jansen, S., Baars, D., and Overbeek, S. (2021). Defning blockchain governance: A framework for analysis and comparison. Information Systems Management, 38(1): 21–41. https://doi.org/10.1080/10580530.2020.1720046

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Wang, S., et al. (2019). Decentralized autonomous organizations: Concept, model, and applications. IEEE Transactions on Computational Social Systems, 6(5): 870–878. https://doi.org/10.1109/TCSS.2019.2938190 Werbach, K. (2018). Te Blockchain and the New Architecture of Trust. Cambridge, MA: Te MIT Press. Zhang, P., White, J., Schmidt, D. C., and Lenz, G. (2017). Applying software patterns to address interoperability in blockchain-based healthcare apps. ArXiv. http://arxiv.org/abs/1706.03700

Chapter 6 Cryptocurrency Crime Arianna Trozze University College London

Introduction As cryptocurrencies have gained popularity since the creation of Bitcoin in 2008, so too has crime involving them risen. In 2021, victims lost approximately $14 billion to cryptocurrency crime, an increase of 79% from the previous year (Chainalysis® 2022). Tis fgure includes many, but not all, of the types of crime we discuss in detail in this chapter. Research from 2018 estimated that, at the time, 46% of all Bitcoin transactions involved illicit activity—attributed to 26% of all its users at the time—totaling $76 billion per year (Foley et al. 2019). Many of the crimes contributing to more recent fgures also involve newer segments of the wider cryptocurrency space, such as decentralized fnance (DeFi) and non-fungible tokens (NFTs). Tis chapter provides an overview of crimes involving cryptocurrencies. It frst introduces the concepts of crypto-enabled and crypto-dependent crimes, which ofer a framework for categorizing the types of crime discussed later in the chapter. It also describes the characteristics of cryptocurrencies which may facilitate their use by criminals. Te chapter then gives an overview of the various types of crime that involve cryptocurrencies, including types of fraud, cybercrimes, cryptocurrency mining crimes, money laundering, terrorism fnancing, 93

94 Cryptocurrency Concepts, Technology, and Applications

sanctions evasion, tax evasion, darknet marketplaces, bribery and corruption, and cryptocurrency-adjacent crimes. Finally, we consider crimes associated with particular areas of the larger cryptocurrency ecosystem—namely, DeFi and NFTs—and ofer conclusions about cryptocurrencies and criminal activity.

Crypto-Enabled and Crypto-Dependent Crimes Many crimes committed involving cryptocurrencies are not new; criminals use cryptocurrencies to commit crimes like money laundering and fraud in new ways. Scholars, therefore, distinguish between crypto-enabled and crypto-dependent crime. Crypto-enabled crimes are merely facilitated by cryptocurrencies (for example, sanctions evasion or Ponzi schemes), while crypto-dependent ones require cryptocurrencies (for example, fake cryptocurrency services or cryptocurrency mining–related crimes). Criminals often commit crypto-enabled crimes in novel ways—for example, Ponzi schemes coded into smart contracts (Kamps et al. 2022; Trozze et al. 2022).

Characteristics of Cryptocurrencies That Facilitate Crime Cryptocurrencies’ characteristics make their use attractive to criminals. Te ecosystem enjoys low barriers to entry (users merely require an internet-connected device). Cryptocurrencies are also pseudo-anonymous, and many cryptocurrency service providers do not collect anti-money laundering (AML) or Know Your Customer (KYC) information. Currently, no global, centralized authority regulates cryptocurrencies, further incentivizing their use. Both international and domestic cryptocurrency transactions are low-cost and irrevocable. Teir security may also entice criminals. Finally, the most secure way to store cryptocurrencies is using a hardware wallet no larger than a fash drive, making them much less cumbersome to transport than physical cash (Kethineni and Cao 2020).

Types of Cryptocurrency Crimes Tere is evidence of cryptocurrencies’ use in various types of fraud, including fraudulent investment schemes, market manipulation, social-engineering

Identity theft

Ransomware

Wire fraud

Hacks

Cryptojacking

Miner extractable value Covert pre-mines

Other frauds

Securities fraud

Insider trading

Other cybercrimes

Stop-loss hunting

Ponzi schemes

High-yield investment schemes

Money laundering Terrorism financing

Pump-and-dumps

Wash trading

Market manipulation

Investment schemes

Mining crimes

Commodities fraud

Spoofing

Sanctions evasion

Ticker stuffing

Crypto crime

Fraud

Tax evasion

SIM swapping

ICO scams

Social engineering

Phishing

Darknet markets

Impersonation scams

Pyramid schemes

Fake tokens Fraudulent services and tokens

Fake exchanges

Advance-fee fraud

Mining scams

Airdrop scams

Bribery and corruption

Fake mixers

Cryptoadjacent crimes

Fake wallets

NFT crime Arbitrage scams

Other scams

Donation scams

Giveaway scams

Blackmail and other extortion

Romance scams

Intellectual property crimes

Figure 6.1 Types of Cryptocurrency Crime [Description follows on next page.]

DeFi crime

Governance attacks

Flash loan attacks Rug pulls

Oracle attacks

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schemes, fraudulent services and tokens, and other types of scams and fraud. Additional types of crimes involving cryptocurrencies include cybercrimes like ransomware and hacks, cryptocurrency mining crimes, money laundering, terrorist fnancing, sanctions evasion, tax evasion, darknet markets, bribery and corruption, and what we refer to as crypto­adjacent crimes. Figure 6.1 (above) ofers a visual summary of these crime types.

Fraud Investment Schemes Ponzi schemes and high­yield investment schemes are commonplace in the cryptocurrency arena, with Bartoletti et al. (2019) fnding evidence of 184 Ponzi schemes on Ethereum alone in 2019. In a Ponzi scheme, fraudsters promise investors absurdly high interest rates for their investments. Tey pay these returns to the original investors by using new investors’ funds, until the scheme can no longer fnd new victims (Bartoletti et al. 2018; Baum 2018; Pryzmont 2016; Reddy and Minnaar 2018; Vasek 2017; Vasek and Moore 2015). Initial coin ofering (ICO) scams are another popular type of cryptocurrencybased fraudulent investment scam. ICOs are similar to traditional initial public oferings, except they sell a new cryptocurrency rather than shares to investors to fundraise. Fraudulent ICOs may also involve theft, pump-and-dump schemes, or Ponzi schemes (Barnes 2018; Baum 2018). Investors lost $687.4 million from just 10 popular ICO scams (Karimov and Wójcik 2021). Scholars also observe cryptocurrency-based pyramid schemes. Tese schemes involve participants earning money for recruiting other members, rather than through the goods and services the company purports to provide (Jiaying 2020). Market Manipulation Various types of manipulation run rampant in cryptocurrency markets, particularly for coins with a low market capitalization. Market manipulation refers to any attempt by market participants to alter the price of a cryptocurrency (ur Rehman et al. 2020). Tere is evidence of the following types of market manipulation in cryptocurrency markets: pump-and-dump schemes (Kamps and Kleinberg 2018); wash trading (Cong et al. 2021); ticker-stufng (Nomics 2019); spoofng (Twomey and Mann 2020); and stop-loss hunting (Banton 2021; CoinBureau 2018). Pump­and­dump schemes involve a party’s buying large volumes of a low market capitalization cryptocurrency, followed by a coordinated misinformation campaign which entices victims to also purchase this cryptocurrency. Tis increase

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in demand, in turn, increases the price of the asset (the “pump”). Following this price increase, the fraudster sells their holdings (the “dump”) for a proft, crashing the price (Barnes 2018; Baum 2018; Chen et al. 2019). One study of 412 pumpand-dump schemes in 2018 and 2019 fnds they produce a monthly “aggregate artifcial trading volume” of $6 million (Xu and Livshits 2019). Wash trading refers to a party’s selling a cryptocurrency among accounts they control (or to colluding parties) (Financial Conduct Authority 2021). Preliminary research suggests that up to 70% of trading volume on unregulated exchanges may involve wash trading (Cong et al. 2021). Ticker­stufng is a related type of fraud, whereby a fraudster falsifes the volume metrics on a trading ticker (Nomics 2019). Spoofng is an act criminalized by the U.S. Dodd Frank Act in 2010, which also occurs in cryptocurrency markets. A party places orders they intend to cancel to manipulate prices (Commodity Futures Trading Commission, n.d.). Finally, stop­loss hunting refers to large-scale holders of cryptocurrency manipulating prices to reach stop-loss level (levels traders set at which to automatically sell their assets). If they succeed—thereby activating these stop-losses—the price of the cryptocurrency will plummet further and the fraudster can purchase it more cheaply (Banton 2021; CoinBureau 2018). Social Engineering Social engineering attacks—which are present in traditional online banking and other areas of cyberspace, such as phishing, SIM swapping, and impersonation scams—also occur in cryptocurrency markets. Phishing refers to a fraudster’s making a fake version of an existing cryptocurrency website, email, or other communication, which tricks victims into inputting private information into the fake site (Chen et al. 2020). Tis could, for example, involve a fake email from a cryptocurrency wallet provider requesting the victim’s private key or seed phrase. SIM swapping involves attackers using social engineering to get enough information about a victim to convince their mobile phone provider to move the victim’s SIM card to one controlled by the attacker (Lee et al. 2020). Te attacker can then complete multi-factor authentication involving the victim’s mobile phone number and gain access to the victim’s cryptocurrency accounts. In 2020, police arrested a man for allegedly stealing $50 million in cryptocurrency in this manner (Duhaime 2020). Finally, in impersonation scams, a fraudster may impersonate someone of authority (for example, a federal employee) or a celebrity to lure victims into joining a cryptocurrency investment scam (Australian Competition & Consumer Commission 2020; Lucking and Aravind 2020).

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Fraudulent Services and Tokens Fraudulent cryptocurrency services and tokens are crypto-dependent frauds (Kamps et al. 2022). Fake cryptocurrency exchanges and fake cryptocurrency wal­ lets generally steal the cryptocurrencies transferred to them (Vasek et al. 2017; Vasek and Moore 2015). Tey may also involve phishing applications, whereby fake cryptocurrency exchanges and wallets imitate existing ones and try to trick victims into using them (Xia, Wang, Zhang, et al. 2020). Fake mixers also exist which similarly steal users’ funds (Möser et al. 2013). Finally, fake tokens may involve tokens that are complete scams (where developers will often “rug pull” the project, see DeFi Crime Risks, below [Xia et al. 2021; Xu et al. 2022]), seek to impersonate existing tokens (again, often using traditional phishing techniques [Higgins 2017]), or advertise scam tokens purported to be associated with existing projects for which there is currently no token. Xia et al. (2021) found that, at the time of their research, 50.14% of tokens on the decentralized exchange Uniswap® were scam tokens. Other Scams and Frauds Tere are various other types of scams and frauds present in the cryptocurrency space of which scholars have, at least, anecdotal evidence. Tese include: • Giveaway scams. A scammer promises to give victims something of value if they send them a cryptocurrency. Te victim never receives the gift (Xia, Wang, Luo, et al. 2020). • Donation scams. Fraudsters pretend to be from a charity raising money using cryptocurrency for a cause. • Airdrop scams. Airdrops involve a cryptocurrency project transferring free cryptocurrency (usually in the project’s native governance token) to reward early adopters. In the case of airdrop scams, fraudsters may send victims counterfeit tokens (Gao et al. 2020) or may try to get victims to approve access to their online wallet (under the guise that this authorizes the airdrop transfer) and then drain their funds. • Advance-fee fraud. Advance-fee frauds are old frauds which are now sometimes carried out with cryptocurrencies. A scammer requests a victim sends them cryptocurrency, promising to return the original amount and more. Instead, the fraudster steals the original funds transferred to them (Phillips and Wilder 2020). •

Romance scams. Cryptocurrency-based romance scams occur similarly

to traditional romance scams, whereby a fraudster builds a fake romantic

Cryptocurrency Crime 99

relationship with the victim and then requests cryptocurrency from them (which, ultimately, they steal) (Navarro 2019). • Arbitrage scams. Arbitrage scams refer to some scam in connection with a scheme to proft of price imbalances across the cryptocurrency market (Gao et al. 2020). • Other blackmail and extortion scams. Cryptocurrency blackmail scams involve a criminal’s extorting an individual for cryptocurrency. Tis may include “sextortion”, where a criminal threatens to release sexual information or nude photos of a victim if they do not send cryptocurrency (O’Malley and Holt 2022; Paquet-Clouston et al. 2019). Another type of blackmail scam involved a criminal’s threatening to spread COVID-19 if the victim does not send cryptocurrency (Xia, Wang, Luo, et al. 2020). Cases of kidnapping (and subsequent extortion) also exist (Kethineni and Cao 2020). • Mining scams. Mining scams refer to a scheme where criminals tell victims they are investing in a mining program; victims never receive their original investment or the promised returns (Vasek 2017; Vasek and Moore 2015) (see Cryptocurrency Mining Crimes, below, for related crimes). Other frauds include identity theft, insider trading, securities fraud, commodities fraud, and wire fraud, which refer to the statutory defnitions of the same. Again, many of these, such as giveaway scams, romance scams, and advance-fee fraud, are crypto-enabled scams, while airdrop scams and mining scams are cryptodependent crimes.

Other Cybercrimes Ransomware Ransomware is a type of malware that encrypts a victim’s fles; once they pay the demanded ransom to the attackers (in cryptocurrency), their fles are decrypted (Conti et al. 2018). Attackers often request ransom payment in Bitcoin or privacy coins like Monero (Paquet-Clouston et al. 2019). Ransomware-as-a-service facilitates this phenomenon, whereby would-be criminals can purchase readymade ransomware to deploy. Ransomware operators also often allow users to infect other computers instead of paying the ransom in order to decrypt their fles, further facilitating its spread (Conti et al. 2018). Tere are more than 500 ransomware families; WannaCry, one of the most prolifc, reached 300,000 victims from 150 countries (Paquet-Clouston et al. 2019). According to blockchain analytics frm Chainalysis (2022), ransomware payments amounted to $692 million in 2020 and $602 million in 2021 (compared to only

100 Cryptocurrency Concepts, Technology, and Applications

$152 million in 2019). However, Paquet-Clousten et al. (2019) urge caution in applying these estimates, given the commercial motivations of their source. Tey analyzed 35 families of ransomware between 2013 and mid-2017 and found ransom payments reached $12,768,536 during that time. Te proceeds from one ransomware family, Locky, account for 50% of this fgure (Paquet-Clouston et al. 2019). Individual schemes range in proftability, from $4,173.12 (the value of the 9.9990 BTC earned at the time) for KeRanger to $2,220,909.12 (the value of the 5,351.2329 BTC received at the time) (Conti et al. 2018). Cryptowall utilized “infection vectors, such as phishing emails, browser exploit kits, and even drive-by downloads via Google Drive” (Ahn et al. 2016). Te malware also automatically evolved and self-encrypted to evade antivirus software detection (Ahn et al. 2016). Hacks (and Theft) Hackers have successfully attacked both centralized and decentralized cryptocurrency exchanges. One study examined 30 Bitcoin hacks, fnding that, depending on when the criminals converted their funds to fat currency, they amounted to between $7 and $88 billion (Charoenwong and Bernardi 2021). Many exchanges have poor security practices, which hackers exploit (Carlisle and Izenman 2019). In fact, 20 of the aforementioned hacks involved security breaches; human errors and insider threats each caused fve (Charoenwong and Bernardi 2021). Recent hacks include the $325 million Wormhole exploit in February 2022, the $540 million Ronin bridge attack in March 2022, and the $611 million Poly Network hack in August 2021 (Tidy 2022). Also, many hacks can be attributed to North Korean state-sponsored hackers such as the Lazarus Group, including the Coincheck hack in 2018 (worth $534 million at the time) and the Bithumb hack of 2018 (worth up to $43 million in various cryptocurrencies at the time) (Carlisle and Izenman 2019). One of the largest (and, perhaps, most famous) exchange hacks was the Mt. Gox hack of 2014. A hack of one of the company’s auditors preceded this attack in 2011, at which point the attacker changed the price of Bitcoin on the exchange to $0.01, stole clients’ private keys, and purchased cheap Bitcoin, ultimately making of with 2,000 BTC. Ten, in 2014, attackers stole the private key fle for the exchange’s Bitcoin wallet and stole 850,000 BTC (Charoenwong and Bernardi 2021).

Cryptocurrency Mining Crimes Nefarious actors exploit the cryptocurrency mining process in numerous ways. One example is cryptojacking, a type of malware that uses the infected computer

Cryptocurrency Crime 101

to mine cryptocurrency on behalf of the attacker (Kamps et al. 2022). Estimates put the total expected revenue from a cryptojacking scheme at $300,000 per month (Kfr 2020). Cryptojacking is a particularly common tactic for mining Monero cryptocurrency (Kamps et al. 2022). Covert pre­mines are another type of cryptocurrency mining crime whereby a project claims there is cap on the supply of its cryptocurrency, but someone is able to mine more. Bitcoin Private is an example of this, with $3.9 million in coins covertly pre-mined prior to its launch (Kamps et al. 2022). Finally, miner front­running (also called miner extractable value) refers to Ethereum miners ordering transactions (including their own) in a way that generates proft for them based on upcoming transactions they observe in the Ethereum mempool* (Daian et al. 2019). Tis is similar to traditional fnancial market front-running, where a trader trades an asset before an upcoming non-public trade (that is known to them) which will afect the asset’s price (Mitchell 2021). In cryptocurrency markets, miners could execute trades to manipulate prices based on knowledge they obtained about upcoming trades (Xu et al. 2022) or extract profts by copying a trade in the mempool, executing theirs frst, and stealing the original trader’s proft. Tey could also deploy bots to this end (Daian et al. 2019). Miner extractable value also includes back-trading (making a trade after another for fnancial gain) or sandwich attacks (combining front-running and back-running) (Xu et al. 2022).

Money Laundering Money laundering is a key cryptocurrency crime concern. Europol estimated that in 2018 criminals laundered $5.5 billion using cryptocurrencies (Corcoran 2018). Money laundering is the process of integrating proceeds of crime into the fnancial system so they can be used without being connected to the predicate ofense from which they derived. Scholars have also pointed to the risk of the use of DeFi (Wronka 2021b) and NFTs (Das et al. 2022)for money laundering. Cryptocurrency-based money laundering tends to follow the same process of placement, layering, and integration seen in fat-based money laundering (Desmond et al. 2019). Placement means putting the “dirty” money into the fnancial system (Albrecht et al. 2019). In the case of cryptocurrencies, this would take the form of a criminal depositing fat currency into a cryptocurrency exchange (Durrant and Natarajan 2019). Te layering process follows, where money launderers seek to obfuscate the origin and trail of their funds (Albrecht et al. 2019). *

A node’s mechanism for keeping track of unconfrmed transactions that the node has seen (but have not yet been added to a block) (Binance Academy [n.d.]).

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Criminals may use the following tactics to execute layering through cryptocurrencies: creating many cryptocurrency accounts, using cryptocurrency mixing services, and exchanging cryptocurrencies for other cryptocurrencies (or moving them to other blockchains), particularly privacy coins and chains (called chain hopping; see Raza and Raza [2021]; Durrant and Natarajan [2019]). Te money-laundering process concludes with integration, which means using the once-dirty money for “mainstream economic activity” (Albrecht et al. 2019) and mingling it with other “clean” funds (Durrant and Natarajan 2019). In cryptocurrency-based money laundering this may involve holding and investing some cryptocurrencies and/or exchanging them for fat currencies (Durrant and Natarajan 2019). Mixers and tumblers are common tools for laundering cryptocurrencies. Mixers let a group of users combine their inputs and outputs from multiple transactions into one transaction. Blockchains record this as a single transaction, and it is not possible to tell which outputs came from which user—i.e., the blockchain may show one Bitcoin was sent from each of A, B, and C addresses to D, E, and F addresses, but it would be impossible to tell if A’s Bitcoin went to address D, address E, or address F. One study evaluated the “success” of mixers in obfuscating fund origins and found that two of the three mixers studied were successful (Möser et al. 2013). Other cryptocurrency-based tools money launderers use include Bitcoin ATMs and gambling services (Fanusie and Robinson 2018). Te Coincheck Exchange hackers (allegedly North Korean state-sponsored hackers; see Carlisle and Izenman 2019) used several of these techniques to launder the proceeds of their crime (Tsuchiya and Hiramoto 2021). Te hackers stole $530 million of the cryptocurrency NEM in January 2018 by infecting a Coincheck terminal with malware and stealing the private keys for the hot wallet containing the exchange’s NEM. Tey sent the stolen funds to 18 addresses and then sent them to many, many other addresses to obfuscate the trail of the funds. Te attackers created their own exchange, through which they ofered to exchange NEM for Bitcoin or Litecoin at a 15% discount. Trough this process, the stolen NEM reached more than 130,000 other cryptocurrency addresses. Te foundation which created NEM tried to use a “mosaic tagging system” to trace the stolen coins, but the hackers thwarted these eforts by “sending the marked NEM to those who had contacted them by sending messages or a small amount of NEM” (Tsuchiya and Hiramoto 2021). Only 30% of the stolen funds have so far been located and been subject to enforcement action in Japan (Tsuchiya and Hiramoto 2021). Criminals also use cryptocurrencies to launder proceeds of “ofine” drug crimes. For example, organized criminal groups have used cryptocurrencies like

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Bitcoin to launder the proceeds of their drug crimes (Majumder et al. 2019). However, cash remains a more viable money-laundering method for crimes of this nature (Butler 2019).

Terrorism Financing Tere is a dearth of systematic, academic research on cryptocurrencies being used for terrorism fnancing; much research is speculative. Research reports specifc case studies, showing that the risk is non-negligible (Kfr 2020). Interviews with both terrorist fnanciers themselves and compliance and prevention experts confrm the same (Teichmann 2018). However, there remains controversy over the severity of its threat (Kfr 2020). For example, in 2018, the European Parliament considered that only a handful of such cases existed and that the advantages of using cryptocurrencies for terrorist fnancing did not outpace current methods (Butler 2019). Tere is anecdotal evidence of extremist groups using the dark web to purchase weapons, drugs, and fake documents or to sell stolen antiquities (Kfr 2020; Teichmann 2018). Te U.S. government has prosecuted cases involving soliciting cryptocurrencies for the Islamic State in Iraq and the Levant (see, for example, United States of America v. Ali Shukri Amin 2015; Kethineni and Cao 2020). Case studies of terrorist fnancing using cryptocurrencies have involved social media or other online campaigns (Dyntu and Dykyi 2019; Kethineni and Cao 2020; Kfr 2020), taking out loans and credit cards using false identities to buy cryptocurrencies to send to terrorist groups (Majumder et al. 2019), as well as kidnapping and ransom (Teichmann 2018). Cryptocurrencies may not yet be used in terrorism fnancing for various reasons. First, mainstream adoption of cryptocurrencies is insufcient, and it remains difcult to purchase goods and services with them (Internal Revenue Service Criminal Investigation 2021; Kfr 2020). Technological barriers to their use may also exist among terrorist groups (Kfr 2020). Tere also remains concern about whether cryptocurrency use is permissible under Islamic law (Kfr 2020). Finally, cryptocurrency prices remain very volatile, which may detract from their attractiveness to terrorists (Internal Revenue Service Criminal Investigation 2021).

Sanctions Evasion Much of the information on sanctions evasion involving cryptocurrencies comes from private companies, think tanks, or news media, particularly spurred by the Russian invasion of Ukraine in February 2022. However, some hypothetical

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academic research does exist. Furthermore, court cases point to the use of cryptocurrencies to evade sanctions, with news of the frst criminal cryptocurrencybased sanctions evasion case being brought in the U.S. (Hansen 2022). Various cryptocurrency-related entities and addresses, such as the Chatex cryptocurrency exchange, have been on the U.S. Ofce of Foreign Assets Control sanctions list (Akartuna et al. 2022), refecting a subset of companies which specifcally cater to sanctioned businesses and individuals (Kfr 2020). As with terrorist fnancing, there remains some debate over the level of risk cryptocurrencies pose to sanctions evasion (Wronka 2021a). For example, concerns about the risk of Russian oligarchs using cryptocurrencies to evade sanctions may be overstated. Tis is, in part, due to the transparent nature of many blockchains, like the Bitcoin blockchain, and the ability to link public keys to known individuals in many cases. Furthermore, to transfer cryptocurrencies into fat currencies (to spend them), users need to use an exchange (even if they use privacy coins), many of which are regulated, have KYC requirements, and ban sanctioned individuals. Using blockchain analysis, detection of sanctioned individuals using even non-regulated exchanges is likely. In addition, many exchanges still do not allow users to exchange funds from mixers (Feeney 2022). Finally, most exchanges operating in Russia have limited liquidity available for users, to the tune of about $200,000 compared to $22 million on U.S. exchanges, creating further difculties for oligarchs wishing to use cryptocurrencies to evade sanctions (Mazzola and Goroch 2022). However, countries like North Korea, Venezuela, and Iran have sought to evade sanctions using cryptocurrencies (Kfr 2020). Venezuela discussed issuing its own cryptocurrency to evade sanctions. Iran has taken up Bitcoin mining; its services account for 44.5% of all Bitcoin mining. Tey earn revenue lost to sanctions against purchasing their oil. Tey also use this Bitcoin to import necessary goods (Wronka 2021a). North Korea uses state-sponsored cybercrime (often involving cryptocurrency) to evade sanctions and earn revenue for its weapons program (Macfarlane 2020). In 2015, South Korean intelligence agencies “estimated that North Korea employs up to 6,000 cyber-warfare experts, a number that has likely grown since then” (Carlisle and Izenman 2019). Tis includes the Lazarus Group (Carlisle and Izenman 2019). North Korea carried out the WannaCry ransomware attack and various cryptocurrency exchange hacks (for example, the Youbit hack in 2017 and, likely, the aforementioned Coincheck hack). Estimates put the value of the country’s cryptocurrency holdings between $15 million and $210 million. Some also suggest they have sold drugs on darknet marketplaces and engaged in cryptocurrency mining (including through cryptojacking) (Carlisle and Izenman 2019).

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Tax Evasion Cryptocurrencies have long been considered as a risk for tax evasion due to certain of their inherent characteristics—namely, their anonymous (or, more accurately, pseudo-anonymous) nature and the lack of reliance on fnancial institutions to use them (Marian 2013). Privacy coins, such as Monero in particular, pose a risk of tax evasion (Noked 2018). Furthermore, there is no global centralized reporting structure for cryptocurrency holdings in the way there is for traditional bank accounts (Kossow and Dykes 2018). In 2021, the IRS seized $3.5 billion in cryptocurrency. However, much of this comes from the Silk Road darknet marketplace case (Internal Revenue Service Criminal Investigation 2021).

Darknet Marketplaces Darknet marketplaces exist on the dark web, which is part of the deep web. Te deep web is a section of the internet not indexed by search engines and which is only accessible “through specifc software, confgurations, or authorization” (Cronin 2018). Generally, one accesses it through a TOR browser, which employs a distributed network to obscure users’ IP addresses. Darknet marketplaces serve as brokers for drugs, cybercrime-as-a-service, counterfeit goods, stolen credentials, fake documents, human trafcking, and child pornography, among other illicit goods and services (Cronin 2018). Perhaps the most famous darknet marketplace was the Silk Road, which the U.S. government took down in 2013; its founder, Ross Ulbricht, is serving life in prison (Burrus 2018). Since then, law enforcement worldwide closed several darknet marketplaces, such as ItalianMafaBrussels and Dark HunTor (Bahamazava and Nanda 2022). As with ransomware, some have attributed the growth in darknet marketplaces to the growth of cryptocurrencies because users make darknet market purchases with them (Almaqableh et al. 2022). Over time, darknet marketplaces have exhibited a shift from the use of Bitcoin to Monero, particularly after Monero’s 2017 privacy update (Bahamazava and Nanda 2022). In 2018, Europol estimated darknet marketplace spending at $1 billion, with 62% of this revenue coming from drug trades (Bahamazava and Nanda 2022). As of October 2018, there were 23 active darknet marketplaces (White et al. 2019). However, it is important to note that “ofine” drug trade remains more signifcant. Te Silk Road earned $100 million per year in sales commissions for all products; the total value of the global cocaine trade alone is around $38 billion (Butler 2019).

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Bribery and Corruption Tere is no systematic research about the use of cryptocurrencies for bribery and corruption, although scholars acknowledge their risk in this regard (Teichmann and Falker 2020; Wawrosz and Lánský 2021). However, some also note that the transparency of many blockchains could reduce the risk of their use in corruption (Kossow and Dykes 2018; Wawrosz and Lánský 2021). Research shows that in countries where corruption is relatively high (but not beyond a certain amount), cryptocurrency use tends to be higher (Wawrosz and Lánský 2021). Further research confrmed these fndings (Alnasaa et al. 2022).

Cryptocurrency-Adjacent Crimes We refer to cryptocurrency­adjacent crimes, where crimes occurred which purported to involve cryptocurrencies or related products and services but, in fact, did not. One example of this is the Cryptoqueen case, in which Ruja Ignatova allegedly stole billions in a Ponzi scheme involving “OneCoin.” She claimed OneCoin was a cryptocurrency; however, it never even existed (BBC 2019).

Crime Risks Associated with Specifc Types of Cryptocurrency Technology Novel aspects of the cryptocurrency ecosystem, such as decentralized fnance (DeFi) and non-fungible tokens (NFTs), exhibit unique crime types, as shown in Figure 6.1 (on page 95). Figure 6.2 depicts the relationships among crime types present in DeFi, NFTs, and the wider cryptocurrency space.

DeFi Crime Risks In addition to and including some of the aforementioned crime risks, DeFi exhibits unique crime types. DeFi refers to an ecosystem of products and services built and delivered through smart contracts, which imitate traditional fnancial products and services (such as currency exchange, lending, insurance, and derivatives trading), except they allow parties to interact directly in a trustless, permissionless manner and retain custody of their own funds (Schär 2021). Some of the crime types present in the wider cryptocurrency space which commonly afect DeFi are miner extractable value (Daian et al. 2019), market manipulation (including pump-and-dump schemes (Hamrick et al. 2021; Mazorra et al. 2022) and wash trading (Qin et al. 2021; Victor and Weintraud

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Cryptocurrencies

Other cybercrimes Mining crimes

Terrorism financing Sanctions evasion Darknet markets Bribery and corruption Crypto-adjacent crimes

DeFi Oracle attacks Governance attacks Flash loan abuse

Fraud (including market manipulation) Money laundering Tax evasion

Rug pulls

NFTs Intellectual property crime

Figure 6.2 Relationships Among DeFi, NFT, and Cryptocurrency Crime Types

2021; Wang et al. 2021), fraudulent investment schemes (Mazorra et al. 2022; Xia et al. 2021), and smart contract fraud and hacks (Katona 2021; Schär 2021). Private sector research (Chainalysis 2022) also points to the use of DeFi in money laundering; however, this is yet to be confrmed through peer-reviewed academic research. In particular, scholars have raised concern about the use of stablecoins (cryptocurrencies whose value is pegged to a government-issued fat currency and a key component of the DeFi ecosystem) for money laundering, as they shield criminals from the volatility of other cryptocurrencies (Dyson et al. 2018). Types of crime which are specifc to DeFi include oracle attacks (Gudgeon et al. 2020; Schär 2021), governance attacks (Gudgeon et al. 2020; Werner et al. 2021), and fash loan abuse (Caldarelli and Ellul 2021; Gudgeon et al. 2020; Qin et al. 2021; Wang et al. 2021; Xu et al. 2022). Tese types of attacks often facilitate some of the other cryptocurrency crime types observed in the wider cryptocurrency ecosystem.

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DeFi smart contracts often extract information from some external data source (called an oracle) (Kamps et al. 2022). Oracle attacks refer to manipulation of these sources of information (oftentimes price information) (Gudgeon et al. 2020; Schär 2021). Flash loan attacks involve a DeFi innovation called a fash loan, which is “an uncollateralized loan that is borrowed and repaid in a single . . . transaction” (Kamps et al. 2022). If the loan is paid back at the end of the transaction, the transactions which use the loan are successful. Otherwise, the transaction and the loan are reversed. Flash loans enable various attacks which would ordinarily require more signifcant amounts of capital, such as oracle attacks, governance attacks, other types of market manipulation, and other crimes (Kamps et al. 2022). Te February 18, 2020, bZx® platform exploit is an example of an oracle attack (to cause price manipulation) facilitated by fash loans, from which victims lost 2,699.97 ETH. In this case, attackers, through a fash loan, manipulated the sUSD/ETH trading pair and then bought sUSD. Tey used this newly purchased sUSD to buy more ETH to repay the original loan, ultimately profting $634,000 (Qin et al. 2021). Many DeFi projects and protocols are governed in a decentralized manner through decentralized autonomous organizations (DAOs). In the case of DAOs, users who hold governance tokens can vote on the use of funds and future of the protocol through smart contracts. Governance attacks exploit this mechanism, in which a nefarious party (or colluding parties) obtains a majority of the available governance tokens and votes in a manner that benefts them but not the protocol (Werner et al. 2021). For example, they may vote to release the entirety of the DAO’s funds to themselves. Te literature explores a theoretical governance attack on MakerDAO; researchers conclude an attacker could obtain enough governance tokens to carry out a governance attack either through crowdfunding mechanisms or using fash loans (Gudgeon et al. 2020). Rug pulls are a fraudulent investment scam unique to DeFi and NFTs (Mazorra et al. 2022). Tey are a type of exit scam that occurs when founders of a project disappear and either (a) steal all the funds deposited into that protocol, or (b) remove the liquidity for the project’s token (Kamps et al. 2022). Scammers may use infuencers or advertising to bolster the pretense of their project’s credibility or combine rug pulls with airdrop or arbitrage scams or pump-and-dump schemes (Xia et al. 2021). One of the most popular decentralized exchanges, SushiSwap, sufered from a $14 million rug pull early in its existence; the founder ultimately returned the funds and the protocol fourished (Smith 2021). Research found 26,957 instances of rug pulls on the Uniswap decentralized exchange alone (Mazorra et al. 2022).

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Tere are three distinct types of rug pulls: the simple rug pull, the sell rug pull, and the smart contract trap door (Mazorra et al. 2022). Simple rug pulls are the most common (with 24,870 of the 26,957 rug pulls in one study ftting this category [Mazorra et al. 2022]) and involve developers removing liquidity from the protocol. A sell rug pull refers to a fraudster creating a token and adding some of its total supply to a liquidity pool (and keeping the rest for themselves). Once enough investors have swapped other (credible) tokens for the newly created token, using the initial liquidity the developer provided, the developer will swap the rest of their personal supply for the credible tokens (Mazorra et al. 2022). Tis may be combined with other eforts to make the token appear legitimate and reassuring investors on Telegram® or Discord® servers (while actually selling all their tokens) (Mackenzie and Bērziņa 2021). Finally, a smart contract trap door rug pull involves designing a token smart contract with rug pull–related features. For example, the mintable function allows a sell rug pull scammer to recoup their original liquidity by automatically creating new tokens to remove the liquidity (Mazorra et al. 2022; Xia et al. 2021). Other examples include mechanisms that secretly charge users a fee for using, minting, swapping, or burning the token (also referred to as advance fee tokens) (Xia et al. 2021) or functions that permit the developer to control an external account that interacts with their smart contract (Mazorra et al. 2022). Te possibilities for smart-contract trap-door rug pulls are diverse, and it is impossible to identify all possible attack vectors for them.

NFT Crime Risks NFTs are digital records of ownership and authenticity of some asset (which may be digital or physical) created by smart contracts and stored on a blockchain. Tey are not directly exchangeable for other NFTs—i.e., they are non­fungible. NFTs sufer from various types of fraud and market manipulation (Das et al. 2022), as well as intellectual property crime (such as copyright infringement violations and counterfeiting) (Çağlayan Aksoy and Özkan Üner 2021; Das et al. 2022). In terms of counterfeiting, one analysis revealed 322 NFT collections with similar names, a total of 2,438,496 identical image URLs, and 59,425 identical or nearly identical NFT images on the OpenSea® NFT marketplace. With regard to potential criminal copyright infringement, several legal questions remain outstanding, but it does seem to pose an issue (Çağlayan Aksoy and Özkan Üner 2021). Types of fraud involving NFTs include forging verifcation badges, impersonation, tampering with of-chain records, and secretly changing royalty costs. Market manipulation is also particularly widespread. One type of common market manipulation in NFT marketplaces is spoofng (also called bid pollution),

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of which, according to one study, 16,215 instances occurred on OpenSea and 15,369 on Rarible® (another NFT marketplace). Scholars also observed shill bid­ ding and bid shielding, which serve to raise and lower auction bids, respectively. Shill bidding afected 282 NFT collections, allowing sellers to proft $13,014,662. Bid shielding saved users between $200.77 and $152,606.31 across 316 instances on OpenSea. Finally, wash trading is rampant, with some suspecting major projects like CryptoKitties® and Decentraland® of benefting from it. Scholars identifed 9,393 incidences of wash trading, generating $96,858,093 (Das et al. 2022). Another study found that between January 2018 and mid-November 2021, wash trading “may have infated the authentic trading volumes by as much as $149.5m for the period” (von Wachter et al. 2022). Tere is also concern over the use of NFTs for money laundering (particularly as NFT marketplaces generally do not collect KYC or AML information [Das et al. 2022]) and tax evasion (Sundaravelu 2021). In fact, HM Revenue and Customs in the UK recently seized its frst NFT (BBC 2022). Rug pulls are also prevalent in the world of NFTs (Das et al. 2022). Finally, the U.S. Department of Justice recently charged its frst insider trading case involving NFTs (United States Department of Justice 2022).

Conclusions Cryptocurrency crime has risen with cryptocurrency’s popularity (Chainalysis 2022). Certain characteristics of cryptocurrencies, such as their pseudo-anonymity, low barriers to entry, and irrevocable transactions, facilitate their use in criminal activities (Kethineni and Cao 2020). Many of the crimes observed in the cryptocurrency space, such as various types of fraud, are crypto-enabled in nature; incorporating cryptocurrencies represents a novel way to execute these types of crime. However, there is also evidence of some crypto-dependent crimes, such as cryptocurrency mining crimes. Tere is a plethora of evidence of certain types of crime involving cryptocurrencies, such as fraud, money laundering, cybercrimes, mining crimes, and darknet marketplace crimes. Crimes such as terrorism fnancing, sanctions evasion, tax evasion, and bribery and corruption require further research, but anecdotal and speculative research suggests they are occurring with cryptocurrencies to some extent. As the space evolves (for example, with the advent of DeFi and NFTs), we see new types of both crypto-enabled crime (such as intellectual property crime) and crypto-dependent crime (such as governance attacks). As these and other areas of the cryptocurrency ecosystem evolve, new types of crypto-dependent crime are likely to arise.

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References Ahn, G.-J., Doupe, A., Liao, Z. Z., and Liao, K. (2016). Ransomware and cryptocurrency: Partners in crime. In: Cybercrime Trough an Interdisciplinary Lens (1st ed., 256). Routledge. https://doi.org/10.4324/9781315618456 Akartuna, E. A., Johnson, S. D., and Tornton, A. (2022). Preventing the money laundering and terrorist fnancing risks of emerging technologies: An international policy Delphi study. Technological Forecasting and Social Change, 179: 121632. https://doi.org/10.1016/j.techfore.2022.121632 Albrecht, C., Dufn, K. M., Hawkins, S., and Morales, R. V. M. (2019). Te use of cryptocurrencies in the money laundering process. Journal of Money Launder­ ing Control, 22(2): 210–216. https://doi.org/10.1108/JMLC-12-2017-0074 Almaqableh, L., et al. (2022). Is it possible to establish the link between drug busts and the cryptocurrency market? Yes, we can. International Journal of Informa­ tion Management, 102488. https://doi.org/10.1016/j.ijinfomgt.2022.102488 Alnasaa, M., et al. (2022). Crypto, corruption, and capital controls: Cross-country correlations. IMF Working Papers, 2022(060): 1. https://doi.org/10.5089/979 8400204005.001 Australian Competition & Consumer Commission (2020). Targeting Scams 2019. https://www.accc.gov.au/system/fles/1657RPT_Targeting%20scams%20 2019_FA.pdf Bahamazava, K., and Nanda, R. (2022). Te shift of DarkNet illegal drug trade preferences in cryptocurrency: Te question of traceability and deterrence. Forensic Science International: Digital Investigation, 40: 301377. https://doi .org/10.1016/j.fsidi.2022.301377 Banton,C.(2021,October28).Stophunting.Investopedia.https://www.investopedia .com/terms/s/stophunting.asp Barnes, N. D. (2018). Blockchain redux—Cracking the code on cryptocurrencies. Information Management, 52(6): 26. Bartoletti, M., Carta, S., Cimoli, T., and Saia, R. (2019). Dissecting Ponzi schemes on Ethereum. https://docs.google.com/spreadsheets/d/1cPanswNPVjoP4xENF7 a2AjQQXX_ByZPbk1qOs6aFEPs/pubhtml Bartoletti, M., Pes, B., and Serusi, S. (2018). Data mining for detecting bitcoin Ponzi schemes. 2018 Crypto Valley Conference on Blockchain Technology (CVCBT), 75–84. https://doi.org/10.1109/CVCBT.2018.00014 Baum, S. (2018). Cryptocurrency fraud: A look into the frontier of fraud. Honors College Theses. https://digitalcommons.georgiasouthern.edu /honors-theses/375 BBC (2019, November 24). Cryptoqueen: How this woman scammed the world, then vanished. BBC News. https://www.bbc.com/news/stories-50435014

112 Cryptocurrency Concepts, Technology, and Applications

BBC (2022, February 13). HMRC seizes NFT for frst time in £1.4m fraud case. BBC News. https://www.bbc.com/news/business-60369879 Binance Academy (n.d.). Mempool. Binance Academy. https://academy.binance .com/en/glossary/mempool Burrus, J. (2018). Fighting fnancial crime in the age of cryptocurrencies, 3. Refnitiv. https://www.refnitiv.com/content/dam/marketing/en_us/documents/expert -talks/world-check-expert-talk-fghting-fnancial-crime.pdf Butler, S. (2019). Criminal use of cryptocurrencies: A great new threat or is cash still king? Journal of Cyber Policy, 4(3): 326–345. https://doi.org/10.1080/23 738871.2019.1680720 Çağlayan Aksoy, P., and Özkan Üner, Z. (2021). NFTs and copyright: Challenges and opportunities. Journal of Intellectual Property Law & Practice, 16(10): 1115–1126. https://doi.org/10.1093/jiplp/jpab104 Caldarelli, G., and Ellul, J. (2021). Te blockchain oracle problem in decentralized fnance—A multivocal approach. Applied Sciences, 11(16): 7572. https://doi .org/10.3390/app11167572 Carlisle, D., and Izenman, K. (2019). Closing the crypto gap guidance for countering North Korean cryptocurrency activity in Southeast Asia [Occasional Paper]. Royal United Services Institute for Defence and Security Studies. Chainalysis (2022). Te 2022 Crypto Crime Report. Charoenwong, B., and Bernardi, M. (2021). A decade of cryptocurrency ‘hacks’: 2011–2021 (SSRN Scholarly Paper No. 3944435). Social Science Research Network. https://doi.org/10.2139/ssrn.3944435 Chen, W., et al. (2020). Phishing scam detection on Ethereum: Towards fnancial security for blockchain ecosystem. Proceedings of the Twenty­Ninth International Joint Conference on Artifcial Intelligence Special Track on AI in FinTech., 4506–4512. Chen, W., et al. (2019). Detecting “pump & dump schemes” on cryptocurrency market using an improved apriori algorithm. 2019 IEEE International Con­ ference on Service­Oriented System Engineering (SOSE), 293–2935. CoinBureau (2018, February 19). Stop-loss hunting: Crypto whales are making a killing on your stops. Coin Bureau. https://www.coinbureau.com/analysis /stop-loss-hunting-crypto-whales/ Commodity Futures Trading Commission (n.d.). Interpretive guidance and policy statement on disruptive practices. https://www.cftc.gov/sites/default/fles/idc /groups/public/@newsroom/documents/fle/dtp_factsheet.pdf Cong, L. W., Li, X., Tang, K., and Yang, Y. (2021). Crypto wash trading. SSRN Scholarly Paper No. 3530220. Social Science Research Network. https://doi .org/10.2139/ssrn.3530220 Conti, M., Gangwal, A., and Ruj, S. (2018). On the economic signifcance of ransomware campaigns: A Bitcoin transactions perspective. Computers & Security, 79: 162–189. https://doi.org/10.1016/j.cose.2018.08.008

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Corcoran, K. (2018, February 12). Criminals in Europe are laundering $5.5 billion of illegal cash through cryptocurrency, according to Europol. Business Insider. https://www.businessinsider.com/europol-criminals -using-cryptocurrency-to-launder-55-billion-2018-2 Cronin, M. J. (2018). Hunting in the dark: A prosecutor’s guide to the dark net and cryptocurrencies fentanyl and related threats. Department of Justice Journal of Federal Law and Practice, 66(4): 65–78. Daian, P., et al. (2019). Flash Boys 2.0: Frontrunning, transaction reordering, and consensus instability in decentralized exchanges. ArXiv:1904.05234 [Cs]. http://arxiv.org/abs/1904.05234 Das, D., et al. (2022). Understanding security issues in the NFT ecosystem. ArXiv:2111.08893. https://doi.org/10.48550/arXiv.2111.08893 Desmond, D. B., Lacey, D., and Salmon, P. (2019). Evaluating cryptocurrency laundering as a complex socio-technical system: A systematic literature review. Journal of Money Laundering Control, 22(3): 480–497. https://doi .org/10.1108/JMLC-10-2018-0063 Duhaime, C. (2020, January 14). Bitcoin SIM swapper charged with stealing $50 million, arrested in British Columbia | Duhaime’s Anti-Money Laundering & Financial Crime News. Duhaime’s Anti-Money Laundering Law in Canada. https://www.antimoneylaunderinglaw.com/2020/01/bitcoin-sim-swapper -charged-with-stealing-50-million-arrested-in-british-columbia.html Durrant, S., and Natarajan, M. (2019). Cryptocurrencies and money laundering opportunities. In M. Natarajan (Ed.). International and Transnational Crime and Justice (2nd ed., 73–79). Cambridge University Press. https://doi .org/10.1017/9781108597296.012 Dyntu, V., and Dykyi, O. (2019). Cryptocurrency in the system of money laundering. Baltic Journal of Economic Studies, 4(5): 75–81. https://doi.org /10.30525/2256-0742/2018-4-5-75-81 Dyson, S., Buchanan, W. J., and Bell, L. (2018). Te challenges of investigating cryptocurrencies and blockchain related crime. Te Journal of Te British Blockchain Association, 1(2): 1–6. https://doi.org/10.31585/jbba-1-2-(8)2018 Fanusie, Y. J., and Robinson, T. (2018). Bitcoin laundering: An analysis of illicit fows into digital currency services [Memorandum], 16. Feeney, M. (2022). Bitcoin won’t rescue Russian oligarchs from sanctions. https://policycommons.net/artifacts/2269610/bitcoin-wont-rescue-russian -oligarchs-from-sanctions/3029439/ Financial Conduct Authority (2021). FCA Handbook. https://www.handbook.fca .org.uk/handbook/MAR/1/6.html?date=2016-03-07 Foley, S., Karlsen, J. R., and Putniņš, T. J. (2019). Sex, drugs, and Bitcoin: How much illegal activity is fnanced through cryptocurrencies? Te Review of Financial Studies, 32(5): 1798–1853. https://doi.org/10.1093/rfs/hhz015

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Gao, B., et al. (2020). Tracking counterfeit cryptocurrency end-to-end. ArXiv Preprint ArXiv:2011.02673. Gudgeon, L., et al. (2020). Te decentralized fnancial crisis. 2020 Crypto Valley Conference on Blockchain Technology (CVCBT), 1–15. https://doi.org/10.1109 /CVCBT50464.2020.00005 Hamrick, J., et al. (2021). Analyzing target-based cryptocurrency pump and dump schemes. Proceedings of the 2021 ACM CCS Workshop on Decentralized Finance and Security, 21–27. https://doi.org/10.1145/3464967.3488591 Hansen, E. (2022, May 16). DOJ to lodge charges over using crypto to evade sanctions—Law360. Law360. https://www.law360.com/articles/1493728 /doj-to-lodge-charges-over-using-crypto-to-evade-sanctions Higgins, S. (2017, January 27). A digital currency scam is misusing the Rothschild family name. CoinDesk. https://www.coindesk.com/rothschild -advisory-warns-fraudulent-digital-currency Internal Revenue Service Criminal Investigation (2021). Annual Report 2021. Jiaying, J. (2020). Regulating blockchain? A retrospective assessment of China’s blockchain policies and regulations. Tsinghua China L. Rev., 12. Kamps, J., and Kleinberg, B. (2018). To the moon: Defning and detecting cryptocurrency pump-and-dumps. Crime Science, 7(1): 18. https://doi.org/10.1186 /s40163-018-0093-5 Kamps, J., Trozze, A., and Kleinberg, B. (2022). Cryptocurrencies: Boons and curses for fraud prevention. In: A Fresh Look at Fraud. Routledge. Karimov, B., and Wójcik, P. (2021). Identifcation of scams in initial coin oferings with machine learning. Frontiers in Artifcial Intelligence, 4: 718450. https:// doi.org/10.3389/frai.2021.718450 Katona, T. (2021). Decentralized fnance: Te possibilities of a blockchain “Money Lego” system. Financial and Economic Review, 20(1): 74–102. https://doi .org/10.33893/FER.20.1.74102 Kethineni, S., and Cao, Y. (2020). Te rise in popularity of cryptocurrency and associated criminal activity. International Criminal Justice Review, 30(3): 325–344. https://doi.org/10.1177/1057567719827051 Kfr, I. (2020). Cryptocurrencies, national security, crime and terrorism. Compara­ tive Strategy, 39(2): 113–127. https://doi.org/10.1080/01495933.2020.1718983 Kossow, N., and Dykes, V. (2018). Blockchain, bitcoin and corruption: A review of the linkages. Transparency International. Lee, K., Kaiser, B., Mayer, J., and Narayanan, A. (2020). An empirical study of wireless carrier authentication for SIM swaps. Proceedings of the Sixteenth Symposium on Usable Privacy and Security, 20. Lucking, D., and Aravind, V. (2020). Cryptocurrency as a commodity: Te CFTC’s regulatory framework. Global Legal Insights ­ Fintech 2020, 8.

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Macfarlane, E. K. (2020). Strengthening sanctions: Solutions to curtail the evasion of international economic sanctions through the use of cryptocurrency notes. Michigan Journal of International Law, 42(1): [i]-230. Mackenzie, S., and Bērziņa, D. (2021). NFTs: Digital things and their criminal lives. Crime, Media, Culture, 17416590211039796. https://doi.org/10.1177 /17416590211039797 Majumder, A., Routh, M., and Singha, D. (2019). A conceptual study on the emergence of cryptocurrency economy and its nexus with terrorism fnancing. In: R. Chandra Das (Ed.). Te Impact of Global Terrorism on Economic and Political Development, 125–138. Emerald Publishing Limited. https:// doi.org/10.1108/978-1-78769-919-920191012 Marian, O. (2013). Are cryptocurrencies super tax havens? Michigan Law Review First Impressions, 112(38): 11. Mazorra, B., Adan, V., and Daza, V. (2022). Do not rug on me: Leveraging machine learning techniques for automated scam detection. Mathematics, 10(6): 949. https://doi.org/10.3390/math10060949 Mazzola, D. P., and Goroch, M. (2022, March 22). Are Russia’s elite really using cryptocurrency to evade sanctions? Te Conversation. http://theconversation .com/are-russias-elite-really-using-cryptocurrency-to-evade-sanctions-179559 Mitchell, C. (2021, November 14). Front-running. Investopedia. https://www .investopedia.com/terms/f/frontrunning.asp Möser, M., Bohme, R., and Breuker, D. (2013). An inquiry into money laundering tools in the Bitcoin ecosystem. 2013 APWG ECrime Researchers Summit, 1–14. https://doi.org/10.1109/eCRS.2013.6805780 Navarro, R. R. (2019). Preventative fraud measures for cryptocurrency exchanges: Mitigating the risk of cryptocurrency scams. MS, Utica College. http://search .proquest.com/docview/2312801484/abstract/7B22A14839754F47PQ/1 Noked, N. (2018). Tax evasion and incomplete tax transparency. Laws, 7(3): 31. Nomics (2019, August 5). Transparency must be trustless: Te Nomics Manifesto. Nomics Blog. https://nomics.com/blog/essays/manifesto O’Malley, R. L., and Holt, K. M. (2022). Cyber sextortion: An exploratory analysis of diferent perpetrators engaging in a similar crime. Journal of Interpersonal Violence, 37(1–2): 258–283. https://doi.org/10.1177/0886260520909186 Paquet-Clouston, M., Haslhofer, B., and Dupont, B. (2019). Ransomware payments in the Bitcoin ecosystem. Journal of Cybersecurity, 5(1): tyz003. https:// doi.org/10.1093/cybsec/tyz003 Phillips, R., and Wilder, H. (2020). Tracing cryptocurrency scams: Clustering replicated advance-fee and phishing websites. 2020 IEEE International Confer­ ence on Blockchain and Cryptocurrency (ICBC), 1–8. https://doi.org/10.1109 /ICBC48266.2020.9169433

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Pryzmont, P. (2016). An empirical study of how Bitcoin related incidents impact its price volatility. Undefned. https://www.semanticscholar.org/paper/An -empirical-study-of-how-Bitcoin-related-incidents-Pryzmont/2872bd0880f7 d06ed98c24629416271229a77ad4 Qin, K., Zhou, L., Livshits, B., and Gervais, A. (2021). Attacking the DeFi ecosystem with fash loans for fun and proft. ArXiv:2003.03810 [Cs]. http://arxiv.org/abs /2003.03810 Raza, H., and Raza, M. R. (2021). A study of blockchain technology, Bitcoin and other cryptocurrencies as means of money laundering, frauds and scams. Global Media and Social Sciences Research Journal (GMSSRJ), 2(1): 73–84. Reddy, E., and Minnaar, A. (2018). Cryptocurrency: A tool and target for cybercrime. Acta Criminologica: Southern African Journal of Criminology, 31(3): 71–92. Schär, F. (2021). Decentralized fnance: On blockchain- and smart contract-based fnancial markets. Federal Reserve Bank of St. Louis Review, Second Quarter. https://doi.org/10.20955/r.103.153-74 Smith, S. S. (2021). Decentralized fnance & accounting—implications, considerations, and opportunities for development. Te International Journal of Digital Accounting Research, 129–153. https://doi.org/10.4192/1577-8517-v21_5 Sundaravelu, A. (2021). IRS views NFTs as tax evasion threat, but cryptocurrency experts disagree. International Tax Review. http://www.proquest.com /docview/2534502639/abstract/8E9FA95E70E34A09PQ/1 Teichmann, F. M. J. (2018). Financing terrorism through cryptocurrencies—A danger for Europe? Journal of Money Laundering Control, 21(4): 513–519. https://doi.org/10.1108/JMLC-06-2017-0024 Teichmann, F. M. J., and Falker, M.-C. (2020). Cryptocurrencies and fnancial crime: Solutions from Liechtenstein. Journal of Money Laundering Control, 24(4): 775–788. https://doi.org/10.1108/JMLC-05-2020-0060 Tidy, J. (2022, March 30). Ronin Network: What a $600m hack says about the state of crypto. BBC News. https://www.bbc.com/news/technology-60933174 Trozze, A., et al. (2022). Cryptocurrencies and future fnancial crime. Crime Science. Tsuchiya, Y., and Hiramoto, N. (2021). How cryptocurrency is laundered: Case study of Coincheck hacking incident. Forensic Science International: Reports, 4: 100241. https://doi.org/10.1016/j.fsir.2021.100241 Twomey, D., and Mann, A. (2020). Fraud and manipulation within cryptocurrency markets. In: Corruption and Fraud in Financial Markets: Malpractice, Misconduct and Manipulation. Wiley. United States Department of Justice (2022, June 1). Former employee of NFT marketplace charged in frst ever digital asset insider trading scheme. U.S. Attorney’s Ofce Southern District of New York. https://www.justice.gov/usao-sdny /pr/former-employee-nft-marketplace-charged-frst-ever-digital-asset-insider -trading-scheme

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United States of America v. Ali Shukri Amin, 1:15-cr-00164-CMH (United States District Court for the Eastern District of Virginia, Alexandria Division 2015). ur Rehman, M. H., Salah, K., Damiani, E., and Svetinovic, D. (2020). Trust in blockchain cryptocurrency ecosystem. IEEE Transactions on Engineering Management. Vasek, M. (2017). Measuring Bitcoin-based cybercrime. [PhD, The University of Tulsa]. http://search.proquest.com/docview/1896552050/abstract /51DBD0D7392A4E59PQ/1 Vasek, M., et al. (2017). Te Bitcoin brain drain: Examining the use and abuse of bitcoin brain wallets. In: J. Grossklags and B. Preneel (Eds.), Financial Cryptography and Data Security, 9603: 609–618. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-54970-4_36 Vasek, M., and Moore, T. (2015). Tere’s no free lunch, even using Bitcoin: Tracking the popularity and profts of virtual currency scams. In: R. Böhme and T. Okamoto (Eds.), Financial Cryptography and Data Security, 8975: 44–61. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-47854-7_4 Victor, F., and Weintraud, A. M. (2021). Detecting and quantifying wash trading on decentralized cryptocurrency exchanges. Proceedings of the Web Conference 2021, 23–32. https://doi.org/10.1145/3442381.3449824 von Wachter, V., Jensen, J. R., Regner, F., and Ross, O. (2022). NFT wash trading: Quantifying suspicious behaviour in NFT markets (arXiv:2202.03866). arXiv. https://doi.org/10.48550/arXiv.2202.03866 Wang, D., et al. (2021). Towards a frst step to understand fash loan and its applications in DeFi ecosystem. Proceedings of the Ninth International Work­ shop on Security in Blockchain and Cloud Computing, 23–28. https://doi .org/10.1145/3457977.3460301 Wawrosz, P., and Lánský, J. (2021). Cryptocurrencies and corruption 1. Ekonomicky Casopis, 69(7): 687–705. https://doi.org/10.31577/ekoncas.2021.07.02 Werner, S. M., et al. (2021). SoK: Decentralized fnance (DeFi). ArXiv:2101.08778 [Cs, Econ, q-Fin]. http://arxiv.org/abs/2101.08778 White, R., Kakkar, P. V., and Chou, V. (2019). Prosecuting darknet marketplaces: Challenges and approaches. Department of Justice Journal of Federal Law and Practice, 67(1): 65–80. Wronka, C. (2021a). Digital currencies and economic sanctions: Te increasing risk of sanction evasion. Journal of Financial Crime (ahead-of-print). https:// doi.org/10.1108/JFC-07-2021-0158 Wronka, C. (2021b). Financial crime in the decentralized fnance ecosystem: New challenges for compliance. Journal of Financial Crime (ahead-of-print). https://doi.org/10.1108/JFC-09-2021-0218 Xia, P., Wang, H., Luo, X., et al. (2020). Don’t fsh in troubled waters! Characterizing Coronavirus-themed cryptocurrency scams. ArXiv Preprint ArXiv:2007.13639.

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Xia, P., Wang, H., Zhang, B., et al. (2020). Characterizing cryptocurrency exchange scams. Computers & Security, 98: 101993. Xia, P., et al. (2021). Trade or trick? Detecting and characterizing scam tokens on Uniswap decentralized exchange. Proceedings of the ACM on Measurement and Analysis of Computing Systems, 5(3): 39:1–39:26. https://doi.org/10.1145/3491051 Xu, J., and Livshits, B. (2019). Te anatomy of a cryptocurrency pump­and­dump scheme (arXiv:1811.10109). arXiv. https://doi.org/10.48550/arXiv.1811.10109 Xu, J., Paruch, K., Cousaert, S., and Feng, Y. (2022). SoK: Decentralized exchanges (DEX) with automated market maker (AMM) protocols. ArXiv:2103.12732 [Cs, q-Fin]. http://arxiv.org/abs/2103.12732

Chapter 7 Following the Virtual Money: Investigating Crypto-Based Money Laundering and Confscating Virtual Assets Federico Paesano Basel Institute on Governance

The Crypto Bombshell In June 2011, I attended a cyber-crime conference in Prague to learn about emerging trends and techniques employed by money launderers to hide their ill-gotten gains. I expected to learn nothing new. As a former fnancial investigator from Italy, I had seen plenty of ingenious money laundering schemes over the years. And in my new training role at the Swiss-based Basel Institute on Governance, my colleagues and I were working hands-on with law enforcement ofcers responsible for investigating and prosecuting fnancial crimes in many diferent countries. What else could there be that we hadn’t already seen? Ten a police ofcer from the U.S. state of Indiana came on stage and dropped a bombshell. Apparently, some criminals had just discovered that they could 119

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move money from one country to another, bypassing the fnancial system, in a matter of seconds, completely anonymously. “Tey are using Bitcoin, a form of virtual money,” he said. He was not able to really explain what this Bitcoin was. He simply said that it was similar to ingame tokens that you buy and sell but have no real value outside the games, with the diference that you could cash out in the real world and efectively move money to another jurisdiction, completely under the radar of law enforcement. Even then, I could sense something new and exciting was happening, both for citizens seeking freer access to fnancial markets and for criminal investigators seeking to trace and recover illicit money. Fast forward. It is 7 April 2014. A group of investigators from all over the world is gathering in a room at the University of Basel. Two FBI ofcers share how they were able to take down the frst drug market in the world, Silk Road. Tis was a place that resembled Amazon® or eBay®, but the products on sale were illegal goods such as drugs, fake passports, and counterfeited money. Te marketplace had vendors, their profles and reputations, listings and ‘product’ reviews. Silk Road was attractive because it was perceived to be highly anonymous. Buyers could have their favorite drugs sent to their homes through the regular postal service. What made it possible to stay anonymous, both for clients and vendors, was the fact that all the transactions were performed in Bitcoin. Moreover, the service was hidden behind Te Onion Router (TOR), an encryption software that allows people to visit places like Silk Road without revealing their IP address. Te two agents described how they were able to track down the identity of the creator of Silk Road, locate the servers where the marketplace was hosted, and, crucially, to fnd the private keys to unlock more than 170,000 Bitcoins he had amassed.* Te workshop organizers—the Basel Institute on Governance, Interpol and Europol—decided to make this ‘group’ permanent and launched the Working Group on Criminal Finances and Cryptocurrencies. From this frst meeting, in which just one case was shared, the topic ballooned in the law enforcement community in the following years. 2021 saw the Working Group’s 5th Global Conference on Criminal Finances and Cryptocurrencies, which was attended by more than 2,000 attendees from law enforcement, academia, and the private sector. Numerous cases were discussed and analysed. *

Federal Bureau of Investigation (25 October 2013). Manhattan U.S. Attorney announces seizure of additional $28 million worth of Bitcoins belonging to Ross William Ulbricht, alleged owner and operator of “Silk Road” website. https://archives .fbi.gov/archives/newyork/press-releases/2013/manhattan-u.s.-attorney-announces -seizure-of-additional-28-million-worth-of-bitcoins-belonging-to-ross-william-ulbricht -alleged-owner-and-operator-of-silk-road-website

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Tis story describes very well how cryptocurrencies have evolved in the last 12 years from an obscure phenomenon known to a few to a global payment method used by millions of people. Obviously, criminals are among those using them, but the unlawful use of cryptocurrencies accounts for only a small part of their overall use. Illicit activity accounted for just 0.34% of transactions, according to Chainalysis®, one of the leading analytic companies in this feld.* As we have seen in the examples above, until a few years ago there were only a few investigators in the world who had encountered cryptocurrencies in the course of their work, and even fewer who had actually successfully conducted an investigation leading to the confscation of the assets. In the next sections, we will analyze and discuss: • The problems posed by cryptocurrencies during a criminal or financial investigation • How the analysis of the blockchain has evolved over time and what information is possible nowadays to extract • Te legal framework that has been built in this feld • Te investigative techniques that can be adopted by law enforcement • How criminals and the crypto community are reacting to law enforcement’s eforts • How asset recovery is (or is not) diferent when it comes to cryptocurrencies

See No Evil, Hear No Evil, Investigate No Evil Moving Under the Radar “Criminals are not using cryptocurrencies in this country.” I can’t even remember how many times I’ve heard those words while sitting with investigators and prosecutors. When I prepare for a crypto-related training program for law enforcement, I usually travel in advance to the country where the training will be conducted to learn about the local judicial system. And that answer always puzzles me. Is it the only place in the world where criminals have decided to completely neglect this new opportunity to move their proceeds undisturbed? Digging deeper, it always becomes clear that most probably the truth is that they have probably encountered cryptocurrencies in their investigations, but

*

Chainalysis (2021). Te 2021 Crypto Crime Report. https://go.chainalysis.com/2021 -Crypto-Crime-Report.html

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they failed to detect them. According to research by Chainalysis,* almost no country in the world has been left untouched by the wave of adoption that occurred in the last few years. And there is no reason to believe that, in a place where ordinary people are using new technologies, criminals are not doing the same. Quite the opposite: criminals are always the frst to take advantage of new techniques, leveraging the so-called legislative gap. Tis refers to the time it takes for law enforcement to understand a new threat and develop countermeasures and for legislators to empower them with the appropriate legal framework. What is preventing law enforcement from investigating cryptocurrencies successfully? Mainly two things: On the one hand, we have to acknowledge that it is a completely new phenomenon, and some investigators still lack basic IT understanding. It is not surprising that many are scared that they will never be able to catch up and understand something so innovative. On the other hand, many are still convinced that in order to investigate and confscate cryptocurrency, one needs to become a cybercrime expert who spends hours and hours behind lines of programming code. Tis is far from the truth. What investigators need is the ability to detect the activity. And to detect it, one needs to understand how cryptocurrency works and moves.

Basic Elements That All Investigators Need to Know If I want to use crypto, I will start by getting a wallet. Tis could be a piece of software on my phone or laptop, or alternatively a hardware wallet, a small machine that I connect to my laptop when I need to make a transaction. Te wallet will create for me two main things. Te frst is an address, an identifer (think about it as the equivalent of a bank account number) that I can share with anybody. Tis identifer will be used by whoever wants to send me some money. Together with it, the wallet will create the corresponding private key, a sort of password that I can use to spend any money that I receive to that address. Te private key must be kept secret, because whoever has access to it can use it to spend that money. In fact, the private key would be the ultimate target of a crypto investigation, as taking control of the suspect’s private key is what allows An address and its private key are mathematically linked. (See Figure 7.1) In fact, the private key is nothing but a random number generated by the wallet from which the corresponding address is generated through a process called hashing. Hashing is the process of converting a certain input (in this case a private key) into another value, called output or hash, using a mathematical *

Chainalysis. (2021). Te 2021 Global Crypto Adoption Index, https://go.chainalysis .com/rs/503-FAP-074/images/Geography-of-Cryptocurrency-2021.pdf

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Figure 7.1 A Bitcoin Address and Its Corresponding Private Key

algorithm. Te generated value is unique and constitutes a fngerprint of the input. A good hash function is one-way only, meaning that by looking at the output, one cannot calculate what the input was. Tis is the reason why I can share my crypto address with anyone, but no one will be able to use it to calculate the corresponding private key. All transactions performed with a cryptocurrency are permanently stored on a ledger, called a blockchain. Tis ledger is replicated on thousands of computers by users who decide to be part of the network. All blockchains are publicly available and can be navigated using one of the many available blockchain explorers. In Figure 7.2 we can see a typical Bitcoin transaction: 1. Transaction’s unique identifer 2. Timestamp 3. Sending address (and the amount sent) 4. Receiving address (and the amount received) 5. Change going back to the sender (and the amount returned) One can click on any of the addresses shown and see each and every transaction done by the individual address. Blockchain explorers are a powerful tool if correctly used. Even if a transaction doesn’t tell anything about its sender and receiver, it is permanently stored in the blockchain for possible future deanonymization, as we will see later. What is important to notice, however, is that while a cash transaction leaves no trace whatsoever, a crypto transaction will leave indelible evidence. And this takes us to the analysis of the blockchain.

Hash

1

ba5ee0ac93ca861d21a9b423d248bafcb24e81a456747cb09...

I

3 3NCyqM4dW8LrLQDcTgeFZwFcGnTBqAQZyt 0.04199170 BTC (;) .

2

2022-05-03 03:59

4 1ErHyQcrXD3Hb6rQUKbXAMSH5Hdc7yoXq7 0.04100000 BTC (:) 5 3NCyqM4dW8LrLQDcTgeFZwFcGnTBqAQZyt 0.00068570 BTC (;)

Figure 7.2 A Bitcoin Transaction (Source: blockchain.info, https://www.blockchain.com/btc /tx/ba5ee0ac93ca861d21a9b423d248bafcb24e81a456747cb0958e764dbd014d11)

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Analyzing the Blockchain How Heuristics Help Te frst time I laid my eyes on Silk Road, I was shocked. How could it possibly be that all those people are selling drugs online? How is it possible that they are able to receive the payments anonymously? In fact, the market relied on the use of Bitcoins as the only means of payment because of its perceived anonymity. If the only information shown in the blockchain is a meaningless string of numbers and letters to represent senders and recipients of transactions, they would be left completely in the dark . . . or not? And then a paper proved that this assumption was completely misleading. In “A fstful of bitcoins: Characterizing payments among men with no names”* by Sarah Meiklejohn and others, the researchers for the frst time described how they used heuristics to cluster together crypto addresses. Imagine sitting with the bank account number of your suspect and having a tool that can immediately disclose if this individual has any other bank accounts anywhere in the world, without the need of a subpoena or a lengthy mutual legal assistance request. Clustering means looking at the blockchain and using some techniques to “cluster together” addresses that are likely controlled by the same person. Tose techniques include multi­input,† one­time change address,‡ and optimal change § heuristics. *

†

‡

§

Meiklejohn, S., Pomarole, M., Jordan, G., Levchenko, K., McCoy, D., Voelker, G. M., and Savage, S. (2013). A fstful of Bitcoins: Characterizing payments among men with no names. IMC 2013—Proceedings of the 13th ACM Internet Measurement Conference, 127–139). https://doi.org/10.1145/2504730.2504747 Also known as common­input­ownership heuristic, it assumes that all the inputs in a transaction are controlled by the same entity. Tis heuristic is frst mentioned in the original Bitcoin paper by Satoshi Nakamoto. However, it is not entirely true because multiple parties can co-sign a transaction using special software (CoinJoin), which spends inputs controlled by diferent entities. Since it is difcult to send the exact specifed amount of funds to the recipient of a transaction, the remainder of the funds are to be returned to the sender via change address. Tis is similar to having to pay 1 USD to someone, and having only a 5 USD note available. Te payor will get 4 USD back as change. When a transaction has two outputs, one of the two is generally the change going back to the sender. Terefore, the address receiving the change is controlled by the sender. Several techniques can be used to assess which of the outputs constitute the change. Te assumption here is that a consumer wallet will not, by design, spend outputs unnecessarily. One can therefore assume that the change is the amount which is smaller than any of the spent inputs.

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An investigator can use the techniques described above to understand whether the address controlled by a suspect is part of a cluster, therefore deanonymizing all the addresses that the target controls. In most cases, an investigation will start with an identifed suspect, and the aim of the investigation is to uncover where the proceeds of crime have gone, especially if a money-laundering investigation has been initiated. However, if the investigators’ target is an unknown or anonymous address, sometimes they have to admit defeat and look for help. Te most common option is to seek the assistance of one of the many blockchain analysis companies which have fourished in recent years. Tey have deeper visibility into the blockchain and can automate research that would keep a police ofcer busy for months, using block explorers and open-source intelligence (OSINT).

Linking Clusters to the Real World Once the clusters are identifed and labelled by those companies, the second step involves naming those labels. Tis is done in many diferent ways, such as web-scraping for self-disclosed addresses. It is not uncommon for individuals to claim the ownership of an address. It used to be common practice in forums related to crypto to include in the signature a crypto address in order to receive tips and donations. Many programmers develop tools and platforms for free and then seek donations via bitcoin payments. Another technique used to deanonymize services (exchanges, providers, custodians) is to open an account with them and send a tiny amount of cryptocurrency. Te receiving address, by defnition controlled by the service targeted, will be then searched inside an existing cluster; once found, it will deanonymize the entire cluster. Even when it is not possible to directly deanonymize the target address, one has to simply broaden the search. Put yourself in the criminal’s shoes: you have your proceeds of crime already in cryptocurrency, or you are using crypto to launder your funds. You will eventually want to cash out and lay your hands on fat currency that you can then spend directly or deposit in a bank account. You will probably try to obfuscate the origin of your cryptocurrency in some way (more later!), but sooner or later you will need to use an exchange to convert your money. Tis is exactly what law enforcement authorities look for—that is, the moment criminals are depositing their funds to have them converted. What blockchain analysis will try to discover is if the targeted address has interacted with an exchange a few hops before or after the transaction you are looking at.

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The Regulation Dilemma How to Harmonize Regulations in a Borderless Industry Surprisingly, the popularity of Bitcoin started in June 2011, when two senators from the United States* asked federal authorities to investigate and take down the Silk Road drug market. Te unexpected consequence was that an unknown technology used by only a small group of enthusiasts was “advertised” on the main media outlets, and both Bitcoin and Silk Road became popular overnight. Since then, the legal framework surrounding cryptocurrencies has evolved, with sudden bursts and frequent interruptions, often with conficting interests from diferent regulators and diferent jurisdictions. Te United States led the way: in 2013, FinCEN, the U.S. Financial Intelligence Unit, said cryptocurrencies do not “have legal tender status in any jurisdiction.” In the same year, instead of trying to regulate the sector, other countries decided to ban Bitcoin outright. In December 2013, the Chinese government, with its Notice on Guarding Against the Risks of Bitcoin,† mandated that all Chinese banks were prohibited from accepting Bitcoin payments to and from exchanges. Even if Japan was the frst country, in 2016, to introduce regulation (probably due to the fact that a Japanese crypto exchange, Mt. Gox, lost almost the entire 850,000 bitcoins it was holding for customers), most of the regulators around the world still didn’t feel the urge to regulate the space. Te frst international efort was put forward in 2018. Following the G20 Leaders’ Summit, in which the 20 leading economies agreed that cryptocurrencies didn’t pose a risk to fnancial stability, it was requested that the Financial Action Task Force‡ (FATF) should further clarify how its anti-money-laundering standards apply to virtual assets. Tose standards are set out in the so-called FATF Recommendations, a framework of measures which countries should implement in order to combat money laundering and terrorist fnancing. Jurisdictions that *

†

‡

Wolf, B. (8 June 2011). Senators seek crackdown on “Bitcoin” currency. Reuters. https:// www.reuters.com/article/us-fnancial-bitcoins-idUSTRE7573T320110608 Chinese Government. (5 December 2013). China bans banks from handling Bitcoin trade. BBC.com. https://www.bbc.com/news/technology-25233224 Te FATF is an intergovernmental organisation founded in 1989 on the initiative of the G7 to develop policies to combat money laundering. In 2001, its mandate was expanded to include terrorism fnancing. Te FATF sets standards and promotes efective implementation of legal, regulatory, and operational measures for combating money laundering, terrorist fnancing, and other related threats to the integrity of the international fnancial system.

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do not comply with the recommendations are put on a gray or black list and may face economic sanctions from institutions such as the International Monetary Fund and the World Bank. Te FATF reacted to the request by adopting changes to its Recommendations to “explicitly clarify that they apply to fnancial activities involving virtual assets, and also added two new defnitions in the Glossary, ‘virtual asset‘ (VA) and ‘virtual asset service provider’ (VASP),” as explained in the guidance issued in 2019.* According to the FATF, a VA is a “digital representation of value that can be digitally traded, or transferred, and can be used for payment or investment purposes.” Tis defnition is broad enough to allow the future inclusion of new technologies. VASPs are essentially defned as legal persons conducting exchange, transfer, and safekeeping of VAs.

International Standards in Practice Te FATF Recommendation relating to VAs, in essence, requires VASPs to act like a bank. Do you recall all the paperwork you had to do when you opened your bank account, and all the eforts your bank put into proving your identity and background? Do you know all the information you have to provide to your bank if a sudden and unexpected sum appeared on your account? Tis is what crypto exchanges are now obliged to do. I still remember that the frst time I opened an account with a crypto exchange many years ago, the only Know Your Customer (KYC) they performed was asking me to provide an email address. Full stop. Tose days are gone now. Onboarding a client, for a crypto exchange, means a lengthy process of progressively deeper questions and documents that must be provided. Te recommendations also extended the so-called travel rule to crypto transactions. Providers of virtual assets need to collect and share customer data for transactions over a certain threshold (USD/EUR 1,000). Te originator’s information “travels” together with the transaction to the receiving VASP. According to the FATF Interpretive Note to Recommendation 16, the originator and benefciary information should include the following: • Originator’s name and account number • Originator’s address, national identity number, date and place of birth • Benefciary’s name and account number *

FATF. (June 2019). Guidance for a risk-based approach to virtual assets and virtual asset service providers. https://www.fatf-gaf.org/media/fatf/documents/recommendations /RBA-VA-VASPs.pdf

128 Cryptocurrency Concepts, Technology, and Applications

Table 7.1 Travel Rule Bank Secrecy Act (threshold 3,000 USD) Name

Originator

Recipient

Required

FATF (Threshold 1,000 USD) Required

Account number

When available

Required

Address

Required

Required

Identity of fnancial Institution

Required

Not required

Amount

Required

Not required

Execution date

Required

Not required

Name

When available

Required

Address

When available

Not required

Identity of fnancial Institution

Required

Not required

Account number

When available

Required

Any other specifc identifer of the recipient

When available

Not required

In Table 7.1, we can see a quick comparison of the travel rule as mandated by the Bank Secrecy Act and under the FATF and the required information. Even cross-border transactions below the USD/EUR 1,000 threshold must include the names and account numbers of the originator and benefciary, but those will not be verifed unless there is a suspicion of money laundering or terrorist fnancing. Te travel rule involves the careful handling of data, the need for privacy, and the use of due diligence measures.

Implementation—Patchy but Positive Te FATF doesn’t dictate how a jurisdiction has to comply with its Recommendation, although it has issued comprehensive guidance. Until now, each jurisdiction has taken a slightly diferent approach. Some, such as Hong Kong, Switzerland, or Singapore, are mandating exchanges to operate exclusively with licenses that enforce the travel rule. Other countries, such as Canada, are still working on it, trying to fnd the best solution. Te U.S. had the rule technically already in place, but it was rarely enforced. In the FATF’s frst 12-month review of the implementation of these new regulations in June 2020, it was noticed that there are numerous problems hampering the eforts toward a harmonized implementation. Tese include identifying

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counterparty VASPs, compliance for private and unhosted wallets conducting transactions with VASPs, interoperability challenges, and the sunrise problem. Te sunrise problem is nothing new. It happens every time a new regulation afecting a global phenomenon is not implemented seamlessly and simultaneously. From a crypto compliance point of view, the issue starts when a client of VASP A (an exchange), which operates in a jurisdiction where the travel rule is implemented, wants to send money to a person using VASP B, located where the travel rule is not a regulatory obligation or is not implemented yet. VASP A, adhering to its obligations, will transmit an information request to VASP B, which will not respond because it doesn’t have the same requirements in its jurisdiction. Has the FATF Recommendation helped law enforcement in taking down criminal activity related to cryptocurrencies? Te answer is defnitely yes. I have helped many investigators to trace illicit transactions in the form of ransom* payments on the blockchain. While in the past it was hard to even locate many VASPs, let alone serve a subpoena and get a reply, the increasingly stringent regulations have dramatically improved the situation, as we will see in the next section.

Law Enforcement: Staying One Step Ahead From 0 to 100 in Just a Few Years “You don’t seem to understand that the police are some of the least technologically skilled in the population. Te police don’t know what Bitcoin is and probably wouldn’t understand how it works. So who exactly is left to be against Bitcoin?” I was navigating the most well-known Bitcoin forum in 2013. People were discussing the possibility that cryptocurrency used for criminal activity could be traced and confscated by law enforcement. Bitcoin was perceived to be almost untraceable, and criminals could achieve almost perfect anonymity simply by using it. At the time, this was not far from the truth. Knowledge about Bitcoin among investigators was really poor. I am sure that most of the law enforcement ofcers would not have been able to detect the use of cryptocurrency even if it *

Ransomware is a type of malware that threatens to encrypt and perpetually block access to the victim’s computer fles (or even publish the victim’s personal data) unless a ransom is paid. Regaining access to the encrypted fles without the decryption key is close to impossible. Ransoms are nearly always demanded in cryptocurrency, making tracing and prosecuting the perpetrators difcult. Te attack is typically carried out employing a Trojan virus that infects the target computer through malicious attachments, embedded links, or exploiting network vulnerabilities.

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130 Cryptocurrency Concepts, Technology, and Applications

Figure 7.3 A Bitcoin Paper Wallet

was happening in plain sight. I imagine the police raiding the apartment of a suspect in a search and simply dismissing a paper wallet* as an advertisement. I am happy to say that, after nine years, the situation has changed dramatically. On the one hand, cryptocurrency’s popularity and the increasing number of cases investigated have pushed law enforcement to invest time and money in education. Tere is a vast number of courses available, starting from a basic understanding of crypto to more in-depth courses on investigative techniques and strategies. On the other hand, tools are available that allow one to trace transactions on the blockchain and automate many of the steps involved until they reach a VASP. Finally, the legal framework surrounding VASPs, as we have seen in the previous section, has created an environment in which law enforcement can successfully cooperate with them in unmasking the identity behind a pseudonymous crypto address. In many cases, a suspicious transaction on the blockchain is not deanonymized directly, but rather it is followed until it reaches an entity that can be subpoenaed for information.

New Investigative Possibilities While a transaction can theoretically be executed in total anonymity, and criminals can adopt tools (as we will see in the next section) to jeopardize the investigators’ eforts to trace it, this is not an easy task. Criminals make mistakes, and to be *

Among the so-called cold wallets, meaning wallets that are ofine, we fnd paper wallets: a piece of paper with the address and private key printed out. Paper wallets were considered the safest ways to store cryptocurrency for several years. Teir popularity declined because they are susceptible to environmental factors (wet paper, fading ink, fre), because of their creation security risks, and because more user-friendly cold storage solutions were developed, such as hardware wallets.

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perfectly anonymous on the web is a mammoth task where one small distraction or a computer glitch can reveal the real identity hiding behind a string of characters. A recent case, described in the box below, gracefully summarizes this concept. “Welcome to Video”: Taking Down the Biggest Child Abuse Website* In the course of an investigation, agents of the U.S. National Crime Agency found, on a suspect’s laptop, the login credentials for a website called “Welcome to Video” (WtV). This website was one of the rare spots in the dark web where people could access, upload, and download child sexual abuse material (CSAM) in exchange for Bitcoins. What very often prevents investigators from taking down websites like this is the widespread use of anonymity tools such as T† or or I2P.‡ The same would have happened with WtV if the investigators had taken the usual approach. Instead, they treated this case like a fnancial investigation and applied a “follow-the-money” approach. When NCA agents identifed some Bitcoin addresses used on the website, they reached out to Chainalysis, one of the leading blockchain analysis frms. What they immediately realized was that users and administrators of WtV, despite hiding in the dark web and using cryptocurrency to pay inside the service, were very naïve both when it came to getting their hands on bitcoin to access WtV and when they wanted to cash out their proceeds. Many users had simply acquired Bitcoins from crypto exchanges and then sent them directly to WtV from their personal wallets. The proceeds from the website were often liquidated directly to a few exchanges in South Korea, which also provided a possible location for the mastermind behind it. Since the service didn’t include any options for the users to take their funds out of the website, all the transactions leaving WtV’s wallet were going to the admin’s pockets. The crucial decision to treat this case as a fnancial investigation meant that agents from the U.S. Internal Revenue Service were brought in. The frst mistake that the agents noticed, when they had the gruesome task of examining WtV’s pages, was that the video’s thumbnails were naïvely not being anonymized. *

†

‡

This case is beautifully illustrated in Andy Greenberg’s article Inside the Bitcoin Bust That Took Down the Web’s Biggest Child Abuse Site, published on Wired, 7 April 2022. https://www.wired.com/story/tracers-in-the-dark-welcome-to-video -crypto-anonymity-myth/ TOR, short for The Onion Router, is free software that enables anonymous communication over the internet directing traffc through a free, worldwide, volunteer network that conceals a user’s location. The Invisible Internet Project is an anonymizing layer that allows peer-to-peer communication. Anonymity is achieved by encrypting the user’s traffc and sending it through a volunteer-run network.

132 Cryptocurrency Concepts, Technology, and Applications

They displayed an IP address located in South Korea, confrming the initial suspicion. The agents subpoenaed the exchanges used to cash out money from WtV and were able to identify the suspect as a Korean man living outside Seoul. Investigators were confronted with a terrible dilemma: to arrest the suspect and take down the website could have been the end of the investigation. But what about the users? Most of the material uploaded to WtV was original, meaning that users were uploading material that they had flmed themselves. Shutting the website down would have left hundreds of child abusers free to continue their criminal activity, ruining the lives of hundreds of children. What they did instead was brilliant in investigative terms but also horrifc in personal ones. They traced users’ payments to the website back to the exchanges where the Bitcoins were purchased. This entailed watching thousands of videos displaying disgusting and atrocious actions towards children, often only one or two years old, being abused, looking for clues that could unveil their locations. When the frst users’ identities were made available to them, the agents made other appalling discoveries. One suspect was a U.S. Homeland Security Investigations agent in Texas. Another was the assistant principal of a high school in Georgia. One suspect appeared to be a former congressional staffer now working at a renowned environmental organization. Another was a Border Patrol agent who, as investigators disclosed, was uploading videos in which he was abusing his stepdaughter. After having identifed and arrested these and many other child abusers, the agents fnally arrested the mastermind in South Korea and seized the website. They found more than 250,000 videos, making Welcome to Video the biggest child sexual abuse materials case in history.

Tis case illustrates some important aspects which investigators should always keep in mind. First, many criminal investigations, when the crime itself generates proceeds of crime, can be treated like a fnancial investigation. Often, following the money can take you where other means will prove unsuccessful. Cui bono? Te destination of the money trail can reveal the crime’s perpetrators. Moreover, in this case we can see the crucial role of cooperation between jurisdictions.

Cooperation Across Borders and with VASPs By nature, cases involving cryptocurrency are cross-border investigations, and mutual legal assistance (MLA) requests are the key to seeking and obtaining help from other countries. Tracing crypto payments to and from VASPs is not enough: once we have identifed the exchange that was used to buy or sell the currency, we need to have clearly in mind the process involved, which actions

Following the Virtual Money 133

we want the foreign jurisdiction (and the VASP) to take, what we can reasonably expect, and what they will expect from our request. First we need to bear in mind the information that VASPs collect from clients and are available to law enforcement upon a properly formalized request. VASPs receive a variety of data depending on the activity a client wants to perform. While sometimes it is sufcient to provide some basic data and an email address to open an account, things change dramatically when this person wants to deposit, trade, or withdraw funds, and also according to the volume of those transactions. A normal KYC process will see the client providing: • Name, date and place of birth, all proved by the submission of a proper form of identifcation (ID), often required to be held in front of a camera where the person can be seen holding the ID to compare the document and the face • Residency, usually by submitting utility bills or bank documents proving the place of residence • Source of wealth, when the client deposits fat currency to be exchanged into crypto • Source of funds, when the client deposits crypto Moreover, VASPs also see and collect the IP address used to connect to the service and maybe also store the unique confguration of the client’s machine (operating system, browser, cookies, two-factor authentication). Te entire account (deposits and withdrawals) and trading history will also be available. In the case of a legal person, among the information provided there will also be incorporation documents and, in some cases, benefcial owners. What must investigators focus on when writing a request for assistance? A few things can defnitely make the diference between a successful cooperation and a painstakingly long and inefective process: • Use of the proper language. When fling a request, be aware of the correct terminology: there is a huge diference between an address, a wallet, and an account. Ethereum is diferent from Bitcoin and from Monero, and they all have a diferent address style. Do your homework and research the terminology before writing. • Attach addresses and transaction IDs in plain text within a Word or Excel document to allow the VASP to copy-paste that data. Copying from a PDF may lead to mistakes. • Reach out to the VASP before writing the request. Most VASPs have big compliance departments that will be happy to communicate with investigators in advance and even guide them while they write the letter. Tey can help investigators to use the right terminology, ask for the correct data, and avoid pointless back and forth of letters.

134 Cryptocurrency Concepts, Technology, and Applications

Hosted vs Unhosted Wallets Te situation above refers to a so-called hosted wallet, meaning a wallet that is held with a VASP. It is important to remember that, in this case, the suspect doesn’t control the private keys, since they are in the possession of the VASP. Te suspect only has a credit with the exchange that is holding the money for them. Te VASP is the only entity that can freeze the funds and allow an efective seizure. Tis is not the case when we consider unhosted wallets, where the suspect is the person directly controlling their funds and the corresponding private keys. Te latter will then be the main target during the investigation. In the case of a hosted wallet, locating the funds controlled by a VASP is sufcient to ask the service to freeze the assets, with no recourse from the suspect—locating the private key is the real objective when dealing with unhosted wallets. First of all, investigators will look for red fags that a suspect is holding cryptocurrency: • Software or apps on the suspect’s devices (phones, laptops). In order to receive, manage, and send cryptocurrency, a person needs to install a wallet. A wallet is a software that creates the keypair (private key/address), which is then used by the user to send and accept transactions. Te presence of a wallet could be an indicator of the use of cryptocurrency. • Browser history. If the suspect is using a VASP to hold and manage their cryptocurrency, traces could be available in the web browser. User IDs and passwords could equally be saved by the browser. • Hardware wallets. Possession of this device, a sort of cold wallet that stores private keys inside the machine, is a strong indicator of the control of cryptocurrency. As we have seen, investigative techniques are constantly evolving. Law enforcement is slowly acquiring the necessary knowledge to trace and locate money laundered through the use of virtual currencies, and an increasing number of tools are becoming available to help them, such as blockchain analytics frms. To counter that, criminals are becoming more sophisticated and are adopting countermeasures to hide their traces deeper in (and outside) the blockchain.

Emerging Criminal Strategies Catching the Wave It is always the same story. When a new technology becomes available that can be exploited to make it easier to launder money, criminals will immediately jump on it. For a few months or years, they will be able to enjoy a double advantage,

Following the Virtual Money 135

legislative and investigative. Te former is a direct consequence of the time needed for legislators to become aware of a potential threat, understand the new technology, and then discuss and defne a possible legal framework to allow law enforcement to try and stop the illegitimate use of the new tools. Investigators, in turn, need to understand how criminals are covering their tracks and their unique laundering processes. Tey then need time and budget to develop new strategies. Te frst wave is used by criminals naïvely, without the need for highly sophisticated strategies. But once a legal framework is there and law enforcement become accustomed to this new typology of criminal activity, a second wave will break. Tis one will take advantage of newly developed secondary tools and mechanisms built on top on the tool. We have seen this in cryptocurrencies, too. While until a few years ago criminals were successfully using Bitcoin to cover their tracks what we described above has proven to be enough for law enforcement to fght back. Te victory is ephemeral, though: it’s a never-ending cat-and-mouse game, and the cryptocurrency world is evolving rapidly.

A Snapshot of Criminal Strategies At the time of writing, criminals are starting to use new strategies: • Non-regulated exchanges are where criminals go to turn their crypto back to fat money. Tey are located in jurisdictions where the regulation is lax and not in line with the FATF Recommendations. Clients are not identifed, and a formal KYC process is light or non-existent. Exchanges of this type can choose to operate in countries which are notoriously secretive when it comes to sharing fnancial information or replying to requests for mutual legal assistance. • Decentralized exchanges emerged within the framework of decentralized fnance (DeFi), which allows fnancial instruments to avoid intermediaries such as banks, brokers, and exchanges. Tis new typology of exchange is non-custodial and therefore unable to be subpoenaed, with the consequence that funds can’t be frozen on request. Money is exchanged thanks to selfexecuting smart contracts that allow peer-to-peer trading. • Chain hopping. While it is sometimes difcult to follow transactions on a blockchain, imagine having to follow the trail of money through a multitude of blockchains. Tis is what chain-hopping does. Using this layering technique means converting one cryptocurrency into another, swapping funds from one blockchain into the next. Criminals, for example, can obtain Bitcoins which are subsequently swapped for one or more privacy-focused cryptos such as Monero to disguise their origin.

136 Cryptocurrency Concepts, Technology, and Applications

• Mixers. Mixers can be used to obfuscate the real origin of funds by mixing diferent streams of identifable cryptocurrency. “Tainted” funds are sent to the service, which mixes them together with those of other users in a sort of “pool.” Funds that are then withdrawn by users (minus a fee collected by the mixer) are not the same as the ones they originally deposited. • Online casinos. Money launderers can turn to online casinos to clean their cryptocurrency. Te scheme is very simple: deposit your dirty money into a gambling site, pretend to play a few games, withdraw your now legitimate coins, and declare them as “winnings.” • Dark markets. Using dark markets is also frequently used to launder cryptocurrency and disguise its real origin. When one opens an account and deposits crypto, the money is kept by the market in one or more pooled accounts. When a user withdraws their funds, they are not taken from the original address used for the deposit but from another one that is completely unrelated. Tis makes it difcult to understand the original criminal activity committed before the deposit. • Crypto ATMs. Crypto ATMs are automated teller machines connected to the internet that allow customers to buy and sell cryptocurrencies. While some perform KYC checks on clients and identify users when they deposit or withdraw funds, many crypto ATM frms allow customers to operate their machines without any sort of identifcation. • Privacy coins. If Bitcoin and Ethereum don’t prove themselves as anonymous as initially thought, criminals can still turn to privacy coins such as Monero. Tey obscure the fow of money and make it more difcult to trace transactions on the respective blockchains. While they work in ways similar to the other cryptocurrencies, they use stealth addresses, where the sender of a transaction is required to create a random, one-time address on behalf of the recipient. Te recipient can therefore have all incoming payments going to unique addresses that cannot be linked to any specifc entity. Another feature used by privacy-focused cryptocurrencies are ring signatures. With most cryptocurrencies, transactions on the blockchain clearly show who the sender is, even if they are behind a crypto address. Ring signatures, on the other hand, can be performed by any member of a group of users, making it impossible to understand which member’s key was used to generate the signature.

Confscation: Deterring Crypto-Enabled Crime Seeking the Key to the Crypto Treasure Undoubtably, when it comes to criminalized digital assets, one of the main hurdles is locating and confscating them. Many still believe that knowing that a criminal

Following the Virtual Money 137

holds crypto in a specifc address and linking those funds to the originating criminal activity is sufcient. While this may lead to a conviction, if the person or their family and associates can still enjoy their illicit proceeds, that doesn’t completely remove the incentive to commit the crime. In contrast, a successful confscation can allow the money to be returned to the victims and deter further crimes from being committed by removing the fnancial incentive. Once the cryptocurrency has been tracked and the address in which it sits had been identifed, law enforcement needs to move those funds away from that address as soon as possible to prevent them from being used or transferred. If a transaction can be initiated by anyone possessing the address’s private key, locating the cryptocurrency is not enough, for two reasons: • First, the investigators cannot consider the money under their control if they don’t possess the private key of the target address. • Second, anyone with that private key can spend the fund. Imagine a criminal holding crypto who shared her private key with an accomplice. She might have left him with the instructions to move the funds if she gets arrested. Once the investigation moves to the recovery of the assets, they will fnd an empty address, as cryptocurrency has been moved to another location, and they have to start tracking it all over. Tere are therefore two crucial steps involved: 1. Finding the private key of the target address. 2. Moving the funds to an address under the control of law enforcement. As we have seen before, when hosted wallets are involved, VASPs can be subpoenaed and, since they control the private keys, they can also prevent the funds from being transferred out of control. Te major obstacle comes when the suspect is holding cryptocurrency in a unhosted wallet—one that they control themselves. In most cases, the treasure trove can be scattered over the suspect’s devices. A search at their premises must be conducted, and any possible evidence must be seized to take possession of the private keys. What to look for? Let’s have a look at where a private key can be stored: • Mobile phones can have wallets installed in the form of apps. Tose wallets can give access to the funds, and a transaction can be initiated from the mobile phone, sending the cryptocurrency to a wallet controlled by the investigators. Te browser history can be scrutinized for possible clues. • Computers can also have wallets as applications, as seen above. Te browser must be also analyzed, since some can have wallets installed as extensions. • Hardware wallets, which we have encountered in previous sections, are an obvious target. Taking possession of them allows the investigators to locate and move the funds.

138 Cryptocurrency Concepts, Technology, and Applications

• When a user initializes a wallet, be it an app or a hardware wallet, they are prompted to create a backup seed. Tis is a list of 12 or 24 words that can be used to restore the wallet in case of loss, damage, or intentional wipe-out of the wallet. Tis list can be crucial: investigators can try to restore a wallet using the seized list of words, recreating the wallet on their device and allowing them to secure the funds elsewhere. Te list can be written anywhere. Law enforcement ofcers have found seeds written on the pages of a TV manual, in a suitcase in a closet, and on a gum wrapper.* Seeds can also be engraved on steel plates to improve durability.

Crypto before the Court Tracing the funds, locating the private key, and moving the cryptocurrency to a secure wallet is useless if the evidence is not admissible in a trial. It must have been obtained keeping in mind the existing legislation and best-practice procedure. In order to be used in court, It must be possible to prove that the evidence has not been corrupted, manipulated, or altered. Tis is possible thanks to the properties of hashing, as we have seen before: the evidence obtained is immediately hashed and electronically fngerprinted, so that any change to the original material is immediately visible. In fact, even the tiniest change to the input (the fles under seizure) will lead to a change to the resulting hash. Tis makes it trivial to prove or disprove the authenticity of digital evidence. Finally, the ultimate goal of the confscation is to sell the cryptocurrency and dispose of the proceeds to compensate victims. Te United States Marshals Service was the frst to take this route with the Bitcoins confscated during the Silk Road case in 2014. More used to the disposal of boats, artworks, jewelry, villas, and cars, they followed a similar approach by auctioning of the Bitcoins they had seized. Nowadays, law enforcement can count on private frms to store and sell the crypto. Often law enforcement will ask themselves when it would be the right moment to realize the cryptocurrency they have confscated: virtual assets are volatile, and their value when it comes to sell the assets may be drastically diferent than what it was at the time of seizure. Law enforcement could sell immediately, with the risk of missing a price increase, or too late, realizing less than expected. It is important to note, however, that practice dictates† that the state is not responsible for trying to maintain the *

†

Milligan, E., and Voreacos, D. (12 April 2022). ‘Staggering’ crypto seizures have cops struggling to keep up. Bloomberg. https://www.bloomberg.com/news/features /2022-04-12/what-happens-when-cops-seize-crypto-and-bitcoin Larkin, A. (23 February 2022) Crypto asset recovery part 2—DeFi, auctions, cryptocurrency bans and capacity. Basel Institute on Governance. https://baselgovernance.org/blog/crypto -asset-recovery-part-2-def-auctions-cryptocurrency-bans-and-capacity

Following the Virtual Money 139

original value of assets in their custody, and that how and when to realize confscated assets depends mostly on a country’s legislation. What is important is that, at the end of the day, the incentive to commit the crime is taken away from criminals and that cryptocurrency is not seen as a safe haven. One of the most prominent Italian criminals, turned State’s witness, Gaspare Mutolo, one day told the Italian Parliamentary Anti-Mafa commission, “Te worst feeling is when our money is taken away from us. People prefer to be put behind bars and keep their money than to stay free without the money. Money is the main thing.”* And this is true for cryptocurrency too.

*

Smith, J. D., and Cooper, G. H. (2015). Disrupting terrorist fnancing with civil litigation. https://www.repatriationgroup.org/wp-content/uploads/2015/05/Terrorist -Financing-Article-Publishers-Final.pdf

Chapter 8 Regulatory and Legal Issues in Cryptocurrencies Usman W. Chohan Centre for Aerospace & Security Studies (CASS) Islamabad, Pakistan

Introduction Te aims of this chapter are (1) to examine the legality of cryptocurrency across various jurisdictions, and to (2) consider the salient legal issues that impact the cryptocurrency space. Tis is a difcult exercise on several levels, since there is considerable variation among countries in terms of their tolerance for crypto and at times the shifting attitude among regulators toward crypto; and, perhaps most importantly, there is a growing litany of concerns about criminality and ill intent among some portion of the cryptocurrency community (Larkin et al. 2021; Corbet et al. 2020; Sanz-Bas et al. 2021; Smith 2019; Dyntu and Dykyi 2018).* One must, therefore, write such a chapter with the recognition that, although a universal ban on cryptocurrencies now seems (nearly) out of the question, the *

To make it more accessible, the tenor of this chapter is tailored toward an informed but non-specialist audience, and it therefore avoids the excessive use of specialist jargon from the felds of computer science, law, or economics. 141

142

Cryptocurrency Concepts, Technology, and Applications

fner points of legal attitudes and regulatory actions risk changing markedly over time (Matei and Baks 2019). At the same time, there are eforts to harmonize international responses toward cryptocurrency regulation (Chohan 2021a), but they face hindrances based on legal precepts (Bailey et al. 2021), regulatory sophistication (Irina 2018), fnancial oversight capabilities (Chohan 2019, 2022a), economic conditions (Szwajdler 2021), and sociocultural norms (Busse et al. 2020; Ben Saad et al. 2022) that come in a great variety around the world. On this point, it is worth explicitly mentioning that this chapter assumes the reader to have a rudimentary knowledge of cryptocurrency, which is otherwise covered in the various chapters of this volume, in addition to useful reviews for non-experts elsewhere (see Chodhury 2019; Nica et al. 2021; Kher et al. 2021; Chohan 2017a). Te world of cryptocurrency is evolving at a rapid pace, and it is difcult for regulators (or anyone else, for that matter) to remain abreast of the breakneck pace of change in a specialist feld of considerable technical sophistication (Garcia-Corral et al. 2022; Matei and Baks 2019; Bailey et al. 2021). Regulatory responses to cryptocurrency have consistently been described as reactive (Chohan 2017a, b), and this problem is inherent since one can never prejudge the path dependency of decentralized and amorphous technologies (Szwajdler 2021). In addition, one cannot shrug away the fact that cryptocurrency is an especially lucrative domain, with a market capitalization that approximates $2.5 trillion as of this writing—which is a quantum of wealth large enough to keep the stakes high for criminals, private institutions, ordinary citizens, and governments alike (Corbet et al. 2020; Ben Saad et al. 2022). With all this in mind, and given the constraints of brevity in covering such a vast topic, the chapter is structured as follows: First, I highlight the primary legal concerns that apply to all jurisdictions grappling with cryptocurrencies, specifcally those concerns which pertain to various types of criminality. Second, I consider the difering legal treatments of cryptocurrencies along a spectrum (mostto-least tolerant) and assign various jurisdictions along that spectrum accordingly. Finally, I discuss international eforts to harmonize cryptocurrency regulation and oversight and ofer some prognostications about the future of cryptocurrency legality in light of aforementioned issues.

Major Legal Concerns in Cryptocurrency Tis section covers broad categories of legal concern regarding cryptocurrency, particularly in terms of their attenuation toward various degrees of criminality as would be understood in most jurisdictions. Tese include hacks and thefts, frauds and scams, money laundering and tax evasion, and terrorist fnancing and

Regulatory and Legal Issues in Cryptocurrencies

143

rogue actors. Tey do not constitute a comprehensive list,* nor are they sorted in any particular order, and although countries may difer in the degree to which they fnd such activities egregious,† these are nevertheless the points of utmost international concern (Corbet et al. 2020; Sanz-Bas 2021; Chohan 2017a; Irina 2018; DoJ 2020; FATF 2019). Tese are presented in Figure 8.1.

Figure 8.1

Legal Concerns Regarding Cryptocurrencies (Source: Author’s elaboration)

Hacks and Thefts As a category of digital instruments, cryptocurrencies are invariably vulnerable to hacks; and as a category of money, they are invariably vulnerable to thefts (Krupa et al. 2021; Angerer et al. 2021; Joo et al. 2019; Smith 2019). Te two go hand in

*

†

For example, there are edicts against cryptocurrencies based on Shariah Law, which stipulate that cryptocurrency should be illegal based on the excessively speculative nature of its returns and the absence of a derivation from an underlying value source (see Chohan, 2017a); such considerations are not universal, however, and so are omitted from this general-view paper. For example, some countries are expressing signifcant concerns about the impact of cryptocurrency mining on their power grids, but this can be treated as more of a nuisance rather than crime per se. Similarly, cryptocurrencies appear to mirror the same conditions of wealth inequality in their ownership that one would fnd in traditional economies (Chohan, 2022c), but inequality is not criminal per se, refecting more of a moral consideration bound in philosophical precepts rather than one requiring legal regulation.

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hand, unfortunately, since the purposes of hacking and thievery fnd common cause among criminal actors. In simplifed language, hacking can be accomplished in multiple ways (see technical breakdowns in Hu and You 2021; Arsi et al. 2022), but it generally relies on (1) overwhelming the production (mining) process with computing power; (2) breaking into owners’ wallets (keys); or (3) intercepting/hijacking transactions; and among these, the second category is the most universally understood defnition as well as the most common. Teft, for the purposes of reference here, can refer to three sorts of expropriations: (1) ransomware attacks which hijack computers and restore them in exchange for cryptocurrency payments, (2) extracting code from wallets (keys), or (3) hijacking/rewiring/re-engineering transactions, the second category being the most common. Tere are many instances of signifcant hacks and thefts over the short time that cryptocurrency has existed (Hu and You 2021; Krupa et al. 2021; Arsi et al. 2022), but a few incidents warrant particular mention, as follows: 1. Mt. Gox. Te Japanese cryptoexchange Mt. Gox was, in its heyday (circa 2013), the largest Bitcoin exchange in the world, handling more than 70% of the global volume of Bitcoin transactions (Ito and Howe 2016). It was frst hacked in 2011, and $8.8 million was stolen, but a gradual hemorrhaging of its hot wallet over time accumulated into nearly 850,000 bitcoins being siphoned of ($600 million at the time), and this was because fake Bitcoins were being pushed onto the exchange as well. Tis was the largest security breach of its kind until then, and it was premised on the absence of a version control security software, which meant that hackers could re-engineer new versions of the exchange without sufcient deterrence. Given the scope of losses, many lawsuits were fled by customers against the company (thus initiating a legal-recourse element), which are still being addressed through a civil rehabilitation plan. 2. Coincheck. Coincheck is another cryptoexchange worthy of mention because of the size of the breach and theft. In January 2018, malicious actors broke into the exchange using a phishing attack, which spread malware across the system and the hot wallets, grabbing hold of cryptocurrency worth $534 million at the time. To date, this has been the largest single attack on an exchange. 3. Continental Pipeline. Continental Pipeline is part of the U.S. energy infrastructure, which sufered a massive ransomware attack in May 2021. Te size of the attack was signifcant because it halted all pipeline transmission, leading to fuel shortages across the eastern United States. It is pertinent to the criminality of cryptocurrencies because the ransomware attackers demanded 75 Bitcoin ($4.4 million at the time) in exchange for

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a software that would gradually unpack the computerized systems held hostage. Due to the decentralized and largely (but not entirely) anonymous nature of blockchain-based systems, it was thought that cryptocurrency hacks and thefts would not be traceable. However, considerable work by law enforcement institutions, particularly the FBI, demonstrated in the Continental case that the perpetrators of hacks and thefts can, with sufcient efort, be located. Tese examples indicate that hacks and thefts remain a signifcant consideration in the legal aspect of cryptocurrency use. Individuals, exchanges, companies, and other stakeholders remain vulnerable, not least because of the lucrative nature of the space and versatility of attacks that can be executed.

Frauds and Scams Cryptocurrency is premised on the notion of trustlessness (Chohan 2019a), which implies that parties can engage in cryptocurrency transactions without needing to know or trust one other. However, the notion of trustlessness has been exaggerated over time because trust is required in many elements of the cryptocurrency economy (Chohan 2019b; Zhang et al. 2021; Karimov and Wojcik 2021; SanzBas et al. 2021), particularly when it comes to initial coin oferings (ICOs; see Karimov and Wojcik 2021; Chohan 2019b). ICOs involve the emission of new tokens (cryptocurrency coins) on the internet. Tis is a space riddled with scams and fraudulent activities, and today social media, as an example, refuses to put up advertisements of ICOs. Te proliferation of unregulated digital instruments has come with a groundswell of misinformation, misleading claims, and scam-artistry, which has caused tens of billions of dollars of damage to unwitting investors (Zhang et al. 2021; Sanz-Bas et al. 2021). Te most signifcant cryptocurrency scam to date was Bitconnect, whose false marketing (“40% returns guaranteed”) and dissolution as a Ponzi scheme led to a loss of $3.5 billion dollars (Chohan 2022b). Other signifcant Ponzi scheme approaches which caused considerable damage to investors include Pincoin (Vietnam, $870m losses to 32,000 people); ACChain (China, $80m); and Plexcoin (USA, $20m). Although ICOs are not as common today as in the past decade, there are new blockchain-based technologies which are gaining in popularity, such as non­fungible tokens (NFTs, see Chohan 2021b) and decentralized fnance (DeFi, see Schuefel 2021; Chohan 2021c). Tese much-hyped emergent felds raise serious concerns of scam-artistry and fraud and thus require careful regulatory and legal attention. It should also be noted that the scope of scams and frauds is widely spread around the world and is not the singular purview of any specifc jurisdiction. Tis allows for lesson-drawing for countries from the experience of one another and to

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guidelines and warnings being issued about investing in such a volatile, high-risk, and vulnerable space (Chohan 2017a, b). Many countries (e.g., U.K., Lithuania, Australia, U.S.) have indeed issued statements (see example in Clayton and Giancarlo 2018) to that efect, urging the public to remain cautious in proceeding with cryptocurrency.

Money Laundering and Tax Evasion Money laundering is a very serious and systemic concern regarding cryptocurrency, as it can be easily used to engage in transactions that would sidestep the traditional fnancial system with comparative ease. For this reason, it is frequently described as “a convenient tool for money laundering” (Dyntu and Dykyi 2018). Much work has been done to identify the scope and threat presented by the use of cryptocurrency for anti-money laundering (AML) purposes, and the fndings of the literature largely point to a grave threat that requires proactive regulatory action at both a national and international scale (Dyntu and Dykyi 2018; Barone and Masciandaro 2019; Dupuis and Gleason 2020; DoJ 2020; Chohan 2017b, 2021a, 2022a). Cryptocurrency can be traded through multiple accounts from multiple locations, and their tracks can be muddled to a large (but not complete) extent. Furthermore, their exchange does not (despite international eforts to this efect, see FATF 2019) need to be grounded in traditional regulatory oversight of KnowYour-Customer (KYC) procedures by banks or governments. Recognizing this, a signifcant efort has been made by regulators and law enforcement agencies to try to curb money laundering eforts (see FATF 2019; DoJ 2020; Chohan 2017b, 2021a, 2022a). At the crux of these initiatives lie the principles of coordination and regularization, with the former recognizing the transboundary nature of the problem and the arbitrage among jurisdictions, and the latter recognizing that an entirely parallel system of monetary exchange poses a threat to the traditional fnancial system with time. Coordination is a function of willful capacity and information sharing eforts by multiple jurisdictions, while regularization is a function of embedding cryptocurrency-based transactions in the existing banking architecture. Eforts along these lines have been mired by challenges including reticence in cooperation (Chohan 2021a) and the complexity of the technologies (Chohan 2022a), but at the same time, in terms of regulatory/legal concerns behind the outlawing of cryptocurrency in certain jurisdictions (Chohan 2017a), money laundering remains a foremost consideration, to the extent that cryptocurrency is at times accused of being “the new face” of money laundering (Mabunda 2018). An important corollary to money laundering is tax evasion, since both are often seen as part of the larger problem of illicit fnance, although the two are not

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one and the same, as one can occur without the other (see typologies in Storm 2013). Bitcoin and other cryptocurrencies are therefore seen as both AML and tax evasion risks (Slattery 2014). On the tax-evasion side specifcally, some authors have argued that cryptocurrencies are “super tax havens” because they could become the “weapon-ofchoice” for tax evaders (Marian 2013, p.38). Given the gravity of the tax evasion risk (Matei and Baks 2019), tax authorities in many countries have begun to synchronize their eforts with fnancial regulatory bodies and law enforcement agencies. For example, in the United States, cryptocurrency is treated as “property” for taxation purposes and either a “security” or a “commodity” for regulatory purposes (depending on its size, see Chohan 2022a-b). Because of this, the Internal Revenue Service (IRS), which is the federal tax authority in the U.S., works with the Securities and Exchange Commission (SEC) and Commodities Futures Trading Commission (CFTC), along with the Department of Justice (DoJ) to combat the legal issues from multiple angles, including the tax evasion element (Chohan 2021a, 2022a; DoJ 2020).

Terrorist Financing and Rogue Actors Although money laundering and terrorist fnancing are often lumped together for the purposes of tracking international illicit fnance (AML/CFT [countering the fnancing of terrorism]), in this chapter the fears of terrorist fnancing and rogue actors (whether state actors, semi-state actors, or non-state actors) are given separate treatment because of the gravity and the motives of their actions (Killick and Parody 2007). Te most worrying category pertains to rogue states, who can muster the wherewithal to engage in hacks and thefts of exchanges and private wallets, so as to extract coins for use in fnancing illicit projects by bypassing sanctions regimes put in place against them. Te next most worrying category includes terrorist outfts’ using similar approaches to bypass sanctions and international anti-terror oversight measures (Chohan 2021a). Te U.S. DoJ has done extensive work on this front and found instances of both sorts of parties engaging with cryptocurrency in illicit ways, as for example the Iranian and North Korean governments, or Al-Qaeda and ISIS subgroups and sympathizers (DoJ 2020). Cryptocurrency is therefore viewed as an “unconventional” but very serious challenge (Dostov and Shust 2014), because it efectively becomes the battleground for ideological and nationalistic interests, along with arenas of contestation through economic warfare. As of this writing, the Russo-Ukrainian confict is growing, and Western countries have announced a swathe of economic sanctions, larger in scope than those declared in the past. However, many observers are dismissing the sanctions as

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inherently inefective, given that the Russians can bypass them with cryptocurrency as a primary medium (see Flitter and Yafe-Bellany 2022). Te wherewithal that state, semi-state, and non-state actors possess means that cryptocurrency faces a political backlash beyond the technocratic arguments for fnancial stability (Prosekov et al. 2021), and this puts further pressure on lawmakers and regulators to reassess their tolerance toward cryptocurrency regulation.

A Spectrum of Regulatory Responses In light of the foregoing discussion about the various forms of illegal and criminal activity to which cryptocurrencies are made subject, one may observe a spectrum of legal attitudes toward cryptocurrency as an asset class and medium of exchange (Chohan 2017a, b). As noted earlier, this variety of attitudes can be explained by a multitude of factors, including legal precepts (Bailey et al. 2021), regulatory sophistication (Irina 2018), fnancial oversight capabilities (Chohan 2019, 2022a), economic conditions (Szwajdler 2021), and sociocultural norms (Busse et al. 2020; Ben Saad et al. 2022), all of which difer among countries. Tere are some countries which are favorably disposed to cryptocurrency but expect it to fall within a reasonable ambit of regulation (U.S. and Canada being prime examples). Ten there are a few countries which either have considered or which are considering the treatment of cryptocurrency (or at least Bitcoin) as an entirely legal instrument at par with their sovereign monetary issuance (with El Salvador at the vanguard). Tere are yet some countries which maintain a deliberate ambiguity about the legality of cryptocurrency and await further legal and technological developments to inform their considered opinion while remaining aloof from international pressures (Pakistan is a prime example). At the same time, some countries have swayed from once exhibiting a level of tolerance to later enforcing outright bans (China), but have sought alternatives such as Central Bank Digital Currencies (CBDCs). Conversely, some other countries which once banned cryptocurrencies have moved toward their reinstatement subject to a regulatory framework (e.g., India). Still other countries have partial restrictions on cryptocurrency use, whether stopping it as a means of payment while tolerating it as a store of value (Indonesia) or stopping banks from processing cryptocurrency transactions (Cambodia), among other iterations. Te various typologies of cryptocurrency legality are laid out in Table 8.1. Although it is beyond the scope of this chapter to examine each of the typologies in detail, the most well-recognized one is that of Level 2 in Table 8.1, which

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Table 8.1 A Spectrum of Cryptocurrency Legality Level

Type of Permissiveness

1

Full legal tender

A cryptocurrency (Bitcoin) serves as a full legal tender, equivalent to and exchangeable with the country’s issued monetary instrument.

El Salvador

2

Permissive with regulatory framework

Cryptocurrencies are fully permitted but are generally subject to a regulatory framework involving legal/fnancial oversight institutions.

U.S., Canada, Australia,

3

Permissive without specifc regulatory framework

Cryptocurrencies are tolerated because they are not considered a part of the ambit of fnancial supervision, as interpreted by regulatory authorities.

New Zealand, Slovakia, U.K.

4

Partial permission/ partial ban

Cryptocurrencies are allowed in certain ways but not others. Examples include bans on bank processing, on local transactions, on mining, etc.

Indonesia, Cambodia, Russia

5

Strategic ambiguity

Offcial regulatory frameworks do not exist, but cryptocurrencies are not explicitly prohibited. This is pursuant to further evolution of the space.

Pakistan

6

Prohibited

Ownership, exchange, and transactions using cryptocurrencies are entirely prohibited; but perhaps with proposed substitutes (CBDCs).

China (digital yuan), Bolivia, Algeria

Explanation

Country Examples

Sorted by level of tolerance/legal permissiveness (Source: Chohan 2017a, b; author’s research)

is to permit the production, use, and ownership of cryptocurrency but make it subject to direct or indirect fnancial oversight (Chohan 2021a, b, 2022a). Tis is the approach taken by the United States, drawing upon a well-formulated strategy that involves the coordination of many diferent institutions to allow for investor protection and market integrity on the one hand, and fnancial innovation and user freedom on the other (see Chohan 2022a). Tis efort to strike a balance between two public values, accountability and freedom, lies at the crux of all mature approaches to citizen-driven technological change (see Chohan 2021a, 2022a). As such, the U.S. approach represents the most sophisticated efort to grapple with the regulatory and legal challenges posed by cryptocurrencies. Level 3 in Table 8.1 is similar in terms of its practical efects for users (permissiveness), but it has ramifcations in terms of the recourse available to users in

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cases of malfeasance or loss. By not treating cryptocurrency as subject to existing fnancial supervision, some countries leave the onus on users to engage with it, but at their own peril. Te clarifcation to be made here is that, while governments in Level 3 may not consider cryptocurrency to be a proper form of money for the purposes of fnancial supervision, this does not mean that licenses, registrations, and other forms of bureaucratic boxes mustn’t be checked; rather, it simply means that cryptocurrency often falls under alternate guidelines or outside the guidelines that are intended for the traditional fnancial sector. Level 4 in Table 8.1 is a more constricted form of tolerance, wherein cryptocurrencies might be allowed for some purposes but not others. For example, a country may allow the possession and trading of crypto but forbid mining (Russia), allow ownership but restrict payability in transactions (Indonesia), or allow people to mine and transact in crypto but not via traditional banking channels (Cambodia). It is worth mentioning here that regulations are subject to change, and what is being observed as of this writing may change in due course. Level 5 in Table 8.1 is a peculiar case in which a country does not have ofcial guidelines or statements regarding cryptocurrency, pursuant to further evolution in the space. Pakistan is a noteworthy case in this regard, since the State Bank (the monetary authority) does not comment decisively on cryptocurrency, even though the volume of trade in cryptocurrency might exceed tens of billions of dollars (Chohan 2017a). Tis “strategic ambiguity” allows the State Bank of Pakistan to avoid domestic and international pressure (from the FATF, for example), particularly since other AML/CFT concerns are continually (and perhaps excessively, for political reasons, see Chohan 2020) raised in the country’s context. Beyond this, there are Level 6 countries in Table 8.1 in which cryptocurrencies are simply prohibited for all purposes: ownership, mining, transactions, and everything else. Bolivia and Nepal are smaller economies that have adopted this stance, but the most prominent economy to do so is China, which has instead piloted a central bank digital currency (CBDC) (as digital yuan) as a centrally governed digital currency. CBDCs are discussed in further detail in the next section, but it is worth mentioning here that they do difer substantially from cryptocurrency as they are typically understood. Countries that ban cryptocurrency entirely tend to cite the issues raised in the earlier section of this chapter, notably AML/CFT issues, but also point to consumer protection, fnancial stability, and economic security as reasons for prohibition. It is not within the remit of this chapter to comment on prohibitions because of the diversity of considerations to which governments must pay heed, all while remaining mindful of their specifc local circumstances. It is also worth noting that, despite oppositional attitudes toward cryptocurrency in some countries, there is a tiny cohort of jurisdictions, led by El Salvador, which ft within Level 1 of Table 8.1 because they treat a particular crypto (Bitcoin) as

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a full legal tender. Tis extraordinary status means that Bitcoin is entirely substitutable, at least according to law, with the sovereign monetary instrument of the country (Gorjon 2021). Treating Bitcoin as a legal tender has had interesting consequences for El Salvador, for on the one hand, it has led to a boost in tourism and international investor interest in the country, but on the other hand, it has worsened the country’s fscal position (IMF 2022). El Salvador has nevertheless stood by this approach, and further time is required before such a generous regulatory posture toward cryptocurrency is vindicated.

International Harmonization Efforts While the previous two sections of this chapter have highlighted (1) broad categories of legal risks and (2) broad typologies of regulatory postures, this section considers international eforts to consolidate or harmonize cryptocurrency regulations across national boundaries. Tis efort is important above all because of the transboundary (or even boundaryless) nature of cryptocurrency, as their owners, dealers, and miners can locate and relocate themselves in adaptive ways. In some instances, such as in Venezuelan refugees’ exodus to Colombia, the boundarylessness has proven to be a blessing (DiSalvo 2019; Rendon 2021). In far more cases, however, and as discussed in previous sections of this chapter, the transboundary nature of cryptocurrency creates various types of risks. Yet the global fnancial regulatory system is highly imbalanced, inefcient, distorted, and politicized, even before one begins to address cryptocurrency; it is riddled with tax havens, duplicitous actors, arbitrage strategies, and armies of lawyers and accountants who legally and illegally perpetuate a distorted world system (Dupuis and Gleason 2020; Shehu 2012; Chohan 2020; Killick and Parody 2007; Storm 2013). Most observers of international fnancial regulation admit this but then consider how cryptocurrency is likely to worsen the system still further (Szwajdler 2021; Nabilou and Prum 2019; Matei and Baks 2019; Chohan 2021c; Larkin et al. 2021), and so something needs to be done, and global regulators must start somewhere ! In this regard, there are several major initiatives that are pushing for better harmonization. Te frst is the FATF, which has issued guidelines for virtual assets (a category larger than crypto alone) that are somewhat stringent (Chohan 2021a) because they include stipulations such as the travel rule, which ground cryptocurrency in the traditional banking system. Te FATF’s guidelines are prescriptive but set out a rigorous template for a tough international approach to cryptocurrency in view of AML/CFT risks.

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Another (more collaborative) approach is that led by the U.S. Department of Justice (DoJ 2020), which asks for the cooperation of agencies both inside and outside the U.S. to work together on targeting specifc violators (usually the most dangerous parties). Te DoJ’s law enforcement–centered approach is inherently reactive and requires considerable will on the part of other institutions to be forthcoming in cooperation, but it does strike at the heart of the most egregious players who pose the greatest regulatory risk. Other major multilateral institutions have also engaged with cryptocurrency regulation, such as the United Nations, the IMF, and the World Bank. However, their eforts are comparatively lackluster. By contrast, the G20 central bankers have been working together, along with the Bank of International Settlements (BIS) and the FATF, to develop ways to cooperate on cryptocurrency harmonization. Te G20 central bank eforts are likely to provide the most concrete results in due course regarding regularization of standards on cryptocurrencies, since central bankers have the highest stake in the macrofnancial/macroprudential stability and are also armed with the maximum wherewithal to take action (see Nabilou and Prum 2019; Chohan 2021a, 2022b). As such, because of their role in fnancial stability and oversight (Chohan 2020), central banks are seen as key national institutions in the cryptocurrency regulatory domain at the international level (Nabilou and Prum 2019). Tat said, central bankers may face a confict of interest in developing mutually recognized global guidelines, given that their own ofces might be pursuing powerful alternatives to existing cryptocurrency, most notably in the form of CBDCs. CBDCs may resemble cryptocurrencies in the sense that they are virtual assets (FATF 2019), but their philosophical underpinnings and practical implications difer signifcantly. For one, cryptocurrency is inspired by the cryptoanarchist ethos (see review in Chohan 2017c), which argues for decentralized, empowering, autonomous approaches to fnancial liberation in cyberspace, detached from the shackles of government oversight. Central banks, as the issuing authority for CBDCs, would have disproportionate control on monetary dynamics (Yuxuan et al. 2018) and surveillance and privacy (Pocher and Veneris 2021). Depending on the legislation that governs central banks, these issues may raise legality and monetary questions of their own. Te counter-argument in favor of CBDCs is that the problems listed earlier in this chapter (hacks and thefts, frauds and scams, AML/CFT, tax evasion) are much better addressed through proactive and direct central bank oversight, perhaps even more so than with the current fnancial system, although challenges may occur in the incipient phases of implementation (Brunnermeier and Niepelt 2019; Viñuela et al. 2020). Insofar as regulatory attitudes are concerned, the countries that fall into Levels 4–6 of Table 8.1 may feel more comfortable gravitating toward Level 2 or 3 as CBDCs come into efect, given the sovereign monetary authority and legal

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architecture that underpins these types. Traditional cryptocurrency may, however, be supplanted or signifcantly marginalized in such circumstances (Viñuela et al. 2020; Pocher and Veneris 2021).

Concluding Remarks Cryptocurrency was originally intended as something lying outside the purview of traditional fnancial/legal systems (Chohan 2017c, 2019a). Yet its wider adoption, ever-increasing complexity, and lucrative nature have amplifed the stakes for all participants in the feld, not least the regulators. At the same time that their popularity has grown, a string of nefarious criminal acts have also been found closely associated with a minority of cryptocurrency actors. Tese acts are of sufcient severity as to mobilize legal and regulatory institutions to curtail the possible public value destruction from such activities (Chohan 2022a). Hacks, thefts, frauds, scams, money laundering, tax evasion, terrorist fnancing, and rogue state and non-state actions all raise considerable alarm among regulatory public managers. Tey also bring what is otherwise a promising area of intellectual, numismatic, technological, and monetary innovation into disrepute. Countries have responded to the emergence of cryptocurrency in a variety of ways, from extreme positivity to outright hostility. Teir responses may vary with time, as new events shape their regulatory postures. Yet while they grapple with this new technology at a national or subnational level, the truth about virtual assets is that they are transboundary in nature and therefore require concerted international action. Tis is easier said than done, given that the present system of international regulation is highly distorted and imperfect in many ways. However, eforts are being made to harmonize regulation. But a larger factor which looms over the subject is that nations may introduce sovereign substitutes to the decentralized cryptocurrencies (CBDCs), which would resolve (to a degree, at least) many of the criminal aspects of cryptocurrency abuse. Nevertheless, the pace of innovation in the cryptocurrency space, and the lucrative nature of its dealings, lead one to surmise that the dialectic between the agents and the regulators (Dupuis and Gleason 2020) will continue to evolve, and at a continually feverish pace.

References Angerer, M., Hofmann, C. H., Neitzert, F., and Kraus, S. (2021). Objective and subjective risks of investing into cryptocurrencies. Finance Research Letters, 40: 101737.

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Arsi, S., Ben Khelifa, S., Ghabri, Y., and Mzoughi, H. (2022). Cryptocurrencies: Key risks and challenges. In: S. Goutte, K. Guesmi, and S. Saadi (Eds.), Cryptofnance: A New Currency for a New Economy, 121–145. World Scientifc Pub Co Inc. Barone, R., and Masciandaro, D. (2019). Cryptocurrency or usury? Crime and alternative money laundering techniques. European Journal of Law and Eco­ nomics, 47(2): 233–254. Ben Saad, S., Allaya, A., Taârit, F., and Hchaichi, R. (2022). Towards a better understanding of the resistance factors of cryptocurrency spread. In: S. Goutte, K. Guesmi, and S. Saadi (Eds.), Cryptofnance: A New Currency for a New Economy, 63–86. Busse, K., Tahaei, M., Krombholz, K., von Zezschwitz, E., Smith, M., Tian, J., and Xu, W. (2020, September). Cash, cards or cryptocurrencies? A study of payment culture in four countries. 2020 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), 200–209. IEEE. Brunnermeier, M. K., and Niepelt, D. (2019). On the equivalence of private and public money. Journal of Monetary Economics, 106: 27–41. Chodhury, N. (2019). Inside Blockchain, Bitcoin, and Cryptocurrencies. CRC Press. Chohan, U. W. (2017a). Cryptocurrencies: A brief thematic review. Economics of Networks eJournal. SSRN. http://dx.doi.org/10.2139/ssrn.3024330 Chohan, U. W. (2017b). Assessing the diferences in Bitcoin & other cryptocurrency legality across national jurisdictions. Available at SSRN 3042248. Chohan, U. W. (2017c). Cryptoanarchism and cryptocurrencies. Regulation of Financial Institutions eJournal. SSRN. http://dx.doi.org/10.2139/ssrn .3079241 Chohan, U. W. (2019a). Are cryptocurrencies truly trustless? In: S. Goutte, K. Guesmi, and S. Saadi (Eds.), Cryptofnance and Mechanisms of Exchange, 77–89. Springer, Cham. Chohan, U. W. (2019b). Initial coin oferings (ICOs): Risks, regulation, and accountability. In: S. Goutte, K. Guesmi, and S. Saadi (Eds.), Cryptofnance and Mechanisms of Exchange, 165–177. Cham: Springer. Chohan, U. W. (2020). Te FATF in the global fnancial architecture: Challenges and implications. Regulation of Financial Institutions eJournal. SSRN. http:// dx.doi.org/10.2139/ssrn.3362167 Chohan, U. W. (2021a). Public Value and the Digital Economy. Routledge. Chohan, U. W. (2021b). Non-fungible tokens: Blockchains, scarcity, and value. Critical Blockchain Research Initiative (CBRI) Working Papers. Chohan, U. W. (2021c). Decentralized fnance (DeFi): An emergent alternative fnancial architecture. Critical Blockchain Research Initiative (CBRI) Work­ ing Papers.

Regulatory and Legal Issues in Cryptocurrencies

155

Chohan, U. W. (2022a). Public value and citizen-driven digital innovation: A cryptocurrency study. International Journal of Public Administration. https:// doi.org/10.1080/01900692.2022.2043365 Chohan, U. W. (2022b). Oversight and regulation of cryptocurrencies: BitLicense. In: S. Goutte, K. Guesmi, and S. Saadi (Eds.), Cryptofnance: A New Currency for a New Economy, 105–120. Chohan, U. W. (2022c). Cryptocurrencies and inequality. In: S. Goutte, K. Guesmi, and S. Saadi (Eds.), Cryptofnance: A New Currency for a New Economy, 49–62. Clayton, J., and Giancarlo, J. C. (2018). Statement by SEC Chairman Jay Clayton and CFTC Chairman J. Christopher Giancarlo: Regulators are looking at cryptocurrency. Press release. DiSalvo, M. (2019). Why are Venezuelans seeking refuge in crypto-currencies? BBC. March 19. https://www.bbc.com/news/business-47553048 DoJ (Department of Justice) (2020, October). Cryptocurrency enforcement framework: Report of the ofce of the Attorney General’s cyber digital task force. Washington, DC: DoJ. Dostov, V., and Shust, P. (2014). Cryptocurrencies: An unconventional challenge to the AML/CFT regulators? Journal of Financial Crime, 21(3): 249–263. Dupuis, D., and Gleason, K. (2020). Money laundering with cryptocurrency: Open doors and the regulatory dialectic. Journal of Financial Crime. Dyntu, V., and Dykyi, O. (2018). Cryptocurrency in the system of money laundering. Baltic Journal of Economic Studies, 4(5): 75–81. Financial Action Task Force (FATF) (2019). Guidance for a Risk­Based Approach to Virtual Assets and Virtual Asset Service Providers. Paris: FATF. Flitter, E., and Yafe-Bellany, D. (2022). Russia could use cryptocurrency to blunt the force of U.S. sanctions. New York Times, February 23. García-Corral, F. J., Cordero-García, J. A., de Pablo-Valenciano, J., and UribeToril, J. (2022). A bibliometric review of cryptocurrencies: How have they grown? Financial Innovation, 8(1): 1–31. Gorjón, S. (2021). Te role of cryptoassets as legal tender: Te example of El Salvador. Banco de Espana Article, 35: 21. Hu, Y., and Hou, Y. G. (2021). An overview and analysis of the process of hacking mining hashing power. Understanding Cryptocurrency Fraud: Te Challenges and Headwinds to Regulate Digital Currencies. Volume 2 of Batten-CorbetLucey Handbooks in Alternative Investments, 149. Walter de Gruyter GmbH & Co. IMF (International Monetary Fund) (2022). El Salvador’s comeback constrained by increased risks. https://www.imf.org/en/News/Articles/2022/02/15/cf -el-salvadors-comeback-constrained-by-increased-risks Ito, J., and Howe, J. (2016). Whiplash: How to Survive Our Faster Future. Grand Central Publishing.

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Irina, C. (2018). Cryptocurrencies legal regulation. BRICS Law Journal, 5(2): 128–153. Joo, M. H., Nishikawa, Y., and Dandapani, K. (2019). ICOs, the next generation of IPOs. Managerial Finance, 46(6): 761–783. Karimov, B., and Wójcik, P. (2021). Identifcation of scams in initial coin oferings with machine learning. Frontiers in Artifcial Intelligence, 4. Killick, M., and Parody, D. (2007). Implementing AML/CFT measures that address the risks and not tick boxes. Journal of Financial Regulation and Compliance. DOI: 10.1108/13581980710744093. Kher, R., Terjesen, S., and Liu, C. (2021). Blockchain, Bitcoin, and ICOs: A review and research agenda. Small Business Economics, 56(4): 1699–1720. Krupa, T., Ries, M., Kotuliak, I., and Bencel, R. (2021, January). Security issues of smart contracts in Ethereum platforms. 2021 28th Conference of Open Innovations Association (FRUCT), 208–214. IEEE. Larkin, C., Pearce, N., and Shannon, N. (2021). Criminality and cryptocurrencies: Enforcement and policy responses–Part II. Understanding Cryptocurrency Fraud, 133. Mabunda, S. (2018, August). Cryptocurrency: Te new face of cyber money laundering. 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD), 1–6. IEEE. Marian, O. (2013). Are cryptocurrencies super tax havens? Mich. L. Rev. First Impressions, 112: 38. Matei, I. G., and Baks, E. W. (2019). Regulating Bitcoin—Te challenges ahead. Acta Universitatis Danubius. Œconomica, 15(3). Nabilou, H., and Prum, A. (2019). Central banks and regulation of cryptocurrencies. Rev. Banking & Fin. L., 39: 1003. Nica, O., Piotrowska, K., and Schenk-Hoppé, K. R. (2022). Cryptocurrencies: Concept and current market structure. In: S. Goutte, K. Guesmi, and S. Saadi (Eds.), Cryptofnance: A New Currency for a New Economy, 1–28. Pocher, N., and Veneris, A. (2021). Privacy and transparency in CBDCs: A regulation-by-design AML/CFT scheme. IEEE Transactions on Network and Service Management. Rendon, M. (2021). How open and public cryptocurrencies can help Venezuelans. Centre for Strategic & International Studies. April 13. https://www.csis.org /analysis/how-open-and-public-cryptocurrencies-can-help-venezuelans Salcedo, E., and Gupta, M. (2021). Te efects of individual-level espoused national cultural values on the willingness to use Bitcoin-like blockchain currencies. International Journal of Information Management, 60: 102388. Sanz-Bas, D., del Rosal, C., Náñez Alonso, S. L., and Echarte Fernández, M. Á. (2021). Cryptocurrencies and fraudulent transactions: Risks, practices, and legislation for their prevention in Europe and Spain. Laws, 10(3): 57.

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Schuefel, P. (2021). DeFi: Decentralized fnance—An introduction and overview. Journal of Innovation Management, 9(3): i–xi. Shehu, A. Y. (2012). Promoting fnancial inclusion for efective anti-money laundering and counter fnancing of terrorism (AML/CFT). Crime, Law and Social Change, 57(3): 305–323. Slattery, T. (2014). Taking a bit out of crime: Bitcoin and cross-border tax evasion. Brook. J. Intl. L., 39: 829. Smith, B. (2019). Te life-cycle and character of crypto-assets: A framework for regulation and investor protection. Journal of Accounting and Finance, 19(1): 156–168. Szwajdler, P. (2021). Considerations on the construction of future fnancial regulations in the feld of initial coin ofering. European Business Organization Law Review, 1–39. Storm, A. (2013). Establishing the link between money laundering and tax evasion. International Business & Economics Research Journal (IBER), 12(11): 1437–1450. Viñuela, C., Sapena, J., and Wandosell, G. (2020). Te future of money and the central bank digital currency dilemma. Sustainability, 12(22): 9697. Yuxuan, C., Kejie, Z., and Wenhao, Y. (2018). Te research on the money supply of central bank digital currency. Journal of Finance Research, 2(2): 27–36. Zhang, Y., Yu, W., Li, Z., Raza, S., and Cao, H. (2021). Detecting Ethereum Ponzi schemes based on improved LightGBM algorithm. IEEE Transactions on Computational Social Systems.

Chapter 9 Cryptocurrencies, Blockchain, and Public Choice Ryan M. Yonk and David Waugh American Institute for Economic Research

Introduction Nobel Economist James M. Buchanan famously described Public Choice as “politics without romance” (Buchanan 1984) to show the political world not through the normative lens of the noble civic but through a more pragmatic and realistic view. For at least the last half-century, the public choice research program has worked to provide a greater understanding of the political world through the application of economic tools. What Buchanan and the other early public choice scholars sought was something that stripped away romantic perceptions. Tey instead applied positive analysis to the realm in which political decisions are made. Tese scholars used the tools of economics to analyze non-market decisions that coordinate political and economic activity. While the feld has developed over the course of the last half-century, this commitment to positive analysis remains the hallmark of public choice analysis. 159

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In applying this positive analysis to non-market, coordinated decisions, the core of public choice relies on two principles: (1) that individuals have preferences, and (2) that they will take action to achieve those preferences. Tese two simple observations of economic coordination rest upon our understanding of market exchange. Tese principles, which are banal in the economic sphere, remain revolutionary when applied in the public sector. Myriad scholars, including James M. Buchanan, Elinor Ostrom, William Riker, and Gordon Tullock, built an understanding of politics from the perspective of coordinated exchange. Teir theory of government uses actual observations of how political decisions are made by emphasizing the incentive structures that shape their decisions. Tis theory replaces normative claims about government with concrete explanations of what truly happens. Public choice applies the analytical techniques of economics to examine political actors and processes. Essentially, it is “the application of economics to political science” (Mueller 1976). Within the economic literature of the 1940s and 1950s, economists criticized the market for failures in the form of greed or institutional misalignment. Buchanan and Tullock* took these critiques of market actors and applied them to politics. Public choice focuses on the decision-making at the individual level (whereas “the people” or “society” at large is not the unit of analysis). Groups do not make choices but individuals––as the fundamental actors––do (Shughart 2018). Traditionally, in political science and most understandings of civics, it is presumed that individuals in government have the public’s best interest in mind while eschewing their personal interests. Public choice theorists reject this romantic notion, arguing that politicians are no diferent from any other rationally selfinterested agent with their own utility expectations. Political actors face diferent constraints, incentives, and decision-making pathways, which suggests that once elected they do not evolve from being rational agents who maximize self-interest to enlightened public servants. Public choice scholars take the position that one cannot simply elect “better people” to fx problems, as all elected ofcials and bureaucrats have the same contextual incentivizing structures and self-interest (Buchanan 1984). As a research program and approach to understanding collective decisionmaking, public choice has a history, stemming from multiple Nobel Prize– winning research agendas in economics,† that takes the best available tools and uses them to better understand how rules, incentives, and expectations can lead to diferent outcomes in the political world. Public choice—in the face of political *

†

Buchanan and Tullock are the founders of the Virginia School of Public Choice. Tere are other schools of public choice, such as the Bloomington and Rochester Schools. Nobel Prize winners include James Buchanan and Elinor Ostrom.

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shifts, new policy approaches, and massive technological changes—has been a productive mechanism to understand how these changes impact politics and the decisions of those involved. Tis chapter looks through the public choice lens to explore how collective decisions can be impacted by technological innovations in cryptocurrency. We rely on the traditional public choice research agenda to analyze how crypto’s blockchain technology might infuence our understanding of political and policy decisions. We present a set of illustrative case studies about non-market decision-making that have been thoroughly studied in public choice research. Tese case studies investigate whether cryptocurrency technology can potentially help improve decision-making. We argue that developments in crypto technology illuminate the institutional arrangements surrounding non-market decision-making and can perhaps alleviate some public choice problems. Our approach ofers unique insights on the relationship between the emerging crypto technology and public choice thought. In each case, we identify an issue raised in the public choice literature and then explore how the emerging cryptocurrency technology––and in some instances the currency itself––provides new insights on non-market decision-making. Our core question specifcally explores whether blockchain technology allows for new ways to coordinate political and economic activity and what the implications of those new ways might be.

Cryptocurrency, Blockchain, and Decentralized Autonomous Organizations (DAOs) To provide context for our discussion of the common public choice problems, explaining the technology behind the development of most cryptocurrencies— namely, blockchains—is necessary. Put simply, blockchains are continuous strings of information. Tey are growing records of information, bundled into blocks and chained together, using cryptography, which is a technique in solving codes. One of the most common uses of blockchain is in the form of a ledger for digital currencies. Unlike typical money, cryptocurrencies are solely electronic and are not backed by a physical currency or asset. Each cryptocurrency network, such as Bitcoin or Ethereum®, maintains a blockchain that serves as a ledger or record for every transaction that takes place (Waugh 2022). Te blockchain links together blocks, or bundles of transactions, in a time-stamped sequence that can be publicly viewed. It is important to note that cryptocurrency blockchains are decentralized. Tey do not require a third-party to maintain them. Tey achieve consensus, or deciding what is valid and what is not, via a consensus mechanism. Te most popular cryptocurrency consensus mechanisms are known as proof­of­work (PoW) and

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proof­of­stake (PoS). On PoW blockchains, blocks are created through mining. For each block of the chain, miners (computers) compete to solve a cryptographic puzzle, using the processing power of their computers. Tis process requires a signifcant amount of resources in the form of computing power and electricity. Once the frst miner solves the problem, a block of the verifed transaction is added to the chain, and the transaction becomes a part of the permanent record. In exchange for its work, the miner receives a reward in cryptocurrency, which provides the incentive for them to expend their computing power to mine more blocks. Mining is the “work” in the term proof-of-work. Miners use their computing power, or work, to “prove” the network. Because the network creates a permanent record of verifed transactions, PoW eliminates the need for a central authority and provides a decentralized validation record. As for proof-of-stake, network participants “stake” or lock up their crypto assets, in exchange for becoming validators of the blockchain. Validators are randomly selected by the network to verify the blockchain. PoS validators are similar to PoW miners, but instead of work, their stake allows them to validate the network. Campbell Harvey argues in DeFi and the Future of Finance, Validators make themselves available by staking their cryptocurrency and then they are randomly selected to propose a block. Te proposed block needs to be attested by a majority of the other validators. Validators proft by both proposing a block as well as attesting to the validity of others’ proposed blocks (Harvey 2021).

In both PoW and PoS, consensus over the blockchain, or the ledger of each network, is arrived at without the need for a centralized actor to ensure that the ledger is valid.

Smart Contracts One innovation that provides a useful example of the potential for blockchain to infuence collective decision-making is a smart contract.* Smart contracts, which *

Nick Szabo conceptualized the smart contract in 1994. He defned it as “a computerized transaction protocol that executes the terms of a contract. Te general objectives of smart contract design are to satisfy common contractual conditions (such as payment terms, liens, confdentiality, and even enforcement), minimize exceptions both malicious and accidental, and minimize the need for trusted intermediaries. Related economic goals include lowering fraud loss, arbitration and enforcement costs, and other transaction costs.”

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are commonly deployed on the Ethereum blockchain network, allow for individuals to write code on top of existing code, creating new products that “live” on that blockchain. Harvey et al. (2021) observe that smart contracts are “code that lives on a blockchain, can control assets and data, and defne interactions between the assets, data, and network participants.” Tey allow for the automatic execution of an agreement, usually a transaction, based on the directions written into their code. Tus, individuals and institutions can automate transactions without the need for a third party or intermediary. Tese applications built using smart contracts are called decentralized applications or dApps. Once dApps are built, individuals can interact peer-to-peer across the app without the need for a central decision-maker. One of the most signifcant innovations that emerge from dApps is decentral­ ized autonomous organizations (DAOs). DAOs are digital organizations that are owned and managed by their members and governed by a smart contract on the blockchain. Te smart contract functions as a set of by-laws, similar to a constitution, which outlines rules governing how the DAO operates and how future changes to the rules can be made. Once the smart contract programming has been fnalized and the funds raised and deposited in a fnancial intermediary, the DAO is deployed on the blockchain, where transactions and changes to the DAO are transparent, verifable, and approved by members (Waugh and Wright 2022). Members can join the DAO by voluntarily acquiring governance tokens in the DAO. DAOs serve a variety of purposes—such as a freelancing network, investment fund, neighborhood watch, or community management tool—for community centers such as a park. DAOs raise funds via selling tokens or shares, similar to a direct public ofering, through which engagement is voluntary and requires afrmative action. Te implications of DAOs, as well as programs that live on top of the blockchain technology, provide a potential alternative to the issues raised by public choice scholars in the study of non-market decision-making. To illustrate this potential, we review three traditional cases studied by public choice scholars, and how these technologies, particularly DAOs, can help alleviate them.

Illustrative Case 1: Questions of Voting: Unanimity, Parties, Coalitions/Interest Groups Our frst illustrative case examines the potential for blockchains to infuence some of the common public choice issues that arise in the use of voting to make collective decisions. We explore whether expanded opportunities for democracy from an institutional perspective can emerge from the use of the technology

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underlying crypto in three key areas: unanimity, parties and vote-trading, and special interest groups.

Public Choice Issue 1: Unanimity One of the key questions that emerged within the voting literature is how to handle decision rules outside the market where allocations are unlikely to beneft all parties. One of the core suggestions that emerges from the public choice literature is the implementation of unanimity or near unanimity rules in these situations, where all parties agree to the outcome. Tis way, everyone gets some beneft from the decision. However, a unanimity rule makes it harder to reach a fnal decision, as everyone involved must reach the same conclusion. Unanimity connotes consensus, or full agreement among a group. In theory, more unanimity means more consensus, which means more voters are in agreement. Economic analysis emphasizes that individuals make decisions based on their unique interests, not necessarily the interests of the group. Terefore, consensus is achieved when the fnal decision coincides with everyone’s own interests. Within blockchain, Bitcoin, a decentralized network with no clear government, changes its protocols and policies through consensus and through the decisions of each individual acting without the necessity of a voting procedure. Network participants need to overwhelmingly agree to use a particular process for it to become standard practice among the miners.

Public Choice Issue 2: Parties and Vote-Trading Another issue raised by public choice scholars is that the institutional arrangements incentivize public ofcials to vote-trade, especially among party allies, to maximize their own interests. Politicians make deals with each other to push their political agendas by agreeing to vote for another’s legislation if they will return the favor. For instance, a politician from Nebraska may vote in favor of wind energy subsidies if his peer from South Carolina votes for legislation that subsidizes farm interests. Tey vote in favor of legislation that benefts a diferent state despite the lack of direct positive impacts for their constituents. Tey engage in vote-trading to ensure their legislation gets passed, often without regard to the total costs of the legislation, so long as they beneft directly from the resulting legislative decision. Tese interactions build trust between politicians, and political parties aid in this process by providing a proxy for reliable political exchangers. Parties act as the grease and glue that allow elected ofcials to engage in political exchange within a legislative body. Public choice has noted that generalized total costs that

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exceed the generalized total benefts are adopted because local benefts to parties and individuals exceed the individual cost to any one taxpayer.

Public Choice Issue 3: Special Interest Groups Te third issue emerges from the tendency of vote-trading and forming party coalitions. Special interest groups (SIGs) seek rents, or “comparative advantage[s] in the market” (Cowden, Prinzinger, and Prinzinger 2009). SIGs will lobby politicians to further their personal goals, and when those goals can be aligned with those of the politician (namely, reelection) they often succeed in obtaining those rents. Tese groups earn political allies to progress interests that may or may not align with the public interest. SIGs receive concentrated benefts, while the costs may be dispersed among the general public. For example, domestic car manufacturers may lobby Congress to raise tarifs against foreign car manufacturers. While domestic manufacturers may beneft by ofering relatively cheaper cars compared to their foreign competitors and garnering more customers, consumers face higher overall prices and fewer options in the car market.

The Impact of Innovative Crypto-Linked Technology on Voting and Unanimity Rules Popular blockchains, such as the Bitcoin and Ethereum blockchains, allow us to observe a form of unanimity in action. When upgrades to the software protocols are proposed, unanimity must arise in order for change to occur. Upgrades to the software protocols can be viewed as amendments to the constitution of each blockchain network, which require consensus in order to implement them on the blockchain. For example, decisions surrounding upgrades to the Bitcoin blockchain must follow the Bitcoin Improvement Proposal (BIP) process. BIPs can be large or small changes to the protocol or to the BIP process itself. Tey begin with a draft, become a proposal, and are eventually accepted. BIPs that signifcantly change Bitcoin take years to go through this process. Since Bitcoin is decentralized, nodes, or computers that run the Bitcoin blockchain, ultimately decide whether BIPs are implemented through choosing which version of Bitcoin they want to run. BIPs that signifcantly change the Bitcoin network require a high level of consensus. Sandor (2022) notes that, “A BIP that proposes implementation with a soft fork [small change] requires a ‘clear miner majority,’ meaning that over 90% of nodes have to approve to upgrade. Tese are called ‘Consensus BIPs.’” Ultimately, blockchains, such as Bitcoin, de-facto elect to use consensus, a variation of unanimity, as a pre-condition for making signifcant changes to the network.

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Tis ensures that changes to the network are not rash and will not backfre in ways that will cause it to diminish in its utility from a technological and monetary standpoint. While unanimity is not required to make changes to a blockchain network, and nor do blockchain networks by rule promote unanimity, the most successful networks likely owe their success, at least in part, to their reliance on various forms of consensus to make changes to the protocol. Tis success is seen in Bitcoin, which is much more decentralized than competitors Terra®, Solana®, and others. Bitcoin users are more secure that the network will not undergo signifcant changes without a rigorous overview. Terefore, they are more likely to invest their time and efort into the network. Successful blockchains have consensus-oriented protocols that approach unanimity. Tis is illuminating for public choice scholars who study political and governmental rules being changed and implemented. Unanimity may beget better political outcomes, and the public will be more inclined to follow changes that are largely supported. Finally, blockchains might serve as a means of implementing quadratic voting. Allen (2017) describes the link as: Quadratic voting [QV] should be understood as a mechanism that is inherently implemented on a blockchain at the point of voter identifcation, robustness and verifcation of the bidding and tallying mechanism, and security and transactional efciency of the vote buying, fund pooling, and redistribution mechanism. By envisaging and implementing the QV mechanism in the context of a platform such as Ethereum, which enables smart contracts in which a citizen preprograms their preferences and then allows their software agent (or Decentralized Autonomous Organization) to efectively automate the trades, voting and to make and receive payments, the transactions cost constraint on QV in an analog world is signifcantly reduced. (Allen 2017)

Tis form of consensus may represent an alternative approach to the unanimity rule often proposed by public choice scholars and may be a more practical approach than many of the large-scale constitutional revisions suggested by some.

Understanding Political Parties and Their Infuence Blockchains provide public choice scholars with a means of observing the infuence of parties and coalitions with respect to managing collective goods. When the traditional protocol upgrade process cannot resolve traditional issues, de-facto “parties” emerge and seek to infuence the protocol update decision. Te most signifcant example of this is the Blocksize War documented by Jonathan Bier in Te Blocksize War: Te Battle over Who Controls Bitcoin’s Protocol Rules. Te blocksize

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war centered around two factions within the Bitcoin community, each with a different vision for how the Bitcoin blockchain ought to operate. Te factions waged “war” against each other over internet forums and public debates, with each side gathering support from institutions and individuals within the Bitcoin community. Eventually, one side “won,” and the other split of to create its own cryptocurrency, known as Bitcoin Cash, or BCH. Historical events, such as the blocksize war, allow public choice scholars to study the infuence of interest groups and factions with respect to key decisions that impact each blockchain. Te ability of an interest group to infuence each blockchain project is, to some extent, dependent on how each blockchain project is designed. Future study from public choice researchers could focus on blockchain projects where founders, foundations, and venture capital frms possess a signifcant amount of voting tokens. In addition to this constituency, there are other constituencies that emerge organically, such as HODLers. Berg et al. (2018) describe this phenomenon by noting that, [T]he incentives and propensity to coordinate activity might provide some interest to Public Choice scholars when constituencies such as HODLers are considered. HODLers are those members of blockchain communities who advocate for a holding strategy when it comes to cryptocurrency trading.

Ultimately, parties and coalitions can play an important role in how blockchains are set up and how decisions are made. Similar to what public choice scholars see in political arenas, factions form in blockchain communities to infuence collective decision-making.

Illustrative Case 2: Questions of Law and Contracts Public Choice Troughout history, contracts have generally relied on third parties as enforcers to resolve disputes or punish non-compliant parties. In the case of a dispute, oftentimes the cost of using a third party to achieve resolution is too high. Within economics, Nobel Prize winner Ronald Coase highlighted a proposition about how legal disputes can be resolved outside of the court system (Coase 1960). Public choice scholars have often suggested just such a solution when considering ways around the requirement for enforcement. Using Coasian logic, they suggest that when an externality exists, parties could theoretically bargain to come to a resolution, provided that transaction costs are low and property rights are clearly defned. Ultimately, parties may come to a more optimal decision without third-party involvement. Coase (1960) acknowledges that,

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. . . in choosing between social arrangements within the context of which individual decisions are made, we have to bear in mind that a change in the existing system which will lead to an improvement in some decisions may well lead to a worsening of others. Furthermore we have to take into account the costs involved in operating the various social arrangements (whether it be the working of a market or of a government department), as well as the costs involved in moving to a new system. In devising and choosing between social arrangements we should have regard for the total efect (Coase 1960, 44).

Te resulting judicial decisions may create some improvements, but they also may be at a cost. Tis is important to recognize when understanding how legal decisions are made versus how individuals independently bargain. Te other important factor to note is that third parties, removed from the actual situation, may not know the details of each situation to make well-informed decisions that lead to the best outcome.

Smart Contracts and the Law Te most signifcant blockchain innovation with respect to the law are smart contracts. While it is not expected that they will replace large swaths of the judicial system in the near future, their application is of interest to public choice scholars who study how the law works. In theory, smart contracts might allow for the replacement of the political judiciary with what appears to be the outset of a form of adjudication rooted in code rather than judicial interpretation. In the near term, however, it appears smart contracts can be used today to reduce the costs of enforcing certain transactions. Levi et al. (2018) argues, Smart contracts are presently best suited to execute automatically two types of “transactions” found in many contracts: (1) ensuring the payment of funds upon certain triggering events and (2) imposing fnancial penalties if certain objective conditions are not satisfed. In each case, human intervention, including through a trusted escrow holder or even the judicial system, is not required once the smart contract has been deployed and is operational, thereby reducing the execution and enforcement costs of the contracting process (Levi et al. 2018).

While smart contracts can replace some of the traditional contracts present in today’s world, they are not risk free. As Carter and Jeng (2021) describe, they are simply “code that automates actions.” Because they replace human agreements and enforcement mechanisms with code, they shift risk from human error to code error. For example, technical exploits (hacks) of smart contracts are common. Werner et. al (2021) identify DeFi attacks that cost users over one hundred million dollars in total value throughout 2020.

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In addition to smart contract risk, decentralized blockchains sufer from broader governance risks that could diminish their adjudicatory power. Tis is particularly an issue with respect to decentralized autonomous organizations that rely on governance via tokens. Per Carter and Jeng, Tokens held by exchanges on behalf of users have been employed to infuence governance outcomes, in some cases against the wishes of these users. Large caches of governance-laden tokens sitting at exchanges could infuence them to accept bribes in order to vote favorably on specifc proposals or simply could be borrowed on an extremely short-term basis (likely not impairing the exchange’s liquidity requirements) to swing a governance vote (Carter and Jeng 2021).

Tis shows how “governance-laden tokens” may not align with the interests of the users. While smart contracts face the risk posed by their exclusive reliance on technology as an enforcement mechanism, the use of smart contracts for adjudication sufers from an additional judicial roadblock. Because they exist on multinational cryptocurrency networks, smart contracts operate across many jurisdictions. Terefore, it is unclear where liability for the smart contract is established if it does not execute as intended. Further, legal bodies face little interest in replacing themselves with code. From a pure self-interest perspective, this enacts an additional roadblock to the implementation of smart contracts as a replacement for a traditional judiciary. Despite these potential limitations, the potential for application remains a signifcant possibility for dealing with some of the issues that arise from political allocation and decision-making that are of interest to public choice scholars.

Illustrative Case 3: Regulation and Governance Politicians delegate tasks and responsibilities to career bureaucrats who are appointed rather than elected to their positions. Career bureaucrats are, in theory, appointed because they possess a unique skill set that enables them to succeed within their relative agency. Tis occurs as a result of specialization and the division of labor. Meanwhile, politicians are ill-equipped with the technical knowledge required to operate certain departments and agencies. While bureaucrats maintain the moniker of “civil servants,” public choice scholars study them through the same lens as they would any other utility-maximizing individual. As Benson (1995) explains, “(1) Only individuals act and make decisions, (2) people are utility-maximizers, and (3) civilian or governmental bureaucratic oversight is difcult because obtaining information is difcult.” In this vein, public

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choice scholars assume that bureaucrats seek to maximize their budgets and power because this will provide them with more opportunities to rise on the bureaucratic ladder and secure employment. Tey assume bureaucrats will use their relevant expertise to obtain the largest budgets possible from the politicians responsible for approving them (Shughart 2018). In addition to the budget maximization model of bureaucratic action, public choice scholars also recognize the ability of congressional committees to infuence bureaucrats. Tis is known as the congressional dominance model. Public choice scholars observe that in many areas of public policy, bureaucrats mirror the opinions of the legislators that maintain power or infuence over them. Both budget maximization and congressional dominance provide public choice scholars with key insights with respect to blockchain and cryptocurrency regulation. Following the budget maximization model, one can assume the agencies will seek to maximize their budgets by acquiring the largest possible share of regulatory power over the cryptocurrency industry. Tis will lead to greater regulations over the industry, as agencies seek to justify their budget increases. Unless another industry within fnancial services eclipses the cryptocurrency industry in terms of popularity and size, and therefore draws attention away from regulators, it is almost certain that, under the budget maximization model, bureaucracies will seek to increase regulation over the sector. Unlike the budget maximization model, the congressional dominance model is less of a straightforward path toward increasing levels of regulation. For example, under the congressional dominance model, one can reasonably expect that an abundance of anti-crypto legislators would infuence agencies, such as the SEC, to pursue more stringent industry-level regulations. On the other hand, procrypto legislators could direct the agencies to take a more hands-of approach. In the following section, we provide an overview of the current regulatory landscape with respect to cryptocurrencies.

Examples from Crypto Regulation Cryptocurrency regulation is increasing in terms of formal rulemaking, as federal agencies jockey for dominant spots with respect to regulating particular aspects of the crypto industry. Regulating the cryptocurrency industry is challenging for regulators because many cryptocurrency networks purport to serve diferent purposes. Bitcoin, for example, seeks to serve as a “peer-to-peer digital cash,”* while other networks, particularly in the decentralized fnance space, seek to emulate *

Te original Bitcoin whitepaper is titled Bitcoin: A Peer-to-Peer Electronic Cash System.

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the world of centralized fnance with options, futures, lending, and more but through decentralization. Many agencies can claim that cryptocurrency regulation falls within their domain. Currently, at the federal level, the Internal Revenue Service (IRS), Securities and Exchange Commission (SEC), and Commodity Futures Trading Commission (CFTC) oversee cryptocurrency regulations. It is expected that the Federal Trade Commission (FTC), Financial Crimes Enforcement Network (FinCEN), and Ofce of the Comptroller of Currency (OCC) may increase their engagement in regulating the industry. In addition to the federal regulation of the space, much of the current cryptocurrency regulation occurs at the state level. Tis regulation is both favorable and unfavorable. Levels of regulation are largely dependent on the political composition of the state legislature. For example, New York recently passed legislation targeting Bitcoin mining, citing environmental concerns. In contrast, Texas maintains highly favorable regulations and laws regarding Bitcoin mining. Tis is consistent with the party politics of each state: New York voters are progressive and less inclined to favor fnancial innovation over environmental concerns, while Texas voters are conservative and likely to think the opposite. Te realities that emerge from the actual use of cryptocurrency and the underlying urge to regulate it provide another avenue for those working in public choice to address a regulatory system from its inception. Tis is of particular interest to those working in public choice because of the newness of the technology and the lack of previous regulatory models that readily apply. What is quickly emerging is an ongoing case study of how cryptocurrency has the potential to upend not just economic considerations but also regulatory models.

Conclusion Te emergence of cryptocurrency and the technology that underlies it has been a source of nearly endless scholarly opportunities, and public choice is no different. Since the implementation of Bitcoin in 2009, blockchain technology has created new institutional possibilities for many of the problems traditionally studied by public choice scholars. While this chapter provides an overview of some of these problems, along with a consideration of possible ways in which they may be implemented, the topic remains an open and productive area for a research agenda. Further areas of study for public choice scholars include blockchain and environmental issues, corporate governance, healthcare, and public fnance, and numerous other topics all have strong possibilities for being impacted by

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blockchain, DAOs, and other innovations that are likely to occur. As we illustrate throughout the chapter, blockchain technology is neither a permanent nor a fnal solution. Rather, it is a technological innovation that allows for new institutional capabilities and may alleviate some (or create new) public choice problems, while carrying along with it new risks and institutional constraints.

References Allen, H. J. (2017). $=€=Bitcoin? Maryland Law Review, 76: 877. Bier, J. (2021). Te Blocksize War: Te Battle Over Who Controls Bitcoin’s Protocol Rules. Independently published. ISBN: 979-8721895609. Benson, B. L. (1995). Understanding bureaucratic behavior: Implications from the public choice literature. Journal of Public Finance and Public Choice, 13(2–3): 89–117. Berg, J., Furrer, M., Harmon, E., Rani, U., and Silberman, M. S. (2018). Digital labour platforms and the future of work. Towards decent work in the online world. Rapport de l’OIT. ISBN: 978-9220310250. Buchanan, J. (1984). Politics without romance: A sketch of positive public choice theory and its normative implications. In: J. Buchanan and R. Tollison (Eds.), Te Teory of Public Choice­I, 1(11). University of Michigan Press. Carter, N., and Jeng, L. (2021, June 14). DeFi protocol risks: Te paradox of DeFi. In: B. Coen and D. R. Maurice (Eds.), Regtech, Suptech and Beyond: Innovation and Technology in Financial Services. RiskBooks. http://dx.doi .org/10.2139/ssrn.3866699 Coase, R. H. (1960). Te problem of social cost. Journal of Law and Economics, 3: 1–44. Cowden, N., Prinzinger, J., and Prinzinger, M. (2009). A public choice model of environmental legislation: MTBE vs. ethanol. Oxford Journal, 8(1). Harvey, C. R., Ramachandran, A., and Santoro, J. (2021). DeFi and the Future of Finance. Wiley. Lemieux, P. (2021). Politics without romance. Econlib. https://www.econlib.org /politics-without-romance/ Levi, S. D. (2018). An introduction to smart contracts and their potential and inherent limitations. Skadden, Arps, Slate, Meagher & Flom LLP. Mueller, D. C. (1976). Public choice: A survey. Journal of Economic Literature, 14(2): 395–433. https://www.jstor.org/stable/pdf/2722461.pdf Sandor, K. (2022). What are BIPs and why do they matter? Coindesk. https://www .coindesk.com/learn/what-are-bips-and-why-they-matter-to-bitcoins-future/

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Shughart, W. (2018). Public choice. Econlib. https://www.econlib.org/library/Enc /PublicChoice.html Waugh, D. (2022, March 23). Not every “crypto” is decentralized: Proof-of-work vs proof-of-stake. American Institute for Economic Research. https://www.aier .org/article/not-every-crypto-is-decentralized-proof-of-work-vs-proof-of-stake/ Waugh, D., and Wright, R. E. (2022). Can DAOs revive civil society? American Institute for Economic Research. https://www.aier.org/article/can-daos-revive -civil-society/ Werner, S., Perez, D., Lewis, G., Klages-Mundt, A., Harz, D., and Knottenbelt, W. (2021). SoK: Decentralized fnance (DeFi). arXiv:2101.08778. https://arxiv .org/pdf/2101.08778.pdf

Chapter 10 Bitcoin Is King Andrew M. Bailey* and Craig Warmke** *Yale-NUS College, **Northern Illinois University

I. Introduction Kerrygold® butter is legendary. For a time, it was the only butter Americans could fnd from grass-fed cows. Rich yellow, favor dense, and imbued with magical Irish qualities. Best butter in the world, and suitable even for blending with morning cofee. Unique. But a visit to Ireland shows something else. Go there and you might be served Connacht Gold for breakfast. Connacht—a province, much like Kerry, a county. If this doesn’t shake your confdence in the uniqueness of Kerrygold, just wait till you see the wall of butters at the Supervalu in Dingle. Tey’re all yellow, and Irish, and seem quite nice. Perhaps Kerrygold isn’t as special as it might seem. Tis essay isn’t about Kerrygold. It isn’t about bovine “gold” in general, either. It’s about digital gold—Bitcoin. Many think that Bitcoin is even less special among cryptoassets than Kerrygold is among Irish butters. After all, 13,457 such assets now have some kind of market value. Many have charismatic leaders, slicker marketing, more apparent utility, and, from time to time, more appealing trading opportunities. Some pundits think Bitcoin’s best days are over. A boomer coin. Perhaps Bitcoin isn’t as special as it might seem.* *

For some comparisons among cryptocurrencies and their design tradeofs, see Bailey et al. (2021a, 2021b). 175

176 Cryptocurrency Concepts, Technology, and Applications

Yet Bitcoin has enjoyed the top spot in market capitalization among cryptocurrencies for 13 years. Te market knows something. What it knows, in our view, is that Bitcoin is special. But its intrinsic machinery—what it is in itself— doesn’t fully explain why.* Bitcoin is also special because of its founding, culture, and product-market ft. Nothing else comes close on these points of comparison. Tis gap between Bitcoin and everything else has implications for policy-making, journalism, and academic research. So, in what follows, we’ll explain why Bitcoin is the king of cryptocurrencies— as of today, all 13,457 of them—and what that crown signifes. We begin with a brief description of what Bitcoin is and how it works.

II. Function Te Bitcoin network ofers fnal settlement without authorities.† It ofers fnal settlement in the sense that transactions are efectively irreversible—Bitcoin has no chargebacks, for example. And it ofers this fnality without authorities like banks or payment providers to oversee, settle, and clear transactions. Te network’s transactions occur in the network’s native asset, also known as Bitcoin. Te network has three main players: 1. Users, who send and receive Bitcoin to and from each other. 2. Nodes, computers that run the Bitcoin software and serve as the network’s referees. Tey reject any invalid transactions and curate the Bitcoin ledger. 3. Miners, computers that run the Bitcoin software and compete to produce blocks of valid transactions for the ledger. About every 10 minutes, a miner successfully produces a block and thereby enjoys that block’s transaction fees, as well as a predefned amount of Bitcoin in accordance with Bitcoin’s automated issuance schedule. A successful block requires a trial-and-error search for the solution to a mathematical puzzle. Network incentives promote honest behavior among these participants. Nodes reject transactions that attempt to spend already spent Bitcoin. Tey also reject any blocks that reward miners beyond the permitted amount. Someone could conceivably spend the same Bitcoin twice by writing a new version of the ledger—an alternative chain of blocks—that erases the original spend and then inserts a new one. But this would likely require a cost-prohibitive amount of energy. Nodes on the network endorse the version of the ledger most likely to be the most energy-intensive—a probabilistic calculation stemming from the difculty required to mine each block in the chain. So in order to succeed, the * †

For the diferent pieces that came together in Bitcoin, see Narayanan and Clark (2017). For technical explainers, see Antonopoulos (2017), Rosenbaum (2019), and Warmke (2021).

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attacker would have to re-mine all the intervening blocks from the original spend and then outpace the rest of the network in creating new blocks. With that kind of energy expenditure, the attacker would likely proft much more from forgoing the attack altogether to net the rewards from mining honestly. You might have heard that the cultivation of Bitcoin’s ledger makes it slow and expensive. In one sense, this is true. Bitcoin’s ledger updates, on average, every 10 minutes, and each transaction includes a fee to whichever miner produces the block that includes it. But each block is small—due to Bitcoin’s consensus rules, the maximum block size is somewhere between two and four megabytes. At today’s average transaction size of around 650 bytes, users shouldn’t expect to squeeze much more than around 2,000–3,000 transactions in a typical block.* With blocks every 10 minutes, this averages to about three to fve transactions per second. Tere is, accordingly, a fee market: transaction fees are bids for space in the ledger. Te more data your transaction involves, the more space on the ledger it’ll take and the more you’ll have to pay. When the network buzzes with activity, users bid over one another—no matter how big or small a payment one seeks. Small value payments become uneconomical. So Bitcoin’s blockchain lacks the transaction throughput of a global payments network. Visa® alone handles, on average, about 1,400 transactions per second, at a cost most are willing to pay. However, unlike Bitcoin, Visa doesn’t ofer transaction fnality. Tey can and do reverse transactions. Visa isn’t a fnal settlement layer. Visa transactions settle, instead, through banks and, ultimately, master accounts with the Federal Reserve. So we can think of Visa as a payments layer built atop the Federal Reserve.† In much the same way, Bitcoin has payment layers built atop, and which ultimately settle on, its blockchain. So we should instead compare Visa to one of these, the most important of which is the lightning network. Technical details aside, users on the lightning network enjoy the security of Bitcoin’s ledger to send and receive Bitcoin nearly instantaneously and practically for free.‡ Te current median fee of one satoshi (the smallest unit of Bitcoin) means that transactions cost a fraction of a penny.§ And its theoretical throughput far exceeds Visa’s own. Amazingly, lightning accomplishes this feat without trusted intermediaries. Consequently, apples to apples and oranges to oranges; Bitcoin is to Fedwire as lightning is to Visa. Te main diference in each case is that the Bitcoin side works without trusted intermediaries. * † ‡

§

https://bitcoinvisuals.com/chain-tx-size Benson et. al. (2017) Poon and Dryja (2016) frst described the network. See Antonopoulos et. al. (2021) for a book-length technical guide. https://1ml.com/statistics

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But there’s one more main diference between Bitcoin and the world of traditional fnance. Unlike the U.S. dollar and other fat currencies, Bitcoin has a non-discretionary monetary policy. Whereas the Federal Reserve manipulates the money supply by tinkering with interest rates, Bitcoin has an automated issuance schedule. Bitcoin has a maximum supply of 21 million Bitcoin, which it’ll reach around the year 2140. Issuance consists in the above-mentioned mining block rewards. At network launch in 2009, the reward was 50 BTC every block. Every four years, this reward halves; today it sits at 6.25 BTC. All of this is auditable—anyone may run the Bitcoin software to verify that the rules have been followed—and the result is an asset with capped supply and highly predictable issuance. As a result, no one can trade on insider knowledge about Bitcoin’s monetary policy. And yet in the last year alone, three highly ranked ofcials with the Federal Reserve have resigned due to several, let’s say, well-timed trades.* Tere is one important point of commonality between Bitcoin’s monetary network and the dollar’s. If you wish to send value using dollars (via cash, a Visa transaction, a bank transfer, PayPal®, etc.), you must frst acquire the native token of that network—the dollar. So also with Bitcoin. If you wish to send value using the Bitcoin network, you must frst acquire some Bitcoin. And once you have some, you may send it (via an on-chain transaction, via lightning, or through some other method). One enters the Bitcoin network just as one enters the dollar network—by earning, purchasing, being gifted, fnding, stealing, or otherwise coming into possession of its native token. In sum, Bitcoin is growing into a self-sufcient monetary stack without trusted intermediaries: the software automates monetary policy, the network efects fnal settlement, and second-layer solutions like lightning enable fast and cheap payments.

III. Fit To understand Bitcoin’s appeal, it is helpful to grasp two things: how Bitcoin works and what the world is like. Without the frst, you might think that Bitcoin is an odd technological fad—little more than an append-only document, as some critics allege. Without the second, you might think that Bitcoin is a solution in search of a problem, a Rube Goldberg® machine whose main purpose is to enrich early adopters at the expense of naïve investors. Critics such as Paul Krugman fall into this second camp. In a June 2022 column, 11 years after his frst critical post about Bitcoin, Krugman writes: “Bitcoin—which *

https://www.nytimes.com/2022/01/10/business/economy/richard-clarida-fed-resign.html

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was introduced in 2009 (!)—has yet to fnd any signifcant real-world uses. In my experience, the answers are always word salad devoid of concrete examples.”* Despite Krugman’s credentials—including a Nobel Prize in economics, the very feld which should help him recognize Bitcoin’s utility—he fails to appreciate certain aspects of how the world works. Tis leaves him unable to discern Bitcoin’s appeal. We’ll describe these aspects when we cover Bitcoin’s real-world uses with concrete examples—no word salad.† Venture capitalist Alyse Killeen says Bitcoin is “fntech for poor people.”‡ Tis is largely what Krugman and other critics fail to understand. A 2021 Chainalysis® study found that adoption of Bitcoin and other cryptocurrencies had skyrocketed 881% in the prior year.§ And, remember, Bitcoin and USD stablecoins (synthetic versions of the U.S. dollar, which do not compete with Bitcoin) together account for well over half of the entire cryptocurrency market value, with Bitcoin itself accounting for 45%. So this growth has not been led by serious competitors to Bitcoin. Chainalysis calculates an adoption index using peer-to-peer exchange trade volume, weighted by purchasing power parity per capita and number of internet users. To data-starved critics like Krugman who think Bitcoin is for Silicon Valley “white tech bros” or alt-right libertarians and anarchists,¶ these facts must come as something of a surprise (see Figure 10.1 on next page). Tis is not a list of the world’s strongest economies.** Te Chainalysis team summarizes their fndings: Our research suggests that reasons for this increased adoption difer around the world—in emerging markets, many turn to cryptocurrency to preserve their * †

‡

§ ¶ **

https://www.nytimes.com/2022/06/06/opinion/cryptocurrency-bubble-fraud.html Some alleged uses for “blockchain”—especially those involving exogenous assets and information—are a bit too coleslaw-like for comfort. For incisive critique of such, see Schuster (2021). On a June 2021 Bitcoin Fundamentals Podcast (with host Preston Pysh), Episode 31, Killeen says: “I think Bitcoin is not political. So it shouldn’t be a Republican, Democrat, libertarian sort of thing. It’s not that. Bitcoiners are not a monolith. And my hope is that it doesn’t become a sort of political U.S. versus them thing. Because I see Bitcoin as fntech for poor people. I understand that that’s not how it’s often spoken about on Bitcoin Twitter and social media spaces, but that’s how I see it. And my hope is that the United States doesn’t miss the opportunity here, or my hope is that folks don’t choose to politicize this.” (https://www.theinvestorspodcast.com/bitcoin-fundamentals /investments-in-bitcoin-tech-w-alyse-killeen/). https://blog.chainalysis.com/reports/2021-global-crypto-adoption-index/ https://www.nytimes.com/2022/06/06/opinion/cryptocurrency-bubble-fraud.html https://blog.chainalysis.com/reports/2021-global-crypto-adoption-index/

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savings in the face of currency devaluation, send and receive remittances, and carry out business transactions, while adoption in North America, Western Europe, and Eastern Asia over the last year has been powered largely by institutional investment.

Vietnam Indio Pakistan Ukraine Kenya Nigeria Venezuelo United States Togo Argentina Colombia Thailand Chino Brazil Philippines South Africa Ghana Russian Federation Tanzania Afghanistan

1.00 0.37 0.36 0.29 0.28 0.26 0.25 0.22 0.19 0.19 0.19 0.17 0.16 0.16 0.16 0.14 0.14 0.14 0.13 0.13

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

4 2 11 6 41 15 29 3 47 14 27 7 1 5 10 18 32 8 60

53

2 3 12 5 28 10 22 4 42 17 23 11 1 7 9 16 37 6 45 38

3 72 8 40

1 18 6 109 2 33 12 76 155 113 80

62 10 122 4 7

Figure 10.1 Global Crypto Adoption Index

People use Bitcoin because it solves their problems. Problems like these:

Lack of Banking According to the most recent Global Findex database from the World Bank, aproximately 31% of adults globally lack a traditional bank account. Of these unbanked, 26% blame the cost of banking, 21% blame distance, and 16% distrust traditional banks. Many worldwide lack access to banking for more reasons than that they just don’t have the money.* But everyone can access Bitcoin’s open monetary network essentially for free, without traveling anywhere, as long as they have an internet-connected device. *

https://globalfndex.worldbank.org/chapters/unbanked

Bitcoin Is King 181

High Infation Around one billion people worldwide live with runaway infation.* Many live with hyperinfation. Recently, the value of currencies in places like Venezuela and Lebanon have been worse than decimated, literally. In these places, using Bitcoin as a medium- to long-term savings vehicle makes sense. Despite wild volatility—sometimes dropping as much as 50% within months—Bitcoin’s purchasing power remains in a steep up-trend against these subpar fat currencies. Over longer time-frames, Bitcoin has outperformed every national currency. And, in shorter time-frames, even cherry-picking its worst months of performance, it still outperforms many national currencies. Since Bitcoin is also easier to attain, verify, transfer, and hide than physical gold, we can respect why some use it to help preserve their purchasing power.

Transaction Costs Intermediaries exist in part to detect, prevent, and reverse fraud. Teir bottom lines require that they levy fees on every transaction. In traditional systems, different kinds of transfers call for diferent plumbing through the fnancial system and specialized business models.† One familiar kind of transfer in the United States is consumer spending through credit cards. A credit card transaction ultimately involves several intermediaries—the card’s issuing bank (e.g., Chase), the credit card company (e.g., Visa), and the merchant’s bank (e.g., PNC). Clearance and settlement of the payment usually takes a few days. So multiple companies with large payrolls need to skim of the top. As a result, consumers often pay 1.5–3% in transaction fees.‡ Remittances take another route through the world’s fnancial plumbing. Someone in one country sends funds to someone in another country, and depending on the particular route one takes, this usually involves at least the money transfer operator as well as the intermediaries that send and receive the funds on each end, respectively. Everyone takes a cut, especially if an intermediary exchanges one currency for another. Although remittance costs have slowly come down over the years, they remain high. Te World Bank’s most recent quarterly report on remittance costs still puts the average global costs at about 6%. But many country pairs face double-digit remittance

https://data.worldbank.org/indicator/FP.CPI.TOTL.ZG See Benson et. al. (2017). ‡ https://www.fool.com/the-ascent/research/average-credit-card-processing-fees-costs -america/ *

†

182 Cryptocurrency Concepts, Technology, and Applications

fees. Remittances from Tanzania, for example, remain extremely high when they’re directed to Kenya (31.45%), Rwanda (24.37%), and Uganda (29.68%).* How does Bitcoin help? Transactions over both Bitcoin and the lightning network have their own network topologies and don’t “care’’ whether you’re buying a cofee at your local Starbucks® or sending money to your relative in Ghana. Te plumbing is the same. Since lightning is basically free and instantaneous regardless of the location of sender and recipient, lightning threatens to obsolete intermediaries involved in both consumer payments and remittances. Lightning is not an incremental improvement over these traditional payment systems. It is a 100× improvement in convenience, speed, and cost.

Capital Controls Suppose you want to fee a totalitarian regime. You might be an independent journalist, a whistleblower, an activist, or a persecuted religious minority. How will you preserve your family’s wealth? Bank accounts can be frozen. You can’t take your house with you. Physical cash is bulky and subject to theft. Gold shows up in metal detectors and is easily confscated. Since Bitcoin is massless and possession involves nothing more than access to a secret passphrase, Bitcoin will often be the most efective way to protect your family’s wealth. Bitcoin serves as a lifeline to many people worldwide. In a recent letter to Congress, 21 human rights advocates from 20 countries write: We can personally attest—as do the enclosed reports from top global media outlets—that when currency catastrophes struck Cuba, Afghanistan, and Venezuela, Bitcoin gave our compatriots refuge. When crackdowns on civil liberties befell Nigeria, Belarus, and Hong Kong, Bitcoin helped keep the fght against authoritarianism afoat. After Russia invaded Ukraine, these technologies (which the critics allege are “not built for purpose”) played a role in sustaining democratic resistance—especially in the frst few days, when legacy fnancial systems faltered.†

Te full letter includes references to several such examples with the more detailed reports from the news media—the very kinds of “concrete examples” that Krugman has requested. Overall, then, Bitcoin is not a solution in search of a problem. It solves real problems for people in dire need. Tese problems generate demand for Bitcoin. Since Bitcoin has a capped supply, increased demand for Bitcoin has but one release *

†

https://remittanceprices.worldbank.org/sites/default/fles/rpw_main_report_and _annex_q421.pdf https://www.fnancialinclusion.tech/

Bitcoin Is King 183

valve: price.* Te people who have speculated on Bitcoin proftably have largely seemed to recognize two things: the true breadth of Bitcoin’s total addressable market as a credibly neutral money and the world’s desire for such a thing. As many elites still fail to grasp—largely because they haven’t needed to—Bitcoin has remarkable product-market ft. It is consistent with all this, to be sure, that Bitcoin’s design involves serious tradeofs or negative externalities. Its public ledger makes privacy difcult, though not impossible. It requires energy for its security. And its fxed supply engenders truly spectacular volatility in its market price. A complete evaluation of Bitcoin would weigh all of its costs and benefts, a project beyond the scope of this chapter.†

IV. Founding Bootstrapping Money As with legacy institutions, Bitcoin’s appeal doesn’t lie only in its intrinsic or technical features, or even in its capacity to solve problems. It also lies in its history and founding values.‡ Imagine that you wanted to create a new money. Te goal here would be twofold: to craft a new monetary species and to nurture its network—to grow the class of people who treated it as money. Tese are not easy goals. You might mint a batch of units—magical beans, as it were—and award them all to yourself. Without doing much more, those beans would be about as useful as an invented language known only to you. Somehow those beans need to get into other hands, and circulate from there. So you might instead give them away. Tat would assure some distribution. But distribution isn’t enough. You also need to get people to value your beans as money. And it’s hard to get people to value something that they’ve only ever freely received. Perhaps, then, you could sell them, frst to friends and family, and later to others. But this, too, would have limits. Why should anyone want to treat as money these magical beans you sold or dispensed to your inner circle? And why should anyone trust you not to mint more beans and dilute the value of the ones *

† ‡

For an argument that a volatile but non-zero price for Bitcoin is to be expected given its fundamentals—and the needs they satisfy—see Andolfatto and Spewak (2019). For an attempt at such a synoptic evaluation, see Bailey et. al. (forthcoming). On the cypherpunk movement that gave birth to Bitcoin and informed its founding anti-authoritarian values, see Brunton (2020) and Beltramini (2021).

184 Cryptocurrency Concepts, Technology, and Applications

you just sold? It makes sense to many that a startup business should have and beneft insiders: the reward for taking the risks inherent in starting a productive enterprise is selling shares. But, for very good reason, we don’t treat shares as money. Money is supposed to be more neutral—more like public market infrastructure than shares in a private frm. So it would be fair to ask: who died and made you king of the money? Overall, why should anyone treat as money those beans that you both created and continued to infuence? It’s a real pickle, one long studied by monetary economists and historians. How can we bootstrap a new money?* Te problem is especially pressing for new private monies. States can force citizens to pay taxes in a given monetary species, thus ensuring non-zero demand for that species, no matter its origins or intrinsic technical features. Not so for typical non-state actors; they must fnd another way to persuade others to treat their new units as money. Here is how Satoshi Nakamoto, Bitcoin’s pseudonymous creator, approached the problem: Fair Distribution Bitcoin’s creator didn’t freely mint money for himself, his friends, or other insiders. Tere is exactly one way to mint new Bitcoin: to complete proofs of work— that is, to burn electricity and processor cycles in the discovery of new blocks and to claim the accompanying reward of newly minted Bitcoin. No exceptions. So Satoshi had to pay for his Bitcoin, just like anyone else. He did not make magic beans out of thin air and hawk them at the local market. He bought them from nature, just like anyone else, and the price was energy. Minting requires mining. So the marginal cost of production for Bitcoin is non-zero, and it’s a price anyone must pay if they wish to mine it. Mining has also been open to all since the network launched. So although Bitcoin has early adopters, it has no insiders. Not all cryptocurrencies follow this model. Some, in stark contrast to Bitcoin, involve early rewards or pre-mines for their creators and other insiders. Here, the marginal cost of production for new monetary units is efectively zero, and early insiders acquire their units under diferent rules than others. Under a so-called “pre-mine,” creators do not purchase their coins from nature in a free and open competition. Instead, they mint their units for free and sell some to others. Here is a typical allocation of tokens from a new network called Optimism (see Figure 10.2), one that many prominent voices have heralded as a partial solution to Ethereum®’s scaling issues: *

Luther (2019).

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Bitcoin Is King 185

Figure 10.2 Of Just over Four Billion OP Tokens at Launch, 5–19% Go to Everyday Users (Source: Chart and data available at https://community.optimism.io/docs/governance/ allocations/)

The lesson we draw is partly normative and partly descriptive. The normative point is this: Bitcoin’s founding is fair in one very important respect: the rules of its monetary system apply to all. We do not claim that pre-mines and the like are always morally wrong or dubious. We do not even claim that they are universally undesirable for investors. But a founding history without insiders and with creators who obey the same rules as anyone else is attractive. The descriptive point follows from this: the market has recognized Bitcoin as king for 13 years. Participants know that it is more fair than many alternatives and accordingly favor Bitcoin in their market behavior. Bitcoin’s fair launch has, we suspect, played an important though subtle role in resolving the bootstrapping problem. It has credibility because it came to be in a credibly neutral way.

Leaderless Money Satoshi—like Keyser Söze—walked away. A creator’s ongoing influence or control poses a risk. Think about it: would you accept some magic internet beans as money, if you knew full well that their creator could later alter them, dilute their supply, or push for technical modifications? You might if you trusted the creator or simply had to accept that creator’s edicts (as with sovereign fiat money).

186 Cryptocurrency Concepts, Technology, and Applications

But this is not a viable path for a private money. Gadgets like Bitcoin aim primarily to be neutral money without trusted authorities.* For any would-be monetary engineer, this is a real bind. You want to make something useful whose usefulness doesn’t rely on you. Failure on this front risks creating a cult of personality or a legacy monetary institution of the kind we all know well, one that relies on trusted authorities. Satoshi did the one thing he could to resolve it: he left. Without pomp or ceremony, he removed his name from the Bitcoin website, handed over its keys to the community of developers, and quietly exited the spotlight. No one can say that Satoshi exerts undue infuence over Bitcoin development, or monetary policy, or culture. Satoshi exerts no infuence over those things, not under the Satoshi name, at any rate. In this way, Bitcoin became leaderless. It is not a sovereign currency—and yet despite being private in that sense, it is not a company money. It is private in the sense of being a non-state money and public in the sense of being non-corporate and open to participation by all. Bitcoin’s leaderless status has made it more robust and resilient. Its central bankers can’t fddle with its supply. Its CEO cannot, in a drunken haze, accidentally tweet out something foolish, tanking market confdence. Bitcoin, Inc., cannot go bankrupt or have its assets frozen. Tere is no Bitcoin Federal Reserve, no Bitcoin CEO, no Bitcoin, Inc. Charismatic leaders are sometimes cited as being an advantage for other cryptocurrencies. Just as Elon Musk or Steve Jobs were great for their companies’ marketing, so also a magnetic or gifted founder can drive interest in a cryptocurrency. But here the Bitcoin network stands apart in its promise to host neutral money. Tis promise is credible to the extent that Bitcoin is leaderless. And Satoshi seems to have realized that Bitcoin’s success required his departure.

From Founding to Now Our description of Bitcoin’s founding may sound lofty and idealistic. But does it have much to do with the real world, now? We think so, and we cite two examples. First, Bitcoin has been, for the entirety of its existence, the most valued, most studied, most used, and most widely known cryptocurrency. Tis is no accident. After all, there are plenty of alternatives—over 13,000, recall. And many of those alternatives have been around for over a decade; their existence is no mystery, and market participants can easily access them. We suspect that Bitcoin’s top ranking among cryptocurrencies reveals a preference for a leaderless cryptocurrency with a fair initial mechanism of distribution. *

See the whitepaper, Nakamoto (2008).

Bitcoin Is King 187

Second, Bitcoin has a unique and healthy coin distribution. For the entirety of its existence, Bitcoin’s ownership has become more decentralized. Two metrics, in particular, support this claim.* First, we have supply equality ratio (SER)—the ratio of “supply held by addresses with less than one ten-millionth of the current supply of native units to the supply held by the top one percent of addresses.”† Comparing Bitcoin’s SER to a few competitors is instructive (Figure 10.3):

Supply Equality Ratio (SER) Source;aeo.,aMetriesaNaaDataaProa

009 008 0.07

006a

=

0.05 004 003

002a 001

0 2016

£::: =

2017

-BTC -LINK

-XRP -XTZ

2019

2018

-ETH -LSK

XLM -LTC

-CRO

-DGB

2020

-DASH -OCR

Figure 10.3 Bitcoin Supply Equality Ratio

As explained by CoinMetrics: A high SER signifies high distribution of supply. As hypothesized, Bitcoin has the highest SER out of the assets evaluated, followed by Ether and Litecoin. This is remarkable, since Bitcoin is also the primary cryptoasset being custodied by large financial institutions; a trend that increases SER’s denominator and puts overall downward pressure on the ratio. The sustained increase in Bitcoin’s SER shows that, in spite of large institutions entering the space, Bitcoin is still very much a grassroots movement.‡

A second metric is network distribution factor (NDF), which is the “ratio of supply held by addresses with at least one ten-thousandth of the current supply *

† ‡

We’re following this CoinMetrics report here in pointing to both of these metrics: https://coinmetrics.io/bitcoin-an-unprecedented-experiment-in-fair-distribution/ https://docs.coinmetrics.io/asset-metrics/supply/ser https://coinmetrics.io/bitcoin-an-unprecedented-experiment-in-fair-distribution/

188 Cryptocurrency Concepts, Technology, and Applications

of native units to the current supply.”* Here, again, is how Bitcoin fares against a few competitors (Figure 10.4):

Figure 10.4 Bitcoin Network Distribution Factor

Lower is presumably better. Bitcoin shines again. As CoinMetrics explains: “A low NDF signifes better distribution as there are fewer entities at the top 0.01%. Conversely, a NDF close to 1 signifes a very low cryptoasset distribution.”† More and more people own Bitcoin. A network of one—Satoshi—has blossomed into an ecosystem involving millions. Bitcoin is young, of course, and it remains a niche money. Its distribution is not nearly as wide as the dollar’s, say, or many other fat currencies. But it seems to be trending in one direction— global adoption. Te point here is not just that Bitcoin is the most valuable cryptocurrency or the most widely used. Rather, its distribution trends in a direction that will be attractive from a wide range of views about apt patterns in the distribution of goods. One need not be an unqualifed egalitarian to suppose that wider distribution of a good is itself good, for example. And any view afrming as much will see Bitcoin’s distribution as trending in the right direction. * †

https://docs.coinmetrics.io/asset-metrics/supply/ndf https://coinmetrics.io/bitcoin-an-unprecedented-experiment-in-fair-distribution/

Bitcoin Is King 189

V. Layer Zero Whereas lightning network is layer 2 and the Bitcoin network is layer 1, we can think of “layer 0” as the people who develop and support the entire Bitcoin ecosystem. Tere are several such groups: software developers, node runners, miners, users, companies, and, fnally, the group of hedge funds, traders, and venture capitalists. Tese aren’t mutually exclusive, but the groups often have competing interests. Some of these competing interests relate to Bitcoin’s history as a credibly neutral money. Bitcoin’s software developers have a reputation for moving slowly precisely so that they don’t break things. One major reason for caution: nodes that run diferent versions of the software risk a chain split that creates a new ledger with a new cryptocurrency. So it’s of utmost importance that proposed changes don’t split the network, whether by accident or disagreement. But, as other networks develop newer technology, some Bitcoin users fear that Bitcoin adoption will lag behind, leading to consistently low transaction fees on the main network. Tis is important because, in a decade, the Bitcoin mining subsidy will drop below a single Bitcoin. If fees don’t increase quickly enough, some fear that Bitcoin will become less secure and lose market share.* Te main counter is that, even if fees don’t increase rapidly enough, Bitcoin’s price will, with the result that, though the Bitcoin-denominated mining subsidy decreases, its value when denominated in the U.S. dollar will sufce to make attacks on the network uneconomical. Given Bitcoin’s product-market ft, as described above, we suspect that concerns about Bitcoin’s security budget are slightly overblown. Bitcoin’s consensus rules (about who has which amounts of Bitcoin) have changed around 20 times. And they seem to occur less frequently as Bitcoin ages—only one such change has occurred in the last fve years.† Te software is open source, available for all to poke and prod, and proposed changes undergo rigorous testing.‡ Bitcoin’s software has a stellar history, especially when we compare it to the hacks, exploits, outages, and unfulflled promises of other cryptocurrency protocols.§ Leading up to 2017, a civil war broke out in the Bitcoin community about whether to increase the block size for higher transaction throughput.¶ Te “big blockers”—which included some of the biggest miners and Bitcoin companies— argued that bigger blocks would hasten adoption by leading to lower transaction * † ‡ § ¶

For a classic statement of the worry, see Carlsten et. al. (2016). https://blog.bitmex.com/a-complete-history-of-Bitcoins-consensus-forks-2022-update/ Lopp (2018). For a list of costly exploits, see https://rekt.news/leaderboard/ Bier (2021).

190 Cryptocurrency Concepts, Technology, and Applications

fees. Te “small blockers” argued that more transactions per block would increase the bandwidth and memory requirements for running a node, leading to fewer nodes on the network and more centralization (and, as you’d expect, more revenue for the companies and miners who pushed for bigger blocks). Te small blockers won handily and signaled an overwhelming commitment to network decentralization. Teir victory owed, in part, to the commitment from node runners to reject bigger blocks. Today, around 15,000 Bitcoin full nodes operate the world over (Figure 10.5).

Figure 10.5 Global Map of Bitcoin Node Distribution (Source: https://bitnodes.io/)

Bitcoin nodes more than double the number of nodes currently on the Ethereum network (the second largest cryptocurrency network).* But the requirements for Ethereum nodes are high and increasing, which has led to a substantial proportion of them being run on centralized servers. For example, AWS® alone handles around 25% of Ethereum work loads.† Te more centralized a network is, the more vulnerable it is to attacks on central points of failure. Some criticize Bitcoin for being similarly vulnerable thanks to miners who pool resources to share block rewards. Given Bitcoin’s consensus mechanism, anyone can hijack the network and attempt to double-spend coins with some reliability once they reach 51% of the network’s hashrate. Currently, mining is an industrial process. As a result, miners often pool resources to operate in pools. For most of Bitcoin’s history, a few pools have had enough hashrate to collude in this way (see Figure 10.6). Although miners and pools have a tradition *

†

According to https://ethernodes.org/, there are currently about 6,000 Ethereum nodes online. https://aws.amazon.com/blockchain/

Bitcoin Is King 191

of avoiding such a high hashrate for fear of devaluing Bitcoin and their large expenditures on specialized mining hardware,* they could conceivably face pressure from the state to, say, blacklist certain addresses if enough pools reside within the same borders. But those involved in the vulnerable pools could leave and join other pools. And technical solutions are also in development.

Figure 10.6 Bitcoin’s Hashrate Distribution across Mining Pools, for Mid-May Through Mid-June, 2022.

Industrial miners have also recently kept most of their mined Bitcoin. When price drops rapidly, and the block reward doesn’t cover the cost of producing a block, they might also need to sell the Bitcoin on their balance sheets to bridge the diference, leading to a kind of price death spiral.† But, though this is a risk, we suppose that these industrial miners will have hedged through derivatives, not too dissimilar from how farmers hedge their future yield. So far, Bitcoin has survived severe price drops. And, as we write, Bitcoin has dropped more than 60%. But, in all these market sell-ofs, Bitcoin drops less on a percentage basis than the rest of the cryptocurrency market. In these sell-ofs, participants treat Bitcoin, alongside stablecoins, as a safe-haven asset.‡ * † ‡

See, for example, the case of Bitfury, as detailed in Popper (2015: p. 299) Shinobi (2021). You can see this in any “Bitcoin dominance” chart.

192 Cryptocurrency Concepts, Technology, and Applications

Figure 10.7 A History of Changes to Ethereum’s and Bitcoin’s Monetary Policies

Bitcoin is also special in how users of all stripes stridently commit to its automated issuance schedule. Te schedule has never changed in design, though developers have had to patch bugs to ensure that it behaves as intended. Bitcoin stands in stark contrast to Ethereum in this regard. Te latter has changed its monetary policy routinely throughout its existence. (See Figure 10.7.) Ethereum’s ever-changing monetary policy owes, in large part, to pockets of infuence within its own community—the presence and continuing involvement of founder Vitalik Buterin, as well as the sway of the Ethereum Foundation, which has had a trademark on the ‘Ethereum’ name since the network originally launched.* Centralized sources of infuence have exercised their power in ways small and large throughout its history. Te most famous is the DAO hard fork of 2016, which left the old network (“Ethereum classic”) behind and instituted a new network (“Ethereum”), all to undo an exploit that, though permissible within the stated rules, resulted in unexpected and wide losses among DAO participants.† We have no opinion on whether this was a good decision, but it speaks to the centralization and manipulability of the second largest cryptocurrency network. In contrast, Bitcoin’s more robust commitment to decentralization has led to more trust in the stability of its native asset. In recent years, we’ve begun to see commitments to hold Bitcoin in the treasuries of publicly traded companies (e.g., Tesla®, Microstrategy®, and Square/Block) and eforts to make Bitcoin legal tender in nation-states like El Salvador and the Central African Republic. * †

https://trademarks.justia.com/866/34/ethereum-86634529.html Shin (2022).

Bitcoin Is King 193

VI. Implications We began with the slogan that Bitcoin is digital gold. Te slogan isn’t literally true, and isn’t intended to be. It’s an analogy or comparison. Is the analogy useful? Is it more illuminating than misleading? We’re now in a position to evaluate such questions. To state the obvious points of disanalogy: Bitcoin is synthetic and digital, whereas gold is a naturally occurring physical element. Human beings have used gold as a monetary good for thousands of years; Bitcoin is a little more than a decade old. Bitcoin and gold do not behave the same in markets, either. Gold’s price isn’t that far of today (around $1,800/oz) from where it was 10 years ago (around $1,500/oz), and it has traded between $1,000 and $2,000 for the entire interval. Bitcoin’s price, by contrast, has shown tremendous volatility. It traded below $20 10 years ago, reached a high of over $69,000 in 2021, and trades around $20,000 today (Summer 2022), over 75% down from the peak—a point not missed in mainstream price coverage.* Tis volatility limits Bitcoin’s potential as a short-term medium of exchange and sets its market reception apart from gold’s, which is tame by comparison. Te points of analogy are perhaps more interesting: like gold and other physical commodities, Bitcoin has a non-zero marginal cost of production. No one can mine more gold or more Bitcoin without paying (whether by paying to blast through rock, as with gold, or for electricity and processor cycles, as with Bitcoin).† Bitcoin is fnite in both stock and fow: its total supply is capped, and additions to that supply in the meantime remain slow and steady. Gold is often thought to have similar properties—a fnite total supply, with additions via mining of perhaps 2% per year (though new ore discoveries could change these expectations and shock markets accordingly). Gold and Bitcoin are, furthermore, censorship resistant in an important sense. One can transfer physical gold without relying on mediating authorities: simply hand over a gold coin to your counterparty. (However, the point does not apply to paper claims for gold). So, too, can one transfer Bitcoin without relying on mediating authorities. Simply sign a transaction and broadcast it to the Bitcoin network. Tese points of similarity reveal something important: Bitcoin is, like gold, neutral. Neutral in initial issuance, neutral in ongoing monetary policy, neutral in transfer. And this makes Bitcoin special—perhaps unique—among cryptocurrencies. In sum: the slogan that Bitcoin is or could be digital gold isn’t just a metaphor, and it isn’t mainly about Bitcoin’s market reception. It’s about Bitcoin as * †

https://www.nytimes.com/2022/05/12/technology/cryptocurrencies-crash-bitcoin.html Selgin (2015), accordingly, classifes Bitcoin as a “synthetic commodity” money.

194 Cryptocurrency Concepts, Technology, and Applications

a piece of neutral infrastructure—rather more like a natural physical element than, say, the U.S. dollar. Suppose that’s right. Suppose that Bitcoin is digital gold. What might follow? We’ll ofer a few suggestions under the categories of policy-making, journalism, and academic research.

Policy-Making Bitcoin has, in fact, already won as a globally neutral monetary network. Nurturing the Bitcoin network, using Bitcoin as a reserve asset, or making payments over Bitcoin would be analogous to deploying gold within the monetary system—only digital, more portable, more divisible, easier to audit and verify, and more difcult to confscate. So attempts to ban Bitcoin or limit its use will meet strong resistance.* It is native to the internet and, as a result, extremely difcult to tamp down. In this regard, we liken Bitcoin to cryptography. Whereas cryptography provides censorship-resistant communication, Bitcoin provides censorship-resistant communication of value. And just as the eforts to limit the strength and spread of cryptography failed in the 1990s, we expect that it will be similarly difcult to limit the strength and spread of Bitcoin.† Whether this is good or bad overall is the subject for another time. But there’s widespread evidence that Bitcoin is helping the underbanked, as well as those who sufer under authoritarian rule and runaway infation.‡ Tere’s also wisdom in not wasting resources fghting the inevitable.

Journalism We must not assume that cryptocurrencies share more in common than they, in fact, do. Bitcoin leads them all precisely because no one leads it. Te policy must begin here from a place of understanding—not of cryptocurrency in general, but of Bitcoin in particular. Te general category isn’t going anywhere precisely because Bitcoin, itself, isn’t going anywhere. We owe it special attention. Too often, journalists lump Bitcoin in with all other cryptocurrency projects. And while these other projects beneft from the association, Bitcoin’s reputation suffers from it. More often, journalists should distinguish between Bitcoin and “crypto.” Our slogan: not Bitcoin only, but Bitcoin frst.

* †

On the high cost of banning Bitcoin, see Hendrickson and Luther (2017). For a history of the battle between cryptographers and cypherpunks against the U.S. Government, see Levy (2001). ‡ See https://www.fnancialinclusion.tech/

Bitcoin Is King 195

Research Bitcoin’s victory as neutral money should have downstream consequences for academic research, too. Presently, there is only one research center mostly devoted to Bitcoin—MIT’s Digital Currency Initiative—and it has a near-exclusive focus on computer science. Tere are several other research centers devoted to cryptocurrency overall, with very few researchers working on Bitcoin exclusively. If we were to apportion research and research centers to importance and long-lasting impact, however, we’d fnd the reverse. We need more Bitcoin-frst research and research centers precisely because Bitcoin is, by far, the most likely to have a long-lasting impact on our world. And, since Bitcoin is so highly interdisciplinary, such centers should be full of experts from all the diferent disciplines that touch on it—economics and computer science, of course, as well as law, philosophy, political science, and business. And we should devote more resources to understanding it rather than projects which, like Icarus, fy too close to the sun before faming out.

VII. Conclusion Bitcoin is not the king of money. Tat honor goes to the U.S. dollar. But Bitcoin is the king of cryptocurrencies. Bitcoin is the most valuable, most secure, and most credibly neutral internet-native asset in the world. Arguably, it is the most valuable cryptocurrency precisely because it is the most secure and credibly neutral. Yet all kings pass away. How long, then, will the dollar or Bitcoin reign over their respective domains? And in the meantime, how far will Bitcoin extend its boundaries? Tis question inspires several others, for those who have an imagination. How many more countries will adopt it as legal tender? Will countries use it to evade sanctions someday? Will Bitcoin serve as a major reserve asset, used in international trade? Will countries look to sign treaties to limit each other’s hashrate? How many lives will it save? Will it overtake physical gold’s market capitalization? Will the lightning network make other payment processors and remittance services obsolete? What new kinds of crime might it enable—or disable, for that matter? How many central banks will it undermine? Tese questions strike some as silly. But we take them quite seriously. And we’d like to encourage others to take them seriously, too.* *

We thank Bradley Rettler and Troy Cross for helpful comments and critique. Disclosures: Te authors are both fellows with the Bitcoin Policy Institute, a Bitcoin research think tank. In addition, Craig Warmke writes for Atomic.Finance, a Bitcoin fnance startup company. Te authors regularly use Bitcoin and fat currencies, including USD and SGD.

196 Cryptocurrency Concepts, Technology, and Applications

References Andolfatto, D., and Spewak, A. (2019). Whither the price of Bitcoin? Economic Synapses, 1. Federal Reserve Bank of St. Louis. Antonopoulos, A. M. (2017). Mastering Bitcoin: Programming the Open Blockchain. O’Reilly Media, Inc. Antonopoulos, A. M., Osuntokun, O., and Pickhardt, R. (2021). Mastering the Lightning Network. O’Reilly Media, Inc. Bailey, A. M., Rettler, B., and Warmke, C. (2021a). Philosophy, politics, and economics of cryptocurrency I: Money without state. Philosophy Compass, 16(11). Bailey, A. M., Rettler, B., and Warmke, C. (2021b). Philosophy, politics, and economics of cryptocurrency II: Te moral landscape of monetary design. Philosophy Compass, 16(11). Bailey, A. M., Rettler, B. and Warmke, C. (forthcoming). Resistance Money: A Qualifed Philosophical Defense of Bitcoin. Routledge. Beltramini, E. (2021). Against technocratic authoritarianism. A short intellectual history of the cypherpunk movement. Internet Histories, 5: 101–118. Benson, C. C., Jones, R., and Loftesness, S. (2017). Payments Systems in the US: A Guide for the Payments Professional (3rd Edition). Glenbrook Press. Bier, J. (2021). Te Blocksize War: Te battle over who controls Bitcoin’s protocol rules. https://blog.bitmex.com/the-blocksize-war-chapter-1-frst-strike/ Brunton, F. (2020). Digital Cash: Te Unknown History of the Anarchists, Utopians, and Technologists Who Created Cryptocurrency. Princeton University Press. Carlsten, M., Kalodner, H., Weinberg, S. M., and Narayanan, A. (2016, October). On the instability of Bitcoin without the block reward. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 154–167. Hendrickson, J., and Luther, W. J. (2017). Banning Bitcoin. Journal of Economic Behavior & Organization,. 141: 188–195. Levy, S. (2001). Crypto: How the Code Rebels Beat the Government—Saving Privacy in the Digital Age. Penguin. Lopp, J. (2018). Who controls Bitcoin Core? https://blog.lopp.net/who-controls -bitcoin-core-/ Luther, W. J. (2019). Getting of the ground: Te case of Bitcoin. Journal of Insti­ tutional Economics, 15(2): 189–205. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. https://bitcoin .org/Bitcoin.pdf. Narayanan, A., and Clark, J. (2017). Bitcoin’s academic pedigree. Communications of the ACM, 60(12): 36–45. Poon, J., and Dryja, T. (2016). Te Bitcoin lightning network: Scalable of-chain instant payments. Lightning Network Paper.

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Popper, N. (2015). Digital Gold: Te Untold Story of Bitcoin. Penguin UK. Selgin, G. (2015). Synthetic commodity money. Journal of Financial Stability, 17: 92–99. Rosenbaum, K. (2019). Grokking Bitcoin. Manning Publications. Schuster, E. (2021). Crypto cloud land. Modern Law Review, 84(5): 974–1004. Shin, L. (2022). Cryptopians: Idealism, Greed, Lies, and the Making of the First Big Cryptocurrency Craze. Public Afairs. Shinobi (2021). How centralized is Bitcoin mining really? Bitcoin Magazine. https://bitcoinmagazine.com/business/is-bitcoin-mining-centralized Warmke, C. (2021). What is Bitcoin? Inquiry, 1–43. Warmke, C. (2022). Electronic coins. Cryptoeconomic Systems, 2(1). https://crypto economicsystems.pubpub.org/pub/warmke-electronic-coins

Chapter 11 Cryptocurrency Options Strategy, Analysis, and Valuation Christopher Droussiotis Seton Hall University

Equity and Cryptocurrency Option Markets: Overview Options play a signifcant role in the fnancial markets. Tese types of securities’ values are derived from other securities such as stocks or bonds and, in recent years, cryptocurrencies. Options and futures are derivative securities, meaning their payof is dependent on other securities’ price movements. Option contracts are traded in many exchange markets such as the Chicago Board Option Exchange (CBOE), Chicago Mercantile Exchange (CME), and over-the-counter markets. Tese option contracts include two types of options: call options and put options. Each type can be used in conjunction with the other for-certain strategies. Te strategies described in this chapter can be used for buying or selling uncovered or covered option contracts. Uncovered or naked options are speculative strategies and entail buying or selling options without owning the underlying investment, stock, or cryptocurrency. Covered strategies involve buying or selling option securities in conjunction with owning the underlying stock or cryptocurrency. 199

200

Cryptocurrency Concepts, Technology, and Applications

Tere are two types of option contract structures: American and European. Te American option contract is structured such that the buyer of the option can exercise his or her right to buy or sell the stock or cryptocurrency anytime from the day the contract is signed until expiration. Te European option contract is structured such that the investor can only exercise the option on the expiration day. Te investor can get out of both options anytime by selling his or her contract at the market premium until expiration. Due to their unique structure, American and European type options are traded at diferent premium prices. Te valuation methods discussed later in the chapter take into consideration each of these structures. When it comes to comparing the equity options market to the cryptocurrency options market, the diferences are very distinct. For starters, cryptocurrency options trade 24 hours a day, seven days a week. Another diference is that the contract size for cryptocurrencies can vary not only by the exchange, but also by the coin. Te major cryptocurrency markets include Binance®, Deribit, OKEx®, BitMex®, and the traditional exchange of CME®. Most of these markets are not regulated in the United States and have varying levels of licensing and regulation from governments around the world. Te largest exchange in the world, Deribit, ofers contracts representing 1 BTC, with a minimum contract size of 0.1. In addition, most of these cryptocurrency option contracts are structured as European types. Deribit uses cash settlement, and contracts expire at 4 am et Saturday. Deribit only accepts deposits and withdrawals in cryptocurrency. Te CME Group ofers Bitcoin derivative trading with rules similar to their other derivative markets; however, CME uses Bitcoin futures as the underlying asset, rather than Bitcoin itself. Regarding the more than 10,000 cryptocurrencies that are in circulation, exchanges usually only ofer derivative markets on the major ones, including Bitcoin and Ethereum. Since this is very new to the markets, the option trading volume can be very low at times. All cryptocurrency contracts use the blockchain technology, but decentralized fnance (DeFi) allows individuals to enter cryptocurrency option contracts that settle by code rather than an exchange. DeFi allows countless possibilities for automation, such as algorithmic market making, but sufers from vulnerability to scams and hacks because of its reliance on code.

Uncovered (Naked) Option Trading Strategy Contracts Uncovered, or naked, options are buying or selling options primarily on speculation, where the investor does not hold any ownership in the underlying asset—in this case stock or cryptocurrency—from which the option premium is derived. Te following three strategies are buying and selling call options, put options, and straddles.

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201

Buying Call Options A traditional call option is a contract that gives the holder the right, not the obligation, to buy the cryptocurrency a set price, called the exercise price (X), at and before the set date, referred to as the expiration date, no matter what happens to the price of the cryptocurrency. Of course, if the holder has the right to buy the cryptocurrency at a set price, he or she is hoping that the price of the cryptocurrency will go up signifcantly. Te holder can then proft from the diference, representing a bullish view on the cryptocurrency. Demonstrated in Figure 11.1, the June call option on Bitcoin with an exercise price of $50,000 entitles its owner to buy one Bitcoin at a price of $50,000 by the third Friday of June (contract expiration date). If the Bitcoin goes to $60,000 before the third week of June, the option holder will exercise the right to buy Bitcoin at $50,000 and then turn around and sell it to the market at $60,000, receiving gross proceeds of $10,000. If the Bitcoin goes below $50,000, the option holder will let the contract expire without exercising. To purchase the option, the investor needs to pay upfront a set price per share called a premium. In this example, the buyer of the June option will need to pay $4,300. Figure 11.1 shows the payof, proft, holding period return (HPR), and break-even (BE) of Bitcoin during various scenarios and prices (using a range of $20,000–$100,000). Figure 11.1 (on next page) shows that if the price of Bitcoin is below $50,000, the call option is not exercisable since the spot price (S) minus the exercise price (X) will be negative, indicating an out­of­the­money option. At $50,000, where the spot price (S) is equal to the exercise price (X), the call option is at the money and not exercisable since there is zero payof (S – X = 0). If the price of Bitcoin is higher than $50,000, the call option is exercisable and is therefore an in­the­money option. It’s important to note that being in the money does not necessarily mean that the option is proftable. It means that there will be a payof if exercised. Using the example in Figure 11.1, it shows that if the price of Bitcoin is at $60,000 on the last day, then the investor needs to exercise the option. In that case, the payof per share is $10,000 (S – X = payof or $60,000 – $50,000 = $10,000), and the investor will always exercise the option when the Bitcoin price is higher than the exercise price, even if the transaction is not proftable. In this case, the investor will show proft because he or she receives $10,000, which more than covers the $4,300 premium, calculating a proft of $5,700. If the investor had locked in the June $30,000 level, for example, and the price of Bitcoin is now at $40,000, the investor will still exercise, even if the $10,000 payof in this case is lower than the $14,000 premium shown in Figure 11.1. Since the investor paid a $14,000 premium for the option, if exercised at any price in the money, the investor can recover some of the initial loss (premium paid). Instead of losing the entire premium of $14,000, by exercising the call option at $40,000, the

1.   UNCOVERED (NAKED) OPTION STRATEGIES ‐ Buying a Call Option CALLS

BTC Exercise Price (X)

APRIL

MAY

BUY Bitcoin June 50,000 CALLS  $35,000.00

JUNE

20,000

            27,000

            25,000

            22,000

30,000

            18,000

            17,000

            14,000

40,000

              8,500

              9,100

              9,000

50,000

              2,000

              3,300

              4,300

60,000

                 290

                 924

              1,530

70,000

                    50

                 209

                 530

100,000

                    10

                    25

                 175

 $20,000.00

ACTION Buy June Buy June Buy June Buy June Buy June Buy June Buy June 

Figure 11.1

Profit

 $15,000.00  $10,000.00  $5,000.00  $‐  $(5,000.00)  $(10,000.00)

 $20,000.00

 $30,000.00

 $40,000.00

 $50,000.00

 $60,000.00

 $70,000.00

 $80,000.00

‐15

‐5

5

15

25

35

45

Break Even = $              54,300.00 Max Loss = $               (4,300.00) Max Gain = Unlimited

Out‐of‐the‐money Option On‐the‐money Option In‐of‐the‐money Option

WHAT IF SCENARIO

INPUT p

Premium Exercise Price  Per Bitcoin (X) (p) $         50,000 $          (4,300) $         50,000 $          (4,300) $         50,000 $          (4,300) $         50,000 $          (4,300) $         50,000 $          (4,300) $         50,000 $          (4,300) $         50,000 $          (4,300)

Payoff

 $25,000.00

ACTION Buy Call @ Exercise (X) = $    50,000.00  Pay Premium  (p)  = $      4,300.00

X

BE $54,300

 $30,000.00

OUTPUT

S

O = max (0,S‐X)

(π) =  O ‐ p

HPR % =  π / p

Bitcoin Price  (S)

Exercise Y/N?

Payoff (O)

Profit (π)

HPR (%)

$                    20,000 $                    30,000 $                    40,000 $                    50,000 $                    60,000 $                    70,000 $                    80,000

No No No No Yes Yes Yes

$                          ‐ $                          ‐ $                          ‐ $                          ‐ $                    10,000 $                    20,000 $                    30,000

$                    (4,300) $                    (4,300) $                    (4,300) $                    (4,300) $                      5,700 $                    15,700 $                    25,700

‐100.0% ‐100.0% ‐100.0% ‐100.0% 132.6% 365.1% 597.7%

Uncovered Option Strategies—Buying Call Options

X + p Break Even Price $                    54,300 $                    54,300 $                    54,300 $                    54,300 $                    54,300 $                    54,300 $                    54,300

Cryptocurrency Options Strategy, Analysis, and Valuation

203

investor recovers the $10,000 back, netting a total loss of $4,000. Te rule is that the investor always exercises the option if the option is in the money or S – X > 0. Te following formulas are used for calculating the output when buying a call option: Call option payof = Maximum (0, cryptocurrency price – exercise price), max (0, S – X) Call option proft = Payof – premium Holding period return % = Proft/premium Break­even cryptocurrency price for call option = Exercise price + premium Maximum loss on buying a call option = Premium Maximum gain on buying a call option = Unlimited

Buying Put Options A put option is a contract that gives the holder the right, not the obligation, to sell cryptocurrency at exercise price (X), at and before the expiration date, no matter what happens to the price of the cryptocurrency. Of course, if the investor has the right to sell the set number of cryptocurrencies at a set price, he or she is then hoping that the price of the cryptocurrency goes down signifcantly and can proft from the diference, representing a bearish view on the cryptocurrency. For example, the June put option on Bitcoin with an exercise price of $60,000 entitles its owner to buy Bitcoin at a price of $60,000 by the third Friday of June (contract expiration date). If the Bitcoin goes down to $40,000 before the third week of June, the option holder will buy it at $40,000 from the market and exercise the right to sell it at $60,000, profting from the $20,000 diference. If the Bitcoin goes above $60,000, the option holder will let the contract expire without exercising. To purchase the option, the investor needs to pay premium upfront. In this example, the buyer of the June Bitcoin option will need pay $14,110 (please note that the investor can purchase a fraction of the Bitcoin). Figure 11.2 shows the payof, proft, HPR and BE of Bitcoin stock during various scenarios and prices (using a range of $20,000–$100,000). Figure 11.2 shows that if the range of the price of Bitcoin is above $60,000, the put option is not exercisable, since the exercise price (X) minus the Bitcoin spot price (S) is negative, representing the out-of-money option. At $60,000, where Bitcoin (S) is equal to the exercise price (X), the call option is at the money and not exercisable since there is zero payof (X – S = 0). If Bitcoin is lower than $60,000, the put option is exercisable, or an in-the-money option. It’s important to note that being in the money does not necessarily mean that the option is proftable. It means that there is a payof. We see in the example in Figure 11.2 that if Bitcoin is at $40,000 on the last day, then the investor needs to exercise the option with

2.   UNCOVERED (NAKED) OPTION STRATEGIES ‐ Buying a Put Option              ‐  PUTS

BTC APRIL

20,000

9

250

225

30,000

90

400

900

40,000

750

1277

2500

50,000

4200

5102

6100

60,000

13500

14000

14110

70,000

22900

23000

29754

100,000

53000

53500

58722

ACTION

MAY

JUNE

   $50,000.00

BE $46,500

   $40,000.00    $30,000.00    $20,000.00

Payoff    $10,000.00    $‐

Profit

   $(10,000.00)

$20,000.00$30,000.00$40,000.00$50,000.00$60,000.00$70,000.00$80,000.00$90,000.00 ‐40

‐30

‐10

0

10

20

30

40

   $(20,000.00)

Buy Put @ Exercise          = $      60,000.00  (X)          Pay Premium  (p)  =           $      14,110.00       

Out‐of‐the‐money Option   On‐the‐money Option   In‐of‐the‐money Option  

Break Even =     $     45,890.00       Max Loss =     $    (14,110.00)      Max Gain =      X ‐ Premium at S=0       WHAT IF   SCENARIO

INPUT

Figure 11.2

BUY APRIL  60,000 PUTS       

  Exercise Price (X)

OUTPUT

S

O =   max (0,X‐S)  

(π) =      O ‐ p

HPR % =        π / p  

X

p

ACTION

Exercise   Price   (X)

Premium Per Bitcoin   (p)

Bitcoin Price    (S)  

Exercise Y/N?

Payoff (O)  

Profit (π)

HPR (%)  

Break Even Price

Buy April 60,000 Put       Buy April 60,000 Put       Buy April 60,000 Put       Buy April 60,000 Put       Buy April 60,000 Put       Buy April 60,000 Put       Buy April 60,000 Put       Buy April 60,000 Put      

            60,000             60,000             60,000             60,000             60,000             60,000             60,000             60,000

                (14,110)                 (14,110)                 (14,110)                 (14,110)                 (14,110)                 (14,110)                 (14,110)                 (14,110)

              20,000               30,000               40,000               50,000               60,000               70,000               80,000               90,000

Yes Yes Yes Yes No No No No

           40,000            30,000            20,000            10,000                  ‐                  ‐                  ‐                  ‐

                               25,890                                15,890                   5,890                 (4,110)               (14,110)               (14,110)               (14,110)               (14,110)

183.5% 112.6% 41.7% ‐29.1% ‐100.0% ‐100.0% ‐100.0% ‐100.0%

              45,890               45,890               45,890               45,890               45,890               45,890               45,890               45,890

Uncovered Option Strategies—Buying Put Options

X + p    

Cryptocurrency Options Strategy, Analysis, and Valuation

205

the payof per Bitcoin being $20,000 (X – S = payof or $60,000 – $40,000 = $20,000). Te proft is calculated to be $5,890 per Bitcoin (Payof – Premium or $20,000 – 14,110 = $5,8900). If Bitcoin price goes down to $50,000, the buyer of the option needs to exercise, since the $50,000 Bitcoin price is lower than the $60,000 exercise price. Please note that the investor will still exercise this option, even though the transaction is not proftable, receiving only a $10,000 payof against a $14,110 premium, showing a loss of $4,110. By exercising the option, the investor at least recovers some of the premium paid, calculating the net loss to be $4,110 per share instead of the entire premium of $14,100 if the buyer of the put option had not exercised. Te rule is that the investor always exercises the option if the option is in the money or the exercise price is higher than the Bitcoin spot price or X – S > 0. Te following formulas are used for calculating the output when buying a put option: Put option payof = Maximum (0, exercise price – cryptocurrency spot price), max (0, X ­ S) Put option proft = Payof – premium Holding period return % = Proft/premium Break­even cryptocurrency spot price for put option = Exercise price – premium Maximum loss on buying a put option = Premium Maximum gain on buying a put option = Exercise price – premium or the price of cryptocurrency = 0

Selling Call and Put Options Selling call and put options are contracts in which the seller has the obligation to sell or buy cryptocurrencies when the buyer exercises his or her option at the exercise price. Te seller of an option receives the premium and is hoping that the price of cryptocurrency remains out of the money until expiration, so he or she can keep the premium. Selling uncovered or naked call options is the most dangerous play, since the loss could be unlimited if, for example, Bitcoin signifcantly rises above the exercise price. Selling uncovered or naked puts is also very dangerous—for example, if Bitcoin goes down to zero. It’s usually advised to sell call or put options in conjunction with either holding Bitcoin (covered later in this chapter) or in combination with a buy of a call option (also covered later in this chapter). Te following formulas are used for calculating the output when selling call and put options:

206

Cryptocurrency Concepts, Technology, and Applications

Call option payof = cryptocurrency price – exercise price Break­even price of cryptocurrency price for call option = Exercise price + premium Maximum loss on selling a call option = Unlimited Maximum gain on selling a call option = Premium Put option payof = Exercise price – Cryptocurrency price Break­even price of cryptocurrency price for put option = Exercise price – premium Maximum loss on selling a put option = Exercise price – premium at price of crypto­ currency = 0 Maximum gain on selling a put option = Premium

Buying and Selling Straddles A straddle contract involves a buy of both calls and puts at the same exercise price. Tis contract gives the holder the right, not the obligation, to buy or sell cryptocurrencies at a set exercise price (X) at and before the expiration date, no matter what happens to the price of the cryptocurrency. Te strategy for buying both call and put options represents the view of buying against the price of the cryptocurrency’s volatility. Tis is a strategy in which the investor expects that, for example, Bitcoin will move signifcantly (scenario based) up or down based on a coming announcement. As an example, let’s say that the government will decide if they will use or not use Bitcoin as a major currency. If it’s approved, the Bitcoin price is expected to increase signifcantly, and the straddle holder will be exercising the option (supported by the call option side of the straddle) to buy it at the exercise price (X). If the government announces that it does not approve using Bitcoin as one of the acceptable currencies, then it is expected to signifcantly decline. Te seller of a straddle is hoping that the news will not have as much as of an efect on the price of cryptocurrency and that it will stay within the break-even points. Of course, the best-case scenario for the seller of straddles and the worst-case scenario for the buyer of straddles is when the cryptocurrency expires at the exercise price (S = X). For example, let’s assume that the May call and put options for Bitcoin with an exercise price of $50,000 entitles its owner to buy or sell it at price of $50,000 at any time up until the third week of May (contract expiration date). If Bitcoin goes above $50,000 by the expiration day, the straddle option holder will exercise the right to buy it at $50,000, respectively, and get paid the diference. If Bitcoin goes below $50,000 by the expiration day, the straddle option holder will exercise the right to sell it at $50,000, respectively, and get paid the diference. To purchase the straddle option, the investor needs to pay both call and put premiums upfront. Figure 11.3 shows the payof, proft, HPR, and two break

Current Price Bitcoin (BTC) So =           =  $47,000.00

3.   UNCOVERED (NAKED) OPTION STRATEGIES ‐ Buying Straddles        ‐   

                9           250

                 225

30,000

          18,000        17,000       14,000

             90           400

                 900

40,000

            8,500

         9,100         9,000

           750

      1,277               2,500

50,000

            2,000

         3,300         4,300

        4,200

      5,102               6,100

60,000

                290

            924         1,530

      13,500

    14,000

           14,110

   $(5,000.00)

70,000

 0                   5

            209

           530

      22,900

    23,000

           29,754

   $(10,000.00)

100,000

  0                25                   1

           175

      53,000

    53,500

           58,722

   $(15,000.00)

ACTION   Buy June   Buy June   Buy June   Buy June   Buy June   Buy June   Buy June   Buy June   Buy June   Buy June   Buy June

X

p

Exercise   Price   (X)        50,000 $               50,000 $               50,000 $               50,000 $               50,000 $               50,000 $               50,000 $               50,000 $               50,000 $               50,000 $               50,000 $       

Premium   Per Bitcoin (p)        $      (10,400)        $      (10,400)        $      (10,400)        $      (10,400)        $      (10,400)        $      (10,400)        $      (10,400)        $      (10,400)        $      (10,400)        $      (10,400)        $      (10,400)

Payoff

   $15,000.00

Profit

   $10,000.00    $5,000.00

‐20

0

10

20

30

40

On‐the‐money Option     In‐of‐the‐money Option

OUTPUT   O =     (S‐X) or (X‐S)

S   Bitcoin Price    (S)

Exercise Y/N?

  Payoff (O)

                                 25,000                                   30,000                                   35,000                                   39,600                                   45,000                                   50,000                                   55,000                                   60,400                                   65,000                                   70,000                                   75,000 

Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes

                       25,000                        20,000                        15,000                        10,400               5,000                   ‐               5,000                        10,400                        15,000                        20,000                        25,000

Uncovered Option Strategies—Buying Straddles

‐10

   $75,000.00

‐30

   $70,000.00

‐40

   $65,000.00

   $‐

Two Break Evens = $             $   60,400.00           39,600.00     $  (10,400.00)    Max Loss =     Unlimited Max Gain =   WHAT IF SCENARIO

BE $60,400  

   $20,000.00

          27,000        25,000       22,000

INPUT

BE $39,600  

   $25,000.00

   $55,000.00

JUNE

   $50,000.00

MAY

20,000

ACTION Strategy: Buying a Call and Put at the same X                   Buy Call & Put @           (X)  = $  50,000.00                 $  10,400.00  Pay both Total Prem. (p)  =    (4300+6100)

Figure 11.3

APRIL

   $45,000.00

JUNE

   $35,000.00

MAY

BUY BITCOIN JUNE 50,000 STRADDLE            $30,000.00

   $30,000.00

APRIL

PUTS

   $25,000.00

CALLS

BTC Exercise Price   (X)

(April 1)  

    (π) =  O ‐ p

      HPR % =    π / p

    X + p

Break Profit   HPR (%) Even (π)   Price 1               14,600 140.4%          60,400                                  9,600 92.3%          60,400                                  4,600 44.2%          60,400                      ‐ 0.0%          60,400                (5,400) ‐51.9%          60,400              (10,400) ‐100.0%          60,400                (5,400) ‐51.9%          60,400                      ‐ 0.0%          60,400                                  4,600 44.2%          60,400                                  9,600 92.3%          60,400               14,600 140.4%          60,400

X  ‐p Break Even   Price 2                    39,600                    39,600                    39,600                      39,600  Breakeven                    39,600                    39,600                    39,600                      39,600  Breakeven                    39,600                    39,600                    39,600

208

Cryptocurrency Concepts, Technology, and Applications

evens (BE1 and BE2) of Bitcoin during various scenarios of its price performance (Range: $20,000–$100,000). Figure 11.3 shows that if the range of Bitcoin is above or below $50,000, the straddle option is in the money and exercisable, with the payof being S – X or X – S. At $50,000, where Bitcoin (S) is equal to the exercise price (X), the call option is at the money and not exercisable since there will be zero payof (X – S = 0). Looking at Figure 11.3, let’s assume that Bitcoin signifcantly increases to $70,000 by the expiration date. Te straddle holder will exercise the right to buy it at $50,000, calculating a payof of $20,000 (S – X = $70,000 – $50,000). With the $20,000 payof, the proft will be $9,600 (payof – total premiums for both call option (premium of $4,300) and put option (premium of $6,100) = $20,000 – $10,400). In general, the straddle holder will always exercise, except in the unlikely event that Bitcoin is equal to the exercise price—basically, no volatility. Te following formulas are used for calculating the output when buying a straddle option: Straddle option payof = Exercise price – cryptocurrency price – exercise price Straddle option proft = Payof – both call and put premiums Holding period return % = Proft/both call and put premium Both break­even cryptocurrency price for straddle option = Exercise price – premium and exercise + premium Maximum loss on buying a straddle option = Premium (assuming S – X = 0) Maximum gain on buying straddle option = Unlimited

Covered Option Trading Strategy Contracts Covered option strategies are buying or selling options primarily for hedging purposes. Te covered option strategy is typically one in which the investor buys or sells options in conjunction with buying or selling the underlying cryptocurrency from which the option’s premium prices are derived. Another reason to use the covered strategy is to enhance the overall exposure by maximizing the gain on both the underlying cryptocurrency and the option securities. Te following three strategies are protective puts, covered calls, and a combination of the two called collars.

Protective Put Strategy A protective put is a strategy in which the investor buys or owns the underlying cryptocurrency and simultaneously buys put options to hedge his or her holdings in case the price of the cryptocurrency drops. Tis is like buying insurance on

Cryptocurrency Options Strategy, Analysis, and Valuation

209

the cryptocurrency. If the cryptocurrency drops in price, the price is protected by exercising the option to sell it at X price. For example, let’s assume the investor buys Bitcoin at the current price of $47,000 (April 1), investing $47,000 for one Bitcoin. Te investor is concerned that Bitcoin will drop in the next few months, so he or she buys the $40,000 May put option, paying a premium of $1,277 per one Bitcoin. Te total cost of the investment including the option is $48,277 ($47,000 + $1,277). Figure 11.4 shows the protective put strategy on various Bitcoin price scenarios, assuming the investor sells Bitcoin at the market price. Te output shows the proft and loss as well as the holding period return on the strategy. Tis fgure shows that the maximum loss on selling Bitcoin is capped at the $40,000 price. If Bitcoin was not hedged by setting a protective put, or if Bitcoin drops to $10,000, the loss will be $37,000 ($47,000 – $10,000). By having the protective put option, the maximum loss is capped at $40,000, as the investor exercises the right to sell Bitcoin at $40,000 no matter what. If Bitcoin increases to $60,000, the investor could sell Bitcoin and record the gain, as demonstrated in Figure 11.4 (on next page), showing $10,500 of proft net of premium. At $60,000, the investor will let the put expire.

Covered Call Strategy A covered call strategy is a one in which the investor buys or owns the underlying cryptocurrency and simultaneously sells a call option of the cryptocurrency. Tis is a popular, low-risk strategy commonly referred to as writing covered calls. What’s interesting is that selling uncovered calls is the riskiest option strategy, with possible unlimited losses, but selling calls while you own the cryptocurrency is thought of as the most conservative strategy. An example of who would be using such a strategy is an investor who is planning to sell the cryptocurrency soon at a higher-than-market level, so instead of setting up a limit order that instructs his or her broker to sell the cryptocurrency when that higher price is met, he or she could sell the equivalent one cryptocurrency in call options, receiving the premium at a set future price. If the cryptocurrency hits that higher price, instead of paying the diference between the exercise price and the market price as a seller of calls, he or she can deliver the cryptocurrency at that price or sell the cryptocurrency in the market and cover the call option obligation from the proft made from selling the cryptocurrency. For example, let’s assume the investor holds one Bitcoin. At the current price of $47,000 (April 1) the investment is worth $47,000 ($47,000 × 1 coin). Te investor is expected to sell Bitcoin when the price gets to $60,000. Instead of putting in a limit order (basically calling the broker with instructions to sell when the price is

1.  COVERED OPTION STRATEGIES ‐ Protective Puts

Current Price Bitcoin (BTC) =  $47,000.00 (April 1) Strategy:   Buying or holding BTC and Buying Put Option at X

PUTS

BTC Exercise Price (X)

APRIL

MAY

JUNE

ACTION

Exercise (X)

30,000

                90  

                400

            900

Buy Bitcoin = 

40,000

               750

             1,277

        2,500

Buy Put at X = 

50,000

           4,200

             5,102

        6,100

Pay Prem. (p)  =         

60,000

         13,500

          14,000

      14,110

70,000

         22,900

          23,000

      29,754

So

STRATEGY Buy BTC & June Puts Buy BTC & June Puts Buy BTC & June Puts Buy BTC & June Puts Buy BTC & June Puts Buy BTC & June Puts Buy BTC & June Puts Buy BTC & June Puts Buy BTC & June Puts

Figure 11.4

Number of BTC

Investment (I)

$47,000.00

1

 $                             (47,000)

$    2,500.00

1

 $                               (2,500)

$   40,000.00

Total Initial Investment = 

INPUT BTC INVEST

BTC (S)/ Premium (p)

$  (49,500)

OUTPUT OPTION SECURITY X

BTC  Purchase

Exercise  Price  (X)

         47,000          47,000          47,000          47,000          47,000          47,000          47,000          47,000          47,000

         40,000          40,000          40,000          40,000          40,000          40,000          40,000          40,000          40,000

p Paid Premium Per BTC (p)            (2,500)            (2,500)            (2,500)            (2,500)            (2,500)            (2,500)            (2,500)            (2,500)            (2,500)

WHAT IF SCENARIO

BTC  INVEST

S

CG = S ‐So

Market Price BTC Price  (S)                 20,000                  30,000                  40,000                  50,000                  60,000                  70,000                  80,000                  90,000               100,000 

Covered Option Strategy—Protective Puts

BTC Capital Gain/ (Loss)           (27,000)           (17,000)             (7,000)              3,000            13,000            23,000            33,000            43,000            53,000

OPTION SECURITY

BOTH BTC AND OPTIONS

O

π ‐ p

(π ‐ p + CG)

(π ‐ p + CG) x Sh

I

 NP / I

Put Option  Payoff (Hedging)

Profit from  the Option per BTC

Net  Profit per BTC

Net Profit  (Total $) (NP)

Initial  Investment

HPR%

         20,000          10,000                ‐                ‐                ‐                ‐                ‐                ‐                ‐

           17,500               7,500             (2,500)             (2,500)             (2,500)             (2,500)             (2,500)             (2,500)             (2,500)

          (9,500)           (9,500)           (9,500)                500          10,500          20,500          30,500          40,500          50,500

                   (9,500)                    (9,500)                    (9,500)                         500                   10,500                   20,500                   30,500                   40,500                   50,500

           49,500            49,500            49,500            49,500            49,500            49,500            49,500            49,500            49,500

‐19.19% ‐19.19% ‐19.19% 1.01% 21.21% 41.41% 61.62% 81.82% 102.02%

Cryptocurrency Options Strategy, Analysis, and Valuation

211

at $60,000), he or she enters a cover call contract at the $60,000 level and is therefore obliged to pay the diference between the market value above $60,000 and the exercise price of $60,000 (S – X). In return, he or she will receive a premium of $1,530 up front, as shown in Figure 11.5 (on next page). If Bitcoin goes above $60,000, he or she will either sell it at the market price or deliver it to the buyer of the call option. If Bitcoin goes to $60,000 exactly (on the money), the option will not be exercised. Te investor holding Bitcoin could sell it at $60,000 and record the gain in addition to the premium he or she received. Te strategy is basically giving up a sudden upside of Bitcoin, but the investor has no need to complain if the price of Bitcoin jumps higher than $60,000. Te alternative is that he or she would have to sell it at the $60,000 mark, being a limit order. Figure 11.5 shows that the proft is capped at $14,530 at any price of Bitcoin price above $60,000.

Collars A collar option strategy is one in which holders of cryptocurrency want to hedge their downside by giving up the upside. In previous sections that discussed protective puts, the investor obtained a protective put to hedge his or her investment in case the cryptocurrency declines, but it costs money to buy put options. Te collar option strategy is customized to minimize or eliminate the premiums paid to buy puts by selling calls at a much higher level of cryptocurrency. Basically, collars are a combination of protective puts and covered calls. Figure 11.6 shows an example in which the investor holding one Bitcoin with a current price of $47,000 wants to set up a foor to cap his or her losses at $30,000 by buying the May puts. Te June puts cost $900 per Bitcoin. Te investor scans the pricing page and fnds that the June $60,000 calls is $1,530. Given that the premiums match, he or she enters a collar option contract and buys the June $30,000 puts and sells the $60,000 calls, netting the premiums and receiving $630 ($1,530 – $900). Figure 11.6 shows the HPR of Bitcoin at various scenarios of the cryptocurrency (using a range of $10,000–$100,000).

Advanced Option Trading Strategies Advanced option strategies involve simultaneously buying and selling multiple options. Te strategies that entail buying and selling at diferent exercise prices but keeping the same expiration dates are called vertical spreads or money spreads, and the buying and selling of options across diferent expiration dates with the same exercise price are called horizontal spreads or time spreads. Spread options

2.  COVERED  OPTION STRATEGIES ‐ Covered Calls       ‐   

Current Price Bitcoin (BTC) So =           =  $47,000.00 (April 1)   Strategy:   Holding BTC and Selling Call Option at X                  

CALLS

BTC   Exercise Price (X)

APRIL

MAY

JUNE

Exercise (X)

ACTION

20,000

          27,000        25,000

     22,000

          Own the Bitcoin (So) = 

30,000

          18,000        17,000

     14,000

          Sell Call at (X) = 

40,000

             8,500

         9,100         9,000

50,000

             2,000

         3,300         4,300

60,000

                290

            924         1,530

70,000

                   50

            209

           530

100,000

                   10

              25

           175

So

Investment (I)

          47,000                   1  .00                                      (47,000)            (1,530)                   1  .00                                          1,530  

                 Recieve Prem. (p)  =         

        Total Initial Investment = 

         $        (45,470)

OUTPUT OPTION SECURITY   X

STRATEGY

Market   Price

Exercise   Price   (X)

Buy Stock & Sell May Calls           Buy Stock & Sell May Calls           Buy Stock & Sell May Calls           Buy Stock & Sell May Calls           Buy Stock & Sell May Calls          

       47,000                        47,000                47,000                47,000                47,000

          60,000                                 60,000                      60,000                      60,000                      60,000

Figure 11.5

Number of BTC

          60,000

INPUT BTC INVEST

  BTC (S)/   Premium (p)

p Received Premium Per BTC   (p)          1,530          1,530          1,530          1,530          1,530

WHAT IF   SCENARIO

BTC INVEST

S

    CG = S ‐So

O

BTC Capital Gain/ (Loss)            (7,000)             3,000           13,000           23,000           33,000

Call Option   Payoff (Hedging)                   ‐                   ‐                   ‐                    (10,000)                    (20,000)

Market Price   BTC Price    (S)                  40,000                                   50,000                                   60,000                                   70,000                                   80,000                   

Covered Option Strategies—Covered Call

OPTION SECURITY  

BOTH BTC AND OPTIONS      

π ‐ p

    (π ‐ p + CG)

(π ‐ p + CG) x Sh        

I

 NP / I      

Profit from      the Option   per BTC  

Net   Profit per BTC  

Net Profit    (Total $)     (NP)

Initial   Investment

HPR%

            1,530                                       1,530             1,530                         (8,470)          (18,470)

           (5,470)             4,530                        14,530           14,530           14,530

              (5,470)                                4,530              14,530                            14,530                            14,530              

          45,470                                 45,470           45,470                                 45,470           45,470           

‐12.03% 9.96% 31.96% 31.96% 31.96%

VERTICAL AND HORIZONTAL SPREADS CALLS

BTC

PUTS

Exercise Price (X)

APRIL

MAY

JUNE

                        20,000

      27,000

       25,000

      22,000

                        30,000

      18,000

       17,000

      14,000

                 90           400

          900

                        40,000

        8,500

         9,100

        9,000

               750

      1,277

       2,500

                        50,000

        2,000

         3,300

        4,300

           4,200

      5,102

       6,100

MAY

                   9           250

JUNE           225

                        60,000

           290              924

        1,530

         13,500

    14,000

     14,110

                        70,000

              50              209

           530

         22,900

    23,000

     29,754

                     100,000

              10

           175

         53,000

    53,500

     58,722

               25

VERTICAL  SPREADS (MONEY SPREADS)

Figure 11.6

APRIL

Covered Option Strategies—Collars

VERTICAL  SPREADS (MONEY SPREADS)

 HORIZONTAL SPREAD   (TIME SPREADS)

214

Cryptocurrency Concepts, Technology, and Applications

typically involve two or more options where the investor buys one option and pays a premium and sells another and receives a premium. Te objective of spread strategies is to minimize the net premium paid. Figure 11.7 shows the diferences between vertical spreads or money spreads and horizontal spreads or time spreads. Choosing vertical spreads as an option strategy, the investor buys and sells call or put options at diferent exercise prices with the same date; in this case April is the chosen expiration day, and the exercise prices are listed vertically. Choosing horizontal spreads as an option strategy, the investor buys and sells options choosing the same exercise price at diferent dates; in this case, the exercise price is $50,000, and the chosen dates are listed horizontally. An investor can be bullish or bearish on Bitcoin, using a combination of spreads to take advantage of his or her view. Te spread strategies include vertical bull and bear spreads for both calls and puts and are described in detail next (please note that the chapter will not cover horizontal or time spreads, which are not as popular as the vertical or money spreads).

Bull Call Vertical Spreads Bull call spreads are vertical spreads that involve buying a call option with a low exercise price and selling the call option with a high exercise price with the same expiration date. To remember this strategy, think of an investor who is bullish on the cryptocurrency. He or she will be buying cryptocurrency at its low price, anticipating selling it at a higher price in the future, as is the case for bull call spreads. Te investor buys the low exercise call price and sells the high exercise price in the hopes that the cryptocurrency price will go up. Spread strategies such as bull call spreads are conservative strategies with maximum proft and maximum loss because an investor is giving up the signifcant upside to be protected on the downside. Figure 11.8 (on page 216) shows a bull call spread strategy in which the investor buys the June $30,000 calls, paying a $14,000 premium, and sells the $60,000 June calls and receives the $4,300 premium, calculating a net premium paid of $9,700 ($14,000 – $4,300). Te investor in this case is bullish, expecting Bitcoin to go up higher than $60,000 to maximize the $21,300 per one Bitcoin. Obviously, if it drops below $30,000, the maximum loss will be capped at $9,700. Te break-even price of Bitcoin is calculated at $39,700 ($30,000 + $9,700). Figure 11.8 shows the payof and proft at various Bitcoin prices.

Bear Put Vertical Spreads Bear put spreads are vertical spreads that involve buying a put option with a high exercise price and selling the put option with a low exercise price with the same

3. COVERED OPTION STRATEGIES‐ Collars       ‐ 

Current Price Bitcoin (BTC) So =           =  $47,000.00 (April 1)   CALLS

BTC

PUTS

  Exercise Price (X)

APRIL

MAY

JUNE

20,000

        27,000

            25,000

30,000

        18,000

            17,000

40,000

           8,500

50,000 60,000

APRIL

MAY

JUNE

     22,000

                   9 

               250

              225

     14,000

                 90

               400

              900

              9,100

        9,000

              750

            1,277

           2,500

           2,000

              3,300

        4,300

           4,200

            5,102

           6,100

              290

                  924

        1,530

         13,500

          14,000

         14,110

70,000

                50

                  209

           530

         22,900

          23,000

         29,754

100,000

                10

                    25

           175

         53,000

          53,500

         58,722

                                            Collars Strategy:  Own BTC, Buy Put, Sell Calls (combination of Protective Puts and Covered Calls) ‐ the intention is to minimize or eliminate the premium               Action:    Own 1 Bitcoin (current price at $47,000)             Buy the June 30,000 Puts ‐pay $900 premium               Sell (Write) the June 60,000 Calls ‐ receive $1530 premium INPUT

OUTPUT

BTC   INVEST So

STRATEGY

Collars 30,000 Puts/60,000 Calls June         Collars 30,000 Puts/60,000 Calls June         Collars 30,000 Puts/60,000 Calls June         Collars 30,000 Puts/60,000 Calls June         Collars 30,000 Puts/60,000 Calls June         Collars 30,000 Puts/60,000 Calls June         Collars 30,000 Puts/60,000 Calls June         Collars 30,000 Puts/60,000 Calls June         Collars 30,000 Puts/60,000 Calls June         Collars 30,000 Puts/60,000 Calls June        

OPTION SECURITY     X puts

Market Price   M2M

Exercise Put      Price   (X1)

          47,000           47,000           47,000           47,000           47,000           47,000           47,000           47,000           47,000           47,000

                 30,000                  30,000                  30,000                  30,000                  30,000                  30,000                  30,000                  30,000                  30,000                  30,000

  X calls

p

Net   Exercise Call      Premium   Price   Recieved (X2) Per BTC   (p)             60,000            630             60,000            630             60,000            630             60,000            630             60,000            630             60,000            630             60,000            630             60,000            630             60,000            630             60,000            630

Figure 11.7 Ver tical and Horizontal Spreads

  WHAT IF SCENARIO

BTC INVEST

S

    CG = S ‐So

O1

O2

Market   Price BTC Price     (S)

BTC Capital Gain/ (Loss)

Put Option   Payoff

Call Option   Payoff

         10,000          20,000          30,000          40,000          50,000          60,000          70,000          80,000          90,000        100,000

         (37,000)          (27,000)          (17,000)            (7,000)             3,000           13,000           23,000           33,000           43,000           53,000

         20,000          10,000                                ‐                                ‐                                ‐                                ‐                                ‐                                ‐                                ‐                                ‐

                 ‐                  ‐                  ‐                  ‐                  ‐                  ‐          (10,000)          (20,000)          (30,000)          (40,000)

OPTION SECURITY  

BOTH BTC AND OPTIONS       π ‐ p

    (π ‐ p + CG)

(π ‐ p + CG) x Sh        

I

  NP / I    

Profit from      the Option   per BTC  

Net   Profit per BTC  

Net Profit     (Total $)   (NP) 1 BTC  

Based on      original April      1 Date  

HPR%

          20,630           10,630                                630                                630                                630                                630            (9,370)          (19,370)          (29,370)          (39,370)

            (16,370)             (16,370)             (16,370)                              (6,370)                 3,630                            13,630                            13,630                            13,630                            13,630                            13,630

              (16,370)               (16,370)               (16,370)                 (6,370)                   3,630                13,630                13,630                13,630                13,630                13,630

          47,000           47,000           47,000           47,000           47,000           47,000           47,000           47,000           47,000           47,000

‐34.83% ‐34.83% ‐34.83% ‐13.55% 7.72% 29.00% 29.00% 29.00% 29.00% 29.00%

1. ADVANCED OPTION STRATEGIES‐ Bull Call Spreads       ‐     

Current Bitcoin (BTC) So =         =  $47,000.00 (April 1)  

CALLS

BTC Exercise Price   (X)

APRIL

MAY

PUTS JUNE

APRIL

MAY

Bull Call Spreads BTC June 30,000/60,000              JUNE

20,000

          27,000

           25,000

          22,000

                   9               250            225

   $15,000.00

          18,000

           17,000

          14,000

                90

   $10,000.00

40,000

            8,500

             9,100

            9,000               750

          1,277

       2,500

   $‐

50,000

            2,000

             3,300

            4,300

          4,200

          5,102

       6,100

               290

                924

            1,530

        13,500

        14,000

     14,110

   $(5,000.00)

70,000

                  50

                209

               530

        22,900

        23,000

     29,754

   $(10,000.00)

     58,722

   $(15,000.00)

                  10                    2  5

               175

        53,000

        53,500

Profit

   $5,000.00

60,000 100,000

Payoff

   $20,000.00

30,000

              400            900

BE  

   $25,000.00

$ $ $ $ $ $ $ $ 10000.00 20000.00 30000.00 39700.00 50000.00 60000.00 70000.00 80000.00 ‐$ 10.00   ‐$ 8.00  

‐$ 6.00  

‐$ 4.00  

‐$ 2.00  

$ 0.00  

$ 2.00  

$ 4.00  

Bull Call Strategy: Buy the Low Exercise Call Price and sell the high Call Exercise Price at the same expiration date (Vertical Spread)                                           Action Example:      Buy the June 30,000 Call  ‐pay $14000 premium             Sell the June 50,000 Calls ‐ receive $4300 premium             INPUT Low X  

p1

OUTPUT High X  

p2

STRATEGY

Buy Exercise   Call   (X1)

Premium   Paid Per BTC   (p1)

Sell Exercise   Call   (X2)

Premium   Received Per BTC   (p2)

Buy Low and Sell High Call           Buy Low and Sell High Call           Buy Low and Sell High Call           Buy Low and Sell High Call           Buy Low and Sell High Call           Buy Low and Sell High Call           Buy Low and Sell High Call           Buy Low and Sell High Call          

          30,000           30,000           30,000           30,000           30,000           30,000           30,000           30,000

          (14,000)           (14,000)           (14,000)          (14,000)           (14,000)           (14,000)           (14,000)           (14,000)

          50,000           50,000           50,000           50,000           50,000           50,000           50,000           50,000

          4,300           4,300           4,300           4,300           4,300           4,300           4,300           4,300

Figure 11.8

p Net   Premium   Paid Per BTC            (9,700)          (9,700)          (9,700)          (9,700)          (9,700)          (9,700)          (9,700)          (9,700)

Advanced Option Strategies—Bull Call Spreads

S

S ‐ X1 + X2    

Market   Price BTC Price    (S)  

Net Payoff  

      10,000       20,000       30,000       39,700       50,000       60,000       70,000       80,000

               ‐                ‐                ‐           9,700                             20,000                  20,000                  20,000                  20,000

Net   Profit

Maximum   Profit

                     (9,700)                      (9,700)                      (9,700)                 ‐                    10,300                    10,300                  10,300                    10,300                  10,300                    10,300                  10,300

Maximum Loss            (9,700)                                    (9,700)                        (9,700)            (9,700)              Breakeven  

Cryptocurrency Options Strategy, Analysis, and Valuation

217

expiration date. To remember this strategy, think of an investor who is bearish on cryptocurrency; he or she will be shorting or selling the cryptocurrency at its high price today and buying the cryptocurrency back at a lower price in the future, as is the case for bear put spreads. Te investor buys the high exercise put price and sells the low exercise price in the hopes that the cryptocurrency price will drop. Spread strategies such as bear put spreads are conservative strategies with maximum proft and minimum loss because the investor is giving up the signifcant upside of the cryptocurrency’s going down to be protected on the downside if it goes down. Figure 11.9 (on next page) shows a put bear spread strategy in which the investor buys the 60,000 June puts, paying a $14,110 premium, and sells the 40,000 June puts, receiving a $2,500 premium, calculating a net premium paid of $11,610. Te investor in this case is bearish, expecting the price of Bitcoin to drop lower than $40,000 to maximize the $9,390 per Bitcoin ($20,000 – $11,610). Obviously, if the price of Bitcoin increases above $60,000, the maximum loss will be capped at $11,610. Te break-even price of Bitcoin is calculated at $48,390 ($60,000 – $11,610). Figure 1.9 shows the payof, proft maximum proft, and maximum loss at various Bitcoin prices.

Bull Put Vertical Spreads Bull put spreads are vertical spreads that involve buying a put option with a low exercise price and selling a put option with a high exercise price with the same expiration date. Like the bull call spreads previously discussed, this strategy is bullish, even though the investor is buying put options. Bull put spreads sound like mixed thinking, as typically an investor who buys puts has a more pessimistic view on the cryptocurrency while the spread is called bull, which represents an optimistic strategy. Te strategy here, though, is still bullish. Te investor is hoping that the cryptocurrency stays high so the options are not exercised, and the investor can keep the net premium received. In all spread strategies where there is contradiction between puts and bulls or calls with bears, the net proceeds are received by the investor. Figure 11.10 shows a bull put spread strategy in which the investor buys the $30,000 April puts, pays a $90 premium, sells the $50,000 April calls, and receives a $4,200 premium, calculating a net premium received of $4,110. Te investor in this case is bullish, expecting the price of Bitcoin to go up or stay higher than $50,000 to keep the $4,110 per Bitcoin proceeds he or she received. Obviously, if the price of Bitcoin drops below $30,000, the maximum loss will be capped at $15,890 ($30,000 – $50,000 + $4,110). Te break-even price of Bitcoin is calculated at $45,990 ($50,000 – $4,110). Figure 11.10 shows the payof and proft at various Bitcoin prices.

2. ADVANCED OPTION STRATEGIES‐ Put Bear Spreads       ‐     

Current Price Bitcoin (BTC) So =           =  $47,000.00 (April 1)  

CALLS

BTC

PUTS

Bear Put Spread BTC June 40,000/60,000          

Exercise Price   (X)

APRIL

20,000

          27,000

           25,000

         22,000

                  9

                250

           225

   $15,000.00

30,000

          18,000

           17,000

         14,000

               90

                400

           900

   $10,000.00

40,000

            8,500

             9,100

           9,000

             750

             1,277

       2,500

   $5,000.00

50,000

            2,000

             3,300

           4,300

          4,200

             5,102

       6,100

60,000

               290

                924

           1,530

       13,500

           14,000

     14,110

   $(5,000.00)

70,000

                  50

                209

               530

       22,900

           23,000

     29,754

   $(10,000.00)

100,000

                  10

                   25

               175

       53,000

           53,500

     58,722

   $(15,000.00)

MAY

JUNE

APRIL

MAY

JUNE

   $25,000.00

BE $48,390  

   $20,000.00

   $‐

Payoff $ 10000.00$ 20000.00$ 30000.00$ 40000.00         ‐8

‐6

‐4

‐2

$ $ 60000.00$ 70000.00$ 80000.00       500000.00 0

2

4

6

Profit

Bear Put Strategy: Buy the High Exercise Put Price and sell the Low Put Exercise Price at the same expiration date (Vertical Spread)                                           Action Example:      Buy the June 60,000 Puts ‐pay $14,110 premium             Sell the June 40,000 Puts ‐ receive $2500 premium             INPUT

OUTPUT

High X  

p1

Low X  

p2

STRATEGY

Buy Exercise   Put   (X1)

Premium   Paid Per BTC   (p1)

Sell Exercise   Call   (X2)

Premium   Received Per BTC   (p2)

Buy High and Sell Low Put           Buy High and Sell Low Put           Buy High and Sell Low Put           Buy High and Sell Low Put           Buy High and Sell Low Put           Buy High and Sell Low Put           Buy High and Sell Low Put           Buy High and Sell Low Put          

          60,000           60,000           60,000           60,000           60,000           60,000           60,000           60,000

         (14,110)          (14,110)          (14,110)          (14,110)          (14,110)          (14,110)          (14,110)          (14,110)

         40,000          40,000          40,000          40,000          40,000          40,000          40,000          40,000

          2,500           2,500           2,500           2,500           2,500           2,500           2,500           2,500

Figure 11.9

p Net   Premium   Paid Per BTC            (11,610)          (11,610)          (11,610)          (11,610)          (11,610)          (11,610)          (11,610)          (11,610)

Advanced Option Strategies—Put Bear Spreads

S

X1 ‐ X2 ‐S

Market   Price BTC Price    (S)  

Net Payoff  

      10,000       20,000       30,000       40,000       48,390       60,000       70,000       80,000

          20,000           20,000           20,000           20,000           11,610                                  ‐                                  ‐                                  ‐

Net   Profit

Maximum Loss  

                   8,390                    8,390                    8,390                    8,390                        ‐                              (11,610)               (11,610)                                             (11,610)               (11,610)                                             (11,610)               (11,610)               

Maximum Profit               8,390                                             8,390                              8,390                              8,390

   Breakeven

3. ADVANCED OPTION STRATEGIES‐ Bull Put Spreads

Current Price Bitcoin (BTC) So =  $47,000.00 (April 1)

CALLS

BTC

PUTS

Bull Put Spread BTC April 30,000/40,000

Exercise Price (X)

APRIL

MAY

JUNE

APRIL

MAY

JUNE

20,000

27000

25000

22000

9

250

225

30,000

18000

17000

14000

90

400

900

40,000

8500

9100

9000

750

1277

2500

50,000

2000

3300

4300

4200

5102

6100

60,000

290

924

1530

13500

14000

14110

70,000

50

209

530

22900

23000

29754

 $(20,000.00)

100,000

10

25

175

53000

53500

58722

 $(25,000.00)

Profit

BE $45,890

 $10,000.00  $5,000.00  $‐  $(5,000.00)  $(10,000.00)

Payoff $ $ $ $ $ $ $ $ 25000.00 30000.00 35000.00 40000.00 45890.00 50000.00 55000.00 60000.00 ‐10

‐8

‐6

‐4

‐2

0

2

4

 $(15,000.00)

Bull Put Strategy: Buy the Low Exercise Put Price and sell the high Exercise Put Price at the same expiration date (Vertical Spread) Action Example:  Buy the April 30,000 Puts  ‐pay $90 premium Sell the April 60,000 Puts ‐ receive $4200 premium INPUT Low X

p1

OUTPUT High X

p2

STRATEGY

Buy Exercise Puts  (X1)

Premium  Sell Exercise Paid Puts  Per BTC (X2) (p1)

Premium  Received Per BTC (p2)

Buy Low and Sell High Put Buy Low and Sell High Put Buy Low and Sell High Put Buy Low and Sell High Put Buy Low and Sell High Put Buy Low and Sell High Put Buy Low and Sell High Put Buy Low and Sell High Put

          30,000           30,000           30,000           30,000          30,000           30,000           30,000           30,000

            (90)             (90)             (90)             (90)             (90)             (90)             (90)             (90)

          4,200           4,200           4,200           4,200          4,200           4,200           4,200           4,200

Figure 11.10

         50,000          50,000          50,000          50,000          50,000          50,000          50,000          50,000

p Net  Premium  Received Per BTC           4,110           4,110           4,110           4,110           4,110           4,110           4,110           4,110

Advanced Option Strategies—Bull Put Spreads

S

S ‐ X1 + X2

Market  Price BTC Price  (S)

Net Payoff

        25,000         30,000         35,000         40,000         45,890         50,000         55,000         60,000

              (20,000)               (20,000)               (15,000)               (10,000)                 (4,110)                      ‐                      ‐                      ‐

Net  Profit

Maximum  Profit

              (15,890)               (15,890)               (10,890)                 (5,890)                      ‐                  4,110           4,110                  4,110           4,110                  4,110           4,110

Maximum Loss          (15,890)          (15,890)          (15,890)          (15,890)

 Breakeven

220 Cryptocurrency Concepts, Technology, and Applications

Bear Call Vertical Spreads Bear call spreads also sound like mixed thinking, as typically an investor who buys calls has a more optimistic view on the cryptocurrency while the spread is called bear, which represents a pessimistic strategy. Te strategy here is net bearish. Te investor is hoping that the cryptocurrency price stays low and the option is not exercised so he or she can keep the net premium received. In all spread strategies that have that contradiction between calls and bears or puts with bulls, as discussed earlier, the net proceeds are received by the investor. Figure 11.11 shows a put bear spread strategy in which the investor buys the $70,000 May calls, pays a $209 premium, sells the $50,000 May calls, and receives a $3,300 premium, calculating a net premium received of $3,091 ($3,300 – $209). Te investor in this case is bearish, expecting the price of Bitcoin to drop lower than $50,000 to keep the premium of $3,091 per Bitcoin. Obviously, if the price of Bitcoin increases above $70,000, the maximum loss will be capped at $16,909 ($50,000 – $70,000 + $3,091). Te break-even price of Bitcoin is calculated at $53,091 ($50,000 + $3,091). Figure 11.11 shows the payof, proft, maximum proft, and maximum loss at various Bitcoin prices.

Option Valuation Methods So far, the chapter covered various option strategies that the investor can use to beneft based on his or her view on the direction of the cryptocurrency spot price (S). Using the right option strategy, based on the view of how the cryptocurrency will perform in the future, the investor used two input variables—the exercise price (X) and premium (p)—to determine the potential payof (O), proft (π), and holding period return (HPR%) at various cryptocurrencies spot prices (S) by expiration day (t) or cryptocurrency spot price at expiration (St). For the call option buyer, the payof is either S – X or 0 if the X > S, and for the put option buyer, the payof is either X – S or 0 if the S > X. To obtain such an option, the investor needs to pay a premium (p), representing the upfront money or the bet per cryptocurrency and hoping the cryptocurrency spot price by expiration day (St) covers the original bet. Tis section of the chapter will focus on various valuation methods to calculate the fair premium that the investor should pay to receive such a payof. Te thinking and calculations should be very similar to betting on a game of chance. Using probability concepts, for example, someone who bets on a coin toss with two outcomes has a 50% chance of winning and a 50% chance of losing. In this

4. ADVANCED OPTION STRATEGIES‐ Bear Call Spreads       ‐     

Current Price Bitcoin (BTC) So =           =  $47,000.00 (April 1)  

CALLS

BTC

PUTS

Bear Call Spreads BTC April 50,000/70,000          

  Exercise Price (X)

APRIL

MAY

JUNE

APRIL

MAY

JUNE

20,000

          27,000

     25,000

          22,000

                   9

              250

           225

30,000

          18,000

     17,000

          14,000

                90

              400

           900

   $(5,000.00)

40,000

            8,500

        9,100

            9,000

              750            1,277

       2,500

   $(10,000.00)

50,000

            2,000

        3,300

            4,300            4,200            5,102

       6,100

60,000

               290

           924

            1,530

        13,500

        14,000

     14,110

70,000

                  5  0

           209

               530

        22,900

        23,000

     29,754

100,000

                  1  0

             25

               175

        53,000

        53,500

     58,722

   $5,000.00    $‐

Profit

BE $53,091  

Pa$yoff $ $ $ $ $ $ $ 10000.00 20000.00 30000.00 40000.00 53091.00 60000.00 70000.00 80000.00 ‐10

‐8

‐6

‐4

‐2

0

2

4

   $(15,000.00)    $(20,000.00)    $(25,000.00)

                                          Bear Call Strategy: Buy the high Exercise Call Price and sell the low Exercise Call Price at the same expiration date (Vertical Spread)     Buy the May 70,000 Calls  ‐pay $209 premium             Action Example:              Sell the May 50,000 Calls ‐ receive $3300 premium INPUT   High X

p1

OUTPUT   Low X

STRATEGY

  Buy Exercise Call (X1)

Premium     Sell Exercise Paid Call     Per BTC (X2) (p1)

BuyHigh and Sell Low Call         BuyHigh and Sell Low Call         BuyHigh and Sell Low Call         BuyHigh and Sell Low Call         BuyHigh and Sell Low Call         BuyHigh and Sell Low Call         BuyHigh and Sell Low Call         BuyHigh and Sell Low Call        

          70,000           70,000           70,000           70,000           70,000           70,000           70,000           70,000

          (209)           (209)           (209)           (209)           (209)           (209)           (209)           (209)

Figure 11.11

          50,000           50,000           50,000           50,000           50,000           50,000           50,000           50,000

p2 Premium   Received   Per BTC (p2)            3,300            3,300            3,300            3,300           3,300            3,300            3,300            3,300

p Net   Premium   Received   Per BTC            3,091            3,091            3,091            3,091           3,091            3,091            3,091            3,091

Advanced Option Strategies—Bear Call Spreads

S

    S ‐ X1 + X2

Market   Price   Stock BTC   (S)

  Net Payoff

       10,000        20,000        30,000        40,000        53,091        60,000        70,000        80,000

                  ‐                   ‐                   ‐                   ‐             (3,091)           (10,000)           (20,000)           (20,000)

Net   Profit

Maximum   Loss

               3,091                                3,091                                3,091                                3,091                                     ‐               (6,909)           (16,909)             (16,909)           (16,909)             (16,909)           (16,909)

Maximum Profit               3,091                              3,091                              3,091                              3,091               

   Breakeven

222

Cryptocurrency Concepts, Technology, and Applications

simple example, let’s assume that the payof is $20 if the coin turns up to be heads (H) and $0 if the coin turns out to be tails (T). On a toss of a die, the winner will be paid $60 if it turns out to be a 6. Te likelihood (probability) that on one single throw the die will turn out to be 6 is one in six (1/6 ), and the probability of not getting a 6 is fve out of six (5/6 ). To evaluate what a fair bet would be based on these games of chance, the formula starts with the following decision tree, shown in Figures 11.12a and 11.12b. Probability (p) Head (H) =

1/2

?

Bet (B) =

Payoff (O) Win (W) =

$20

B = 1/2 W + 1/2 L Tail (T) =

1/2

Loss (L) =

$0

B = (p) W + (1‐p) L Figure 11.12a

Calculating the Fair Bet Based on Probability of Winning on Coin Toss

Probability (p)  6 = Bet (B) =

1/6

?

Payoff (O) Win (W) =

$60

B = 1/6 W + 5/6 L 1, 2, 3, 4, 5 

5/6

Loss (L) =

$0

Figure 11.12b Calculating the Fair Bet Based on Probability of Getting a “6” on Die Toss

Te fgures show that the winnings or payofs as $20 and $60 for the coin toss and dice throw, respectively. Using the probability formulas derived from the decision tree, the bets are calculated as follows: Coin toss bet a Die toss bet a

1 1 a$20a a a$0a a $10  2 2

1 5 a$60a a a$0a a $10 6 6

Cryptocurrency Options Strategy, Analysis, and Valuation

223

Te wins or losses of these games of chance represent the option payof, and the outcome from the toss of a coin and throw of a die represent the cryptocurrency spot price. Tis probability formula concept can be used to determine the fair bet that the player needs to pay up front to enter this game; this fair bet represents the premium that the investor needs to pay upfront to have the right to exercise such an option when there is a positive payof outcome. Te challenge is to derive such probabilities when the investor is betting on the direction of the cryptocurrency in the future. Such direction is measured by historical cryptocurrency spot price movements, expressed in statistics as the variance (σ2) of the cryptocurrency spot price or standard deviation (σ). A few such valuation models that use cryptocurrencies spot price variances and standard deviations are the binomial option pricing model and Black-Scholes.*

Binomial Option Pricing Model: SinglePeriod Probability Method Te binomial option pricing model (BOPM) continuously measures the two possible outcomes of the cryptocurrency, up or down, from any point in time until expiration. As the spot price of cryptocurrency goes up, the probability it will continue to go up can change versus the probability of going down from that new level. Te up (u) and down (d) values are derived from historical experiences and are used in the BOPM to derive such probabilities. Te probability of the cryptocurrency spot price’s (S) going up or down is ultimately measured to be the fair bet or the premium an investor is willing to pay to meet his or her expectation. Figure 11.13 uses an example on how to calculate the premium using BOPM. Figure 11.13 shows there is a 50% probability that the price of Bitcoin will go up from its current level of $47,000 to $56,400 and a 50% probability that it will go down to $42,300 within the measurable period, in this case one year. Te probability calculation is as follows: pA

AA1 A iA A dA AA1 A 0.05A A 0.90A 1.05 A 0.90 0.15 A A A A 0.5 and i A p A 0.5  uAd 1.20 A 0.90 1.20 A 0.90 0.30

If the price of Bitcoin goes up to $56,400, the call option payof will be $6,400 (C = max (0, S – X) = max (0, $56,400 – $50,000). If the price of Bitcoin goes down to $42,300, the call option will be out of the money, and the payof will be $0 based on C = max (0, S – X) = max (0, $42,300 – $50,000). Given the *

“Te Black-Scholes model, also known as the Black-Scholes-Merton (BSM) model, . . . estimates the theoretical value of derivatives based on other investment instruments, taking into account the impact of time and other risk factors.” https://www.investopedia .com/terms/b/blackscholes.asp

ONE‐PERIOD BINOMIAL OPTION PRICING MODEL ‐ Calculating the Call Option Premium                 INPUT Current Bitcoin (S)=     $    47,000 Up (u) =     1.20x Down (d) =     0.90x Exercise Price (X) =       $    50,000 Risk Free Rate (i) =         5.00% Time in Years (t) =         1 Periods =   1

OUTPUT PERIOD 0  

FORMULAS PERIOD 1   Su=       56,400

 S =    

      47,000.0

Cu=  6,400.00 C =    3,047.62

Sd =         42,300

Cd=            ‐            

p =                0.50               1‐p=              0.50               C=       3,047.62 European Option Premium    

Figure 11.13

One-Period Binomial Option Pricing Model—Calculating Call Option Premium

Su = S . u        Sd = S . d        Cu = Max (0, Su ‐ X)         Cd = Max (0, Sd ‐ X)         p = [(1+i) ‐ d )] / (u ‐ d)           C= [ (p . Cu) + [(1‐p) Cd)] ] / [(1+i)^t                 

Cryptocurrency Options Strategy, Analysis, and Valuation

225

binomial outcome of 50%, in this case the payof is $6,400 and the 50% payof is $0; using the probability theory explained below, the fair bet or premium should be at $3,200, representing 50% × $6,400 + 50% × $0. If we calculate the present value of the $3,200 back to today’s value, since it is what are we paying today for the future outcome, the value is calculated to be $3,047.62 based on the present-value calculation of

AA A

AA

AAAAAA

or

$A,AAA

AAAA.AAAA

A $3,047,62 

Figure 11.14 calculates the put option premium using the same probabilities. Looking at the put option scenario using the same variables as the call option described above, the payofs and premiums are calculated diferently. If the price of Bitcoin goes up to $56,400, the out-of-the-money put option payof will be at $0 (P = max (0, X – S) = max (0, $50,000 – $56,400). If the p goes down to $42,300, the put option will be in the money, and the payof will be $7,700 based on P = max (0, X – S) = max (0, $50,000 – $42,300). Given the binomial outcome of 50%, in this case the payof is $7,700 and the 50% payof is $0; using the probability theory explained below, the fair bet or premium should be at $3,850, representing 50% × $7,700 + 50% × $0. If we calculate the present value the $3,850 back to today’s value, since it is what are we paying today for the future outcome, the value is calculated to be $3,666.67 based on the present-value calculation of 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹

$3,850

𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 = (1+𝑖𝑖𝑖𝑖)𝑡𝑡𝑡𝑡 or (1+0.05)1 = $3,666.67

Binomial Option Pricing Model: Two-Period Probability Method Te BOPM using two periods measures four possible outcomes of the cryptocurrency—up and then up, up and then down, down and then up, and down and then down again—from any point in time until expiration. Te four outcomes illustrated in Figure 11.15 show three possible payofs, since the cryptocurrency that is going up and then down (Sud) is the same as its going down and then up (Sdu). As before with the single-period BOPM, the up (u) and down (d) values are derived from historical experiences and are used to derive such probabilities. Tis probability of the cryptocurrency’s going up or down ultimately measures the fair bet or the premium an investor is willing to pay to meets his or her expectation. Figure 11.15 uses an example of how to calculate the premium for both call and put options using two-period BOPM. Te example calculates both the European and American structures.

ONE‐PERIOD BINOMIAL OPTION PRICING MODEL ‐ Calculating the Put Option Premium                 INPUT Current Stock (S)=     $    47,000 Up (u) =     1.20x Down (d) =     0.90x Exercise Price (X) =       $    50,000 Risk Free Rate (i) =         5.00% Time in Years (t) =         1 Periods =   1

OUTPUT PERIOD 0  

FORMULAS PERIOD 1   Su=              56,400

  S =  

             47,000

Cu=                            ‐ C =      3,666.67

Sd =                42,300

Cd=    7,700.00

p =              0.50 1‐p=            0.50 P=    3,666.67 European Option Premium    

Figure 11.14

One-Period Binomial Option Pricing Model—Calculating Put Option Premium

Su = S . u        Sd = S . d        Pu = Max (0, X‐Su)         Pd = Max (0, X‐Sd)         p = [(1+i) ‐ d )] / (u ‐ d)                            P= [ (p . Pu) + [(1‐p) Pd)] ] / [(1+i)^Freq

TWO‐PERIOD BINOMIAL OPTION PRICING MODEL ‐ Call and Put Options               INPUT

OUTPUT

CALL OPTION  

PERIOD 0  

PERIOD 1  

Su = S . u       

Su^2=          67,680

  Cu^2=        17,680.00 (Payoff)

S =   $         47,000   u =

Su=     56,400

1.20x

  d = 0.90x   $         50,000 X =   i = 5.00% Frequency= 1 Periods= 2 Frequency:         ( Annual =1,        Semiannual = 2,  Quarterly=4)

  S =  

p =              0.50                 1‐p=                            0.50

      6,400

     8,780.95

    50,760     

(Payoff)

Sd =       42,300           ‐            (Payoff)

760.00

       361.90

Sd^2=          38,070

         Su^2 = S  . u^2          Sd^2 = S  . d^2

Cud= Cd=

       Sd = S . d

  Cd^2=

0.00 (Payoff)

        Cu^2 = Max (0, Su^2 ‐ X)         Cd^2 = Max (0, Sd^2 ‐ X)         Cud = Max (0, Sud ‐ X)           p = [(i+1) ‐ d )] / (u ‐ d)                  Cu= [ (p . Cu^2) + [(1‐p) Cud)] ] / [(1+i)^Freq]                  Cd= [ (p . Cud) + [(1‐p) Cd^2)] ] / [(1+i)^Freq]                  C= [ (p . C1) + [(1‐p) C2)] ] / [(1+i)^Freq]

C(E)=            4,353.74 European Option Premium     C(A)=            3,047.62 American Option Premium     PERIOD 0  

Frequency:         ( Annual =1,        Semiannual = 2,  Quarterly=4)

Figure 11.15

         47,000

Cu=

(Payoff)

PUT OPTION   S =   u =   d =   X =   i =   Frequency= Periods=

FORMULAS

PERIOD 2  

$         47,000 $              1 $              1 $         50,000 $              0 1 2

PERIOD 1  

PERIOD 2   Su^2=          67,680

  Pu^2=

0.00 (Payoff)

  S =  

         47,000

Su=     56,400           ‐           

Pu=

             ‐              

    50,760     

(Payoff)

Pud=

0.00 (Payoff)

p =              0.50                 1‐p=                            0.50

Sd =       42,300       7,700 (Payoff)

Pd= Sd^2=          38,070

     5,680.95   Pd^2=

11,930.00 (Payoff)

    P(E)=      2,705.22 European Option Premium           P(A)=      3,666.67 American Option Premium

Two-Period Binomial Option Pricing Model—Calculating Call and Put Option Premiums

       Su = S . u        Sd = S . d          Su^2 = S  . u^2          Sd^2 = S  . d^2         Pu^2 = Max (0, X ‐ Su^2)         Pd^2 = Max (0, X ‐ Sd^2)           Pud = Max (0, X ‐ Sud )           p = [(i+1) ‐ d )] / (u ‐ d)                  Pu= [ (p . Pu^2) + [(1‐p) Pud)] ] / [(1+i)^Freq]                  Pd= [ (p . Pud) + [(1‐p) Pd^2)] ] / [(1+i)^Freq]                  P= [ (p . P1) + [(1‐p) P2)] ] / [(1+i)^Freq]

228

Cryptocurrency Concepts, Technology, and Applications

Option Premium Calculations Based on European Structure Te probability calculations for both the call and put option premiums are the same. After the investor calculates the call and put option payofs for the second period, the probability is applied to the upper/upper, upper/lower, and lower/ lower payofs as follows: Call Option Premium

Cu A

AAAAA AAA AAAAAAAAA AAAA/AA

and then C A

and Cd A

AAAAAAA AAAAAAAA AAAA/AA

Put Option Premium

Pu =

p(Pu2 )+( 1−p)(Pud) (1+i/f)

AAAAAAAA AAAAAAAA A

and then P A

and Pd =

 

AAAA/AA

p(Pud)+( 1−p)(Pd2 )

AAAAAAA AAAAAAAA AAAA/AA

 

(1+i/f)

Option Premium Calculations Based on American Structure Te probability calculations for both the call and put option premiums are the same. After the investor calculates the call and put option payofs for the frst period, the probability is applied to the upper and lower payofs as follows: Call Option Premium

C = ApAC higher payoffA A A 1 A pAAC lower payoffA Put Option Premium

P=

A1 A i/fA

ApAP lower payoffA A A 1 A pAAP higher payoffA A1 A i/fA

Black-Scholes-Merton (BSM) Model Black-Scholes-Merton model (BSM) is one of the frst pricing models that was used to calculate the fair price for a call or a put option premium based on fve variables—current spot price (cryptocurrency), strike price, volatility, time, and

Cryptocurrency Options Strategy, Analysis, and Valuation

229

risk-free rate. Before the Black-Scholes formula is stated, the chapter will focus on these fve variables and other statistical concepts. Five Variables All option valuation methods are consistent, using the same fve variables: the current spot price (cryptocurrency) as the underline asset, the exercise or future price, the variance or standard deviation representing the volatility, the time, and the risk-free rate described below. 1. Current Spot Price (S0) of Cryptocurrency. Like the BOPM, the current price of the underlying spot price of the cryptocurrency is one of the most important variables in determining the intrinsic value of the expected option payof in the future. As mentioned earlier, the relationship between the current cryptocurrency price and the future exercise price is the frst natural comparison to determine how high the spot needs to go to exceed the exercise price for a call option and how low the cryptocurrency needs to go below the exercise price for a put option. Te value, or intrinsic value, of S – X and X – S for the call option and put option payofs, respectively, is the basis of the Black-Scholes formula. 2. Exercise Price (X). Tis is the future price of the underlying cryptocurrency at expiration of the option. Tis price is constant, and the investor is constantly comparing the current spot price (S) to the exercise price as the expiration time comes near and the intrinsic value of S – X or X – S is expanding or contracting to calculate the call option and put option payof, respectively. 3. Volatility and Standard Deviation (σ). Tis is the volatility measured by the standard deviation of the historical underlying cryptocurrency price as it changes from day to day, week to week, or month to month. Bigger swings in the spot price of the underlying cryptocurrency translate to higher volatility and a higher standard deviation. Basically, this swing determines how probable it is for the cryptocurrency to go higher or lower than the constant exercise price. Te higher the standard deviation, the more likely the cryptocurrency will be in the money, with positive intrinsic value of S – X or X – S for the call and put option, respectively. Te higher the standard deviation, the higher the expected option premium. Time (t). Time to maturity or time to option expiration is an important 4. variable to measure the value of the option. In general, the premium paid to buy the option is higher if there is more time to exercise. It also gives a higher chance for the cryptocurrency to reach the exercise price.

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5. Risk-Free Rate (i). Use the risk-free rate to calculate the present value of the exercise price. The rate used should be the annualized rate and be adjusted for maturity. The other adjustment is compounding.

Other Statistical Concepts It’s important to reintroduce some mathematical concepts that are essential for valuating options, including the natural logarithms, probability theory, and normal distribution.

1. Compounding Using Exponential (e) The number e is a mathematical constant 2.7182 that is the base of the natural logarithm and is equal to one. The future value calculation is calculated based on the following formula: i t FV = PV (1 + ) f

where PV is the present value, i is the interest, t is the time, and f is the frequency or compounding payment per year. As the frequency increases from annually to semi-annually, to quarterly, to monthly, and to infinite, compounding the future value will increase, but it won’t get higher than 2.718 times the original present value, as illustrated in Figure 11.16. The infinite compounding e is used in the Black-Scholes model to calculate the present value of the exercise price (X) as e–it and is derived based on the following mathematical calculation: e = PV (1 + i)t then PV =

e then PV = e−it (1 + i)t

2. Natural Logarithm (ln) The model assumes spot prices follow a lognormal distribution because asset prices cannot be negative (they are bounded by zero). This is also known as a Gaussian distribution. Often, asset prices are observed to have significant right skewness and some degree of kurtosis (fat tails). This means high-risk downward moves happen more often in the market than a normal distribution predicts. The assumption of lognormal underlying asset prices such as cryptocurrencies should thus show that implied volatilities are similar for each strike price according to the Black-Scholes model.

Cryptocurrency Options Strategy, Analysis, and Valuation

231

COMPOUND INTEREST USING e Present Value = $               1.00 Interest = 10% Years = 10 Description

Annual Semi Quarterly Monthly Daily Hourly By Minute By Second Infinite

Figure 11.16

Compound per year Freguency (f)

                      1                       2                       4                     12                   365                8,760           525,600     31,536,000 e

Future Value Compunded

      2.5937425       2.6532977       2.6850638       2.7070415       2.7179096       2.7182663       2.7182816       2.7182819       2.7182818

Calculating the Compound Interest Using the Exponential “e”

3. Normal Probability Distribution (N) Te normal probability distribution in a bell-shaped curve gives the probability that a standard normal random variable will be less than equal to a given value. Figure 11.17 shows that the normal distribution graph represents a standard normal distribution with mean μ = 0 and standard deviation σ = 1. For example, if the spot price of the cryptocurrency has a standard deviation of 10%, that means that the price of the cryptocurrency such as Bitcoin “swings” 10% either way from the average, representing, for example, that a the price of Bitcoin with an average of $30,000 in the upper and lower limits within the period is between $27,000 and $33,000 (–10% to +10%). Within one standard deviation, the probability that the spot price of the cryptocurrency will stay at 10% is 64%. In a game of chance, the investor will bet $6.40 to get a $10 payof for the spot price of the cryptocurrency to move within 10%. Te values of N (d) are given by Figure 11.17. Te value of d is between negative infnite and positive infnite, but for valuation purposes showing three decimals, the levels are basically between –3.00 and 3.00 for almost ~100% of the normal distribution of positive values between zero and one. For example, if d = 1.3, the normalized value is 0.903. Te table is used in the Black-Scholes model to determine the normal probability of the spot price of the cryptocurrency to determine the fair value of the premium. Te formulas for calculating the probability distribution to be used in the calculation of the intrinsic value of S – X or X – S for the call and put options, respectively, using the Black-Scholes model N(d1), which is the probability of the

Normal Probability Distribution

0.45

NORMAL DISTRIBUTION TABLE

0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0

‐3.75 ‐3.25 ‐2.75 ‐2.25 ‐1.75 ‐1.25 ‐0.75 ‐0.25 0.25 0.75 1.5

2.5

3

3.5

4

μ ‐ 2σ  μ ‐ σ  0.13%

Figure 11.17

2.14%

13.59%

μ + σ  μ + 2σ  μ 64.26% 13.59%

2

2.14%

d 3.200 3.100 3.000 2.900 2.800 2.700 2.600 2.500 2.400 2.300 2.200 2.100 2.000

N(d) 0.999 0.999 0.999 0.998 0.997 0.997 0.995 0.994 0.992 0.989 0.986 0.982 0.977

d 1.900 1.800 1.700 1.600 1.500 1.400 1.300 1.200 1.100 1.000 0.900 0.800 0.700

N(d) 0.971 0.964 0.955 0.945 0.933 0.919 0.903 0.885 0.864 0.841 0.816 0.788 0.758

d 0.600 0.500 0.400 0.300 0.200 0.100 0.000 ‐0.100 ‐0.200 ‐0.300 ‐0.400 ‐0.500 ‐0.600

0.13%

Normal Distribution Table Showing –3.2 To +3.2 Diviations Normalized from 0 to 1

N(d) 0.726 0.691 0.655 0.618 0.579 0.540 0.500 0.460 0.421 0.382 0.345 0.309 0.274

d ‐0.700 ‐0.800 ‐0.900 ‐1.000 ‐1.100 ‐1.200 ‐1.300 ‐1.400 ‐1.500 ‐1.600 ‐1.700 ‐1.800 ‐1.900

N(d) 0.242 0.212 0.184 0.159 0.136 0.115 0.097 0.081 0.067 0.055 0.045 0.036 0.029

d ‐2.000 ‐2.100 ‐2.200 ‐2.300 ‐2.400 ‐2.500 ‐2.600 ‐2.700 ‐2.800 ‐2.900 ‐3.000 ‐3.100 ‐3.200

N(d) 0.023 0.018 0.014 0.011 0.008 0.006 0.005 0.003 0.003 0.002 0.001 0.001 0.001

Cryptocurrency Options Strategy, Analysis, and Valuation

233

spot price of the cryptocurrency being in the money and N(d2) representing the probability of being out-of-the-money, are as follows: d1 =

S X

ln( )+ (i+ σ√t

σ2 )t 2

and d2 = d1 − σ√t

where S is the current spot price of the cryptocurrency, X is the exercise price, i is the risk-free rate, t is time to expiration, and σ is the standard deviation. The Black-Scholes formula for call option (C) and put option (P) are as follows: C = S N(d1) − Xe−it N(d2) and P = Xe−it [1 − N(d2)] − S [1 − N(d1)]

For example, let’s assume that the current spot price of Bitcoin (S) is $47,000 and the future exercise price (X) that expires in 6 months is $50,000. This outof-the-money call option and in-the-money put option have a standard deviation (σ) of 0.40. Given the risk-free rate of 5%, what is the fair value for both call and put option premiums? Input

S = $47,000 X = $50,000 t = 0.50 (6 months) i = 5.0% σ = .40 Formulas

and

d1 =

ln (

. 42 47k ) + (0.05 + 2 ) 0.50 50k = 0.0110 0.40√0.5

d2 = 0.0110 –0.2828 = –0.2718 N (d1) = N (0.0110) = 0.5044 N (d2) = N (­0.2718) = 0.3929 The call option calculated using the Black-Scholes formula: C = 47k (0.5044) − 50k e−(0.05)(0.5) (0.33929) = = 23.71k − 50k (0.9753)(0.3929) = 4.55

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Cryptocurrency Concepts, Technology, and Applications

Te put option is calculated as follows: P = 50k e−(0.05)(0.5) [1 − (0.3929)] − 47k [1 − 0.5044] = 6,313.17

Te call option calculations using Excel® is shown in Figure 11.18; the put option calculations using Excel is shown in Figure 11.19.

Put Call Parity Te investor needs to calculate either the call option or the put option, and given that the probability concepts used are the same pricing for the same period, the call price should be in parity of the put price. Te diference between the call premium and the put premium should equal the intrinsic value S – X or X – S, with the exception that the X will be adjusted to represent the present value, since the exercise price is in the future. Te call put parity is as follows:

C – P = S – PV (X) or C – P = S – Xe –it If an investor knows the call price and needs to determine the put price and vice versa, the formulas translate to the following:

C = S – Xe –it + P and P = Xe –it – S + C Using this example where the current price of Bitcoin is $47,000 and the future exercise price (X) that expires in six months is $50,000, the call option is priced at $4,547.88. Given the risk-free rate of 5%, what is the fair value of the put option premium? Using the call put parity formula, calculate the put option as follows:

P = X – S + C = 50k – 47k + 4.55k = 6.31k

Hedge Ratio (h) and Leverage (Borrowing) Methods: Using BOPM Tere is another method that can be used to determine the option pricing, which is like the BOPM discussed above, but instead of using the probability, the values can be derived by using the leverage method. Tis leverage method is important when the investor is using the BOPM valuation to determine the fully hedged position for option strategies, such as covered calls and protective puts. Te method to calculate the call price is demonstrated. Tis six-step method shown in Figure 11.20 frst determines the hedge ratio and then, using a leverage component, calculates the fair value of both call and put options.

BLACK-SCHOLES VALUATION

CALL OPTION A

B

C

D

E

F

G

4 5 INPUT OUTPUT 6 7 Standard Deviation  (σ) = 0.4 d1 = 8 Expiration (in years)  (T) = 0.5 6 mon. d2 = 9 Risk‐Free Rate (Annual) (i) = 5.0% N(d1) = 10 Stock Price (S ) = 47,000 N(d2) = 11 Exercise Price (X) = 50,000 12 C= 13

Figure 11.18

H

I

FORMULAS 0.0110 ‐0.2718 0.5044 0.3929

=(LN(D10/D11)+(D9+(D7^2)/2)*D8)/(D7*SQRT(D8)) =+G7‐D7*SQRT(D8) =NORMSDIST(G7) =NORMSDIST(G8)

4,547.68

=+D10*EXP(-D9*D8)*G9-D11*EXP(-D9*D8)*G10

Calculating Call Option Premiums Using Black-Scholes

BLACK-SCHOLES VALUATION

PUT OPTION A

B

C

D

4 5 INPUT 6 7 Standard Deviation  (σ) = 0.4 8 Expiration (in years)  (T) = 0.5 9 Risk‐Free Rate (Annual) (i) = 0.05 10 Stock Price (S ) = 47,000 11 Exercise Price (X) = 50,000 12 13

Figure 11.19

E

F

G

P=

Calculating Put Option Premiums Using Black-Scholes

I

FORMULAS

OUTPUT

d1 = d2 = N(d1) = N(d2) =

H

0.0110 ‐0.2718 0.5044 0.3929

=(LN(D10/D11)+(D9+(D7^2)/2)*D8)/(D7*SQRT(D8)) =+G7‐D7*SQRT(D8) =NORMSDIST(G7) =NORMSDIST(G8)

6,313.17

=D11*EXP(-D9*D8)*(1-G10)-D10*(1-G9)

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Cryptocurrency Concepts, Technology, and Applications

Figure 11.20 shows that the European call option should be priced at $3.048 and the put option at $3,667, with hedge ratios of 4/9 and 5/9, respectively. Te 4/ hedge ratio for the call option suggests that the investor that is seeking 100% 9 hedging when planning to buy, let’s say Bitcoin, is to hold nine call contracts for every four Bitcoins (cover call). For a protective put strategist, he or she needs to buy nine put contracts for every fve Bitcoins.

Summary Options have been around since the tulip mania of 1636, when options on tulips were widely traded on speculation that tulip prices would continue to soar. Cryptocurrency doubters have compared the tulip mania with the signifcant increase in Bitcoin price between 2015 and 2017, and 2019 and 2021, predicting a similar fate. While these rapid price increases were followed by sharp retractions, major cryptocurrency markets generally remained stable, and prices have continued to rise long term. Options markets frst began to develop for Bitcoin in 2018, and interest in these options helped propel further innovation in the industry. Despite the controversy surrounding this new technological disruption of this digital asset, major investment frms, government regulatory agencies such as the SEC, and major exchanges including the Chicago Mercantile Exchange (CME) have continued to develop derivatives markets in step with each other. Tese markets provide investors with the ability to hedge risk and implement complex trading strategies in ways not previously possible. Te options market for cryptocurrencies has been only one of the latest introductions to the cryptocurrency momentum in recent years. When CME introduced Bitcoin and Ethereum futures a few years ago, it provided an efcient tool to access exposure for these cryptocurrencies and manage risk in a more regulated market than traditional cryptocurrency exchanges. Tese cryptocurrency options follow very closely the equity options model, in which the investor has a view of the price and buys or sells calls and puts based on this view. As a result, many of the option trading strategies used on equity markets can be used on cryptocurrency markets as well. Te purchase of options is used primarily for hedging investors’ underlying exposure or for speculating on the direction of the price with minimal investment. Given the recent volatility of the cryptocurrency market, the volume of options trading has picked up signifcantly and is expected to continue to grow as more portfolio managers discover this new asset class as a result of increasing interest from their customers.

Step 1: (Su - Sd) = $          14,100 Range between Upper and Lower Stock Step 2: (Cu - Cd) = $            6,400 Range between Upper and Lower Payoff Step 3: h = ( Cu - Cd) / (Su - Sd) = 4/9  Hedge Ratio (Buy 1 Stock / Sell 3 Calls) Step 4: (PV of Sd) Step 5: (So - PV(Sd)) Step 6: ([So - PV(Sd)] x h) =

$    40,285.71 Present Value of the Stock ‐ Borrowing) $      6,714.29 Intrinsic Value (S ‐ Expected X) $      3,047.62 Intrinsic Value times the hedge ratio

Premium

$

3,047.62

$ 53,047.62  Stock Price + Premium

= Break Even Distance to BE ($) = Distance to BE (%) =

$         14,100 Range between Upper and Lower Stock $           7,700 Range between Upper and Lower Payoff 5/9  Hedge Ratio (Buy 2 Stocks / Buy 3 Puts)

Step 4: (PV of Su) Step 5: (So - PV(Su)) Step 6: ([PV(Su) - S0] x h) =

$   53,714.29 Present Value of the Stock ‐ Borrowing) $     6,714.29 Intrinsic Value (Expected X‐S) $     3,666.67 Intrinsic Value times the hedge ratio

= Premium = Break Even

$      6,047.62  Break Even $116.06  ‐ Current Stock $100 12.87%  Break Even / Stock Price ‐ 1

CALL‐PUT PARITY ERROR CHECK                                              3,666.67

Figure 11.20

Step 1: (Su - Sd) = Step 2: (Pu - Pd) = Step 3: h = ( Pu - Pd) / (Su - Sd) =

Binomial Option Pricing Model–Leveraged: Six-Step Method

Distance to BE ($) = Distance to BE (%) =

$

3,666.67

$ 46,333.33  Stock Price ‐ Premium

$       (666.67)  Break Even $96.36  ‐ Current Stock $100 ‐1.42%  Break Even / Stock Price ‐ 1

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Cryptocurrency Concepts, Technology, and Applications

Even after the rapid growth of the past few years, the cryptocurrency options market is still at its infancy and is expected to grow and continue to expand its reach to other cryptocurrencies. As these markets grow and mature, they will likely attract increasing attention from traditional frms in the fnancial industry, as well as increasing regulatory scrutiny. It is hoped that this will result in a safer and more standardized cryptocurrency derivatives market that works to the beneft of all investors.

Chapter 12 The Role of Blockchain and Smart Contracts in International Relations Stephan Unger* and Hossein Hassani** *Saint Anselm College; **University of Tehran

12.1 Introduction Tis chapter deals with the current and potential role of blockchain technology in the context of international relations. Te development of blockchain technology spreads increasingly across various branches and sectors, from IT to fnancial and politics applications. Te most important characteristic of this development is the implementation of a more efcient and more secure data verifcation—more efcient because it enables a decentralized storage within the network, and more secure because the data is secured by the proof of work concept, which is independent of a centralized server or entity. Te utilization of smart contracts enables users to newly set up the network of international relations and make it more resilient and secure. Since smart contracts allow any logic to be implemented on top of transactions, any type of relationship,

239

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Cryptocurrency Concepts, Technology, and Applications

agreement, planned course of action, defned goals, organizational searches, and constraints can be included in the establishment of a smart contract. A smart contract logic may include distributions of budget allocations, negotiations of policy changes, implementations of agreements, and mitigation of disputes. Since the resolution is automated, potential conficts could be resolved even before they emerge. Te basic functions of a smart contract (i.e., to exchange money, property rights, securities, or anything else of value) allow one to defne rules under which they are automatically enforced. Once executed, the outcome is stored on a blockchain in an immutable way and can be used not only for recordkeeping but also as an input for future optimization (Hines 2021). Te advantages over a human-centered approach are obvious: the elimination of human errors and emotional behavior, faster and safer execution, minimization of costs, resolution of conficts, and the construction of a more resilient system. Te potential of smart contracts ranges from simple process optimization to the resolution of geo-political conficts. Almost all smart contract or blockchain applications associated with geopolitical risk deal with the mitigation of trading risk through hedging, but not the actual implementation into the political decision-making process. However, Savelyev (2017) is one of the few researchers to investigate the potential implications of smart contracts in contract law. He analyzes legal issues associated with the application of existing contract law provisions to so-called smart contracts, or “agreements existing in the form of software code implemented on the blockchain platform, which ensures the autonomy and self-executive nature of smart contract terms based on a predetermined set of factors.” He outlines the peculiarities of blockchain technology, as currently implemented in Bitcoin cryptocurrency, which forms the core of smart contracts. Blockchain has already been tested for geopolitical risk assessment and decision-making. Lithopia is a prototype of a fctional blockchain-managed village that uses satellite and drone data to trigger smart contracts on the open-source blockchain platform Hyperledger®. Te project is testing the possibility of anticipatory governance of emerging blockchain and distributed ledger technologies (DLTs) by involving stakeholders in the design process over templates. Te goal is to question the promises of blockchain governance happening over automation and smart contracts and to ofer an alternative to the misuses of emerging technologies in the so-called predictive and anticipatory design (Kera et al. 2019). Van Bodegraven (2017) highlights the ethical and design issues arising with the emergence of anticipatory design and predictive user experiences. Also, Guston (2014) dives into the roles of new technology in governance and policy making. He elaborates the notion of anticipatory governance, which is “a broad-based capacity extended through society that can act on a variety of inputs to manage emerging knowledge-based technologies while such management is still possible.” While he

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241

mainly applies the concept of anticipatory governance to nano-technology in the U.S., his theoretical framework is also relevant to the integration of smart contracts and blockchain technology into today’s decision-making processes. Wright and De Filippi (2015) explore the benefts and drawbacks of this emerging decentralized technology and argue that its widespread deployment will lead to expansion of a new subset of law, which they term Lex Cryptographia: rules administered through self-executing smart contracts and decentralized (autonomous) organizations. Tey argue that as blockchain technology becomes widely adopted, centralized authorities, such as governmental agencies and large multinational corporations, could lose the ability to control and shape the activities of disparate people through existing means. From this, they imply that there will be an increasing need to focus on how to regulate blockchain technology and how to shape the creation and deployment of these emerging decentralized organizations in ways that have yet to be explored under current legal theory (Wright and De Filippi 2015). Cannarsa (2018) highlights the diference between natural language, in which contracts are written, and computer language, in which codes are written. He argues that courts will play an important role, through interpretation, in disclosing the “true meaning” of a contract. Coding contracts and relying on computer-code language can hence have a signifcant impact on the civil law approach and bring the two legal systems closer as far as contract drafting and contract interpretation are concerned (Cannarsa 2018). Oxford Analytica (2020) stresses the existing problems which manifest a barrier for smart contracts to be implemented in the current regulatory framework. Experiments were conducted which tested the technology for its potential to save time and money and for its capacity to boost transparency of the fnancial sector. However, these trials have exposed major limitations of the technology’s design. Te identifed constraints include, for example, that only some governments have amended legislation to include blockchain-based transactions; more would need to follow to aid legal certainty. Another major hurdle to wider adoption of smart contracts is the lack of efective dispute resolution mechanisms and that simpler contracts executed through blockchain could lower administrative costs (Oxford Analytica 2020). However, blockchain technology will fnd its way not only into geopolitics but also into space politics. De Filippi and Leiter (2021) argue that blockchain technology is relevant for outer space because it fosters novel narratives advancing possible futures characterized by new modes of governance. Tey illustrate how the possible applications of blockchain technology could be applied to outer space governance. Most applications of blockchain technology utilize issued tokens to speculate on or hedge against outcomes of geopolitical actions. Bouri et al. (2020) investigate

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Cryptocurrency Concepts, Technology, and Applications

whether price discontinuities in cryptocurrencies are jointly related to large swings in geopolitical risk. Tey examine the jump incidence of daily returns for Bitcoin and other leading cryptocurrencies and study the co-jumps between cryptocurrencies and the geopolitical risk index using logistic regressions. Tey fnd that the price behavior of all cryptocurrencies under study fuctuates, but only Bitcoin jumps are dependent on jumps in the geopolitical risk index, which indicates that Bitcoin is a hedge against geopolitical risk. Su et al. (2020) investigate the role of the Bitcoin currency in avoiding and surpassing the risks that are associated with global geopolitical events. Tey perform Granger causality tests in order to explore the mutual infuences between geopolitical risks and Bitcoin prices and fnd that there are positive and negative infuences that stem from geopolitical risks toward Bitcoin prices. Further literature (Selmi et al. 2022) shows that Bitcoin, and thus the underlying blockchain technology, is used to mitigate geopolitical risk. Overall, it is important to stress that even though blockchain technology has the power and potential to solve many decision-making problems—for example, through implementation of smart contracts—the fnal decision-making body or oversight entity should always be and remain human beings. Te reason for this lies in the complexity of new situations which might overwhelm even the smartest machine-learning algorithms, not only because of a lack of historical data to learn from, but also because of situations which change the rules. We base our analysis about the potential applications of blockchain in international relations on the theoretical framework provided by Allison (1971). His main assertion is that the interactions between countries cannot be reduced to actions between two players. Instead, the decision-making process in international relations underlies the decision-making process by many players on many diferent levels, even though they are or can be embedded within the same country. Graham (1971) proposes three models which account for this distinction of diferent entities involved in decision-making processes—a rational actor model, an organizational process model, and a government politics model. Te rational actor model attempts to explain international events by recounting the aims and calculations of nations or governments. Te organizational process model attempts to explain international events by processes and procedures of large organizations that constitute a government. Te government politics model attempts to explain international events by the outcomes of bargaining games among players in national governments. We will now explore in detail the potential of blockchain technology in the context of international relations by analyzing the three theoretical models of international relations by Allison (1971) and evaluate which role smart contracts and blockchain could play and take over in the corresponding frameworks. We present these three proposed models describing international relations and show

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243

how blockchain technology and smart contracts can be applied to each. Each of the models ofers a variety of possible adaption points where smart contracts can take over processes, optimize them, and execute them in a highly efcient way.

12.2 Models of International Relations 12.2.1 The Rational Actor Model Te frst model, the rational actor model, assumes that decisions are made and actions are taken by governments as primary actors. Te action is chosen according to a calculated solution to a strategic problem. Tis corresponds to the classical model in international relations, which explains an occurrence in foreign policy by the rational action chosen by a government. What are the basic concepts of rational action? In the rational actor model, the goals and objectives of the players can be expressed through payofs, utility, or preference, which represent the value or utility of alternative sets of consequences. Terefore, their values can be ranked, and depending on their assigned utility, the rational actor would choose the action with the highest value. Applied to a blockchain, any entry on a distributed ledger can be ranked as well. Tese entries can then be used by a smart contract to automatically execute the action with the highest value. Alternatives can be represented as a decision tree, and consequences are outcomes of choice if a particular alternative was chosen. Choice represents a selection of alternatives whose consequences rank highest in the decision-maker’s payof function—hence, predictive behavior that can be fully explained in terms of the goals the actor is trying to achieve. Te nature of machine-learning algorithms are decision trees which learn from backward propagation and error optimization. Te application of such learning algorithms can be the basis of a smart contract which approximates the optimal choice among various alternatives through simulation and automatically executes the action which realizes the highest payof. Te underlying assumption in the rational actor model is that rational choice consists of value-maximizing adaption within the context of a given payof function, fxed alternatives, and known consequences. Te divergence in the choice of action for an actor, and thus for an analyst who tries to forecast an actor’s action, arises when it comes to the tradeof between rational choice and optimal choice. As optimal choice assumes limited rationality, this leads to a potential overlook of a wide range of values and consequences. Tis problem will be solved by the implementation of smart contracts, since a rule-based framework needs to be set up in order for the algorithm to take into account the wide range of values and consequences. Trough simulation, all

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potential outcomes can be calculated, and only the ones which fulfll the pre-set requirements are executed. Te problem in reality is that there doesn’t exist a pattern of activity for which one cannot write a large number of objective functions such that the pattern of activity maximizes each function. Machine-learning algorithms would change this situation. Depending on the calculation capacity, algorithms can perform almost infnite scenario calculations which can even generate new objective functions such that a pattern of activity maximizes each function. One approach to tackle this problem is to package activities of various ofcials of government as actions chosen by a unifed actor, analogous to an individual human being. All these packaging activities correspond to blockchain entries and verifcations, which can then be used for simulations and determination of optimal, respectively rational action, depending on the chosen objective function.

12.2.2 The Organizational Behavior Model Organizational choice requires the generation of all possible alternatives, the assessment of the probabilities of all consequences, and the evaluation of each set of consequences for all relevant goals. Tis leads to a comprehensive rationality in the behavior of organizations. Nevertheless, there exist fve deviations from comprehensive rationality due to the physical and psychological limitations of humans working in organizations which become obsolete with the implementation of blockchain technology. Tese deviations are factored problems, satisfcing, search, uncertainty avoidance, and repertoires. Factored problems are problems which are so complex that humans split them up into quasi-independent parts. Blockchain technology functions algorithmically. Satisfcing often replaces optimization, which means that a “good enough” solutions often satisfes. Due to the automatic and sequential processing of operations through algorithms, all ledgers are screened, and a global optimum can be found with enough available calculation power. When it comes to search, organizations very often generate alternatives by relatively stable, sequential search processes which create a very limited spectrum. Te same characteristic applies to the search process in a blockchain. Te decentralized nature of a blockchain and parallel mining process allows one to search without limiting the spectrum. Uncertainty avoidance makes organizations reluctant to base actions on estimates of an uncertain future. Tis problem can be tackled by a blockchain as well, since smart contracts can be programmed in such a way that they take into account several potential outcomes and automatically execute a certain task. Repertoires refer to action programs which constitute the range of efective choice in recurring situations. Artifcial Intelligence can be integrated into smart contracts such

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that they extend the range of choices, even in recurring situations, and thus avoid repetitive behavior. Te limitation of organizational learning is often characterized by a process wherein behavior remains stable but goals change over time. In such a case, smart contracts would need to be adapted or re-programmed. Another point where smart contracts can be helpful is the identifcation of a threshold when certain goals are not only not met, but also can’t be reached anymore, and thus might indicate a deviation toward a new goal which would need to be defned. Organizational routines in a new context pose uncertainty, which smart contracts can minimize by evaluating the ledgers of all nodes in the network, simulating potential outcomes, and choosing the action which meets the pre-determined goals as closely as possible. If governmental leaders intend to trigger several diferent organizations’ programs simultaneously, smart contracts would defnitely perform more efciently in parallel execution of these actions.

12.2.3 The Governmental Model Te governmental model assumes that the leaders at the top of organizations are players in a competitive game. According to Allison (1971), these leaders are bargaining along regularized circuits among players positioned hierarchically within the government. Terefore, in this model, government behavior is not assumed to be an organizational output but rather a result of bargaining games. Te characteristics of the governmental model is that the players don’t focus only on single strategies but on many diverse intra-national problems. Tis implies that there is no consistent set of strategic objectives, but of national, governmental, and personal goals. In order to include intra-governmental agreements into smart contracts, all parties need to join the blockchain. Te verifcation process could even promote trans-national cooperation if the participating servers are distributed across the diferent nation states, thus contributing to the joint efort of running a blockchain and all associated smart contracts and tokens on it. Allison (1971) suggests that decisions need to be decentralized because of the broad spectrum of foreign policy problems. Te blockchain network would fulfll this requirement because any node on the network could perform a certain action through the execution of a smart contract made by the node and verifed by the whole network. As we can see, blockchain technology both fattens hierarchies by taking out bargaining advantages through automatization and shifts negotiation skills to an earlier stage in the smart contract rule determination. Te shift of the education and skill set of negotiators over time surrounding the negotiation of the rules performed by the smart contract will create a stable environment which will not be exposed to uncertainty or decision fuctuations arising out of evolving situations.

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12.3 Conclusion Tis chapter presents the potential roles of blockchain technology in international relations and what impact the replacement or enhancement of human-centered diplomacy by the implementation and application of blockchain technology and smart contracts would have. Te three theoretical models by Allison (1971) constitute a description and reasoning of decision-making processes in international relations, to which blockchain technology and smart contracts can be applied to. His three proposed models are the rational actor model, the organizational behavior model, and the governmental model. Each of the models is described, including its potential interconnection points to blockchain technology. We describe which role smart contracts could play in each model—that is, through tokenization and automatized allocation of governmental budget resources according to rule-based smart contract executions.

References Allison, G. T. (1971). Essence of Decision, Explaining the Cuban Missile Crisis. Little, Brown & Company Limited. van Bodegraven, J. (2017, March). How anticipatory design will challenge our relationship with technology. 2017 AAAI Spring Symposium Series. Bouri, E., Gupta, R., and Vo, X. V. (2022). Jumps in geopolitical risk and the cryptocurrency market: Te singularity of Bitcoin. Defence and Peace Economics, 33(2): 150–161. DOI: 10.1080/10242694.2020.1848285 Cannarsa, M. (2018). Interpretation of contracts and smart contracts: Smart interpretation or interpretation of smart contracts? European Review of Private Law, 6: 773–785. https://kluwerlawonline.com/journalarticle/European +Review+of+Private+Law/26.6/ERPL2018054 De Filippi, P., and Leiter, A. (2021). Blockchain in outer space. AJIL Unbound, 115: 413–418. DOI:10.1017/aju.2021.63 Guston, D. H. (2014). Understanding ‘anticipatory governance’. Social Studies of Science, 44(2): 218–242. Hines, B. (2021). Digital Finance. Security Tokens and Unlocking the Real Potential of Blockchain, 31. John Wiley & Sons. Kera, D. R., Sourek, P., Krainski, M., Reshef, Y., Rodriguez, J. M. C., and Knobloch, I. M. (2019). Lithopia: Prototyping blockchain futures. Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (CHI EA ’19). New York: Association for Computing Machinery. Paper LBW1219: 1–6. https://doi.org/10.1145/3290607.3312896

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Oxford Analytica (2020). Smart contracts are likely to have limited application. Emerald Expert Briefngs. oxan-db. Savelyev, A. (2017). Contract law 2.0: ‘Smart’ contracts as the beginning of the end of classic contract law. Information & Communications Technology Law, 26(2): 116–134. DOI: 10.1080/13600834.2017.1301036 Selmi, R., Bouoiyour, J., and Wohar, M. E. (2022). “Digital Gold” and geopolitics. Research in International Business and Finance, 59: 101512. Su, C.-W., Meng, Q., Ran, T., Xue-Feng, S., Lucian, A., and Muhammad, U. (2020). Can Bitcoin hedge the risks of geopolitical events? Technological Forecasting and Social Change, 159. 120182. 10.1016/j.techfore.2020.120182. Wright, A., and De Filippi, P. (2015, March 10). Decentralized blockchain technology and the rise of Lex Cryptographia. SSRN: https://ssrn.com/abstract =2580664 or http://dx.doi.org/10.2139/ssrn.2580664

Glossary AML

Anti-money laundering

DoJ

Department of Justice

AUM

Asset under management

DPoS

Delegated proof-of-stake

BCH

Bitcoin Cash

EBSI

European Blockchain Services Infrastructure

BE

Breakeven

BFT

Byzantine Fault Tolerance

BIP

Bitcoin Improvement Proposal

BIS

Bank of International Settlements

BOPM

Binomial option pricing model

ETP

Exchange traded product

FATF

Financial Action Task Force

Black-Scholes-Merton

FinCEN

Financial Crimes Enforcement Network

FOREX

Foreign Exchance Market

BSM CBDC

Central bank digital currency

CBOE

Chicago Board Option Exchange

CFTC

Commodities Futures Trading Commission

CME

Chicago Mercantile Exchange

DAO

Decentralized autonomous organizations

ECB

European Central Bank

EIP

Ethereum collects improvement proposal

ETF

Exchange-traded fund

ETN

Exchange-traded note

FTC

Federal Trade Commission

HPR

Holding period return

ICO

Initial coin offering

IRS

Internal Revenue Service

KYC

Know Your Customer

NDF

Network distribution factor Non-fungible token

DApp

Decentralized application

NFT

DeFi

Decentralized fnance

NPP

New Payments Platform

DGP

Decentralized governance protocol

OCC

Offce of the Comptroller of Currency

DLT

Distributed ledger technology

OSINT

Open-source intelligence

PoA

Proof-of-authority

249

250 Cryptocurrency Concepts, Technology, and Applications

PoS

Proof-of-stake

SER

Supply equality ratio

PoW

Proof-of-work

SIG

Special interest groups

QV

Quadratic voting

TOR

The Onion Router

Securities and Exchange Commission

VA

Virtual asset

VASP

Virtual asset service provider

SEC

Index # 51% attack, 84 1-megabyte rule, 87 A accountability, 16, 17, 85–87, 149, 154 advanced option strategies, 211, 216–221 advance-fee fraud, 98, 99 advance fee tokens, 109 airdrop scams, 98, 99 American option contract, 200 Amplify Transformational Data Sharing, 11 analyzing the blockchain, 124 anti-money laundering (AML), 94, 110, 129, 146, 147, 150–152, 155–157 application architecture, 79 arbitrage scams, 99, 108 Asset Under Management (AUM), 11 attention–crash risk relationship, 31 attention–volatility relationship, 31, 32 automated governance, 88 AWS®, 190, 191 B Bank of International Settlements (BIS), 152

bear call spreads, 220, 221 bear put spreads, 214, 217 bid pollution, 109 bid shielding, 110 big blockers, 190 Binance®, 3, 101, 112, 200 binomial option pricing model (BOPM), 223–227, 230 Bitcoin Cash (BCH), 167 Bitcoin futures, 28, 55, 56, 200 Bitcoin Improvement Proposal (BIP), 165 Bitcoin’s consensus rules, 177, 189 Bitconnect, 145 Bitfnex®, 7 Bitmex®, 189, 196, 200 Black-Litterman model, 42, 46–51, 54, 57 Black-Litterman Portfolio (PORT), 47 blackmail and extortion scams, 99 Black-Scholes-Merton (BSM), 223, 228 Black-Scholes model, 223, 230, 231 blockchain governance, 75–91, 240 blockchain technology, 64, 77, 89, 91, 111, 114, 116, 161, 163, 171, 172, 200, 239–247 Block Legitimate Index, 8 Blocksize War, 166, 167, 172, 196 251

252 Cryptocurrency Concepts, Technology, and Applications

bootstrapping money, 183 bounded curves, 89 bribery and corruption, 94, 96, 106, 110 Buchanan, James M., 159, 160 Bufett, Warren, 14 bull call spreads, 214–217 bull put spreads, 217, 219 buy-and-hold strategy, 52 Byzantine Fault Tolerance (BFT), 4 C call options, 199–202, 206, 211 capital controls, 111, 182 central bank digital currency (CBDC), 59–74, 148–153, 157 Chainalysis®, 93, 121, 179 chain hopping, 102, 135 change management, 86 Chicago Board Option Exchange (CBOE), 199 Chicago Mercantile Exchange (CME), 199, 236 child pornography, 105 child sexual abuse material, 131, 132 choice asymmetry, 26, 30 Coase, Ronald, 167 coercion, 86, 87 Coinbase®, 6, 7 Coincheck, 100–104, 116, 144 Coincheck Exchange hackers, 102 CoinMetrics, 187, 188 cold wallets, 130 collars, 208, 211, 213 commodities fraud, 99 Commodity Futures Trading Commission (CFTC), 14, 97, 112, 114, 147, 155, 171 confscation, 121, 136–138 consensus, 4, 14, 75, 78–84, 87, 90, 113, 161–166, 177, 189, 190

consensus BIPs, 165 consensus mechanism, 4, 75, 78, 81–84, 161, 191 Continental Pipeline, 144 control mechanism, 87 cooperation across borders, 132 coordinated mining action, 85 copyright infringement violations, 109 correlation coefcients, 11, 48, 50 counterfeiting, 109 covered calls, 208–211, 234 covered call strategy, 209 covered option, 199, 202, 204, 207–213 crypto ATMs, 136 crypto before the court, 138 cryptocurrency-adjacent crimes, 94, 106 cryptocurrency-based pyramid schemes, 96 cryptocurrency crime, 93–95, 101, 107, 110 cryptocurrency exchange, 6, 7, 11, 54, 98–101, 104, 115–118, 236 cryptocurrency goods and services market, 2, 14 cryptocurrency mining crimes, 93, 96, 99, 100, 110 cryptocurrency options strategy, analysis, and valuation, 199 cryptocurrency returns, 29, 30, 34, 35 cryptocurrency wallet, 6, 19, 97, 98 crypto-enabled and crypto-dependent crimes, 93, 94 cryptojacking, 100, 101, 104 crypto wallets, 6, 7 cyberattacks, 84 cybercrime, 93, 96, 99, 104, 110, 122 cybercrime-as-a-service, 105 D dark market, 136

Index 253

darknet marketplace, 94, 104, 105, 110, 117 data privacy issues, 64 Decentraland®, 110 decentralized applications (DApps), 78–80, 85–88, 163 decentralized autonomous organizations (DAOs), 80, 87, 88, 91, 92, 108, 161, 163, 169, 172, 173, 192 decentralized exchanges, 7, 108, 113, 118, 135 decentralized fnance (DeFi), 93–94, 98, 101, 106–108, 116–118, 116–118, 135, 138, 145, 155, 157, 162, 168, 172–173, 200 decentralized governance, 85–88 DeFi crime risks, 98, 106 delegated proof-of-stake (DPoS), 83, 84 denial-of-service attacks, 84 Department of Justice (DoJ), 143, 146, 147, 152, 155, 249 Deribit, 200 derivatives markets, 236 development of cryptocurrency, 2 digital euro, 65, 72, 73 digital gold, 175, 193, 194, 197, 247 dispute resolution, 86, 241 disruptive innovation, 75 diversifcation efcacy, 49, 50 Dogecoin, 83 donation scams, 98 drug crimes, 102, 103 E emerging criminal strategies, 134 emerging research, 23 Ethereum®, 3, 7–15, 79–87, 96, 101, 111, 112, 133, 136, 156, 157, 161, 162, 165, 166, 184, 190, 192, 200, 236

European option contract, 200 exchange-traded funds (ETFs), 2, 4, 9, 11, 18, 19, 48–50 exchange-traded notes (ETNs), 9, 14 exchange-traded products (ETPs), 1, 4, 11, 94, 104, 105, 109, 110, 116, 117, 120 F factors market, 2, 14 fake cryptocurrency exchanges, 98 fake cryptocurrency wallets, 98 fake mixers, 98 fake tokens, 98 Federal Reserve, 14, 16, 46, 116, 177, 178, 186, 196 Federal Trade Commission (FTC), 14, 112, 114, 147, 155, 171 Fedwire®, 177 fettered governance, 88 fat money, 5, 7, 11, 17, 18, 62, 135, 185 Financial Action Task Force (FATF), 126–129, 135, 143, 146, 150–155 Financial Crimes Enforcement Network (FinCEN), 126, 171 fnancial system regulators, 60, 70 fngerprint, 123, 138 fash loan attacks, 108 follow-the-money, 131 founding, 176, 183–186 framework regulation, 86, 87 fraud, 84, 93–99, 107–116, 143, 145, 152–157, 162, 179, 181 fraudulent investment schemes, 94, 107 fraudulent services and tokens, 96, 98 futures contracts, 2, 4, 8, 11 G Gaussian distribution, 230

254 Cryptocurrency Concepts, Technology, and Applications

Gensler, Gary, 14 giveaway scams, 98, 99 Global Findex database, 180 gold, 13, 14, 17, 42, 48, 49, 175, 179–182, 193–197, 247 governance attacks, 107–110 governance paradox, 77, 89 Grayscale Bitcoin Investment Trust, 9 H hacks, 96, 100, 104, 107, 112, 142– 147, 152, 153, 168, 189, 200 hard fork, 87, 192 hardware wallet, 94, 122, 130, 134, 137, 138 hashing, 122, 138, 155 hashrate distribution, 191 hedge ratio (h), 234, 236 heuristics, 21, 124 high infation, 181 high-yield investment schemes, 96 HODLers, 167 holding period return (HPR), 201, 203, 206, 211, 220 horizontal spreads, 211, 214, 215 hosted wallets, 134, 137 human trafcking, 105 hyperinfation, 16, 181 I identity theft, 99 impersonation scams, 97 incentive mechanism, 79–83 industrial miners, 191 infrastructure architecture, 78, 79 initial coin ofering (ICO) scams, 96 insider trading, 99, 110, 116 intellectual property crime, 109, 110 Internal Revenue Service (IRS), 14, 103, 105, 114, 116, 131, 147, 171, 228, 229, 234, 236

international harmonization eforts, 151 interoperability, 78–81, 91, 92, 129 in the money, 111, 201–205, 208, 225, 229, 233 investment frontier, 50 investor attention in cryptocurrency markets, 21–24, 28, 32, 34 investor attention vs investor sentiment, 22 Invisible Internet Project, 131 J Jobs, Steve, 186 K Know Your Customer (KYC), 94, 104, 110, 127, 133–136, 146 Kraken, 7 Krugman, Paul, 178 L law enforcement, 105, 119–122, 125, 129, 130, 133–138, 145–147, 152 layer 0, layer zero, 189 leaderless money, 185 legislative gap, 122 lex cryptographica, 86 lightning network, 177, 182, 189, 195–196 liquidity, 7, 16, 23, 24, 31–34, 70, 104, 108, 109, 169 M macro-level governance, 85, 87 major legal concerns in cryptocurrency, 142 market capitalizations, 3, 5 market development, 1–4, 9 market manipulation, 94, 96, 106–109 measures of investor attention, 23, 24

Index 255

meso-level governance, 81 metrics, 7, 23, 55, 97, 187, 188 micro-level governance, 78 milestones for cryptocurrencies, 18 mining pools, 83, 191 mining scams, 99 mintable, 109 MIT’s Digital Currency Initiative, 195 mixers, 98, 102, 104, 136 monetary policies, 2, 6, 11, 14, 18, 76, 192 money laundering, 73, 93–96, 101, 102, 107, 110–116, 119, 126, 128, 142, 146, 147, 153–157 money spreads, 214 Mt. Gox, 100, 126, 144 Musk, Elon, 14, 186 multi-input, 124 N Nakamoto consensus, 83 Nakamoto, Satoshi, 37, 124, 184 naked options, 199 NASDAQ Composite Index, 16 network distribution factor (NDF), 103, 151, 187, 188 non-fungible tokens (NFTs), 17, 83, 93, 106–113, 116, 117, 145, 155 non-regulated exchanges, 104, 135 non-regulatory risk, 54 normal probability distribution (N), 231 O of-chain governance, 81, 82, 86, 91 Ofce of the Comptroller of Currency (OCC), 171 OKEx®, 200 on-chain governance, 81–83, 86 one-time change address, 124 online casinos, 136

OpenSea®, 109, 110 open-source decentralized systems, 85 optimal change heuristics, 124 options of cryptocurrencies, 9 option trading strategy, 200, 208 option valuation methods, 220, 229 OP tokens, 185 oracle attacks, 107, 108 oracles, 88 Ostrom, Elinor, 160 out of the money, 201, 205, 223, 225, 233 P PayPal®, 6, 178 permissionless blockchains, 79 phishing, 97–100, 112, 115, 144 Pincoin, 145 Plexcoin, 145 political actors, 160 polycentric governance, 85, 86 Ponzi schemes, 94, 96, 111, 157 pools, 83, 190, 191 Powell, Jerome, 14 power distribution, 85 premium, 27, 33, 43–45, 200–211, 214, 217, 220, 233–235 price discovery process, 30 privacy coins, 99, 102–105, 136 private key, 17, 97, 100, 122, 123, 130, 134, 137, 138 proof-of-authority (PoA), 4, 83–86 proof-of-stake (PoS), 83–86, 90, 162, 173 proof-of-work (PoW), 4, 83–86, 161, 162, 173 protective puts, 208–211, 234 proxies, 23, 24, 28–32 public blockchains, 84 public choice, 159–173 public ledger, 183

256 Cryptocurrency Concepts, Technology, and Applications

pump-and-dump schemes, 96, 97, 106, 108 put options, 199, 200, 203–208, 214, 217, 225, 231, 234 Q quadratic voting (QV), 82, 89, 166 quasi-fat money, 2, 17 R ransomware, 96, 99, 100, 104, 105, 111, 112, 115, 129, 144 regulatory and legal issues, 141 remittances, 63, 180–182 Riker, William, 160 rogue actors, 143, 147 romance scams, 98, 99 rug pull, 98, 108, 109 S sanctions evasion, 93–96, 103, 104, 110 satoshi, 37, 124, 177, 184–188 script currencies, 16 script money, 2, 3, 16 Securities and Exchange Commission (SEC), 14, 147, 155, 170, 171, 236 securities fraud, 99 semi-autonomous governance, 88 semi-centralized or hybrid governance, 86 semi-centralized systems, 86 Shariah Law, 143 Sharpe Ratio, 42, 47, 48, 52, 58 Sharpe Reward-to-Variability Ratios, 47, 53, 55 shill bidding, 110 Silk Road, 105, 120, 124, 126, 138 SIM swapping, 97 single-period probability method, 223 small blockers, 190

smart contracts, 78–80, 83, 86–90, 94, 106–109, 135, 156, 162, 163, 166–169, 172, 239–247 smart contract trap door, 109 snapshot of criminal strategies, 135 social engineering attacks, 97 social engineering schemes, 94 soft fork, 81, 86, 87, 165 special interest groups, 164, 165 spectrum of cryptocurrency legality, 149 spoofng, 96, 97, 109 Stablecoin, 17, 63, 69–73, 107, 179, 192 state-sponsored cybercrime, 104 stop-loss hunting, 96, 97, 112 straddles, 200, 206, 207 sunrise problem, 129 supply equality ratio (SER), 187 SushiSwap, 108 T targeting, 86, 87, 111, 152, 171 tax evasion, 63, 94, 96, 105, 110, 115, 116, 1423, 146, 147, 152, 153, 157 terrorism fnancing, 93, 103, 110, 115, 126 ticker-stufng, 96, 97 timeline of development, 1, 2 time spreads, 211, 214 token economic models, 89 transaction fees, 83, 176, 177, 181, 189 travel rule, 127–129, 151 trust, 2, 3, 9, 37, 63, 75–79, 83, 85, 89–92, 106, 115, 117, 145, 154, 162, 164, 168, 178, 181, 184, 186, 192 Tullock, Gordon, 160 tumblers, 102 Twitter®, 24, 25, 28–34, 179 two-period probability method, 225

Index 257

U uncovered option contracts, 199 unhosted wallets, 129, 134, 137 uninformed trading activity, 30 Upbit®, 7 V valuation methods, 200, 220, 229 value accountability, 16, 127 Vanguard 500 Index (VAN), 38, 39, 46–54 vertical spreads, 214 veto, 82 virtual asset (VA), 119, 126, 127, 138, 151–155

virtual asset service provider (VASP), 127–134, 137, 155 virtual money, 119, 120 Visa®, 166, 177, 178, 181 volatility accountability, 16, 17 volatility and crash risk, 30 voluntarism, 86, 87 vote-trading, 164, 165 voting processes, 85 W WannaCry ransomware attack, 104 wash trading, 96, 97, 106, 110, 112, 117 Welcome to Video, 131, 132 wire fraud, 99