Options Trading In Bear Mkts 0070152721, 9780070152724

Table of contents :
Cover
Contents
Chapter 1: INTRODUCTION
1.1 Objectives
1.2 Indian Derivatives Market: An Overview
1.3 What are Derivatives?
1.4 Evolution of Derivative Trading in India
1.5 Participants in Derivatives Market
1.6 Types of Derivatives
Summary
Keywords
Chapter 2: UNDERSTANDING OPTIONS
2.1 Objectives
2.2 Introduction
2.3 Options: An Overview
2.4 Types of Options
2.5 Advantages of Options
2.6 Terminologies in Options
2.7 Trading System
2.8 Procedure for Margin Collection
2.9 Types of Orders
2.10 Settlement Schedule for Option Contracts
2.11 Settlement Mechanism
2.12 Writing of Options
Summary
Keywords
Chapter 3: OPTION TRADING
3.1 Objectives
3.2 Introduction
3.3 Market-wide Limits
Summary
Keywords
Chapter 4: PRICE INDEX
4.1 Objectives
4.2 Introduction
4.3 What is an Index?
4.4 Eligibility Criteria of Indices
4.5 Construction of Index
4.6 Desirable Attributes of an Index
Summary
Keywords
Chapter 5: PRICING OF OPTIONS
5.1 Objectives
5.2 Introduction
5.3 Black–Scholes Option Pricing Model
5.4 Pricing of Equity Options
5.5 Pricing of Options on Dividend Paying Scrips
5.6 Binomial Model of Option Pricing
5.7 Pricing of Binomial Put Option
5.8 Binomial Multiple Period Model
Summary
Keywords
Appendix
Chapter 6: STRATEGIC DERIVATIVE TOOLS
6.1 Objectives
6.2 Introduction
6.3 Put–call Parity
6.4 PC Ratio
6.5 Weighted PC Ratio
6.6 Volume PC Ratio
6.7 Tools to Measure Market Sentiment
Summary
Keywords
Chapter 7: VOLATILITY
7.1 Objectives
7.2 Introduction
7.3 Types of Volatility
7.4 Estimating Volatility
7.5 Estimating Historical Volatility
7.6 Factors Affecting the Computation of Historical Volatility
7.7 Implied Volatility
7.8 Volatility Smile
7.9 GARCH
7.10 Impacts of Implied Volatility and Underlying Asset Price on Purchase of Options
7.11 Volatility Trading
7.12 NSE Volatility Index
7.13 Behavioral Study of Nifty Options during Distress
7.14 Impacts of Events on Volatility—A Case Study
7.15 Comparative Study of the Behaviour of Nifty and IT Stocks During an Event
7.16 Impact of Quarterly Results on Stock Futures
7.17 Volatility Skew
7.18 Stochastic Volatility
7.19 Volatility Arbitrage
7.20 Volatility Change
Summary
Keywords
Chapter 8: OPTION GREEKS
8.1 Objectives
8.2 Introduction
8.3 Delta
8.4 Gamma
8.5 Vega
8.6 Theta
8.7 Rho
Summary
Keywords
Appendix
Chapter 9: OPTION TRADING STRATEGIES
9.1 Objectives
9.2 Introduction
9.3 Advantages of Strategies
9.4 Buying Put Option
9.5 Bear Spread with Puts
9.6 Long Put Ratio Spread
9.7 Bear Spread with Call
9.8 Synthetic Short
9.9 Short Put Ladder
9.10 Long Combo
9.11 Long Call Christmas Trees
9.12 Short Put Albatross
9.13 Short Straddle versus Put
9.14 Short Strip with Calls
9.15 Long Guts
9.16 Long Call Ladder
9.17 Long Iron Butterfly
9.18 Long Put Spread versus Short Call
9.19 Basic Option Strategies
9.20 Trading Strategy Adopted by Nick Leeson
9.21 Short Straddle
9.22 Long Straddle
9.23 Covered Call Writing
9.24 Probability
9.25 Spread Trading
9.26 Contango and Backwardation
9.27 Trading Strategies with Long-Term Options
9.28 Portfolio Hedging by Call Writing
9.29 Portfolio Hedging Through Delta Hedge
9.30 Diagonal Spread
9.31 Scalping
Summary
Keywords
Chapter 10: MARKET INFORMATION
10.1 Objectives
10.2 Introduction
Summary
Keywords
Chapter 11: RISK IN DERIVATIVES
11.1 Objectives
11.2 Introduction
11.3 Risk in Options
11.4 Is Writing Options a High Risky Strategy?
11.5 Classification of Risks
11.6 Elimination of Market Risk through Hedging
Summary
Keywords
Chapter 12: ACCOUNTING AND TAXATION OF OPTION TRADING
12.1 Objectives
12.2 Introduction
12.3 Accounting Norms for Equity and Index Options
12.4 Charges in F&O Segment
12.5 Taxation of Derivatives
12.6 Income Tax
Summary
Keywords
Chapter 13: FAQs ON OPTIONS
13.1 Objectives
Summary
Keywords
Chapter 14: DERIVATIVE GLOSSARY
14.1 Objectives
Summary
Keywords
Index

Citation preview

OPTION TRADING BEAR MARKET STRATEGIES

OPTION TRADING BEAR MARKET STRATEGIES

Sasidharan K Director Derivative Research Forum Centre for Resource Development and Research Kochi, Kerala

Alex K Mathews Head—Research Geojit PNB Paribas Financial Services Ltd. Kochi, Kerala

Tata McGraw Hill Education Private Limited NEW DELHI McGraw-Hill Offices New Delhi New York St Louis San Francisco Auckland Bogotá Caracas Kuala Lumpur Lisbon London Madrid Mexico City Milan Montreal San Juan Santiago Singapore Sydney Tokyo Toronto

Tata McGraw Hill Published by Tata McGraw Hill Education Private Limited 7 West Patel Nagar, New Delhi 110 008. Copyright © 2010, by Tata McGraw Hill Education Private Limited. No part of this publication may be reproduced or distributed in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise or stored in a database or retrieval system without the prior written permission of the publishers. The program listings (if any) may be entered, stored and executed in a computer system, but they may not be reproduced for publication. This edition can be exported from India only by the publishers, Tata McGraw Hill Education Private Limited. ISBN (13): 978-0-07-015272-4 ISBN (10): 0-07-015272-1 Managing Director: Ajay Shukla Head—Professional and Healthcare: Roystan La’Porte Executive Publisher—Professional: R Chandra Sekhar Asst. Sponsoring Editor— BGR: Dipankar Das Manager—Production: Sohan Gaur Manager—Sales & Marketing: S Girish Sr. Product Specialist—BGR: Priyanka Goel General Manager—Production: Rajender P Ghansela Asst. General Manager—Production: B L Dogra Information contained in this work has been obtained by Tata McGraw Hill, from sources believed to be reliable. However, neither Tata McGraw Hill nor its authors guarantee the accuracy or completeness of any information published herein, and neither Tata McGraw Hill nor its authors shall be responsible for any errors, omissions, or damages arising out of use of this information. This work is published with the understanding that Tata McGraw Hill and its authors are supplying information but are not attempting to render engineering or other professional services. If such services are required, the assistance of an appropriate professional should be sought. NSE Disclaimer: Wherever data from www.nseindia.com is used in this book, NSE India Ltd will not be responsible for the authenticity/mistake in the interpretation of the information and readers may refer to the original information given on the website. Typeset at Script Makers, 19, A1-B, DDA Market, Paschim Vihar, New Delhi 110 063, and printed at Rashtriya Printers, M-135, Panchsheel Garden, Naveen Shahdara, Delhi 110 032 Cover Printer: Rashtriya Printers Cover Design: Kapil Gupta, New Delhi RZZCRRBFRQBLA

To Late Smt. Sreedevi and Late Smt. Anse K. Mathews

PREFACE

Capital market in India has been witnessing high volatility since the last quarter of 2007. The global financial market meltdown has adversely affected the Indian capital market with the two stock market indices BSE Sensex and NSE Nifty touching multi-year lows. Though the fall in the market was sharp, many investors hoped for recovery and hence held on to their stocks. Unfortunately, adding fuel to fire, the US sub-prime crisis and the consequent fall in the American stock indices played a significant role in bringing down almost all the major stock indices across the world and India could not escape from this tragedy. For over six months bears have ruled the Indian capital market smashing the hopes of many investors. We hear losses of investors in bear markets, but seldom have we heard stories of gains, whereas investors are buoyant when the markets are bullish and gains are lucrative. Hence, people are afraid of and hesitant to venture into a bear market. This prompted us to think of strategies which would enable an investor to make reasonable profit even in weak bear markets. From our market experience, we identified options as the best hedging tools in weak bear market conditions and certain strategies that would help the investors to reap gains even in weak bear market conditions. This book on option trading specifically spells out these strategies. Before writing this book, we extensively researched the availability of books on this theme. Although books like Trade Options Like a Professional by James Bittmann (Powell’s Books), Bear Market Investing Strategies by Harry D. Shultz (Wiley Trading, Wiley), etc., are available in the international market we did not come across any book dealing exclusively with the weak bear market strategies suitable for all Indian market conditions. The books available in India on derivatives are specifically on futures and options and even those are more generic in nature and not market trend-specific. Hence, we felt the need for a reference book that can be used by traders, stock market professionals, FIIs arbitrageurs, hedgers and investors to protect their investments from the vagaries of a falling market. Since the book is structured around market conditions in India, all the data and trading information have been collected from the option trading platform of NSE which has the highest volume in the F&O segment in the

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Preface

Indian stock market. Even the example of pricing has been worked out using data from the F&O segment of Indian stock market. Similarly, the examples of market quotations have been culled out from Indian newspapers. The book is divided into 14 chapters. The first chapter, presents the history of derivative trading in India and the structure of Indian derivative market. The second chapter provides the basic understanding of options and option terminology. Basics of option trading, contract structure, trading mechanism, etc., prevalent in the Indian market are covered in the third chapter. The fourth chapter deals with price indices, construction of indices and components of stock indices in the Indian stock market. The fifth chapter covers the Black Scholes and Binomial Models of option pricing based on prices in the Indian derivative market. The sixth chapter on strategic option trading tools extensively deals with put/call relationship, put/call ratio and its use in option trading, open interest and volume analysis, impact cost, rollover, etc. The seventh chapter is exclusively devoted to volatility, covers both historical and implied volatility and explains the use of volatility in option trading. Another important factor in option trading is the use of Greeks which have been explained in the eighth chapter. The most important chapter is the ninth one in which 21 option trading strategies are explained with diagrammatic presentation and two reallife examples of losses in derivative trading, viz., Barings Bank and the Fall of Amaranth Advisors LLC. Besides, this chapter explains the concepts of backwardation, contango and spread, citing the Ranbaxy–Daiichi deal as example of backwardation. It also throws light on the use of probability in framing option trading strategies. The tenth chapter details the sources from which market information can be drawn. Though derivatives are risk management tools, they themselves bring in various risks. The potential risks in derivative transactions and the method of hedging them are covered in the eleventh chapter. The twelfth chapter deals with the accounting and taxation of derivative transactions and answers some of the commonly raised questions about option trading in the FAQ section— questions raised in course of various seminars conducted by us at different centres on trading in futures and options. The book concludes with a glossary of derivative terms. The book is unique in its complete coverage of all aspects of option trading specific to the Indian stock market. The book will be immensely useful for traders, investors and option dealers. The students pursuing advanced courses in behavioral finance, capital markets and derivatives will also find this book as an excellent reference material. That the book is written from the real experiences of option trader gives it another edge. We hope the readers will find this book highly useful. We encourage suggestions for further improvement. SASIDHARAN K. ALEX K. MATHEWS

ACKNOWLEDGEMENTS We have cherished the desire to write a book on derivative strategies specific to bear markets for the past 12 years. The prolonged and prevailing bearish conditions in the Indian capital market since January 2008 encouraged us to complete this task as fast as possible. We could write this book in a short time span only due to the immense support, guidance and encouragement received from various quarters—traders, dealers, professionals and academicians. First of all, we are thankful to Mr. C. J. George, Managing Director of Geojit BNP Paribas Financial Services Ltd., who greatly encouraged us to complete this task. Since the book covers real market practices, we needed the assistance of National Stock Exchange Ltd. for using market information. Hence, we thank NSE for permitting us to reproduce the data available on their website in this book. We also express our sincere gratitude to the management of The Economic Times for giving us permission to reproduce the articles from the ET in this book. We are thankful to the editorial team of Tata McGraw Hill Education (India)— R Chandra Sekhar, Dipankar Das, Sindhu Ullas and their colleagues—for the efforts they have put in bringing out our book in an excellent shape. We owe a special thanks to Mr. Tapas Maji of Tata McGrawHill who persuaded us to complete this task and referred us to their professional publication team. We are thankful to the top management team of Geojit BNP Paribas Financial Services Ltd.—Mr. Satish Menon, Mrs. Jaya Jacob Alexander and Mr. Balakrishnan—for their immense support and encouragement. We also thank Mr. Johny Varghese, Mr. Sudheer Kumar, Mr. K.K. Alexander, Mr. Saju Varghese, Mr. Tency Kurien, Mr. Ashon Mathew, Mr. Ganesh, Mr. Vishnu Das and Mr. Franklin for their support and assistance. We are grateful to Mr Sanil Abraham of MM news channel who was literally a helping hand in proof reading the book. We are extremely thankful to our family members who spared us from many of our commitments to them and helped us go on with this task. Finally, we thank the Almighty for giving us health, intelligence and power to complete this task witshin the scheduled time. SASIDHARAN K. ALEX K. MATHEWS

x

Contents

CONTENTS Preface Acknowledgements 1

INTRODUCTION 1.1 1.2 1.3 1.4 1.5 1.6

2.

1

Objectives 1 Indian Derivatives Market: An Overview 1 What are Derivatives? 3 Evolution of Derivative Trading in India 3 Participants in Derivatives Market 4 Types of Derivatives 5 Summary 8 Keywords 8

UNDERSTANDING OPTIONS 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 2.12

3.

vii ix

Objectives 9 Introduction 9 Options: An Overview 9 Types of Options 10 Advantages of Options 11 Terminologies in Options 12 Trading System 16 Procedure for Margin Collection 18 Types of Orders 21 Settlement Schedule for Option Contracts Settlement Mechanism 22 Writing of Options 23 Summary 23 Keywords 23

OPTION TRADING 3.1 3.2 3.3

Objectives 25 Introduction 25 Market-wide Limits Summary 39 Keywords 39

9

22

25

25

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4.

PRICE INDEX 4.1 4.2 4.3 4.4 4.5 4.6

5.

PRICING OF OPTIONS 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8

6.

89

Objectives 89 Introduction 89 Black–Scholes Option Pricing Model 89 Pricing of Equity Options 94 Pricing of Options on Dividend Paying Scrips Binomial Model of Option Pricing 96 Pricing of Binomial Put Option 98 Binomial Multiple Period Model 99 Summary 101 Keywords 101 Appendix 101

95

STRATEGIC DERIVATIVE TOOLS 6.1 6.2 6.3 6.4 6.5 6.6 6.7

7.

41

Objectives 41 Introduction 41 What is an Index? 41 Eligibility Criteria of Indices 42 Construction of Index 42 Desirable Attributes of an Index 43 Summary 87 Keywords 88

Objectives 103 Introduction 103 Put–call Parity 103 PC Ratio 109 Weighted PC Ratio 110 Volume PC Ratio 111 Tools to Measure Market Sentiment Summary 120 Keywords 121

103

112

VOLATILITY 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8

Objectives 123 Introduction 123 Types of Volatility 124 Estimating Volatility 125 Estimating Historical Volatility 125 Factors Affecting the Computation of Historical Volatility Implied Volatility 130 Volatility Smile 130

123

128

Contents xiii

7.9 GARCH 133 7.10 Impacts of Implied Volatility and Underlying Asset Price on Purchase of Options 136 7.11 Volatility Trading 137 7.12 NSE Volatility Index 138 7.13 Behavioral Study of Nifty Options during Distress 139 7.14 Impacts of Events on Volatility—A Case Study 141 7.15 Comparative Study of the Behaviour of Nifty and IT Stocks During an Event 145 7.16 Impact of Quarterly Results on Stock Futures 150 7.17 Volatility Skew 151 7.18 Stochastic Volatility 152 7.19 Volatility Arbitrage 153 7.20 Volatility Change 153 Summary 154 Keywords 154

8.

OPTION GREEKS 8.1 8.2 8.3 8.4 8.5 8.6 8.7

9.

Objectives 155 Introduction 155 Delta 155 Gamma 159 Vega 161 Theta 164 Rho 167 Summary 169 Keywords 169 Appendix 170

OPTION TRADING STRATEGIES 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 9.9 9.10 9.11 9.12 9.13

155

Objectives 171 Introduction 171 Advantages of Strategies 171 Buying Put Option 172 Bear Spread with Puts 175 Long Put Ratio Spread 177 Bear Spread with Call 178 Synthetic Short 179 Short Put Ladder 181 Long Combo 182 Long Call Christmas Trees 183 Short Put Albatross 185 Short Straddle versus Put 187

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9.14 9.15 9.16 9.17 9.18 9.19 9.20 9.21 9.22 9.23 9.24 9.25 9.26 9.27 9.28 9.29 9.30 9.31

Short Strip with Calls 188 Long Guts 189 Long Call Ladder 191 Long Iron Butterfly 191 Long Put Spread versus Short Call 193 Basic Option Strategies 195 Trading Strategy Adopted by Nick Leeson 197 Short Straddle 210 Long Straddle 212 Covered Call Writing 213 Probability 214 Spread Trading 217 Contango and Backwardation 220 Trading Strategies with Long-Term Options 221 Portfolio Hedging by Call Writing 226 Portfolio Hedging Through Delta Hedge 226 Diagonal Spread 227 Scalping 227 Summary 227 Keywords 228

10. MARKET INFORMATION 10.1 10.2

Objectives 229 Introduction 229 Summary 240 Keywords 240

11. RISK IN DERIVATIVES 11.1 11.2 11.3 11.4 11.5 11.6

241

Objectives 241 Introduction 241 Risk in Options 241 Is Writing Options a High Risky Strategy? 241 Classification of Risks 242 Elimination of Market Risk through Hedging 243 Summary 246 Keywords 247

12 ACCOUNTING AND TAXATION OF OPTION TRADING 12.1 12.2 12.3 12.4 12.5

229

Objectives 249 Introduction 249 Accounting Norms for Equity and Index Options 249 Charges in F&O Segment 251 Taxation of Derivatives 251

249

Contents

12.6

Income Tax 251 Summary 252 Keywords 252

13 FAQs ON OPTIONS 13.1

Index

253

Objectives 253 Summary 261 Keywords 262

14. DERIVATIVE GLOSSARY 14.1

xv

263

Objectives 263 Summary 275 Keywords 276 279

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CHAPTER

01

INTRODUCTION

1.1

OBJECTIVES

The objective of this chapter is to familiarize the readers with the concept of derivatives, Indian derivative market and the different types of derivatives available in the market.

1.2

INDIAN DERIVATIVES MARKET: AN OVERVIEW

Though derivative trading has been in existence in India in commodity markets since ancient times, the financial derivatives came into existence in the late 1990s. The first step was the promulgation of the Securities Laws (Amendment) Ordinance, 1995, which withdrew the prohibition on option trading in securities. The L.C. Gupta panel, appointed by Securities and Exchange Board of India (SEBI) to develop appropriate regulatory framework for derivatives trading played a crucial role in the introduction of equity derivative in the Indian capital market. Later, the J.R. Verma Committee brought out extensive risk containment measures which facilitated the launching of stock derivatives and index derivatives in India. The trading on index futures was commenced on 12 June 2000, followed by index options on 4 June 2001, options on individual securities on 2 July 2001, and individual stock futures on 2 November 2001. The two major indices traded in the Indian capital market are Sensex of Bombay Stock Exchange (BSE) with 30 scrips in its basket and Nifty of National Stock Exchange Limited (NSE) with 50 stocks in its fold. Simultaneously, the derivatives were introduced in foreign currencies (USD/INR). Although Reserve Bank of India permitted banks to use credit derivatives for managing their credit risk and interest rate derivatives (IRDs) for managing the interest rate risks, these instruments did not pick up as expected. The derivative trading in commodity market also became active with the initiative of three major exchanges, viz. National Multi Commodity Exchange of India, Multi Commodity Exchange of India, and National Commodity and Derivative Exchange. The picture shows the growth in derivative segment as a whole (NSE) which includes stock futures, index futures, index options and stock options. The growth in number of contracts in 2007 was partly due to reduction

2

Option Trading

in options’ lot size; for example the Nifty lot size has been reduced from 200 to 100. Later, in 2007 it was again reduced to 50 (Fig. 1.1). 450000000 400000000 350000000 300000000 250000000

No. of companies

200000000

Turnover

150000000 100000000 50000000

9 –0

8 20

08

–0

7 20

07

–0

6 20

06

–0

5 20

20

05

–0

4

Fig. 1.1

04

–0

3 20

03

–0

2 02

–0 20

01 20

20

00

–0

1

0

Derivative growth in India

Over a period of time, the growth in Nifty options has increased spontaneously due to higher participation from foreign financial institutions, domestic investors including arbitrage funds and portfolio hedgers. In the beginning of 2008, we had seen a drastic drop in individual stock option segment due to extreme market volatility (Fig. 1.2). 1400000 1200000 1000000

Now options or Stock options or

800000 600000 400000 200000

20 00 – 20 01 01 – 20 02 02 – 20 03 03 20 –04 04 20 –05 05 20 –06 06 20 –07 07 20 –08 08 –0 9

0

Fig. 1.2

Options growth in India

Introduction

1.3

3

WHAT ARE DERIVATIVES?

Derivatives are financial contracts structured on an underlying asset, which could be shares, bonds, loans, commodities, indices etc. These contracts help the investor to mitigate the price risk arising out of movements in prices of the underlying assets. Derivatives have helped corporations to maximize their profits by reducing their risk in respect of financial transactions. A derivative contract helps a firm or an individual to fix the price of the underlying asset for delivery on a future date. Some derivative contracts such as options have only the right but not the obligation to deliver the underlying asset. As a result a buyer of a derivative contract can opt for not taking giving delivery of the underlying asset if the price moves against the buyer of the contract by paying a price known as premium while entering into the contract. However, the derivatives have turn out to be too risky when the dealers resort to speculation. They have also caused substantial loss to the corporations and financial institutions, ultimately leading to closure of some of them. The latest among these is the story of Northern Rock Bank in USA.

1.4

EVOLUTION OF DERIVATIVE TRADING IN INDIA

The derivative markets developed as a result of the consciousness that derivatives could be used to manage the risk involved in capital markets. Derivatives have been in existence in India in some form or the other for a long time. If we speak about commodities, the Bombay Cotton Trade Association started futures trading in 1875, and by the early 1900s India had one of the largest futures industry. These derivative instruments protect the investors against adverse market conditions and can also be used to hedge investors’ position thereby helping in reducing cost. As the government in 1952 imposed a ban on cash settlement, option trading and derivative trading shifted to informal forward contracts. The RBI in June 2000 allowed the trading in security derivatives on stock exchanges. The major contributory factors for downfall/success of derivative markets include the underlying market and of course the market culture. The Securities Contracts Regulation Act of 1956 was implemented to prevent gambling in contracts by prescribing appropriate regulations to restrict such activity. Before the inception of derivatives in India, there was ‘forward trading’ in securities using the instruments like ‘Teji’, ‘Mandi’ and ‘Fatak’. A series of reforms of the stock market between 1993 and 1996 paved the way for the development of exchange-traded equity derivatives market in India. Further, the improvements brought to NSE unleashed of new avenues. In 1996, the NSE had sent a proposal to SEBI for listing exchangetraded derivatives. The liberalization policy of the Congress government in the 1990s helped the introduction of derivatives based on interest rates and foreign exchange.

4

Option Trading

The NSE had launched interest rate futures in 2003, but there has been little trading in these interest rate futures when compared to the equity derivatives. The main reason for less interest in these instruments was faults in the contracts’ specifications, leading to deviation of the underlying interest rate from the reference rate used by the investor. One another less active derivative counter was foreign exchange derivatives which were less active than IRDs when introduced. But, the whole scenario changed with the foreign exchange derivatives becoming the active derivative instrument rather than interest rate derivative. In foreign exchange derivatives, the most popular ones are currency forwards and swaps. In a currency swap, banks and corporations may swap its rupee-denominated debt into another currency or vice versa. Exchange-traded commodity derivatives have been traded only since 2000, and we have seen a significant growth in this market. The number of commodities eligible for futures trading increased from 8 in 2000 to 80 in 2004 with the trading value clocking almost four times. The users of derivatives in India include many financial institutions such as banks that have assets and liabilities of different maturities and that too in different currencies which are exposed to different risks. The IRDs and the foreign exchange derivatives help them in managing their credit risk. Transactions between banks dominate the market for IRDs but the state-owned banks contribute only a small part. The corporations on the other hand are active in the currency forwards and swaps markets, buying these instruments from banks.

1.5

PARTICIPANTS IN DERIVATIVES MARKET

Participants in derivative trading include dealers, hedgers, speculators, and arbitrageurs according to the nature of investment.

Dealers are those who put orders to buy or sell on behalf of clients, institutions such as banks, mutual funds, securities houses etc., and they are the end-users of the trading channel.

Hedgers consist of corporations, investment institutions, banks, governments and individual clients who want to reduce exposure to market variables such as interest rates, share values, bond prices, currency exchange rates and commodity prices. Their aim is to mitigate the risks associated with the underlying price of an asset, whether in equities or in commodities. Consider an institutional investor holding 3000 Reliance shares bought at a price of Rs. 2000. They can hedge their open position in Reliance by entering into an opposite position in the futures. Here, they can sell 40 lots of Reliance futures (1 lot = 75 shares) for the same price. If the price moves down drastically, they can cover their shorts in the futures at a profit.

Speculators are people who expect higher returns than average profits. They will take high risks in the expectation of making quick bucks. A speculator is interested in capital gains rather than the income from the investment.

Introduction

5

Arbitrageurs constitute another group of participants who takes the advantage of a price differential between two or more markets. If futures of a commodity or a stock trades substantially above the cost of carry, arbitrageurs sell the expensive futures and they will buy the spot. Thereby, they will make a riskless profit.

1.6

TYPES OF DERIVATIVES

The derivatives can be classified into commodity derivatives, financial derivatives, weather derivatives etc. The innovations in the market may give rise to new class of derivatives based upon the customer demand.

Commodity derivatives Commodity derivatives are those contracts where the underlying assets are commodities. The best example for commodity derivatives in India is pepper futures, oil futures, jute futures, hessian futures etc. Rubber derivatives were introduced to hedge the risk arising out of the price movement in the rubber market. This mechanism will enable the growers to get a fair return on their investments.

Financial derivatives As mentioned, a financial derivative is a contract based on a financial asset. The asset can be a loan or a deposit. Financial derivatives are traded in the financial market. The major financial markets where the derivatives are traded are Mumbai and Chennai. Financial derivatives can further be classified into:

Equity derivatives where the underlying assets are shares. Interest rate derivatives are hedging tools to manage the risk arising out of interest rate movement. The IRDs can be based on debentures or bonds or simple plain vanilla derivative like bond futures, treasury bill futures, interest rate options, interest rate swaps etc.

Credit derivatives are structured based on credit instruments or loans where the Pay off is decided based on a credit event. These contracts are linked to a third party reference asset. Credit default swaps, credit default options, collateralized bond obligations etc. are examples of credit derivatives.

Index derivatives are contracts structured based on indices. The pay off is determined based o n the movement of indices. The indices can be that of stock, commodities etc. Examples of index derivatives are CNX Nifty futures, CNX Nifty options etc.

Currency derivatives represent contracts covering foreign currency obligations or claims. The payoff is decided based on the price of the currency. The important types of currency derivatives are currency futures, currency options, currency swaps etc.

Weather derivatives Weather derivatives are financial products that enable an organization to offset the financial risk due to a weather variable. They allow companies to control the effects of weather on demand for their

6

Option Trading

products. This hedging reduces the volatility of future revenue to a more predictable cash flow. A common measure of temperature that arose from the market is a degree-day. A degree-day is the deviation of a day’s average temperature from the reference temperature. This was found to be a useful measure that the energy suppliers could use to hedge their supply in adverse temperature conditions. The common forms of weather derivatives are call options, put options, caps, floors, collars and swaps. Some of the exotic varieties like one-touch, digitals, barrier and basket options are also structured to meet the specific needs.

Forwards Forwards contracts are the traditional form of hedging tools extensively used in India. They are OTC derivatives and are not traded on exchange floors. Forward contracts are not standardized; hence, full hedging is possible. Another important aspect of forward contracts is that the underlying assets are to be delivered upon receipt of payment from the counterparty. Cancellation of forward contracts before the expiry date involves cost. Similarly, the parties have to pay the charges for early delivery and extension of contracts, if the price moves against the bank.

Futures Futures are contracts structured for the delivery of the underlying assets on an agreed future date; the delivery may or may not take place. Where the cash settlement happens, the counterparty will take delivery or give delivery. Otherwise, the contract will be wound up by settling the difference between the contract value and the spot price at the time of maturity. The futures can be equity futures, index futures, interest rate futures, currency futures, commodity futures or weather futures. Futures are generally exchange-traded. Therefore, they are less risky and highly liquid. The advantage of futures contract is that the investor can liquidate his position at any time since the exchange is the counterparty. The exchange ensures the settlement under the contract. For this purpose, they collect margin from the contracting parties.

Options Options are financial contracts entered between two parties where one party has the right to give or take delivery of an underlying asset but has no obligation to do so, whereas the other party has the obligation to give or take delivery. Options can be a call option where a person having short position in the asset buys the option. In the case of put option, a person holding long position sells the option to hedge against the downward movement of price. The seller of the option is otherwise known as writer and the counterparty is known as the option buyer. The process is known as writing of option. Options can be American or European. In the case of American option, the buyer can exercise the contract at any point of time before the maturity. In the case of European option, the contact can be exercised only on the expiry date. All equity options are American options whereas all index options are European options. The options can be plain vanilla options, compound options or complex options such as futures with option, swaption, exotic options, etc.

Introduction

7

Swaps A swap is a contract entered between two parties for exchange of cash flows with identical maturities for the purpose of taking advantage of comparative advantage enjoyed by either one or both. The swap can be an interest rate swap or a currency swap. In the case of an interest rate swap, the contracting parties exchange a floating rate with a fixed rate, a fixed rate with a fixed one or a floating rate with floating. The currency swaps enables the contracting parties to exchange the cash flows in two different currencies for taking advantage of the interest rate differentials as well as the exchange rate differentials. The principal under swap contracts are notional and only the interest rate differentials are exchanged by the contracting parties. Unlike options or futures, the swaps can be liquidated only on maturity. Therefore, swaps are illiquid. The swaps can be equity swaps, commodity swaps, currency swaps or compound swaps like swaptions.

Forward rate agreements A forward rate agreement (FRA) is a derivative contract, which protects the buyer of FRA from changes in interest rate. Normally, FRAs are long-term contracts, say for 3 years, 5 years etc., with intermittent reset dates. For example, an FRA with maturity of 3 years can have interest rate reset dates at the end of every 6 months. An FRA by a company, which has decided to avail a loan from a bank for a period of 6 months on a date 3 months from then, ensures an interest rate which is mutually agreed upon by the FRA buyer and the seller. In this contract, the FRA seller, normally a bank, agrees to pay the interest rate differential in case the interest rate prevailing at the time of availing the loan as well as on the reset dates are more than the agreed rate. If the interest rate happens to be less than the agreed rate, the buyer has to pay the differential to the seller. A typical FRA transaction is given in Fig. 1.3. 6 Months 3 Months

Current Date

3 Months

Sanction Date

3 Months

Reset Date

Maturity Date

Fig. 1.3 A typical FRA transaction In this diagram, the contract is entered on date CD for a loan to be availed on date SD, 3 months after CD. The loan period is 6 months. During the period of 6 months, the interest will be reset on RD. If the rate of interest agreed upon at the time of signing the contract is 9% and the actual rate of interest at the time of availing the loan is 10%, the FRA seller will pay 1% to the buyer. If on the reset date the interest rate falls to 8.5%, the FRA buyer has to pay 0.5% to the FRA seller. FRAs can be used effectively for hedging the risks in respect of investments as well as borrowings.

8

Option Trading

Caps, floors and collars Caps and floors are set of option contracts entered into to hedge the risk arising out of movement of interest rates. When there is more than one cap or floor, it is known as caplets or floorlets. The caps protect the cap buyer from an upward movement of interest rate. The cap differs from FRA because in the case of cap the buyer can abandon the contract if the rate moves downwards, in which case his maximum loss is the upfront premium paid by him. However in the case of FRA, the FRA buyer cannot abandon the contract, but has to pay the difference. A floor is an option contract, which protects the floor buyer from downward movement of interest rate. In the event of a fall in the interest rate, the FRA seller will pay the difference to the FRA buyer, whereas if the rate moves upwards the buyer need not pay anything to the seller. His maximum loss is the upfront premium paid by him to the seller at the time of writing the option. A collar is a combination of caps and floors. The collar helps the investor to hedge against both upward and downward movement of interest rates. In India, the major transactions take place in futures and options segment of the capital market, commodity market and currency market . In this book, we will focus on option especially on option strategies which are meant for a bearish stock market. The options and their features, various strategies in a bear market, accounting and taxation will be discussed in detail in the subsequent chapters.

Summary In this chapter we have broadly discussed the concept of derivatives, evolution and structure of derivative markets in India, market participants and different types of derivatives. This will remain as a basic platform on which we have built up the remaining chapters.

Keywords Derivatives Dealers Speculators Options FRA

National Stock Exchange Hedgers Arbitrageurs Futures Caps

Bombay Stock Exchange Commodity exchanges Forwards Swaps Collars

CHAPTER

02

UNDERSTANDING OPTIONS

2.1

OBJECTIVES

Having understood the concept of derivatives and their varieties, we shall now move on to the specific area of derivative products. One of the most common derivative products is options. In this chapter, we will discuss the basic concepts of options and the option terminologies.

2.2

INTRODUCTION

Options are generally classified into call options and put options. These are traded based on their premiums. American options can be exercised during the lifetime of the contract whereas the European options can be exercised on the day of expiry. The risk of option buyers is limited to the extent of the premium that they have paid for while purchasing the same. But option writers undertake high risk and, therefore, high margins are required for writing options.

2.3

OPTIONS: AN OVERVIEW

The basic parameter for a professional investor to choose a financial product is its risk–return profile. This paved the way for the origin of derivatives in the financial market worldwide. In the previous chapter, we have discussed various types of derivative products. Derivative markets around the world act as a price protection mechanism for the investors. The values of these products are derived from their underlying value whether it is an asset, an index or reference rate. These derivative products are gaining day-to-day importance in the rising volatile conditions of the financial market and on increased participation of investors. In the initial days of derivative market, they were meant simply for hedging, but later on they began to be used for speculation and arbitrage opportunities. Of the several variants in derivatives, options are preferred by investors for investing, hedging and speculative purposes. To recapitulate what we have

10

Option Trading

discussed in the previous chapter: ‘An option buyer has the right to buy/ sell an underlying at a fixed price (strike) on a future date. The seller has the obligation to deliver/buy the underlying if the buyer desires to exercise the option. Purchasing an option requires an upfront payment, while it costs nothing to enter into a futures and forwards contract (Tables 2.1 and 2.2). Table 2.1 Distinction between Futures and Forwards Futures Futures contracts are traded on an organized exchange.

Forwards Forward contracts are OTC (over-the-counter) in nature.

Futures contracts are standardized contracts.

Forward contracts are customized.

Futures contracts are more liquid.

Forward contract is less liquid.

Futures require margin payment.

No margin payment is needed for Forward contracts.

Futures contracts are settled on a daily basis.

Forward contracts are settled only at the end of a period.

No counterparty risk.

Counterparty risk may happen.

Table 2.2 Distinction between Options and Forwards Options Options are traded on an organized exchange or OTC.

Forwards Forward contracts are OTC (overthe-counter) only in nature.

Option contracts are standardized contracts.

Forward contracts are customized.

Option contracts are more liquid.

Forward contract is less liquid.

Exchange traded options require margin payment.

No margin payment needed for forward contracts.

Option contracts are settled on a daily basis (American options).

Forward contract settlement is only at the end of a period.

No counterparty risk.

Counterparty risk may happen.

2.4

TYPES OF OPTIONS

Options are broadly classified into two categories: (1) call option and (2) put option (Fig. 2.1). Call option may be defined as an instrument that gives the option buyer the right but not the obligation to buy an agreed quantity of a financial instrument from the option seller on a certain date at a previously agreed-upon price. The put option gives the put buyer the right but not the

Understanding Options

11

obligation to sell a financial instrument on a certain date at a previously agreed-upon price. Further, the options are classified into American and European options. The American options give the investor the choice to exercise his right at any time during the lifetime of the contract, while the holder of the European option can only exercise his option at the end of the life of the contract. CALL OPTION AMERICAN OPTIONS

STOCK OPTIONS PUT OPTION OPTIONS CALL OPTION

EUROPEAN OPTIONS

INDEX OPTIONS PUT OPTION

Fig. 2.1

2.5

Types of options

ADVANTAGES OF OPTIONS

The options offer the advantages as discussed further.

Cost effectiveness. The cost incurred by an investor in options is just the premium amount multiplied by the market lot of an index or stock option, instead of investing a huge amount to purchase a stock. For example, if you want to buy 200 Infosys shares from the cash market when it is trading at around Rs. 1300, then you have to pay an amount equal to Rs. 2,60,000, but if you are buying Infosys option at the strike price of 1300, you will have to pay the premium, for example, Rs. 40 multiplied by the lot size which is 200. So the amount that you must pay will be just Rs. 8000.

Low risk and high returns In options, the maximum amount of money lost in the case of buying call and put is the premium amount only. The loss of the option buyer is limited and the profit is unlimited, while the option seller’s profit is limited to the option premium and the loss, on the other hand, is unlimited. Suppose you are holding 75 shares of Reliance Industries at a purchase price of Rs. 2000, but you are afraid that there can be a fall in price due to political uncertainties. So, you thought of buying put options of Reliance Industries. The price of Reliance 2000 put option is Rs. 50 when the spot is trading at Rs. 2100. You can buy one lot of Reliance 2000 put option by paying Rs. 3750 (75 ´ 50). The put option will expire on 30 December, 2008.

Points to remember: 1. The expiry of Reliance Industries’ put option is on 30 December, 2008. So, you can hold on the position till 30 December.

12

Option Trading

2. You bought the Rs. 2000 put option at a premium of Rs. 50. The maximum loss is limited to Rs. 50 ´ 75 = Rs. 3750. 3. The investor will get a protection below Rs. 1950 (2000 – 50). 4. If the stock does not fall below Rs. 2000, then your investments are safe, but you will lose the premium of Rs. 3750. An investor who is bearish on the market will buy put options. Investors with bearish view on Nifty (market) will buy Nifty put options. Assume that Nifty is trading at 5000 and you are expecting Nifty to test 4500 in the month of October. So you buy 100 Nifty put October options at Rs. 100. Premium can be the maximum loss. If Nifty falls below 5000, then you start making profits (5000 –100), and if it is assumed to close at 4000 then you may earn Rs. 99,900 ((1000 ´ 100) –100). Here, the premium is the maximum loss. Low margin requirement: Another distinct advantage of options is the low margin requirement. The purchase of call and put option attracts only the premium for the options. In other words, the margin is limited to the premium of the options. The seller of the call option and put options has the obligation to pay higher margin because of the unlimited risk exposure. The margins are calculated using VaR model (value at risk) by the National Stock Exchange.

2.6

TERMINOLOGIES IN OPTIONS

The following are some of the major terms used in option trading. Long: One is long in a stock when he is having a purchase position in it. In the same way, buying positions in call or put options can be termed as long positions.

Short: The term ‘short’ is used when one has selling positions in call or put options.

Opening buy (buy open): Opening buy refers to the purchase of an option contract either call or put.

Opening sell (sell open): Opening sell means a sale position in either the call or put options to create fresh short positions. Close out: Close out is the opposite transaction of buy open and sell open which closes the open positions fully or partly. For example, a short position in an option contract can be offset by the purchase of a contract having same characteristics while a purchase position in an option contract can be offset by the sale of a contract having the same characteristics.

Closing buy (buy close): Closing buy refers to a purchase transaction that offsets a short position either wholly or partly (sale position). For example, a call option seller can close his short position through the purchase of the same contract.

Understanding Options

13

In order to cover a short position in call having some special features such as underlying asset, strike price, exercise date, etc., one should select a call option having the same characteristics. For example, one sold Nifty 2500 call option expiring on March 2009, if s/he wants to close out the short, s/he has to buy 2500 call option expiring on March 2009. Note that a call option cannot be closed out by a put option or vice versa. Though both the options should fundamentally be the same, the premium on which they are bought and sold may be different since this is determined by the market forces from time to time. It gives an opportunity to the trader to make gains from buying and selling of option contracts.

Closing sell (sell close): Closing sell means a sale transaction, which offsets a long position either wholly or partly. For example, the buyer of a put or call can eliminate his long position by effecting a sale in the same type of contract, and this is similar to squaring up of long positions in the equity market. As stated earlier, the difference in premium, if any, is the profit/loss of the trader.

Option class: A set of options that are identical with respect to type and underlying asset.

Option series: An option series consists of all the listed option contracts of a given class that have the same strike price and expiry date.

Open interest: Open interest on a contract refers to the total number of such contracts outstanding at any point of time. Open interest increases when a fresh contract of the same variety is bought or sold in the market and decreases as an existing contract is extinguished by squaring up/settlement by the buyers and sellers.

Strike price: It is the price at which an underlying stock can be purchased or sold. Alternatively, it is the price at which a specific derivative contract can be exercised. Strike prices are mostly used to describe stock and index options, in which strike prices are fixed in the contract. For call options, the strike price is the price at which the security can be bought (up to the expiration date), while for put options the strike price is the price at which shares can be sold.

Expiration date: It is the date on which the contract expires and the holder of an option can opt to exercise the option or can allow it to expire worthless. In India, expiration date for option contract is fixed as the last Thursday of the month. In case of holiday on Thursday, expiration will happen on the previous trading session. Let us assume that we had taken a long position in August 2008 put option at Rs. 35. The August 2008 series will expire on the last

14

Option Trading

Thursday of August 2008, which is 28th. If you want to carry over the purchase put option to September 2008 on or before 28 August, you should sell August put option and you have to buy a new contract expiring on 25 September (last Thursday of September). Purchase or sale position of put option or call option can be kept till the expiry of that particular series or one can trade on premium. Say, for example, you bought a call option for Rs. 105 September 2008 series and you can sell call position when the call premium increased to Rs. 120. Therefore, you will make a profit of Rs. 15 per share.

Premium: It is the price that the option buyer pays to the option seller. The option buyer and seller determine option premium. Option premium consists of intrinsic value and time value. The premium will change according to the demand and supply of the option. If the market is bearish, there will be more demand for the put option thereby causing an increase in premium. If the market is bullish, then the call buyer will pay more premium for the call options. Many investors are calculating option premium using various methods like Black Scholes formula, binomial trees and trinomial trees. In India, investors normally track Black Scholes formula for finding out the theoretical option prices. The Black Scholes formula is commonly used by investors to find out the theoretical price of the Nifty options. Pricing of options using Black Scholes formula is discussed in Chapter 5.

Intrinsic value: In options, intrinsic value is the amount of in the money portion in the premium value. This means the real value for both call and put option, with respect to their different strike prices against respective spot prices. For a call option, intrinsic value is the difference between the underlying asset’s spot price and strike price. But, for a put option, intrinsic value is the difference between strike price and the underlying asset’s spot price. For example, a 170 call option of RPL trading at a premium of Rs. 17, with spot price of Rs. 180; will have an intrinsic value of Rs. 10 (180 - 170). And a put option of 200 RPL put trading at a premium of Rs. 25 will have an intrinsic value of Rs. 20 (200-180).

Time value: It is the difference between the premium and intrinsic value of an option. It is the real demand for the stock by investors on their expectation towards stock price. It is also referred to as the value that an option has in addition to its intrinsic value. In the above example of call option of RPL, Rs. 7 (17-10) is the time value of RPL 170 call, whereas, for put option, it has a time value of Rs. 5 (25-20). An option is said to have time value, if it is at the money or out of the money. At the money option has the maximum time value as the intrinsic value will be zero when both the strike and spot prices are same.

Understanding Options

15

At-the-money: An option is said to be at the money when the current spot price equals the strike price in the case of both call and put options.

In the money: An option is said to be in the money if the exercise of the option leads to cash inflow to the holder of the option. A call option is said to be in the money if the option’s strike price is below the market price of the underlying. And, if the spot price is much higher than strike price, that call option is said to be deep in the money. But, for a put option, it is the inverse,that is a put option is said to be in the money only if market price of the underlying is below the strike price. If the spot price lies far below the strike price, that put option is said to be deep in the money.

Out of the money: An option is said to be out of the money, if the option becomes worthless on expiry. A call option is said to be out of the money if the spot price of the option is below the strike price. If the spot price is far lower than the strike price, that call option is said to be deep out of the money. In the case of a put option, an option is said to be out of the money if the strike price lies lower than the spot price and is said to be deep out of the money, if the strike price is much lower then the spot price. A brief view on different option types are given in Tables 2.3 and 2.4. Table 2.3 Strike Price

Spot Price

Call Premium

Option Type

1500

1600

150

Deep ITM

1550

1600

100

ITM

1600

1600

60

ATM

1650

1600

35

OTM

1700

1600

15

Deep OTM

Table 2.4 Strike Price

Spot Price

Put Premium

Option Type

1500

1600

15

Deep OTM

1550

1600

30

OTM

1600

1600

90

ATM

1650

1600

120

ITM

1700

1600

160

Deep ITM

Source: www.nseindia.com

16

Option Trading

Price Bands: To prevent erroneous order entry, operating ranges or daily minimum and maximum ranges are fixed for derivatives segment. · For Index Futures: 10% of the base price · For Index and Stock Options: Upper Operating range + 99 % of base price or Rs. 20, whichever is higher, · For Stock Ffutures: 20 % of the base price Source: www.nseindia.com

Volatility: Volatility represents the changes in the prices of underlying asset. Volatility is one of the factors considered for calculating option price. Volatility can be historical volatility or implied volatility. While historical volatility is computed from past data relating to the prices of the underlying asset, implied volatility is embedded in the option price (premium) and varies according to the market conditions. Volatility is dealt with in a more detailed manner in another chapter. American and European options: In the European model of options, contract buyers are allowed to exercise their right to buy or sell (the asset) on the settlement day alone and the settlement day may probably be the expiry day of the contract. However, buying and selling positions could be squared up at any time in the market by entering into a reverse transaction. In the American model of options, call or put option holders can settle their claims by exercising the right to buy or sell on any day that falls between the date of entry and the expiry date. In India, both call and put options are settled in cash whereas in US, it is settled in stock.

2.7

TRADING SYSTEM*

The futures and options trading system provides a fully automated trading environment for screen-based, floor-less trading on a nation-wide basis and an online monitoring and surveillance mecha-nism. The system supports an order-driven market and provides complete trans-parency of trading operations. Orders, as and when they are received, are first stamped and then immediately processed for potential match. If a match is not found, then the orders are stored in different ‘books’. Orders are stored in price–time priority in various books in the following sequence: · Best price · Within price, by time priority.

2.7.1 2.7.1.1

Margin Requirements for Investors Margin on Purchases of Options

An investor who has taken long position in an option (call or put) will have to pay a margin equal to the premium of the option. In this case, the risk of the investor is limited. For example, if an investor goes long on Infosys by buying a 1300 call option when the premium is at Rs. 64 and the lot size being 200, she has to pay a margin amount equal to Rs. 12,800 (64 ´ 200). *

Source: www.nseindia.com

Understanding Options

2.7.1.2

17

Margin on Selling of Options

An investor who has taken short position in an option will have to pay not only the initial margin but also the mark–to-market margin. This is because of the risk factor involved with a short position on an option. For example, if an investor goes short on Infosys by selling a call option of 1300 when the premium is at Rs. 64 and the lot size being 200, he will receive an amount equal to Rs. 12,800 but has to pay the initial margin prescribed by the NSE span margin (e.g. if the initial margin fixed by NSE is 15%, then the investor has to pay Rs. 40920 as the initial margin amount [(1300 + 64) ´ (200 ´ 15/100)]) and also the mark-to-market margin at the end of every day as the difference between the sale price and the closing price multiplied by the lot size which will be debited to the client account (e.g. if the premium increased from Rs. 64 to Rs. 70 at the close of the day, then the sellers have to pay Rs. 1200 at the end of the day as mark-to-market margin apart from the initial margin).

2.7.2

Margins for Trading Members

Online position monitoring and margining system is the most critical component of risk containment mechanism for futures and options segment introduced by NSE. Actual margining and position monitoring is done on an intra-day basis. A portfolio-based system called Standard Portfolio Analysis of Risk (SPAN) is used by National Securities and Clearing Corporation Ltd. (NSCCL) for the purpose of margining. The overall risk in a portfolio of futures and options are identified using SPAN for each member. F&O contracts are treated in the same way by the system, identifying unique exposures associated with options portfolios like extremely deep out of the money short positions, inter-month risk and commodity risk. The main objective of SPAN is to identify the largest loss, which may incur by a portfolio from one day to the next day. SPAN then sets margin requirement at a level sufficient to cover the day’s loss. The factors affecting value of an option for Standard Pricing Models are: · Underlying market price · Volatility of underlying instrument · Time to expiration SPAN uses delta information to form spreads between futures and options contracts. Futures and option’s value change in relation to the changes in the value of the underlying is measured as Delta. It is 1.0 for Futures and -1.0 to +1.0 for Options. Option deltas are dynamic, by which a change in the underlying will cause change in both option’s price and delta.

2.7.3

Margins for Option Trading

The loss incurred for the buyer of a call/put option is the premium amount which is paid on the initial, while that for writer of an option, the loss is equal

18

Option Trading

to the difference between strike price and spot price multiplied by the number of options. Often, the buyer of an option, in the case of both call and put options need to pay only the premium amount as margin, whereas the seller of an option (call or put) has to pay short option minimum charge towards the exchange. The seller of an option has the obligation to buy or sell the underlying on the discretion of the buyer and so the loss for the seller of the option is unlimited. Hence, the seller has to pay an initial margin to the exchange, because of probability of unlimited losses that can occur for a day, as default from the seller may cause settlement issues in option contract. This initial margin will be refunded to the seller upon the expiry of the contract after netting the losses and gains on each day, which is same as that in futures contracts, as it is mark-to-market on a daily basis. That means any loss at the end of a day’s trade is netted against the initial margin from seller’s account according to the positions taken in call and put options in both index and stock options.

2.7.4

Short Option Minimum Charge

Short positions in extreme deep out of the money strikes may change wildly and may start generating losses and may change into in-the money. Hence, NSCCL has set up a minimum margin for every short option position in a portfolio called Short Option Minimum charge. The Short Option Minimum charge serves as a minimum charge towards margin requirements for each short position in an option contract. If the short option minimum charge is Rs. 50 per short position, a portfolio containing 20 short options will have a margin requirement of at least Rs. 1000, even if the scanning risk charge plus the inter month spread charge on the position is only Rs. 500. Premium margin is the client-wise margin amount payable for the day and has to be paid by the buyer till the premium settlement is complete.

2.8

PROCEDURE FOR MARGIN COLLECTION

SEBI has prescribed two types of margins in derivatives market—initial margin and mark to market margin. · Initial Margin is adjusted from the available liquid net worth of the clearing member on an online real time basis. Initial margin is based on 99% VaR and worst case loss over a specified horizon, depending on the time in which mark to market margin is collected. NSCCL collects initial margin up-front for all the open positions of a clearing member based on the margins computed by NSCCL-SPAN. A CM (clearing member) is in turn required to collect the initial margin from the TMs and his respective clients. Similarly, a TM should collect upfront margins from his/her clients.

Understanding Options

19

Futures contracts. The open positions (gross against clients and net of proprietary/self trading) in the futures contracts for each member are marked to market to the daily settlement price of the futures contracts at the end of each trading day. The daily settlement price at the end of each day is the weighted average price of the last half an hour of the futures contract. The profits/losses arising from the difference between the trading price and the settlement price are collected by/given to all the clearing members.

Option contracts. The marked to market for option contracts is computed and collected as part of the SPAN margin in the form of net option value. The SPAN margin is collected on an online real time basis based on the data feeds given to the system at discrete time intervals. The Initial Margin is the higher of (Worst Scenario Loss + Calendar Spread Charges) Or short option minimum charge.

2.8.1

Short Option Minimum Charge

The worst scenario loss are required to be computed for a portfolio of a client and is calculated by valuing the portfolio under 16 scenarios of probable changes in the value and the volatility of the index/individual stocks. The options and futures positions in a client’s portfolio are required to be valued by predicting the price and the volatility of the underlying over a specified horizon so that 99% of times the price and volatility so predicted does not exceed the maximum and minimum price or volatility scenario. In this manner, initial margin of 99% VaR is achieved. The specified horizon is dependent on the time of collection of mark to market margin by the exchange. The probable change in the price of the underlying over the specified horizon, i.e., ‘price scan range’, in the case of Index futures and Index option contracts are based on three standard deviation (3s ) where ‘s ’ is the volatility estimate of the Index. The volatility estimate ‘s ’ is computed as per the exponentially weighted moving average methodology. This methodology has been prescribed by SEBI. In case of option and futures on individual stocks, the price scan range is based on three and a half standard deviation (3.5s) where ‘s’ is the daily volatility estimate of individual stock. If the mean value (taking order book snapshots for past 6 months) of the impact cost for an order size of Rs. 0.5 million exceeds 1%, the price scan range would be scaled up by square root three times to cover the close out risk. This means that stocks with impact cost greater than 1% would now have a price scan range of - Sqrt (3) ´ 3.5s, or approx. 6.06s. For stocks with impact cost of 1% or less, the price scan range would remain at 3.5s. For index futures and stock futures, it is specified that a minimum margin of 5% and 7.5% would be charged. This means, if for stock futures the 3.5s value

20

Option Trading

falls below 7.5%, then a minimum of 7.5% should be charged. This could be achieved by adjusting the price scan range. The probable change in the volatility of the underlying, i.e., ‘volatility scan range’ is fixed at 4% for Index options and is fixed at 10% for options on individual stocks. The volatility scan range is applicable only for option products. Calendar spreads are offsetting positions in two contracts in the same underlying across different expiry. In a portfolio-based margining approach, all calendar-spread positions automatically get a margin offset. However, risk arising due to difference in cost of carry or the ‘basis risk’ needs to be addressed. It is, therefore, specified that a calendar spread charge would be added to the worst scenario loss for arriving at the initial margin. For computing calendar spread charge, the system first identifies spread positions and then the spread charge, which is 0.5% per month on the far leg of the spread with a minimum of 1% and maximum of 3%. Presently, calendar spread position on exchange-traded equity derivatives has been granted calendar spread treatment till the expiry of the near month contract. In a portfolio of futures and options, the non-linear nature of options makes short option positions most risky. Especially, short deep out of the money options, which are highly susceptible to, changes in prices of the underlying. Therefore, a short option minimum charge has been specified. The short option minimum charge is 3% and 7.5% of the notional value of all short Index option and stock option contracts, respectively. The short option minimum charge is the initial margin if the sum of the worst-scenario loss and calendar spread charge is lower than the short option minimum charge. To calculate volatility estimates, the exchanges are required to use the methodology specified in the Prof. J.R. Varma Committee Report on Risk Containment Measures for Index Futures. Further, to calculate the option value the exchanges can use standard option pricing models—Black-Scholes, binomial, Merton, Adesi-Whaley. The initial margin is required to be computed on a real-time basis and has two components: · The first is the creation of risk arrays taking prices at discreet times taking latest prices and volatility estimates at the discreet times, which have been specified. · The second is the application of the risk arrays on the actual portfolio positions to compute the portfolio values and the initial margin on a real-time basis. The initial margin so computed is deducted from the available liquid net worth on a real time basis.

Understanding Options

2.8.2

21

Calendar Spread Charge

The margin on calendar spread shall be calculated on the basis of delta of the portfolio consisting of futures and options contracts in each month. A calendar spread positions will be treated as non-spread (naked) positions in the far month contract, three trading days prior to expiration of the near month contract. Source: www.nseindia.com

2.9

TYPES OF ORDERS

Orders in the F&O segment are entered by the trading members (TMs) as per their requirements based on time, price and other conditions.

2.9.1

Time Condition

Orders can be entered into the system based on a timeframe basis by the TMs. There are two types of orders based on time condition: day order and immediate or cancel (IOC) order. A day order is an order valid only for the day on which it is entered into the system. The system will cancel the order automatically at the end of the day, if the order is not executed by the end of the day. An immediate or cancel order allows user to place an order, either to buy or sell a contract in an immediate manner into the trading system. If the order does not match with that in the system, then that order is cancelled at once from the system. If the order satisfies partial match with the system, the remaining unmatched portion of the order is cancelled instantly.

2.9.2

Price Condition

The stop loss price condition allows a user to release order into the system in the case of buying and selling with a stop loss price, beyond which the client cannot bear a loss on the particular stock or index. For a stop loss buy order, with a trigger price1 of Rs. 120 and limit price of Rs. 130, the order will be executed once the market price reaches Rs. 120. For a stop loss sell order, the trigger price will be higher than the limit price.2

2.9.3

Other Conditions

Besides time and price conditions, there is another type of order known as market order by which orders are placed, based on the current market price, which is determined by the system itself and clients cannot buy or sell their orders according to their choice of price. 1 2

Trigger price is the price at which an order gets triggered from the stop loss book. Limit price is the price of the orders after triggering from the stop loss book.

22

Option Trading

2.10

SETTLEMENT SCHEDULE FOR OPTION CONTRACTS

· The premium settlement of index and securities option contracts works on T+1 working day settlement at or after 11.30 am for pay-in, whereas pay-out works on T+1 working day at or after 12.00 pm. (T is the trade day.) · Exercise and final settlement for index options is in the order of T+1 working day at or after 11:30 am for pay-in and T+1 working day at or after 12.00 pm for pay-out. (T is the expiration day of the contract.) · Interim exercise settlement for option contracts on individual securities is in the order of T+1 working day at or after 11.30 am for pay-in and T+1 working day at or after 12.00 pm for pay-out. (T is the exercise day.) · Exercise and final settlement for option contract on individual securities is in the order of T+1 working day at or after 11.30 am for pay-in and T+1 working day at or after 12.00 pm for pay-out. (T is the expiration day of the contract.)

2.11 2.11.1

SETTLEMENT MECHANISM Premium Settlement

Premium settlement in the case of option contracts on index or individual securities is cash settled, and the settlement style is premium. Net premium payable or receivable at the end of each day for each CM is calculated by netting across all the premium payable and premium receivable positions of the client level. Those CMs having premium payable positions are required to pay the premium amount to NSCCL that is passed on to the members with premium receivable position, which is known as daily premium settlement. Premium amounts from TMs and clients are collected and settled by CMs. Premium settlement for pay-in and pay-out is done on T+1 days and the premium payable amount and premium receivable amount is directly debited or credited to the clearing bank account of CM.

2.11.2

Interim Exercise Settlement

Interim exercise settlement for option contracts on individual securities is allowed only for valid exercised option positions at in-the-money strike prices on the closing of trading hours on the exercise day. These valid option positions of in-the-money contracts are matched to short positions in the same series on a random basis. The interim exercise settlement value is computed as the difference between strike price and settlement price of the relevant option contract. The exercise settlement value is debited or credited on the T +1 day on the clearing bank account of the CMs.

2.11.3

Final Exercise Settlement

Final exercise settlement for option positions of in-the-money strike prices at the closing hours of trading hours on the expiration day of an option contract are eligible for settlement. The long positions of in-the-money strike prices are assigned automatically to short positions in option contracts with the same

Understanding Options

23

series. Exercise style is European for index option contracts and is American style for individual securities. The CMs will assign and allocate those option contracts, which have been exercised at the client level. Exercise settlement is cash settled and is debited or credited to the relevant CM’s clearing accounts with the respective clearing bank. Final settlement loss or profit amount for option contracts on index and individual securities are credited or debited to the relevant CM’s clearing bank account on the T+1 working day, where T is the day of expiry. After the expiration day, open positions in option contracts do not exist. The pay-in and pay-out of funds for a CM on a day is the net amount across settlements of all TMs and clients.

2.12

WRITING OF OPTIONS

The process of selling an option contract is known as writing of options. The seller of the option is the writer and the counterparty is the buyer. A call writer has the obligation to sell the underlying asset to the option buyer whereas the put buyer has the obligation to buy the underlying asset from the put buyer. An option buyer can abandon the option which an option writer cannot do. If the buyer decides to exercise the option, the seller has to meet his commitment. Writing of options is explained with examples in the next chapter.

Summary In this chapter, we have discussed the basics of option contracts and various terminologies used in option-related transactions. We have also discussed the trading systems, margins and settlement for investors and TMs in a brief manner. These will be revisited at appropriate places in the remaining chapters.

Keywords Call option Deep in the money Out of the money Time value Long position Opening buy Margin American option

Put option Strike price At the money Intrinsic value premium Short position Open interest Close out Closing buy Short option minimum charge European option

In the money Deep out of the money Volatility Opening sell Closing sell Settlement

CHAPTER

03

OPTION TRADING

3.1

OBJECTIVES

In the last chapter we explained the concept of options, features of option contracts, how options are different from forwards and futures, option terminologies, margins and settlement mechanism. In this chapter we will discuss the art of writing put options.

3.2

INTRODUCTION

Trading in options involve buying or selling options. Selling of options is called writing of options. An investor can write an option by paying the initial margin, which is the same as in the case of buying options, and also the mark-to-market margin calculated by the NSE at the end of every day. The investor should be aware of the trading mechanism, contract cycle and charges other than the margin amount. This chapter will help the reader to gain knowledge in these fields.

3.3 MARKET-WIDE LIMITS Everyday at the end of the trading session, the exchange will check whether the total trade done in calls and puts exceed 95% of the market-wide position limit for that scrip set by the exchange. The exchange will collect details of positions taken by clients at the end of the trading session on that particular scrip and will see to it that trading done on the scrip from next day will be to reduce such positions held by the clients till normal trading in the scrip is resumed. Normal trading will only resume when the outstanding position in the scrip moves down to 80% of the market wide position limits. An alert comes on the trading screen when the open interest in a security moves above 60% of the market-wide position limits set for the stock. If any of the clients who places new order in spite of the ban on trade due to the crossing of market-wide limits, apart from reducing his existing positions in the stock, will be charged a penalty of 1% of the value of the increased position subject to a minimum of Rs. 5000 and maximum of Rs. 10,00,00. This penalty will be recovered from the clearing member affiliated with the trading members/clients on a T+1 basis along with the pay-in.

26

Option Trading

Trading Member-Wise Position Limits*: The position limits set for the equity index options shall be higher of about 15% of the total open interest in the market in all equity index options. The limit will be applicable on all option contracts open positions in a particular underlying index. Client Level Position Limits*: The total open position held by a client in all the derivative contracts on any underlying index should not exceed the following: (1) 1% of the free float market capitalization. (2) 5% of the open interest in all derivative contracts in the same underlying stock. (whichever is higher in the above two cases)

Collateral Limits for Trading Members*: The clearing members who are clearing and settling for trading members can specifically mention the maximum collateral limit towards initial margin for each and every trading member. These limits can be set through the facility provided on the trading system at any time till the close of trading hours by the clearing member. The limits thus set are applicable to the trading members and other participants for that particular day unless otherwise modified by the clearing member.

3.3.1

Trading Mechanism*

NEAT-F&O trading system provides fully automated screen-based trading for Nifty Futures & Options and Stock Futures & Options on a nationwide basis, with an online monitoring and surveillance mechanism. Anonymous orderdriven market provides complete transparency of trading operations and operates on a strict price-time priority. Trading Members and Clearing Members only have the access to the NEAT-F&O trading system, of which, trading members can perform functions such as order entry, order matching and order and trade management. Clearing Members monitors trading member’s trader workstation for which they clear the trades. Also, Clearing Members can enter and set limits to positions just like that of a trading member. Orders are matched automatically through the NEAT F&O system, done on the basis of security’s price, time and quantity of which quantity fields are in units and price in rupees. The lot size and tick size for each option contracts traded are notified by the stock exchange from time to time. A trade is generated when an active order finds a match on the other side of the order book. Active order is the most recent order entering into the system. If there is no match in terms of the price, quantity and time, the order will become inactive and will go and sit into the respective outstanding order book in the system.

3.3.2

Contract Cycle*

In India, Futures and Options contracts on Stocks and Index expire on the last Thursday of every month. At any point of time, there exists three contracts available for trading for Stock options, one will be the current month contract, second one will be the next month contract and the other one being the third month contract. For example, in the month of November, there will be three * Source: www.nseindia.com.

Option Trading 27

contracts: first is the November contract which should expire on the last Thursday of November, second one is the December contract which expires on the last Thursday of December and the last one is the January contract expiring on the last Thursday of January. But on the other hand, option contracts available for Nifty has now been extended to three years from three months. That means on November 2008, option contracts available for Nifty options are those ranging from Nov 2008 to Dec 2011. Table 3.1 Contract Specification for S&P CNX Nifty Options* Underlying Index

S&P CNX NIFTY

Exchange of Trading

NSE

Security Descriptor

OPTIDXNIFTY

Contract Size

Permitted Lot size is 50

Price Steps

Rs. 0.05

Trading Cycle

Maximum of three year trading cycle

Expiry Day

Last Thursday of the expiry month or previous trading day, if the last Thursday is a trading holiday

Settlement Basis

Cash settlement on a T+1 basis

Style of Option

European

Strike Price Interval

Rs. 50

Final Settlement Price

Closing Value of Index on the last trading day

Table 3.2

Contract Specification for Stock Options*

Underlying Index

Individual Securities Available for Trade in the F&O Segment

Exchange of Trading

NSE

Security Descriptor

OPTSTK

Style of Option

American

Strike Price Interval

As specified by the exchange

Contract Size

As specified by exchange

Price Steps

Rs. 0.05

Price Bands

Not Applicable

Trading Cycle

Option contracts have a maximum of three-month trading cycle—near month, next month and far month(third)

Expiry Day

Last Thursday of expiry month or previous trading day, if the last Thursday is a holiday (Contd.)

*Source: www.nseindia.com

28

Option Trading

Settlement Basis

Daily settlement on T+1 basis and final option exercise settlement on T+3 basis

Daily Settlement Price

Premium Value (Net)

Final Settlement Price

Closing price of underlying on exercise day or expiry day

Settlement Day

Last trading day

3.3.3

Generation of Strikes*

Based on the range in which previous day’s closing value of the index falls, various strikes are introduced in Nifty and Stock options and are given below: · For Nifty Options Table 3.3 Nifty Index Level

Strike Price Interval

Scheme of Strikes to be Introduced 3-1-3†

Up to 1500

10

>1500 up to 2000

10

5-1-5

>2000 up to 2500

50

7-1-7

>2500

50

9-1-9

† Up to 1500 Nifty level, the strike interval will be 10 and should have one at the money strike price, three out of money and three in the money strike prices.

· For Stock options Table 3.4 Underlying Price

Strike Price Interval

Scheme of Strikes Introduced 3-1-3

Less than or equal to Rs.50

2.5

>Rs.50 – Rs.250

5

3-1-3

>Rs.250-Rs.500

10

3-1-3

>Rs.500-Rs.1000

20

3-1-3

>Rs.1000-Rs.2500

30

3-1-3

>Rs.2500

50

3-1-3

* Source: www.nseindia.com.

Option Trading 29

3.3.4

Charges*

In the F&O segment, the maximum brokerage chargeable by a trading member fixed by NSE is at 2.5% of the contract value in the case of Index and Stock Futures. (Contract Value = Futures price * Market Lot). For Index and Stock options, it is 2.5% of the product of notional value of premium and quantity, exclusive of statutory levies. Trading members are advised to charge brokerage from the clients only on the Premium price, rather than Strike price.

3.3.5

Corporate Action Adjustments*

The basis for any adjustment for corporate actions shall be such that the value of the position of the market participants, on the cum and ex-dates for the corporate action, shall continue to remain the same as far as possible. This will facilitate in retaining the relative status of positions viz., in-the-money, atthe-money and out-of-money. This will also address issues related to exercise and assignments.

3.3.5.1

Corporate Actions to be Adjusted*

The corporate actions may be broadly classified under stock benefits and cash benefits. The various stock benefits declared by the issuer of capital are: · Bonus · Rights · Merger / De-merger · Amalgamation · Splits · Consolidations · Hive-off · Warrants, and · Secured Premium Notes (SPNs) among others. The cash benefit declared by the issuer of capital is cash dividend.

3.3.5.2

Time of Adjustment

Any adjustment for corporate actions would be carried out on the last day on which a security is traded on a cum basis in the underlying equities market, after the close of trading hours.

3.3.5.3

Adjustment

Adjustments may entail modifications to positions and/or contract specifications as listed below, such that the basic premise of adjustment laid down above is satisfied: (a) Strike Price (b) Position (c) Market Lot/Multiplier * Source: www.nseindia.com.

30

Option Trading

The adjustments would be carried out on any or all of the above, based on the nature of the corporate action. The adjustments for corporate actions would be carried out on all open, exercised as well as assigned positions.

3.3.5.4

Methodology for Adjustment

The methodology to be followed for adjustment of various corporate actions to be carried out are as follows: A. Bonus, Stock Splits and Consolidations Ž Strike Price: The new strike price shall be arrived at by dividing the old strike price by the adjustment factor as under. Ž Market Lot / Multiplier: The new market lot / multiplier shall be arrived at by multiplying the old market lot by the adjustment factor as under. Ž Position: The new position shall be arrived at by multiplying the old position by the adjustment factor as under. Ž Adjustment factor: Bonus - Ratio A:B Adjustment factor : (A+B)/B Stock Splits and Consolidations Ratio - A : B Adjustment factor : A/B Ž The above methodology may result in fractions due to the corporate action, e.g., a bonus ratio of 3:7. With a view to minimizing fraction settlements, the following method is adopted : 1. Compute value of the position before adjustment 2. Compute value of the position taking into account the exact adjustment factor 3. Carry out rounding off for the Strike Price and Market Lot 4. Compute value of the position based on the revised strike price and market lot The difference between 1 and 4 above, if any, is decided in the manner laid down by the relevant authority by adjusting Strike Price or Market lot so that no forced closure of open position is mandated. B. Dividends Ž  Dividends which are below 10% of the market value of the underlying stock, would be deemed to be ordinary dividends and no adjustment in the Strike Price would be made for ordinary dividends. For extra-ordinary dividends, above 10% of the market value of the underlying security, the Strike Price would be adjusted. Ž To decide whether the dividend is “extra-ordinary” (i.e., over 10% of the market price of the underlying stock.), the market price would mean the closing price of the scrip on the day prior to the date on

Option Trading 31

which the announcement of the dividend is made by the company after the meeting of the Board of Directors. However, in cases where the announcement of dividend is made after the close of market hours, the same day’s closing price would be taken as the market price. Further, if the shareholders of the company in the AGM change the rate of dividend declared by the Board of Directors, then to decide whether the dividend is extraordinary or not would be based on the rate of dividend communicated to the exchange after AGM and the closing price of the scrip on the day prior to the date of the AGM. Ž In case of the declaration of “extraordinary” dividend by any company, the total dividend amount (special and/or ordinary) would be reduced from all the strike prices of the option contracts on that stock. Ž The revised strike prices would be applicable from the ex-dividend date specified by the exchange. C. Mergers Ž On the announcement of the record date for the merger, the exact date of expiration (Last Cum-date) would be communicated to members. Ž After the announcement of the Record Date, no fresh contracts on Futures and Options would be introduced on the underlying, that will cease to exist subsequent to the merger. Ž Unexpired contracts outstanding as on the last cum-date would be compulsorily settled at the settlement price. The settlement price shall be the closing price of the underlying on the last cum-date. Ž GTC/GTD orders for the futures & options contracts on the underlying, outstanding at the close of business on the last cumdate would be cancelled by the Exchange. D. Rights Rights Ratio A : B, Benefit per right entitlement (C) : P – S Benefit per share (E) : (P – S) / A+B Underlying close price on the last cum date (P) Issue price of the rights (S)

Adjustment factor = (P–E)/P To be multiplied by old strike price and divided into old lot size to arrive at the new strike price and lot size

E. The relevant authority may, on a case by case basis, carry out adjustments for other corporate actions in conformity with the above guidelines, including compulsory closing out, where it deems necessary.

32

Option Trading

3.3.6

Collateral for Margins

Margin for options trading by clients in the F&O segment can be provided in the form of collaterals. The collaterals can be segregated into cash and non cash components- Cash, Bank Guarantee, Fixed Deposit Receipts (FDRs) and Treasury Bills. Cash component includes cash, bank guarantee, fixed deposit receipts, T-bills and dated government securities. Non-cash component includes all other forms of collateral deposits like deposit of approved demat securities. It is mandatory to have 50% of the Effective deposits in the form of cash. The margin granted for trading in F&O segment is the sum of cash and specified securities. Actual payment is needed for F&O segment in the case of settlement dues from mark to market losses.

Cash: Margins given in the form of cash is done by way of allocation of funds from the client’s bank account. For this, cash has to be made available in client’s bank account on the end of trading hours on the date of which the amount is due. In case, cash is not available with the client’s bank account, trading member has the right to sell the securities deposited as margin. Payin obligations of the clients are thus met by the trading members on sale of securities.

Securities: Securities included in Group I are eligible as collateral for options trading and has to be in the demat form. They are required to be valued or marked to market on a daily basis after applied haircut, which is equivalent to the VAR of the equity security. The securities are chosen by way of depositing securities allocated from the client’s demat account. These securities are selected by NSE and are attached in the website for the margin purpose. Also, trading member has the right of choice to select securities on selected criteria such as liquidity, volume etc. The securities would be subject to a minimum margin of 15% on Nifty securities, and a minimum margin of 30% margin on other securities or such other margin percentage as may be decided by NSCCL from time to time. It is mandatory that at least weekly marking to market is to be carried out on all securities. In the case of debt securities, collateral is sanctioned only for those having an investment grade. In addition, 10% haircut with weekly mark to market is applied on debt securities.

Units of Mutual Funds and Gilt Funds: Based on the Net Asset Value, collaterals are allowed on Money market Mutual funds and Gilt Funds after applying a 10% haircut on the NAV and any exit load charged by these mutual funds. Other Mutual Funds are valued based on their NAV after applying an haircut which is same as the VAR of the units NAV and any exit load charged by the mutual fund. Source: www.nseindia.com.

Option Trading 33

3.3.7 Eligibility Criteria for Securities in Options Trading * The eligibility of a stock / index for trading in Derivatives segment is based upon the criteria laid down by SEBI through various circulars issued from time to time. The latest circular issued in this respect is circular No. : SEBI/ DNPD/Cir-31/2006 dated September 22, 2006. Based on various circulars, the following criteria will be adopted by the Exchange w.e.f. September 22, 2006, for selecting stocks and indices on which Futures & Options contracts would be introduced: 1. Eligibility criteria of stocks · The stock shall be chosen from amongst the top 500 stocks in terms of average daily market capitalisation and average daily traded value in the previous six months on a rolling basis. · The stock’s median quarter-sigma order size over the last six months shall be not less than Rs. 0.10 million (Rs. 1 lakh). For this purpose, a stock’s quarter-sigma order size shall mean the order size (in value terms) required to cause a change in the stock price equal to onequarter of a standard deviation. · The market wide position limit in the stock shall not be less than Rs. 500 million (Rs. 50 crore). The market wide position limit (number of shares) shall be valued taking the closing prices of stocks in the underlying cash market on the date of expiry of contract in the month. The market wide position limit of open position (in terms of the number of underlying stock) on futures and option contracts on a particular underlying stock shall be 20% of the number of shares held by non-promoters in the relevant underlying security i.e. freefloat holding. 2. Continued Eligibility · For an existing stock to become ineligible, the criteria for market wide position limit shall be relaxed upto 10% of the criteria applicable for the stock to become eligible for derivatives trading. To be dropped out of Derivatives segment, the stock will have to fail the relaxed criteria for 3 consecutive months. · If an existing security fails to meet the eligibility criteria for three months consecutively, then no fresh month contract shall be issued on that security. · Further, the members may also refer to circular no. NSCC/F&O/ C&S/365 dated August 26, 2004, issued by NSCCL regarding Market Wide Position Limit, wherein it is clarified that a stock which has remained subject to a ban on new position for a significant part of the month consistently for three months, shall be phased out from trading in the F&O segment. However, the existing unexpired contracts may be permitted to trade till expiry and new strikes may also be introduced in the existing contract months. *Source: www.nseindia.com.

34

Option Trading

3. Re-introduction of dropped stocks A stock which is dropped from derivatives trading may become eligible once again. In such instances, the stock is required to fulfill the eligibility criteria for three consecutive months to be re-introduced for derivatives trading. 4. Eligibility criteria of Indices · Futures & Options contracts on an index can be introduced only if 80% of the index constituents are individually eligible for derivatives trading. However, no single ineligible stock in the index shall have a weightage of more than 5% in the index. The index on which futures and options contracts are permitted shall be required to comply with the eligibility criteria on a continuous basis. · SEBI has subsequently modified the above criteria, vide its clarification issued to the Exchange “The Exchange may consider introducing derivative contracts on an index if the stocks contributing to 80% weightage of the index are individually eligible for derivative trading. However, no single ineligible stocks in the index shall have a weightage of more than 5% in the index.” · The above criteria is applied every month, if the index fails to meet the eligibility criteria for three months consecutively, then no fresh month contract shall be issued on that index, However, the existing unexpired contacts shall be permitted to trade till expiry and new strikes may also be introduced in the existing contracts.

3.3.8 Calculation of Quarter Sigma Order Size of Stock* The following procedure is adopted for calculating the Quarter Sigma Order Size: 1. The applicable VAR (Value at Risk) is calculated for each security based on the J. R. Varma Committee guidelines. (The formula suggested by J. R. Varma for computation of VAR for margin calculation is statistically known as ‘Exponentially weighted moving average (EWMA)’ method. In comparison to the traditional method, EWMA has the advantage of giving more weight to the recent price movements and less weight to the historical price movements.) 2. Such computed VAR is a value (like 0.03), which is also called standard deviation or Sigma. (The meaning of this figure is that the security has the probability to move 3% to the lower side or 3% to the upper side on the next trading day from the current closing price of the security). 3. Such arrived at standard deviation (one sigma), is multiplied by 0.25 to arrive at the quarter sigma. (For example, if one sigma is 0.09, then quarter sigma is 0.09 * 0.25 = 0.0225.) * Source: www.nseindia.com.

Option Trading 35

4. From the order snapshots (taken four times a day from NSE’s Capital Market Segment order book) the average of best buy price and best sell price is computed which is called the average price. 5. The quarter sigma is then multiplied with the average price to arrive at quarter sigma price. The following example explains the same : Security Best Buy (in Rs.) Best Sell (in Rs.) Average Price One Sigma Quarter sigma Quarter sigma price (Rs.) (Average Price *Quarter sigma)

XYZ 306.45 306.90 306.70 0.009 0.00225 0.70

6. Based on the order snapshot, the value of the order (order size in Rs.), which will move the price of the security by quarter sigma price in buy and sell side is computed. The value of such order size is called Quarter Sigma order size. (Based on the above example, it will be required to compute the value of the order (Rs.) to move the stock price to Rs. 306.00 in the buy side and Rs. 307.40 on the sell side. That is Buy side = average price – quarter sigma price and Sell side = average price + quarter sigma price). Such an exercise is carried out for four order snapshots per day for all stocks for the previous six months period. 7. From the above determined quarter sigma order size (Rs.) for each order book snap shot for each security, the median of the order sizes (Rs.) for buy side and sell side separately, are computed for all the order snapshots taken together for the last six months. 8. The average of the median order sizes for buy and sell side are taken as the median quarter sigma order size for the security. 9. The securities whose median quarter sigma order size is equal to or greater than Rs. 0.1 million (Rs. 1 Lakh) qualify for inclusion in the F&O segment. Futures & Options contracts may be introduced on new securities which meet the above mentioned eligibility criteria, subject to approval by SEBI. New securities being introduced in the F&O segment are based on the eligibility criteria which take into consideration average daily market capitalization, average daily traded value, the market wide position limit in the security, the quarter sigma values and as approved by SEBI. The average daily market capitalisation and the average daily traded value would be computed on the 15th of each month, on a rolling basis, to arrive at the list of top 500 securities. Similarly, the quarter sigma order size in a stock would also be calculated on the 15th of each month, on a rolling basis, considering the order book snapshots of securities in the previous six months and the market wide position limit (number of shares) shall be valued taking the * Source: www.nseindia.com.

36

Option Trading

closing prices of stocks in the underlying cash market on the date of expiry of contract in the month. The number of eligible securities may vary from month to month depending upon the changes in quarter sigma order sizes, average daily market capitalisation and average daily traded value calculated every month on a rolling basis for the past six months and the market wide position limit in that security. Consequently, the procedure for introducing and dropping securities on which option and future contracts are traded will be as stipulated by SEBI in its circular no. SEBI/DNPD/Cir-26/2004/07/16 dated July 16, 2004.

3.3.9

Selection Criteria for Mini Derivative Contracts*

Mini derivative contracts (Futures and options) shall be made available for trading on such indices/securities as specified by SEBI from time to time.

3.3.10 Eli.gibility Criteria for Long Term Option Contracts* Vide its circular no. SEBI/DNPD/Cir-34/2008 dated January 11, 2008 SEBI has specifically permitted introduction of option contracts with longer tenure on S&P CNX Nifty index.

3.3.11

Selection Criteria for Unlisted Companies*

For unlisted companies coming out with initial public offering, if the net public offer is Rs. 500 crore. or more, then the Exchange may consider introducing stock options and stock futures on such stocks at the time of its’ listing in the cash market. Source: www.nseindia.com.

3.3.12

Stocks in F&O Segment*

Stock Futures available in the F&O segment of NSE as on 31st March, 2009 are given below. Currently, 240 scrips have been trading in the F&O segment. Derivatives on Individual Securities Aban Offshore Ltd.

Asian Layland Ltd

Abb Ltd.

Asian Paints Limited

Aditya Birla Nuvo Limited

Associated Cement Co.Ltd.

Adlabs Films Ltd

Aurobindo Pharma Ltd.

Allahabad Bank

Axis Bank Ltd.

Alstom Projects India Ltd

Bajaj Auto Limited

Ambuja Cements Ltd.

Bajaj Hindustan Ltd.

Andhra Bank

Bajaj Holdings & Investment Ltd.

Ashok Leyland Ltd

Balrampur Chini Mills Ltd

* Source: www.nseindia.com.

Option Trading 37 Bank of Baroda

Financial Technologies (I) Ltd.

Bank of India

Firstsource Solutions Limited

Bharat Earth Movers Ltd.

Gail (India) Ltd.

Bharat Electronics Ltd.

Glaxosmithkline Pharma Ltd.

Bharat Forge Co. Ltd

GMR Infrastructure Ltd.

Bharat Heavy Electricals Ltd.

Grasim Industries Ltd.

Bharat Petroleum Corporation Ltd.

Great Offshore Ltd.

Bharti Airtel Ltd

GTL Infrastructure Limited

Bhushan Steel & Strips Ltd.

GTL Ltd.

Biocon Limited.

Gujarat State Petronet Ltd.

Bombay Rayon Fashions Ltd. Cairn India Limited Canara Bank Century Textiles Ltd. Cesc Ltd. Chambal Fertilizers Ltd. Chennai Petroleum Corporation Ltd. Cipla Ltd. Colgate Palmolive Ltd. Container Corpation of India Ltd. Corporation Bank Crompton Greaves Ltd. Cummins India Ltd.

GVK Power & Infrastructure Ltd. HCL Technologies Ltd. HDFC Bank Ltd. Hero Honda Motors Ltd. Hindalco Industries Ltd. Hindustan Construction Co. Hindustan Petroleum Corporation Ltd. Hindustan Unilever Ltd. Hindustan Zinc Limited Hotel Leela Ventures Ltd. Housing Development and Infrastructure Ltd.

Dabur India Ltd.

Housing Development Finance Corporation Ltd.

Deccan Chronicle Holdings Ltd.

ICICI Bank Ltd.

Dena Bank

ICSA (India) Limited

Dish TV India Ltd.

Idea Cellular Ltd.

Divi’s Laboratories Ltd.

IFCI Ltd.

DLF Limited Jaiprakash Hydro-Power Ltd. Jindal Saw Limited Dr. Reddy’s Laboratories Ltd. Educomp Solutions Ltd. Essar Oil Ltd. Everest Kanto Cylinder Ltd. Federal Bank Ltd.

India Cements Ltd. India Infoline Limited Indiabulls Real Estate Limited Indian Bank Indian Hotels Co. Ltd. Indian Oil Corporation Ltd. Indian Overseas Bank

38

Option Trading Indusind Bank Ltd.

Noida Toll Bridge Company Ltd.

Industrial Development Bank of India Ltd.

Oil & Natural Gas Corp. Ltd.

Infosys Technologies Ltd.

Opto Circuits (India) Ltd. Oracle Financial Service Software Ltd.

Infrastructure Development Finance Company Ltd.

Orchid Chemicals Ltd.

Ispat Industries Limited

Oriental Bank of Commerce

ITC Ltd.

Pantaloon Retail (I) Ltd.

IVRCL Infrastructure & Projects Ltd.

Patel Engineering Ltd.

Jaiprakash Associates Ltd.

Patni Computer System Ltd.

Reliance Industries Ltd.

Petronet LNG Limited

Reliance Infrastucture Limited

Piramal Healthcare Ltd

Jindal Steel & Power Ltd.

Polaris Software Lab Ltd.

JSW Steel Ltd.

Power Finance Corporation Ltd.

K S Oils Limited Kingfisher Airlines Ltd.

Power Grid Corporation of India Ltd.

Kotak Mahindra Bank Ltd.

Praj Industries Ltd.

Lanco Infratech Ltd.

PTC India Limited

Larsen & Toubro Ltd.

Punj Lloyd Ltd.

LIC Housing Finance Ltd.

Punjab National Bank

Lupin Ltd.

Ranbaxy Laboratories Ltd.

Mahanagar Telephone Nigam Ltd.

Rel. Nat. Resources Ltd.

Mahindra & Mahindra Ltd.

Reliance Capital Ltd.

Mangalore Refinery and Petrochemicals Ltd.

Reliance Communications Ltd.

Maruti Udyog Ltd. Mercator Lines Limited Moser-Baer (I) Ltd. Motor Industries Co. Ltd. Mphasis Ltd.

Reliance Petroleum Ltd. Reliance Power Ltd. Rolta India Ltd. Rural Electrification Corporation Ltd. Sesa Goa Ltd.

Nagarjuna Constrn. Co. Ltd.

Shipping Corporation of India Ltd.

Nagarjuna Fertiliser & Chemicals Ltd.

Shree Renuka Sugars Ltd.

National Aluminium Co. Ltd.

Sintex Industries Ltd.

National Thermal Power Corporation Ltd.

State Bank of India

Neyveli Lignite Corporation Ltd.

Siemens Ltd

Steel Authority of India Ltd.

Option Trading 39 Sterling Biotech Ltd.

The Great Eastern Shipping Co. Ltd.

Sterlite Industries (I) Ltd.

Titan Industries Ltd.

Sun Pharmaceuticals India Ltd.

Triveni Engg. & Inds. Ltd.

Sun TV Network Ltd.

Tulip IT Services Ltd.

Suzlon Energy Ltd.

UCO Bank

Syndicate Bank

Ultratech Cement Ltd.

Tata Chemicals Ltd.

Union Bank of India

Tata Communications Ltd

Unitech Ltd.

Tata Consultancy Services Ltd.

United Phosphorous Ltd.

Tata Motors Ltd.

United Spirits Ltd.

Tata Power Co. Ltd.

Vijaya Bank

Tata Steel Ltd.

Voltas Ltd.

Tata Tea Ltd. Tata Teleserv(Maharashtra) Tech Mahindra Limited Television Eighteen India Ltd.

Welspun Guj. St. Ro. Ltd. Wipro Ltd. Yes Bank Limited Zee Entertainment Enterprises Ltd.

* These are stocks that are excluded from the F&O segment from March 2009 onwards.

Summary In this chapter we have explained the market-wide limits and the trading mechanism that exists in the option market which the investors should keep in mind. More details regarding the contract cycle, charges involved and collateral margins are also given in this chapter. Finally, we have explained the criteria for including stock in the F&O segment, detailed the scrips included in the F&O segment and discussed the calculation of quarter sigma. In the next chapter we present the price indices and how they are constructed, their uses etc.

Keywords Market-wide limits Quarter sigma Dividend

Contract cycle Bonus Mergers

Collateral for margins Stock split Rights

40

Option Trading

CHAPTER

04

PRICE INDEX

4.1

OBJECTIVES

In the previous chapter, we discussed about the basics of options as well as the process of writing options together with some of the market practices. We have also explained some of the basic practices followed in option trading. Apart from individual stock options there are index options also. In this chapter, we will explain the concept of price index, construction of index and the scrips included in the index.

4.2

INTRODUCTION

Market sentiment is expressed through index. We can say that traders are the barometer of a stock market. Various sectors have different indices such as the IT index, which relates to the information and technology stocks. Nifty is the most preferred index in India. Fifty stocks constitute Nifty.

4.3

WHAT IS AN INDEX?

An Index is a barometer for the stock market. It also represents a country’s growth, weakness, and strength. as it is a numerical measure indicating the movement of prices of a basket of items specified over a period of time based on the prices prevalent on a base year. Index can be created for any series of data. Consumer price index, wholesale price index etc. are examples of indices. In stock market, the price movement is measured using stock index. BSE has its index, which is called as Sensex and NSE having S&P CNX Nifty. Each of these have sub-indices representing sectors like Banking, FMCG, Automobile, etc. Similarly, the indices based on size of market capitalization like midcap, small cap, large cap, etc. also is used for measuring the price movement. Newspapers also have created their own indices for evaluation purpose. Economic Times 100, Economic Times midcap, ET Automobiles etc. are examples of the indices created by Economic Times. Other newspapers also have created similar indices.

42

Option Trading

4.4

ELIGIBILITY CRITERIA OF INDICES

· Futures & Options contracts on an index can be introduced only if 80% of the index constituents are individually eligible for derivatives trading. However, no single ineligible stock in the index shall have a weightage of more than 5% in the index. The index on which futures and options contracts are permitted shall be required to comply with the eligibility criteria on a continuous basis. · SEBI has subsequently modified the above criteria; vide its clarification issued to the Exchange “The Exchange may consider introducing derivative contracts on an index if the stocks contributing to 80% weightage of the index are individually eligible for derivative trading. However, no single ineligible stocks in the index shall have a weightage of more than 5% in the index.” · The above criteria is applied every month, if the index fails to meet the eligibility criteria for three months consecutively, then no fresh month contract shall be issued on that index, However, the existing unexpired contacts shall be permitted to trade till expiry and new strikes may also be introduced in the existing contracts.

4.5 CONSTRUCTION OF INDEX In NSE, indices are owned and managed by India Index Services and Products Ltd. (IISL), a joint venture between NSE and CRISIL. Standard & Poors (S&P) has marketing and licensing agreement with IISL, which is India’s first specialized company with index as the core product. In NSE, nine indices are maintained, computed and traded. Table 4.1 provides the market capitization of an index. Table 4.1 Company

Market Capitalization of an Index

Current market cap (lakhs)

Base market cap (lakhs)

XYZ

1665682

1613285.5

NMO

821545

813005.25

1289632.55

1295489.6

QAR

2482149.8

2421882.05

GTA

594229.25

598117.4

Total

6853238.6

6741779.8

TES

Index = (Current market capitalization/base market capitalization) ´ Base value Current market capitalization = 6853238.6 Base market capitalization = 6741779.8 Base value = 100 So the index value works out to be 101.65.

Price Index

43

4.6 DESIRABLE ATTRIBUTES OF AN INDEX The three main attributes of an index are: 1. It should capture the behavior of a large variety of different portfolios in the market. This is achieved by diversification in such a manner that a portfolio is not vulnerable to any individual stock or industry risk. If illiquid stocks are included in the index calculation, then the index will not reflect the current price behaviour of the market and it may show stale price behavior. 2. It should include liquid stocks. Liquidity is the ability to transact at a price which is very close to the current price. A liquid stock has very tight bid- ask spread. 3. It should be maintained professionally. The index should contain stocks with little impact cost as possible. A good index methodology must therefore incorporate a steady pace. It is quite healthy to make a few changes every year, each of which is small and does not dramatically alter the character of the index.

S&P CNX NIFTY*: S&P CNX Nifty is a well diversified 50-stock index accounting for 22 sectors of the Indian economy. It is used for a variety of purposes such as benchmarking fund portfolios, index based derivatives and index funds. S&P CNX Nifty is owned and managed by India Index Services and Products Ltd. (IISL), which is a joint venture between NSE and CRISIL. IISL is India’s first specialised company focused upon the index as a core product. IISL has a Marketing and licensing agreement with Standard & Poor’s (S&P), who are world leaders in index services. · The average total traded value for the last six months of all Nifty stocks is approximately 62.45% of the traded value of all stocks on the NSE · Nifty stocks represent about 63.98% of the total market capitalization as on January 30, 2009. · Impact cost of the S&P CNX Nifty for a portfolio size of Rs. 2 crore is 0.16% · S&P CNX Nifty is professionally maintained and is ideal for derivatives trading.

Criteria for Selection of Constituent Stocks The constituents and the criteria for the selection judge the effectiveness of the index. Selection of the index set is based on the following criteria:

Liquidity (Impact Cost) For inclusion in the index, the security should have traded at an average impact cost of 0.50% or less during the last six months for 90% of the observations for a basket size of Rs. 2 crore.

* Source: www.nseindia.com

44

Option Trading

Impact cost is the cost of executing a transaction in a security in proportion to the weightage of its market capitalization as against the index market capitalisation at any point of time. This is the percentage mark-up suffered while buying/selling the desired quantity of a security compared to its ideal price (best buy + best sell)/2.

Floating Stock Companies eligible for inclusion in S&P CNX Nifty should have atleast 10% floating stock. For this purpose, floating stock means stocks which are not held by the promoters and associated entities (where identifiable) of such companies.

Others (a) A company which comes out with a IPO will be eligible for inclusion in the index, if it fulfills the normal eligibility criteria for the index like impact cost, market capitalization and floating stock, for a 3 month period instead of a 6 month period. (b) Replacement of Stock from the Index: A stock may be replaced from an index for the following reasons: (i) Compulsory changes like corporate actions, delisting etc. In such a scenario, the stock having largest market capitalization and satisfying other requirements related to liquidity, turnover and free float will be considered for inclusion. (ii) When a better candidate is available in the replacement pool, which can replace the index stock, i.e., the stock with the highest market capitalization in the replacement pool has at least twice the market capitalization of the index stock with the lowest market capitalization. With respect to (2) above, a maximum of 10% of the index size (number of stocks in the index) may be changed in a calendar year. Changes carried out for (2) above are irrespective of changes, if any, carried out for (1) above. Table 4.2

List of Nifty 50 Stocks*

Company Name

Industry

Symbol

ABB Ltd.

ELECTRICAL EQUIPMENT

ABB

ACC Ltd.

CEMENT AND CEMENT PRODUCTS

ACC

Ambuja Cements Ltd.

CEMENT AND CEMENT PRODUCTS

AMBUJACEM

Axis Bank Ltd.

BANKS

AXIS BANK

Bharat Heavy Electricals Ltd.

ELECTRICAL EQUIPMENT

BHEL

Bharat Petroleum Corporation Ltd.

REFINERIES

BPCL

Price Index

45

Company Name

Industry

Symbol

Bharti Airtel Ltd.

TELECOMMUNICATION SERVICES

BHARTIARTL

Cairn India Ltd.

OIL EXPLORATION/ PRODUCTION

CAIRN

Cipla Ltd.

PHARMACEUTICALS

CIPLA

DLF Ltd.

CONSTRUCTION

DLF

GAIL (India) Ltd.

GAS

GAIL

Grasim Industries Ltd.

CEMENT AND CEMENT PRODUCTS

GRASIM

HCL Technologies Ltd.

COMPUTERS - SOFTWARE

HCLTECH

HDFC Bank Ltd.

BANKS

HDFCBANK

Hero Honda Motors Ltd.

AUTOMOBILES - 2 AND 3 WHEELERS

HEROHONDA

Hindalco Industries Ltd.

ALUMINIUM

HINDALCO

Hindustan Unilever Ltd.

DIVERSIFIED

HINDUNILVR

Housing Development Finance Corporation Ltd.

FINANCE - HOUSING

HDFC

I T C Ltd.

CIGARETTES

ITC

ICICI Bank Ltd.

BANKS

ICICIBANK

Idea Cellular Ltd.

TELECOMMUNICATIONSERVICES

IDEA

Infosys Technologies Ltd.

COMPUTERS -SOFTWARE

INFOSYSTCH

Jindal Steel & Power Ltd.

STEEL AND STEEL PRODUCTS

JINDALSTEL

Larsen & Toubro Ltd.

ENGINEERING

LT

Mahindra & Mahindra Ltd.

AUTOMOBILES -4 WHEELERS

M&M

Maruti Suzuki India Ltd.

AUTOMOBILES -4 WHEELERS

MARUTI

NTPC Ltd.

POWER

NTPC

National Aluminium Co.Ltd.

ALUMINIUM

NATIONALUM

Oil & Natural Gas Corporation Ltd.

OIL EXPLORATION/ PRODUCTION

ONGC

46

Option Trading

Company Name

Industry

Symbol

Power Grid Corporation of India Ltd.

POWER

POWERGRID

Punjab National Bank

BANKS

PNB

Ranbaxy Laboratories Ltd. PHARMACEUTICALS

RANBAXY

Reliance Capital Ltd.

FINANCE

RELCAPITAL

Reliance Communications Ltd.

TELECOMMUNICATION SERVICES

RCOM

Reliance Industries Ltd.

REFINERIES

RELIANCE

Reliance Infrastructure Ltd.

POWER

RELINFRA

Reliance Power Ltd.

POWER

RPOWER

Siemens Ltd.

ELECTRICAL EQUIPMENT

SIEMENS

State Bank of India

BANKS

SBIN

Steel Authority of India Ltd.

STEEL AND STEEL PRODUCTS

SAIL

Sterlite Industries (India) Ltd.

METALS

STER

Sun Pharmaceutical Industries Ltd.

PHARMACEUTICALS

SUNPHARMA

Suzlon Energy Ltd.

ELECTRICAL EQUIPMENT

SUZLON

Tata Communications Ltd.

TELECOMMUNICATION SERVICES

TATACOMM

Tata Consultancy Services Ltd.

COMPUTERS - SOFTWARE

TCS

Tata Motors Ltd.

AUTOMOBILES - 4 WHEELERS

TATAMOTORS

Tata Power Co. Ltd.

POWER

TATAPOWER

Tata Steel Ltd.

STEEL AND STEEL PRODUCTS

TATASTEEL

Unitech Ltd.

CONSTRUCTION

UNITECH

Wipro Ltd.

COMPUTERS - SOFTWARE

WIPRO

CNX Nifty Junior* The next rung of liquid securities after S&P CNX Nifty is the CNX Nifty Junior. It may be useful to think of the S&P CNX Nifty and the CNX Nifty Junior as making up the 100 most liquid stocks in India. As with the S&P CNX Nifty, stocks in the CNX Nifty Junior are filtered for liquidity, so they are the most liquid of the stocks excluded from the S&P CNX Nifty. The maintenance of the S&P CNX Nifty and the CNX Nifty Junior are synchronized so that the two * Source: www.nseindia.com

Price Index

47

indices will always be disjoint sets, i.e., a stock will never appear in both indices at the same time. Hence, it is always meaningful to pool the S&P CNX Nifty and the CNX Nifty Junior into a composite 100 stock index or portfolio. · CNX Nifty Junior represents about 9.62 % of the total market capitalization as on Jan 30, 2009. · The average traded value for the last six months of all Junior Nifty stocks is approximately 16.86% of the traded value of all stocks on the NSE · Impact cost for CNX Nifty Junior for a portfolio size of Rs. 50 lakh is 0.23%.

Criteria for Selection of Constituent Stocks The constituents and the criteria for the selection judge the effectiveness of the index. Selection of the index set is based on the following criteria:

Liquidity (Impact Cost) For inclusion in the index, the security should have traded at an average impact cost of 0.5% or less during the last six months for 90% of the observations for a basket size of Rs. 50 lakh.

Floating Stock Companies eligible for inclusion in the CNX Nifty Junior should have atleast 10% floating stock. For this purpose, floating stock means stocks which are not held by the promoters and associated entities (where identifiable) of such companies.

Others A company which comes out with a IPO will be eligible for inclusion in the index, if it fulfills the normal eligiblity criteria for the index like impact cost, market capitalization and floating stock, for a 3 month period instead of a 6 months period Replacement of Stock from the Index: A stock may be replaced from an index for the following reasons: (i) Compulsory changes like corporate actions, delisting etc. In such a scenario, the stock having largest market capitalization and satisfying other requirements related to liquidity, turnover and free float will be considered for inclusion. (ii) When a better candidate is available in the replacement pool, which can replace the index stock, i.e., the stock with the highest market capitalization in the replacement pool has at least twice the market capitalization of the index stock with the lowest market capitalization. However where a stock is replaced due to a stock being transferred to S&P CNX Nifty then the stock coming into CNX Nifty Junior need not have twice the market capitalization of the stock which is being transferred to S&P CNX Nifty. With respect to (ii) above, a maximum of 10% of the index size (number of stocks in the index) may be changed in a calendar year. Changes carried out for (ii) above are irrespective of changes, if any, carried out for (i) above.

48

Option Trading

Table 4.3

List of Nifty Junior Stocks

Company Name

Industry

Symbol

Aditya Birla Nuvo Ltd.

TEXTILES - SYNTHETIC

ABIRLANUVO

Andhra Bank

BANKS

ANDHRABANK

Adani Enterprises Ltd.

TRADING

ADANIENT

Apollo Tyres Ltd.

TYRES

APOLLOTYRE

Ashok Leyland Ltd.

AUTOMOBILES - 4 WHEELERS

ASHOKLEY

Asian Paints Ltd.

PAINTS

ASIANPAINT

Bank of Baroda

BANKS

BANKBARODA

Bank of India

BANKS

BANKINDIA

Bharat Electronics Ltd.

ELECTRONICS - INDUSTRIAL

BEL

Bharat Forge Ltd.

CASTINGS/FORGINGS

BHARATFORG

Biocon Ltd.

PHARMACEUTICALS

BIOCON

Canara Bank

BANKS

CANBK

Chennai Petroleum Corporation Ltd.

REFINERIES

CHENNPETRO

Container Corporation of India Ltd.

TRAVEL AND TRANSPORT

CONCOR

Corporation Bank

BANKS

CORPBANK

Cummins India Ltd.

DIESEL ENGINES

CUMMINSIND

Dr. Reddy’s Laboratories Ltd.

PHARMACEUTICALS

DRREDDY

Glaxosmithkline Pharmaceuticals Ltd.

PHARMACEUTICALS

GLAXO

GMR Infrastructure Ltd.

CONSTRUCTION

GMRINFRA

Glenmark Pharmaceuticals Ltd.

PHARMACEUTICALS

GLENMARK

Hindustan Petroleum Corporation Ltd.

REFINERIES

HINDPETRO

Housing Development and Infrastructure Ltd.

CONSTRUCTION

HDIL

IDBI Bank Ltd.

BANKS

IDBI

IFCI Ltd.

FINANCIAL INSTITUTION

IFCI

Price Index Company Name

Industry

Symbol

Indian Hotels Co. Ltd.

HOTELS

INDHOTEL

Indian Overseas Bank

BANKS

IOB

49

Infrastructure FINANCIAL INSTITUTION Development Finance Co. Ltd.

IDFC

JSW Steel Ltd.

STEEL AND STEEL PRODUCTS

JSWSTEEL

Jaiprakash Associates Ltd.

DIVERSIFIED

JPASSOCIAT

Kotak Mahindra Bank Ltd.

BANKS

KOTAKBANK

LIC Housing Finance Ltd.

FINANCE-HOUSING

LICHSGFIN

Lupin Ltd.

PHARMACEUTICALS

LUPIN

Mangalore Refinery & Poetrochemicals Ltd.

REFINERIES

MRPL

Moser Baer India Ltd.

COMPUTERS-HARDWARE

MOSERBAER

MphasiS Ltd.

COMPUTERS-SOFTWARE

MPHASIS

Mundra Port and Special Economic Zone Ltd.

TRAVEL AND TRANSPORT

MUNDRAPORT

Oracle Financial Services Software Ltd.

COMPUTERS-SOFTWARE

OFSS

Patni Computer Systems Ltd.

COMPUTERS-SOFTWARE

PATNI

Power Finance Corporation Ltd.

FINANCIAL INSTITUTION

PFC

Raymond Ltd.

TEXTILE PRODUCTS

RAYMOND

Reliance Natural Resources Ltd.

GAS

RNRL

Sesa Goa Ltd.

MINING

SESAGOA

Syndicate Bank

BANKS

SYNDIBANK

Tata Teleservices (Maharashtra) Ltd.

TELECOMMUNICATIONSERVICES

TTML

Tech Mahindra Ltd.

COMPUTERS - SOFTWARE

TECHM

UltraTech Cement Ltd.

CEMENT AND CEMENT PRODUCTS

ULTRACEMCO

Union Bank of India

BANKS

UNIONBANK

United Spirits Ltd.

BREW/DISTILLERIES

MCDOWELL-N

Vijaya Bank

BANKS

VIJAYABANK

Wockhardt Ltd.

PHARMACEUTICALS

WOCKPHARMA

50

Option Trading

S&P CNX IT Index* Information Technology industry has played a major role in the Indian economy during the last few years. A number of large, profitable Indian companies today belong to the IT sector and a great deal of investment interest is now focused on the IT sector. In order to have a good benchmark of the Indian IT sector, IISL has developed the CNX IT sector index. CNX IT provides investors and market intermediaries with an appropriate benchmark that captures the performance of the IT segment of the market. Companies in this index are those that have more than 50% of their turnover from IT related activities like software development, hardware manufacture, vending, support and maintenance.The average total traded value for the last six months of CNX IT Index stocks is approximately 91% of the traded value of the IT sector. CNX IT Index stocks represent about 96% of the total market capitalization of the IT sector as on March 31, 2005. The average total traded value for the last six months of all CNX IT Index constituents is approximately 14% of the traded value of all stocks on the NSE. CNX IT Index constituents represent about 14% of the total market capitalization as on March 31, 2005.

Methodology The index is a market capitalization weighted index with its base period being December 1995 and the base date and base value being January 1, 1996 and 1,000 respectively. The Base Value of the index is being revised from 1000 to 100 w.e.f. 28 May 2004.

Selection Criteria Selection of the index set is based on the following criteria : 1. Company’s market capitalization rank in the universe should be less than 500. 2. Company’s turnover rank in the universe should be less than 500. 3. Company’s trading frequency should be at least 90% in the last six months. 4. Company should have a positive networth. 5. A company which comes out with a IPO will be eligible for inclusion in the index, if it fulfills the normal eligibility criteria for the index for a three-month period instead of a six-month period. Table 4.4

List of Stocks in CNX IT Index

Company Name

Industry

Symbol

CMC Ltd.

COMPUTERS - HARDWARE

CMC

Core Projects & Technologies Ltd.

COMPUTERS - SOFTWARE

COREPROTEC

* Source: www.nseindia.com

Price Index Company Name Educomp Solutions Ltd.

Industry COMPUTERS - SOFTWARE

Symbol EDUCOMP

Financial Technologies (India) Ltd.

COMPUTERS - SOFTWARE

FINANTECH

Firstsource Solutions Ltd.

COMPUTERS - SOFTWARE

FSL

GTL Ltd.

TELECOMMUNICATION SERVICES

GTL

HCL Infosystems Ltd.

COMPUTERS - HARDWARE

HCL-INSYS

HCL Technologies Ltd.

COMPUTERS - SOFTWARE

HCLTECH

Hexaware Technologies Ltd.

COMPUTERS - SOFTWARE

HEXAWARE

Infosys Technologies Ltd.

COMPUTERS - SOFTWARE

INFOSYSTCH

MindTree Ltd.

COMPUTERS - SOFTWARE

MINDTREE

Moser Baer India Ltd.

COMPUTERS - HARDWARE

MOSERBAER

Mphasis Ltd.

COMPUTERS - SOFTWARE

MPHASIS

Oracle Financial Services Software Ltd.

COMPUTERS - SOFTWARE

OFSS

Patni Computer Systems Ltd.

COMPUTERS - SOFTWARE

PATNI

Polaris Software Lab Ltd.

COMPUTERS - SOFTWARE

POLARIS

Rolta India Ltd.

COMPUTERS - SOFTWARE

ROLTA

Tata Consultancy Services Ltd.

COMPUTERS - SOFTWARE

TCS

Tech Mahindra Ltd.

COMPUTERS - SOFTWARE

TECHM

Wipro Ltd.

COMPUTERS - SOFTWARE

WIPRO

51

CNX Bank Index* CNX Bank Index is an index comprised of the most liquid and large capitalized Indian Banking stocks. It provides investors and market intermediaries with a benchmark that captures the capital market performance of Indian Banks.The index will have 12 stocks from the banking sector which trade on the National Stock Exchange. The average total traded value for the last six months of CNX Bank Index stocks is approximately 82% of the traded value of the banking sector. CNX Bank Index stocks represent about 88% of the total market capitalization of the banking sector as on March 31, 2005. * Source: www.nseindia.com

52

Option Trading

The average total traded value for the last six months of all the CNX Bank Index constituents is approximately 7% of the traded value of all stocks on the NSE. CNX Bank Index constituents represent about 7% of the total market capitalization as on May 31, 2008.

Methodology The index is a market capitalization-weighted index with base date of 1 January, 2000, indexed to a base value of 1000.

Selection Criteria Selection of the index set is based on the following criteria: 1. Company’s market capitalization rank in the universe should be less than 500. 2. Company’s turnover rank in the universe should be less than 500. 3. Company’s trading frequency should be at least 90% in the last six months. 4. Company should have a positive networth. 5. A company which comes out with a IPO will be eligible for inclusion in the index, if it fulfills the normal eligiblity criteria for the index for three months instead of a six month. Table 4.5

List of Stocks in Bank Index

Company Name

Industry

Symbol

Axis Bank Ltd.

BANKS

AXISBANK

Bank of Baroda

BANKS

BANKBARODA

Bank of India

BANKS

BANKINDIA

Canara Bank

BANKS

CANBK

HDFC Bank Ltd.

BANKS

HDFCBANK

ICICI Bank Ltd.

BANKS

ICICIBANK

IDBI Bank Ltd.

BANKS

IDBI

Kotak Mahindra Bank Ltd.

BANKS

KOTAKBANK

Oriental Bank of Commerce

BANKS

ORIENTBANK

Punjab National Bank

BANKS

PNB

State Bank of India

BANKS

SBIN

Union Bank of India

BANKS

UNIONBANK

CNX 100 Index* CNX 100 is a diversified 100 stock index accounting for 35 sectors of the economy. *Source: www.nseindia.com

Price Index

53

CNX 100 is owned and managed by India Index Services & Products Ltd. (IISL), which is a joint venture between CRISIL & NSE. IISL is India’s first specialized company focused upon the index as a core products. IISL has a licensing and marketing agreement with Standard & Poor’s (S&P), who are leaders in index services. · CNX 100 represents about 73.60% of the total market capitalization as on Jan 30, 2009. · The average traded value for the last six months of all CNX100 stocks is approximately 79.31 % of the traded value of all stocks on the NSE · Impact cost for CNX 100 for a portfolio size of Rs. 3 crore is 0.18%.

Method of Computation CNX 100 is computed using market capitalization weighted method, wherein the level of the index reflects the total market value of all the stocks in the index relative to a particular base period. The method also takes into account constituent changes in the index and importantly corporate actions such as stock splits, rights, etc., without affecting the index value.

Base Date and Value The CNX 100 Index has a base date of Jan 1, 2003 and a base value of 1000.

Criteria for Selection of Constituent Stocks CNX 100 index would comprise of the securities, which are constituents of S&P CNX Nifty, and CNX Nifty Junior. In other words this index is a combination of the S&P CNX Nifty and CNX Nifty Junior. Any changes i.e. inclusion and exclusion of securities in S&P CNX Nifty and CNX Nifty Junior would be automatically mirrored in this new index. Table 4.6

List of Stocks in CNX 100 Index

Company Name

Industry

Symbol

ABB Ltd.

ELECTRICAL EQUIPMENT

ABB

ACC Ltd.

CEMENT AND CEMENT PRODUCTS

ACC

Adani Enterprises Ltd.

TRADING

ADANIENT

Aditya Birla Nuvo Ltd.

TEXTILES - SYNTHETIC

ABIRLANUVO

Ambuja Cements Ltd.

CEMENT AND CEMENT PRODUCTS

AMBUJACEM

Andhra Bank

BANKS

ANDHRABANK

Apollo Tyres Ltd.

TYRES

APOLLOTYRE

Ashok Leyland Ltd.

AUTOMOBILES - 4 WHEELERS

ASHOKLEY

Asian Paints Ltd.

PAINTS

ASIANPAINT

Axis Bank Ltd.

BANKS

AXISBANK

54

Option Trading

Company Name Bank of Baroda. Bank of India

Industry BANKS BANKS

Symbol BANKBARODA BANKINDIA

Bharat Electronics Ltd.

ELECTRONICS INDUSTRIAL

BEL

Bharat Forge Ltd.

CASTINGS/FORGINGS

BHARATFORG

Bharat Heavy Electricals Ltd.

ELECTRICAL EQUIPMENT

BHEL

Bharat Petroleum Corporation Ltd.

REFINERIES

BPCL

Bharti Airtel Ltd.

TELECOMMUNICATION SERVICES

BHARTIARTL

Biocon Ltd.

PHARMACEUTICALS

BIOCON

Cairn India Ltd.

OIL EXPLORATION/ PRODUCTION

CAIRN

Canara Bank

BANKS

CANBK

Chennai Petroleum Corporation Ltd.

REFINERIES

CHENNPETRO

Cipla Ltd.

PHARMACEUTICALS

CIPLA

Container Corporation of India Ltd.

TRAVEL AND TRANSPORT

CONCOR

Corporation Bank

BANKS

CORPBANK

Cummins India Ltd.

DIESEL ENGINES

CUMMINSIND

DLF Ltd.

CONSTRUCTION

DLF

Dr. Reddy’s Laboratories Ltd.

PHARMACEUTICALS

DRREDDY

GAIL (India) Ltd.

GAS

GAIL

GMR Infrastructure Ltd.

CONSTRUCTION

GMRINFRA

Glenmark Pharmaceuticals Ltd.

PHARMACEUTICALS

GLENMARK

Grasim Industries Ltd.

CEMENT AND CEMENT PRODUCTS

GRASIM

Glaxosmithkline Pharmaceuticals Ltd.

PHARMACEUTICALS

GLAXO

HCL Technologies Ltd.

COMPUTERS - SOFTWARE

HCLTECH

HDFC Bank Ltd.

BANKS

HDFCBANK

Hero Honda Motors Ltd.

AUTOMOBI LES - 2 AND 3 WHEELERS

HEROHONDA

Hindalco Industries Ltd. Hindustan Unilever Ltd.

ALUMINIUM DIVERSIFIED

HINDALCO HINDUNILVR

Price Index Company Name

Industry

Symbol

Hindustan Petroleum Corporation Ltd.

REFINERIES

HINDPETRO

Housing Development Finance Corporation Ltd.

FINANCE - HOUSING

HDFC

Housing Development and Infrastructure Ltd.

CONSTRUCTION

HDIL

I T C Ltd.

CIGARETTES

ITC

ICICI Bank Ltd.

BANKS

ICICIBANK

IDBI Bank Ltd.

BANKS

IDBI

IFCI Ltd.

FINANCIAL INSTITUTION

IFCI

Idea Cellular Ltd.

TELECOMMUNICATION SERVICES

IDEA

Indian Hotels Co. Ltd.

HOTELS

INDHOTEL

Indian Overseas Bank

BANKS

IOB

Infosys Technologies Ltd. COMPUTERS - SOFTWARE

INFOSYSTCH

Infrastructure Development Finance Co. Ltd.

FINANCIAL INSTITUTION

IDFC

JSW Steel Ltd.

STEEL AND STEEL PRODUCTS

JSWSTEEL

Jaiprakash Associates Ltd.

DIVERSIFIED

JPASSOCIAT

Jindal Steel & Power Ltd.

STEEL AND STEEL PRODUCTS

JINDALSTEL

Kotak Mahindra Bank Ltd.

BANKS

KOTAKBANK

LIC Housing Finance Ltd.

FINANCE - HOUSING

LICHSGFIN

Larsen & Toubro Ltd.

ENGINEERING

LT

Lupin Ltd.

PHARMACEUTICALS

LUPIN

Mangalore Refinery & Petrochemicals Ltd.

REFINERIES

MRPL

Mahindra & Mahindra Ltd.

AUTOMOBILES - 4 WHEELERS

M&M

Maruti Suzuki India Ltd.

AUTOMOBILES - 4 WHEELERS

MARUTI

Moser Baer India Ltd.

COMPUTERS - HARDWARE

MOSERBAER

Mphasis Ltd.

COMPUTERS - SOFTWARE

MPHASIS

Mundra Port and Special Economic Zone Ltd.

TRAVEL AND TRANSPORT

MUNDRAPORT

55

56

Option Trading

Company Name

Industry

Symbol

NTPC Ltd.

POWER

NTPC

National Aluminium Co. Ltd.

ALUMINIUM

NATIONALUM

Oil & Natural Gas Corporation Ltd.

OIL EXPLORATION/ PRODUCTION

ONGC

Oracle Financial Services Software Ltd.

COMPUTERS - SOFTWARE

OFSS

Patni Computer Systems Ltd.

COMPUTERS - SOFTWARE

PATNI

Power Finance Corporation Ltd.

FINANCIAL INSTITUTION

PFC

Power Grid Corporation of India Ltd.

POWER

POWERGRID

Punjab National Bank

BANKS

PNB

Ranbaxy Laboratories Ltd.

PHARMACEUTICALS

RANBAXY

Raymond Ltd.

TEXTILE PRODUCTS

RAYMOND

Reliance Capital Ltd.

FINANCE

RELCAPITAL

Reliance Communications Ltd.

TELECOMMUNICATION SERVICES

RCOM

Reliance Industries Ltd.

REFINERIES

RELIANCE

Reliance Infrastructure Ltd.

POWER

RELINFRA

Reliance Natural Resources Ltd.

GAS

RNRL

Reliance Power Ltd.

POWER

RPOWER

Siemens Ltd.

ELECTRICAL EQUIPMENT

SIEMENS

Sesa Goa Ltd.

MINING

SESAGOA

State Bank of India

BANKS

SBIN

Steel Authority of India Ltd.

STEEL AND STEEL PRODUCTS

SAIL

Sterlite Industries (India) Ltd.

METALS

STER

Sun Pharmaceutical Industries Ltd.

PHARMACEUTICALS

SUNPHARMA

Suzlon Energy Ltd.

ELECTRICAL EQUIPMENT

SUZLON

Price Index Company Name

Industry

Symbol

Syndicate Bank

BANKS

SYNDIBANK

Tata Communications Ltd.

TELECOMMUNICATION SERVICES

TATACOMM

Tata Consultancy Services Ltd.

COMPUTERS - SOFTWARE

TCS

Tata Motors Ltd.

AUTOMOBILES - 4 WHEELERS

TATAMOTORS

Tata Power Co. Ltd.

POWER

TATAPOWER

Tata Steel Ltd.

STEEL AND STEEL PRODUCTS

TATASTEEL

Tata Teleservices (Maharashtra) Ltd.

TELECOMMUNICATION SERVICES

TTML

Tech Mahindra Ltd.

COMPUTERS - SOFTWARE

TECHM

UltraTech Cement Ltd.

CEMENT AND CEMENT PRODUCTS

ULTRACEMCO

Union Bank of India

BANKS

UNIONBANK

Unitech Ltd.

CONSTRUCTION

UNITECH

United Spirits Ltd.

BREW/DISTILLERIES

MCDOWELL-N

Vijaya Bank

BANKS

VIJAYABANK

Wipro Ltd.

COMPUTERS - SOFTWARE

WIPRO

Wockhardt Ltd.

PHARMACEUTICALS

WOCKPHARMA

57

S&P CNX 500* The S&P CNX 500 is India’s first broadbased benchmark of the Indian capital market. The S&P CNX 500 represents about 93.95% of total market capitalisation and about 93.20% of the total turnover on the NSE as on, 30 January, 2009. The S&P CNX 500 companies are disaggregated into 72 industry indices viz., S&P CNX Industry Indices. Industry weightages in the index reflect the industry weightages in the market. For example, if banking sector has a 5% weightage in the universe of stocks traded on NSE, banking stocks in the index would also have an approx. representation of 5% in the index.

S&P CNX 500* Method of Computation S&P CNX 500 is computed using market capitalization weighted method, wherein the level of the index reflects the total market value of all the stocks in * Source: www.nseindia.com

58

Option Trading

the index relative to a particular base period. The method also takes into account constituent changes in the index and importantly corporate actions such as stock splits, rights, etc without affecting the index value.

Base Date and Value The calendar year 1994 has been selected as the base year for S&P CNX 500. The base value of the index is set at 1000.

Criteria for Selection of Constituent Stocks The constituents and the criteria for the selection judge the effectiveness of the index. Selection of the index set is based on the following criteria:

Market Capitalization A company’s rank on market capitalization is an important consideration for its inclusion in the Index.

Industry Representation S&P CNX 500 Equity Index reflects the market as closely as possible. In order to ensure that this is accomplished, industry weightages in the index mirror the industry weightages in the universe. Consequently, companies to be included in the index are selected from the industries which are under represented in the index S&P CNX 500 Equity Index currently contains 72 industries, including one category of diversified companies and one category of miscellaneous. The number of industries in the Index and the number of companies within each industry have been kept flexible, in order to ensure that the index retains its objective of being an dynamic market indicator.

Trading Interest S&P CNX 500 Equity Index includes those companies which have a minimum listing record of six months on the Exchange. In addition these companies must have demonstrated high turnover and trading frequency.

Financial Performance S&P CNX 500 Equity Index includes companies that have a minimum record of three years with a positive networth.

Others A company which comes out with a IPO will be eligible for inclusion in the index, if it fulfills the normal eligiblity criteria for the index for a three-month period instead of a six-month period.

Price Index

Table 4.7

List of Stocks in CNX 500

Company Name

Industry

Symbol

3M India Ltd.

TRADING

3MINDIA

ABB Ltd.

ELECTRICAL EQUIPMENT

ABB

ABG Shipyard Ltd.

SHIPPING

ABGSHIP

ACC Ltd.

CEMENT AND CEMENT PRODUCTS

ACC

Aarti Industries Ltd.

CHEMICALS - ORGANIC

AARTIIND

Aban Offshore Ltd.

OIL EXPLORATION/ PRODUCTION

ABAN

Abhishek Industries Ltd

TEXTILES - COTTON

ABSHEKINDS

Adani Enterprises Ltd.

TRADING

ADANIENT

Aditya Birla Nuvo Ltd.

TEXTILES - SYNTHETIC

ABIRLANUVO

Adlabs Films Ltd.

MEDIA & ENTERTAINMENT

ADLABSFILM

Ador Welding Ltd.

ELECTRODES

ADORWELD

Advanta India Ltd.

FOOD AND FOOD PROCESSING

ADVANTA

Aftek Ltd.

COMPUTERS - SOFTWARE

AFTEK

Agro Dutch Industries Ltd.

FOOD AND FOOD PROCESSING

AGRODUTCH

Agro Tech Foods Ltd.

SOLVENT EXTRACTION

ATFL

Ajanta Pharmaceuticals Ltd.

PHARMACEUTICALS

AJANTPHARM

Akruti City Ltd.

CONSTRUCTION

AKRUTI

Aksh Optifibre Ltd.

CABLES - TELECOM

AKSHOPTFBR

Alembic Ltd.

PHARMACEUTICALS

ALEMBICLTD

Alfa Laval (India) Ltd.

ENGINEERING

ALFALAVAL

Allahabad Bank

BANKS

ALBK

Allcargo Global Logistics Ltd.

TRAVEL AND TRANSPORT

ALLCARGO

Alok Industries Ltd.

TEXTILES - SYNTHETIC

ALOKTEXT

Alstom Projects India Ltd.

POWER

APIL

Amara Raja Batteries Ltd.

AUTO ANCILLARIES

AMARAJABAT

Ambuja Cements Ltd.

CEMENT AND CEMENT PRODUCTS

AMBUJACEM

Amtek Auto Ltd.

AUTO ANCILLARIES

AMTEKAUTO

Amtek India Ltd.

AUTO ANCILLARIES

AMTEKINDIA

59

60

Option Trading

Company Name

Industry

Symbol

Anant Raj Industries Ltd.

CONSTRUCTION

ANANTRAJ

Andhra Bank

BANKS

ANDHRABANK

Andhra Sugars Ltd.

DIVERSIFIED

ANDHRSUGAR

Ansal Properties & Infrastructure Ltd.

CONSTRUCTION

ANSALINFRA

Apollo Hospitals Enterprises Ltd.

MISCELLANEOUS

APOLLOHOSP

Apollo Tyres Ltd.

TYRES

APOLLOTYRE

Aptech Ltd.

COMPUTERS - SOFTWARE

APTECHT

Areva T&D India Ltd.

ELECTRICAL EQUIPMENT

AREVAT&D

Arvind Ltd.

TEXTILE PRODUCTS

ARVIND

Asahi India Glass Ltd.

AUTO ANCILLARIES

ASAHIINDIA

Ashok Leyland Ltd.

AUTOMOBILES - 4 WHEELERS

ASHOKLEY

Asian Electronics Ltd.

ELECTRONICS INDUSTRIAL

ASIANELEC

Asian Hotels Ltd.

HOTELS

ASIANHOTEL

Asian Paints Ltd.

PAINTS

ASIANPAINT

AstraZenca Pharma India Ltd.

PHARMACEUTICALS

ASTRAZEN

Atul Ltd.

DYES AND PIGMENTS

ATUL

Aurobindo Pharma Ltd.

PHARMACEUTICALS

AUROPHARMA

Automotive Axles Ltd.

AUTO ANCILLARIES

AUTOAXLES

Avaya Global Connect Ltd.

TELECOMMUNICATION EQUIPMENT

AVAYAGCL

Aventis Pharma Ltd.

PHARMACEUTICALS

AVENTIS

Axis Bank Ltd.

BANKS

AXISBANK

Ballarpur Industries Ltd.

PAPER AND PAPER PRODUCTS

BALLARPUR

B L Kashyap & Sons Ltd.

CONSTRUCTION

BLKASHYAP

BASF India Ltd.

CHEMICALS - SPECIALITY

BASF

BEML Ltd.

ENGINEERING

BEML

BOC India Ltd.

GAS

BOC

BPL Ltd.

CONSUMER DURABLES

BPL

Bajaj Auto Finance Ltd.

FINANCE

BAJAUTOFIN

Bajaj Auto Ltd.

AUTOMOBILES - 2 AND 3 WHEELERS

BAJAJ-AUTO

Price Index Company Name

Industry

61

Symbol

Bajaj Finserv Ltd.

FINANCE

BAJAJFINSV

Bajaj Hindusthan Ltd.

SUGAR

BAJAJHIND

Bajaj Holdings & Investment Ltd.

FINANCE

BAJAJHLDNG

Balaji Telefilms Ltd.

MEDIA & ENTERTAINMENT

BALAJITELE

Balmer Lawrie & Co. Ltd.

DIVERSIFIED

BALMLAWRIE

Balrampur Chini Mills Ltd.

SUGAR

BALRAMCHIN

Bank of Baroda

BANKS

BANKBARODA

Bank of India

BANKS

BANKINDIA

Bannari Amman Sugars Ltd.

SUGAR

BANARISUG

Bata India Ltd.

LEATHER AND LEATHER PRODUCTS

BATAINDIA

Berger Paints India Ltd.

PAINTS

BERGEPAINT

Bhansali Engineering Polymers Ltd.

PETROCHEMICALS

BEPL

Bharat Electronics Ltd.

ELECTRONICS INDUSTRIAL

BEL

Bharat Forge Ltd.

CASTINGS/FORGINGS

BHARATFORG

Bharat Heavy Electricals Ltd.

ELECTRICAL EQUIPMENT

BHEL

Bharat Petroleum Corporation Ltd.

REFINERIES

BPCL

Bharti Airtel Ltd.

TELECOMMUNICATION SERVICES

BHARTIARTL

Bhushan Steel Ltd.

STEEL AND STEEL PRODUCTS

BHUSANSTL

Biocon Ltd.

PHARMACEUTICALS

BIOCON

Birla Corporation Ltd.

CEMENT AND CEMENT PRODUCTS

BIRLACORPN

Blue Dart Express Ltd.

TRAVEL AND TRANSPORT

BLUEDART

Blue Star Ltd.

AIRCONDITIONERS

BLUESTARCO

Bombay Dyeing & Manufacturing Co. Ltd.

TEXTILES - SYNTHETIC

BOMDYEING

Bombay Rayon Fashions Ltd.

TEXTILE PRODUCTS

BRFL

62

Option Trading

Company Name Bosch Ltd

Industry AUTO ANCILLARIES

Symbol BOSCHLTD

Britannia Industries Ltd.

FOOD AND FOOD PROCESSING

BRITANNIA

Cadila Healthcare Ltd.

PHARMACEUTICALS

CADILAHC

CESC Ltd.

POWER

CESC

CMC Ltd.

COMPUTERS HARDWARE

CMC

CRISIL Ltd.

FINANCE

CRISIL

Cairn India Ltd.

OIL EXPLORATION/ PRODUCTI ON

CAIRN

Can Fin Homes Ltd.

FINANCE - HOUSING

CANFINHOME

Canara Bank

BANKS

CANBK

Carborundum Universal Ltd.

ABRASIVES

CARBORUNIV

Carol Info Services Ltd.

FOOD AND FOOD PROCESSING

CAROLINFO

Castrol (India) Ltd.

PETROCHEMICALS

CASTROL

Century Enka Ltd.

TEXTILES - SYNTHETIC

CENTENKA

Century Textile & Industries Ltd.

DIVERSIFIED

CENTURYTEX

Century Plyboards (India) Ltd.

CONSTRUCTION

CENTURYPLY

Chettinad Cement Corporations Ltd.

CEMENT AND CEMENT PRODUCTS

CHETTINAD

Chambal Fertilizers & Chemicals Ltd.

FERTILISERS

CHAMBLFERT

Chemplast Sanmar Ltd.

PETROCHEMICALS

CHEMPLAST

Chennai Petroleum Corporation Ltd.

REFINERIES

CHENNPETRO

Cholamandalam DBS Finance Ltd.

FINANCE

HOLADBS

Cipla Ltd.

PHARMACEUTICALS

CIPLA

City Union Bank Ltd.

BANKS

CUB

Clariant Chemicals (India) Ltd.

DYES AND PIGMENTS

CLNINDIA

Colgate Palmolive (India) Ltd.

PERSONAL CARE

COLPAL

Consolidated Finvest & Holdings Ltd.

FINANCE

CONSOFINVT

Container Corporation of India Ltd.

TRAVEL AND TRANSPORT

CONCOR

Price Index Company Name

Industry

63

Symbol

Coromandel Fertilisers Ltd.

FERTILISERS

COROMNFERT

Corporation Bank

BANKS

CORPBANK

Cosmo Films Ltd.

PACKAGING

COSMOFILMS

Crest Animation Studios Ltd.

MEDIA & ENTERTAINMENT

CRESTANI

Crompton Greaves Ltd.

ELECTRICAL EQUIPMENT

CROMPGREAV

Cummins India Ltd.

DIESEL ENGINES

CUMMINSIND

D-Link India Ltd

COMPUTERS - HARDWARE

D-LINK

D.S. Kulkarni Developers Ltd.

CONSTRUCTION

DSKULKARNI

DCM Shriram Consolidated Ltd.

DIVERSIFIED

DCMSRMCONS

DCW Ltd.

PETROCHEMICALS

DCW

DLF Ltd.

CONSTRUCTION

DLF

Dabur India Ltd.

PERSONAL CARE

DABUR

Dalmia Cement (Bharat) Ltd.

CEMENT AND CEMENT PRODUCTS

DALMIACEM

Deccan Chronicle Holdings Ltd.

PRINTING AND PUBLISHING

DCHL

Deepak Fertilisers & Petrochemicals Corp. Ltd.

FERTILISERS

DEEPAKFERT

Dena Bank

BANKS

DENABANK

Dhampur Sugar Mills Ltd.

SUGAR

DHAMPURSUG

Dishman Pharmaceuticals & Chemicals Ltd.

PHARMACEUTICALS

DISHMAN

Divi’s Laboratories Ltd.

PHARMACEUTICALS

DIVISLAB

Donear Industries Ltd.

TEXTILES - SYNTHETIC

DONEAR

Dr. Reddy’s Laboratories Ltd.

PHARMACEUTICALS

DRREDDY

Dredging Corporation of India Ltd.

MISCELLANEOUS

DREDGECORP

Dwarikesh Sugar Industrial Ltd.

SUGAR

DWARKESH

Dynamatic Technologies Ltd.

COMPRESSORS/PUMPS

DYNAMATECH

E.I.D. Parry (India) Ltd.

DIVERSIFIED

EIDPARRY

EIH Ltd.

HOTELS

EIHOTEL

64

Option Trading

Company Name ESAB India Ltd.

Industry ELECTRODES

Symbol ESABINDIA

Edelweiss Capital Ltd.

FINANCE

EDELWEISS

Educomp Solutions Ltd.

COMPUTERS - SOFTWARE

EDUCOMP

Eicher Motors Ltd.

AUTOMOBILES - 4 WHEELERS

EICHERMOT

Elder Pharmaceuticals Ltd.

PHARMACEUTICALS

ELDERPHARM

Electrosteel Castings Ltd.

CASTINGS/FORGINGS

ELECTCAST

Elgi Equipments Ltd.

COMPRESSORS / PUMPS

ELGIEQUIP

Engineers India Ltd.

ENGINEERING

ENGINERSIN

Entertainment Network India Ltd.

MEDIA & ENTERTAINMENT

ENIL

Era Infra Engineering Ltd.

CONSTRUCTION

ERAINFRA

Escorts Ltd.

AUTOMOBILES - 4 WHEELERS

ESCORTS

Essar Oil Ltd.

REFINERIES

ESSAROIL

Essar Shipping Ltd.

SHIPPING

ESSARSHIP

Essel Propack Ltd.

PACKAGING

ESSELPACK

Everest Industries Ltd.

CEMENT AND CEMENT PRODUCTS

EVERESTIND

Exide Industries Ltd.

AUTO ANCILLARIES

EXIDEIND

FDC Ltd.

PHARMACEUTICALS

FDC

Fag Bearings India Ltd.

BEARINGS

FAGBEARING

Federal Bank Ltd.

BANKS

FEDERALBNK

Federal-Mogul Goetze (India) Ltd.

AUTO ANCILLARIES

FMGOETZE

Financial Technologies (India) Ltd.

COMPUTERS - SOFTWARE

FINANTECH

Finolex Cables Ltd.

MISCELLANEOUS

FINCABLES

Finolex Industries Ltd.

PETROCHEMICALS

FINPIPE

First Leasing Co. of India Ltd.

FINANCE

FIRSTLEASE

Firstsource Solutions Ltd.

COMPUTERS - SOFTWARE

FSL

Fortis Healthcare Ltd.

MISCELLANEOUS

FORTIS

Fresenius Kabi Oncology Ltd.

PHARMACEUTICALS

FKONCO

Future Capital Holdings Ltd.

FINANCE

FCH

GAIL (India) Ltd.

GAS

GAIL

Price Index Company Name

Industry

Symbol

GHCL Ltd.

CHEMICALS - INORGANIC

GHCL

GMR Infrastructure Ltd.

CONSTRUCTION

GMRINFRA

GTL Infrastructure Ltd.

TELECOMMUNICATION EQUIPMENT

GTLINFRA

GTL Ltd.

TELECOMMUNICATION SERVICES

GTL

Gammon India Ltd.

CONSTRUCTION

GAMMONIND

Garden Silk Mills Ltd.

TEXTILES - SYNTHETIC

GARDENSILK

Gateway Distriparks Ltd.

TRAVEL AND TRANSPORT

GDL

Geojit BNP Paribas Financial Services Limited.

FINANCE

GEOJIT BNPP

Geometric Ltd.

COMPUTERS - SOFTWARE

GEOMETRIC

Gillette India Ltd.

PERSONAL CARE

GILLETTE

Gitanjali Gems Ltd.

GEMS JEWELLERY AND WATCHES

GITANJALI

GlaxoSmithkline Consumer Healthcare Ltd.

FOOD AND FOOD PROCESSING

GSKCONS

Glaxosmithkline Pharmaceuticals Ltd.

PHARMACEUTICALS

GLAXO

Glenmark Pharmaceuticals Ltd.

PHARMACEUTICALS

GLENMARK

Godfrey Phillips India Ltd.

CIGARETTES

GODFRYPHLP

Godrej Consumer Products Ltd.

PERSONAL CARE

GODREJCP

Godrej Industries Ltd.

CHEMICALS - INORGANIC

GODREJIND

Gokaldas Exports Ltd.

TEXTILE PRODUCTS

GOKEX

Graphite India Ltd.

ELECTRODES

GRAPHITE

Grasim Industries Ltd.

CEMENT AND CEMENT PRODUCTS

GRASIM

Great Eastern Shipping Co. Ltd.

SHIPPING

GESHIP

Greaves Cotton Ltd.

DIESEL ENGINES

GREAVESCOT

Gujarat Alkalies & Chemicals Ltd.

CHEMICALS INORGANIC

GUJALKALI

Gujarat Ambuja Exports Ltd.

TRADING

GAEL

65

66

Option Trading

Company Name Gujarat Fluorochemicals Ltd. Gujarat Gas Co. Ltd.

Industry

Symbol

GAS

GUJFLUORO

GAS

GUJRATGAS

Gujarat Industries Power Co. Ltd.

POWER

GIPCL

Gujarat Mineral Development Corporation Ltd.

MINING

GMDCLTD

Gujarat NRE Coke Ltd.

MINING

GUJNRECOKE

Gujarat Narmada Valley Fertilisers Co. Ltd.

FERTILISERS

GNFC

Gujarat State Fertilizers & Chemicals Ltd.

FERTILISERS

GSFC

GVK Power & Infrastructure Ltd

POWER

GVKPIL

H.E.G. Ltd.

ELECTRODES

HEG

HCL Infosystems Ltd.

COMPUTERS HARDWARE

HCL-INSYS

HCL Technologies Ltd.

COMPUTERS SOFTWARE

HCLTECH

HDFC Bank Ltd.

BANKS

HDFCBANK

HMT Ltd.

AUTOMOBILES - 4 WHEELERS

HMT

HT Media Ltd.

PRINTING AND PUBLISHING

HTMEDIA

Harrisons Malayalam Ltd.

TEA AND COFFEE

HARRMALAYA

Havell’s India Ltd.

ELECTRICAL EQUIPMENT

HAVELLS

Heritage Foods (India) Ltd.

FOOD AND FOOD PROCESSING

HERITGFOOD

Hero Honda Motors Ltd.

AUTOMOBILES - 2 AND 3 WHEELERS

HEROHONDA

Hexaware Technologies Ltd.

COMPUTERS - SOFTWARE

HEXAWARE

Hikal Ltd.

PESTICIDES AND AGROCHEMICALS

HIKAL

Himachal Futuristic Communications Ltd.

TELECOMMUNICATION EQUIPMENT

HIMACHLFUT

Himatsingka Seide Ltd.

TEXTILE PRODUCTS

HIMATSEIDE

Hindalco Industries Ltd.

ALUMINIUM

HINDALCO

Hindustan Construction Co. Ltd.

CONSTRUCTION

HCC

Price Index Company Name Hindustan Motors Ltd.

Industry

67

Symbol

AUTOMOBILES - 4 WHEELERS OIL EXPLORATION/ PRODUCTION

HINDMOTOR

Hindustan Petroleum Corporation Ltd.

REFINERIES

HINDPETRO

Hindustan Unilever Ltd.

DIVERSIFIED

HINDUNILVR

Hindustan Zinc Ltd.

METALS

HINDZINC

Honda SIEL Power Products Ltd.

ELECTRICAL EQUIPMENT

HONDAPOWER

Honeywell Automation India Ltd.

ELECTRONICS INDUSTRIAL

HONAUT

Hotel Leelaventure Ltd.

HOTELS

HOTELEELA

House of Pearl Fashions Ltd.

TEXTILE PRODUCTS

HOPFL

Housing Development Finance Corporation Ltd.

FINANCE - HOUSING

HDFC

Housing Development and Infrastructure Ltd.

CONSTRUCTION

HDIL

HSIL Ltd.

CONSTRUCTION

HSIL

I T C Ltd.

CIGARETTES

ITC

ICI India Ltd.

PAINTS

ICI

ICICI Bank Ltd.

BANKS

ICICIBANK

IDBI Bank Ltd.

BANKS

IDBI

IL&FS Investsmart Ltd.

FINANCE

INVSTSMART

ING Vysya Bank Ltd.

BANKS

INGVYSYABK

IVRCL Infrastructures & Projects Ltd.

CONSTRUCTION

IVRCLINFRA

Idea Cellular Ltd.

TELECOMMUNICATIONSERVICES

IDEA

Ind-Swift Laboratories Ltd.

PHARMACEUTICALS

INDSWFTLAB

India Cements Ltd.

CEMENT AND CEMENT PRODUCTS

INDIACEM

India Glycols Ltd.

PETROCHEMICALS

INDIAGLYCO

Hindustan Oil Exploration Co. Ltd.

HINDOILEXP

68

Option Trading

Company Name

Industry

Symbol

India Infoline Ltd. Indiabulls Financial Services Ltd.

FINANCE FINANCE

INDIAINFO INDIABULLS

Indiabulls Real Estate Ltd.

CONSTRUCTION

IBREALEST

Indian Hotels Co. Ltd.

HOTELS

INDHOTEL

Indian Oil Corporation Ltd.

REFINERIES

IOC

Indian Overseas Bank

BANKS

IOB

Indo Rama Synthetics Ltd.

TEXTILES - SYNTHETIC

INDORAMA

Indraprastha Gas Ltd.

GAS

IGL

Indraprastha Medical Corporation Ltd.

MISCELLANEOUS

INDRAMEDCO

IndusInd Bank Ltd.

BANKS

INDUSINDBK

Info Edge (India) Ltd.

COMPUTERS - SOFTWARE

NAUKRI

Infosys Technologies Ltd.

COMPUTERS - SOFTWARE

INFOSYSTCH

Infotech Enterprises Ltd.

COMPUTERS - SOFTWARE

INFOTECENT

Infrastructure Development Finance Co. Ltd.

FINANCIAL INSTITUTION

IDFC

Ingersoll Rand (India) Ltd.

COMPRESSORS / PUMPS

INGERRAND

Inox Leisure Ltd.

MEDIA & ENTERTAINMENT

INOXLEISUR

Ipca Laboratories Ltd.

PHARMACEUTICALS

IPCALAB

Jai Corp Ltd.

STEEL AND STEEL PRODUCTS

JAICORPLTD

J.B. Chemicals & Pharmaceuticals Ltd.

PHARMACEUTICALS

JBCHEPHARM

JM Financial Ltd.

FINANCE

JMFINANCIL

JSL Ltd.

STEEL AND STEEL PRODUCTS

JSL

JSW Steel Ltd.

STEEL AND STEEL PRODUCTS

JSWSTEEL

Jagran Prakashan Ltd.

PRINTING AND PUBLISHING

JAGRAN

Jain Irrigation Systems Ltd.

PLASTIC AND PLASTIC PRODUCTS

JISLJALEQS

Jaiprakash Associates Ltd.

DIVERSIFIED

JPASSOCIAT

Jammu & Kashmir Bank Ltd.

BANKS

J&KBANK

Price Index Company Name

Industry

Symbol

Jay Shree Tea & Industries Ltd. Jet Airways (India) Ltd.

TEA AND COFFEE

JAYSREETEA

TRAVEL AND TRANSPORT

JETAIRWAYS

Jindal Poly Films Ltd.

PACKAGING

JINDALPOLY

Jindal Saw Ltd.

STEEL AND STEEL PRODUCTS

JINDALSAW

Jindal Steel & Power Ltd.

STEEL AND STEEL PRODUCTS

JINDALSTEL

Jubilant Organosys Ltd.

CHEMICALS - ORGANIC

JUBILANT

Jyoti Structures Ltd.

TRANSMISSION TOWERS

JYOTISTRUC

KCP Ltd.

CEMENT AND CEMENT PRODUCTS

KCP

KPIT Cummins Infosystem Ltd.

COMPUTERS - SOFTWARE

KPIT

KSB Pumps Ltd.

COMPRESSORS/PUMPS

KSBPUMPS

Kajaria Ceramics Ltd.

CONSTRUCTION

KAJARIACER

Kalpataru Power Transmission Ltd.

TRANSMISSION TOWERS

KALPATPOWR

Kansai Nerolac Paints Ltd.

PAINTS

KANSAINER

Karnataka Bank Ltd.

BANKS

KTKBANK

Karur Vysya Bank Ltd.

BANKS

KARURVYSYA

Kesoram Industries Ltd. CEMENT PRODUCTS

CEMENT AND

KESORAMIND

Kirloskar Brothers Ltd.

COMPRESSORS / PUMPS

KBL

Kirloskar Oil Engines Ltd.

DIESEL ENGINES

KIRLOSOIL

Kohinoor Foods Ltd.

FOOD AND FOOD PROCESSING

KOHINOOR

Kotak Mahindra Bank Ltd.

BANKS

KOTAKBANK

K.S. Oils Ltd.

SOLVENT EXTRACTION

KSOILS

Koutons Retail India Ltd.

TEXTILE PRODUCTS

KOUTONS

LIC Housing Finance Ltd.

FINANCE - HOUSING

LICHSGFIN

Lakshmi Energy and Foods Ltd.

FOOD AND FOOD PROCESSING

LAKSHMIEFL

Lakshmi Machine Works Ltd.

TEXTILE MACHINERY

LAXMIMACH

Lakshmi Vilas Bank Ltd.

BANKS

LAKSHVILAS

Lanco Infratech Ltd.

CONSTRUCTION

LITL

Larsen & Toubro Ltd.

ENGINEERING

LT

69

70

Option Trading

Company Name

Industry

Symbol

Lumax Industries Ltd. Lupin Ltd.

AUTO ANCILLARIES PHARMACEUTICALS

LUMAXIND LUPIN

MRF Ltd.

TYRES

MRF

MRO-TEK Ltd.

COMPUTERS HARDWARE

MRO-TEK

Madras Cements Ltd.

CEMENT AND CEMENT PRODUCTS

MADRASCEM

Mahanagar Telephone Nigam Ltd.

TELECOMMUNICATION SERVICES

MTNL

Maharashtra Seamless Ltd.

STEEL AND STEEL PRODUCTS

MAHSEAMLES

Mahindra & Mahindra Financial Services Ltd.

FINANCE

M&MFIN

Mahindra & Mahindra Ltd.

AUTOMOBILES - 4 WHEELERS

M&M

Mahindra Lifespace Developers Ltd.

CONSTRUCTION

MAHLIFE

Mahindra Ugine Steel Co. Ltd.

STEEL AND STEEL PRODUCTS

MAHINDUGIN

Mangalore Refinery & Petrochemicals Ltd.

REFINERIES

MRPL

Marico Ltd.

PERSONAL CARE

MARICO

Maruti Suzuki India Ltd.

AUTOMOBILES - 4 WHEELERS

MARUTI.

Mastek Ltd.

COMPUTERS - SOFTWARE

MASTEK

Matrix Laboratories Ltd.

PHARMACEUTICALS

MATRIXLABS

Max India Ltd.

PACKAGING

MAX

McLeod Russel India Ltd.

TEA AND COFFEE

MCLEODRUSS

Mercator Lines Ltd.

SHIPPING

MLL

Merck Ltd.

PHARMACEUTICALS

MERCK

Micro Inks Ltd.

CHEMICALS SPECIALITY

MICRO

Mid-Day Multimedia Ltd

PRINTING AND PUBLISHING

MID-DAY

Price Index Company Name

Industry

71

Symbol

MindTree Ltd. Mirc Electronics Ltd.

COMPUTERS - SOFTWARE CONSUMER DURABLES

MINDTREE MIRCELECTR

Mirza International Ltd.

LEATHER AND LEATHER PRODUCTS

MIRZAINT

Monnet Ispat Ltd.

STEEL AND STEEL PRODUCTS PESTICIDES AND

MONNETISPA

Monsanto India Ltd.

MONSANTO

AGROCHEMICALS Moser Baer India Ltd.

COMPUTERS - HARDWARE

MOSERBAER

Motherson Sumi Systems Ltd.

AUTO ANCILLARIES

MOTHERSUMI

Motial Oswal Financial Services Ltd.

FINANCE

MOTILALOFS

Mphasis Ltd.

COMPUTERS - SOFTWARE

MPHASIS

Mukta Arts Ltd

MEDIA & ENTERTAINMENT

MUKTAARTS

Mundra Port and Special Economic Zone Ltd.

TRAVEL AND TRANSPORT

MUNDRAPORT

Munjal Showa Ltd.

AUTO ANCILLARIES

MUNJALSHOW

National Fertilizers Ltd.

FERTILISERS

NFL

NDTV Ltd.

MEDIA & ENTERTAINMENT

NDTV

NELCO Ltd.

ELECTRONICS INDUSTRIAL

NELCO

NIIT Ltd.

COMPUTERS - SOFTWARE

NIITLTD

NMDC Ltd.

MINING

NMDC

NRB Bearings Ltd.

BEARINGS

NRBBEARING

NTPC Ltd.

POWER

NTPC

Nagarjuna Construction Co. Ltd.

CONSTRUCTION

NAGARCONST

Nagarjuna Fertilizers & Chemicals Ltd.

FERTILISERS

NAGARFERT

National Aluminium Co. Ltd.

ALUMINIUM

NATIONALUM

Nava Bharat Ventures Ltd.

METALS

NBVENTURES

Navneet Publications (India) Ltd.

PRINTING AND PUBLISHING

NAVNETPUBL

Neyveli Lignite Corporation Ltd.

POWER

NEYVELILIG

72

Option Trading

Company Name Nilkamal Ltd.

Industry

Symbol

Nirma Ltd.

PLASTIC AND PLASTIC PRODUCTS DETERGENTS

NILKAMAL

Noida-Toll Bridge Co. Ltd.

TRAVEL AND TRANSPORT

NOIDATOLL

Oil & Natural Gas Corporation Ltd.

OIL EXPLORATION/ PRODUCTION

ONGC

Omax Autos Ltd.

AUTO ANCILLARIES

OMAXAUTO

Omaxe Ltd.

CONSTRUCTION

OMAXE

Oracle Financial Services Software Ltd.

COMPUTERS - SOFTWARE

OFSS

Orchid Chemicals & Pharmaceuticals Ltd.

PHARMACEUTICALS

ORCHIDCHEM

Orient Paper & Industries Ltd.

DIVERSIFIED

ORIENTPPR

Oriental Bank of Commerce

BANKS

ORIENTBANK

Oriental Hotels Ltd.

HOTELS

ORIENTHOT

Oswal Chemicals & Fertilizers Ltd.

FERTILISERS

BINDALAGRO

PNB Gilts Ltd.

FINANCE

PNBGILTS

PSL Ltd.

STEEL AND STEEL PRODUCTS

PSL

PVP Ventures Ltd.

COMPUTERS - SOFTWARE

PVP

Panacea Biotec Ltd.

PHARMACEUTICALS

PANACEABIO

Pantaloon Retail (India) Ltd.

MISCELLANEOUS

PANTALOONR

Paper Products Ltd.

PAPER AND PAPER PRODUCTS

PAPERPROD

Parsvnath Developer Ltd.

CONSTRUCTION

PARSVNATH

Patel Engineering Ltd.

CONSTRUCTION

PATELENG

Patni Computer Systems Ltd.

COMPUTERS - SOFTWARE

PATNI

Petronet LNG Ltd.

GAS

PETRONET

Pfizer Ltd.

PHARMACEUTICALS

PFIZER

Phoenix Mills Ltd.

CONSTRUCTION

PHOENIXLTD

Piramal Healthcare Ltd.

PHARMACEUTICALS

PIRHEALTH

Pidilite Industries Ltd.

CHEMICALS - ORGANIC

PIDILITIND

Polaris Software Lab Ltd.

COMPUTERS - SOFTWARE

POLARIS

NIRMA

Price Index Company Name Power Finance Corporation Ltd. Power Grid Corporation of India Ltd.

Industry

Symbol

FINANCIAL INSTITUTION

PFC

POWER

POWERGRID

Praj Industries Ltd.

ENGINEERING

PRAJIND

Pricol Ltd.

AUTO ANCILLARIES

PRICOL

Prism Cement Ltd.

CEMENT AND CEMENT PRODUCTS

PRISMCEM

Procter & Gamble Hygiene & Health Care Ltd.

PERSONAL CARE

PGHH

Provogue (India) Ltd.

TEXTILE PRODUCTS

PROVOGUE

Punj Lloyd Ltd.

CONSTRUCTION

PUNJLLOYD

Punjab National Bank

BANKS

PNB

Puravankara Projects Ltd.

CONSTRUCTION

PURVA

Radico Khaitan Ltd

BREW/DISTILLERIES

RADICO

Rajesh Exports Ltd.

GEMS JEWELLERY AND WATCHES

RAJESHEXPO

Rallis India Ltd.

PESTICIDES AND AGROCHEMICALS

RALLIS

Ramco Industries Ltd.

CEMENT AND CEMENT PRODUCTS

RAMCOIND

Ramco Systems Ltd.

COMPUTERS SOFTWARE

RAMCOSYS

Ranbaxy Laboratories Ltd.

PHARMACEUTICALS

RANBAXY

Rashtriya Chemicals & Fertilizers Ltd.

FERTILISERS

RCF

Raymond Ltd.

TEXTILE PRODUCTS

RAYMOND

Rei Agro Ltd.

FOOD AND FOOD PROCESSING

REIAGROLTD

Reliance Capital Ltd.

FINANCE

RELCAPITAL

Reliance Communications Ltd.

TELECOMMUNICATION - SERVICES

RCOM

Reliance Industrial Infrastructure Ltd.

ENGINEERING

RIIL

Reliance Industries Ltd.

REFINERIES

RELIANCE

Reliance Infrastructure Ltd.

POWER

RELINFRA

73

74

Option Trading

Company Name

Industry

Symbol

Reliance Natural Resources Ltd.

GAS

RNRL

Reliance Power Ltd.

POWER

RPOWER

Religare Enterprises Ltd.

FINANCE

RELIGARE

Rico Auto Industries Ltd.

AUTO ANCILLARIES

RICOAUTO

Rolta India Ltd.

COMPUTERS - SOFTWARE

ROLTA

Ruchi Soya Industries Ltd.

SOLVENT EXTRACTION

RUCHISOYA

Rural Electrification Corporation Ltd.

FINANCIAL INSTITUTION

RECLTD

S. Kumars Nationwide Ltd.

TEXTILE PRODUCTS

SKUMARSYNF

SEAMEC Ltd.

OIL EXPLORATION/ PRODUCTION

SEAMECLTD

SKF India Ltd.

BEARINGS

SKFINDIA

SREI Infrastructure Finance Ltd.

FINANCE

SREINTFIN

SRF Ltd.

TEXTILES - SYNTHETIC

SRF

Sakthi Sugars Ltd.

SUGAR

SAKHTISUG

Saregama India Ltd.

MEDIA & ENTERTAINMENT

SAREGAMA

Sesa Goa Ltd.

MINING

SESAGOA

Seshasayee Paper & Boards Ltd.

PAPER AND PAPER PRODUCTS

SESHAPAPER

Shanthi Gears Ltd.

AUTO ANCILLARIES

SHANTIGEAR

Shasun Chemicals & Drugs Ltd.

PHARMACEUTICALS

SHASUNCHEM

Shipping Corporation of India Ltd.

SHIPPING

SCI

Shoppers Stop Ltd.

MISCELLANEOUS

SHOPERSTOP

Shree Cement Ltd.

CEMENT AND CEMENT PRODUCTS

SHREECEM

Price Index Company Name

Industry

Symbol

Shree Renuka Sugars Ltd.

SUGAR

RENUKA

Shrenuj & Co. Ltd.

GEMS JEWELLERY AND WATCHES

SHRENUJ

Shriram Transport Finance Co. Ltd.

FINANCE

SRTRANSFIN

Siemens Ltd.

ELECTRICAL EQUIPMENT

SIEMENS.

Simplex Infrastructures Ltd.

CONSTRUCTION

SIMPLEXINF

Sintex Industries Ltd.

PLASTIC AND PLASTIC PRODUCTS

SINTEX

Sirpur Paper Mills Ltd.

PAPER AND PAPER PRODUCTS

SIRPAPER

Sobha Developers Ltd.

CONSTRUCTION

SOBHA

Sona Koyo Steering Systems Ltd.

AUTO ANCILLARIES

SONASTEER

Sonata Software Ltd.

COMPUTERS - SOFTWARE

SONATSOFTW

South Indian Bank Ltd.

BANKS

SOUTHBANK

State Bank of India

BANKS

SBIN

State Trading Corporation of India Ltd.

TRADING

STCINDIA

Steel Authority of India Ltd.

STEEL AND STEEL PRODUCTS

SAIL

Sterling Biotech Ltd

PHARMACEUTICALS

STERLINBIO

Sterlite Industries (India) Ltd.

METALS

STER

Sterlite Technologies Ltd.

ELECTRICAL EQUIPMENT

STRTECH

Strides Arcolab Ltd.

PHARMACEUTICALS

STAR

Sun Pharmaceutical Industries Ltd.

PHARMACEUTICALS

SUNPHARMA

Sun TV Network Ltd.

MEDIA & ENTERTAINMENT

SUNTV

Sundaram Finance Ltd.

FINANCE

SUNDARMFIN

75

76

Option Trading

Company Name

Industry

Symbol

Sundram Fasteners Ltd. Supreme Industries Ltd.

FASTNERS PLASTIC AND PLASTIC PRODUCTS

SUNDRMFAST SUPREMEIND

Supreme Petrochem Ltd.

PETROCHEMICALS

SUPPETRO

Surya Roshni Ltd.

STEEL AND STEEL PRODUCTS

SURYAROSHNI

Suzlon Energy Ltd.

ELECTRICAL EQUIPMENT

SUZLON

Swaraj Engines Ltd.

DIESEL ENGINES

SWARAJENG

Syndicate Bank

BANKS

SYNDIBANK

TV Today Network Ltd.

MEDIA & ENTERTAINMENT

TVTODAY

TVS Motor Company Ltd.

AUTOMOBILES - 2 AND 3 WHEELERS

TVSMOTOR

Taj GVK Hotels and Resorts Ltd.

HOTELS

TAJGVK

Tamil Nadu Newsprint & Papers Ltd.

PAPER AND PAPER PRODUCTS

TNPL

Tamilnadu Petroproducts Ltd.

PETROCHEMICALS

TNPETRO

Tata Chemicals Ltd.

CHEMICALS INORGANIC

TATACHEM

Tata Coffee Ltd.

TEA AND COFFEE

TATACOFFEE

Tata Communications Ltd.

TELECOMMUNICATION SERVICES

TATACOMM

Tata Consultancy Services Ltd.

COMPUTERS - SOFTWARE

TCS

Tata Elxsi Ltd.

COMPUTERS - HARDWARE

TATAELXSI

Tata Investment Corporation Ltd.

FINANCE

TATAINVEST

Tata Metaliks Ltd.

STEEL AND STEEL PRODUCTS

TATAMETALI

Tata Motors Ltd. WHEELERS

AUTOMOBILES - 4

TATAMOTORS

Tata Power Co. Ltd.

POWER

TATAPOWER

Tata Sponge Iron Ltd.

METALS

TATASPONGE

Price Index Company Name Tata Steel Ltd.

Industry

77

Symbol

Tata Tea Ltd.

STEEL AND STEEL PRODUCTS TEA AND COFFEE

TATASTEEL TATATEA

Tech Mahindra Ltd.

COMPUTERS - SOFTWARE

TECHM

Television Eighteen India Ltd.

MEDIA & ENTERTAINMENT

TV-18

Thermax Ltd.

ELECTRICAL EQUIPMENT

THERMAX

Thomas Cook (India) Ltd.

TRAVEL AND TRANSPORT

THOMASCOOK

Titan Industries Ltd.

GEMS JEWELLERY AND WATCHES

TITAN

Torrent Pharmaceuticals Ltd.

PHARMACEUTICALS

TORNTPHARM

Tourism Finance Corporation of India Ltd.

FINANCIAL INSTITUTION

TFCILTD

Trent Ltd.

MISCELLANEOUS

TRENT

Triveni Engineering & Industries Ltd.

SUGAR

TRIVENI

Tube Investments of India Ltd.

CYCLES

TUBEINVEST

Tulip Telecom Ltd.

TELECOMMUNICATIONSERVICES

TULIP

UCAL Fuel Systems Ltd.

AUTO ANCILLARIES

UCALFUEL

UCO Bank

BANKS

UCOBANK

UFLEX Ltd.

PACKAGING

UFLEX

UltraTech Cement Ltd.

CEMENT AND CEMENT PRODUCTS

ULTRACEMCO

Unichem Laboratories Ltd.

PHARMACEUTICALS

UNICHEMLAB

Union Bank of India

BANKS

UNIONBANK

Unitech Ltd.

CONSTRUCTION

UNITECH

United Phosphorous Ltd.

PESTICIDES AND AGROCHEMICALS

UNIPHOS

United Spirits Ltd.

BREW/DISTILLERIES

MCDOWELL-N

Unity Infraprojects Ltd.

CONSTRUCTION

UNITY

Usha Martin Ltd.

STEEL AND STEEL PRODUCTS

USHAMART

Uttam Galva Steels Ltd.

STEEL AND STEEL PRODUCTS

UTTAMSTL

78

Option Trading

Company Name

Industry

Symbol

Uttam Sugar Mills Ltd. UTV Software Communication Ltd.

SUGAR MEDIA & ENTERTAINMENT

UTTAMSUGAR UTVSOF

V.I.P. Industries Ltd.

PLASTIC AND PLASTIC PRODUCTS

VIPIND

VST Industries Ltd.

CIGARETTES

VSTIND

Value Industries Ltd.

CONSUMER DURABLES

VALUEIND

Vardhman Textiles Ltd.

TEXTILES - COTTON

VTL

Varusn Shipping Co. Ltd.

SHIPPING

VARUNSHIP

Venky’s (India) Ltd.

FOOD AND FOOD PROCESSING

VENKEYS

Vesuvius India Ltd.

REFRACTORIES

VESUVIUS

Videocon Industries Ltd.

CONSUMER DURABLES

VIDEOIND

Vijaya Bank

BANKS

VIJAYABANK

Vishal Retail Ltd.

MISCELLANEOUS

VISHALRET

Voltas Ltd.

AIRCONDITIONERS

VOLTAS

Welspun Gujarat Stahl Rohren Ltd.

STEEL AND STEEL PRODUCTS

WELGUJ

West Coast Paper Mills Ltd.

PAPER AND PAPER PRODUCTS

WSTCSTPAPR

Wipro Ltd.

COMPUTERS - SOFTWARE

WIPRO

Wockhardt Ltd.

PHARMACEUTICALS

WOCKPHARMA

Wyeth Ltd.

PHARMACEUTICALS

WYETH

Zandu Pharmaceutical Works Ltd.

PHARMACEUTICALS

ZANDUPHARM

Zee Entertainment Enterprises Ltd.

MEDIA & ENTERTAINMENT

ZEEL

Zee News Ltd.

MEDIA & ENTERTAINMENT

ZEENEWS

Zensar Technolgies Ltd.

COMPUTERS - SOFTWARE

ZENSARTECH

Zodiac Clothing Co. Ltd.

TEXTILE PRODUCTS

ZODIACLOTH

Zuari Industries Ltd.

FERTILISERS

ZUARIAGRO

ibn18 Broadcast Ltd.

MEDIA &

IBN18

ENTERTAINMENT

Price Index

79

CNX Midcap* The medium capitalized segment of the stock market is being increasingly perceived as an attractive investment segment with high growth potential. The primary objective of the CNX Midcap Index is to capture the movement and be a benchmark of the midcap segment of the market.

Method of Computation CNX Midcap is computed using market capitalization weighted method, wherein the level of the index reflects the total market value of all the stocks in the index relative to a particular base period. The method also takes into account constituent changes in the index and importantly corporate actions such as stock splits, rights, etc., without affecting the index value.

Base Date and Value The CNX Midcap Index has a base date of 1 January 2003 and a base value of 1000.

Criteria for Selection of Constituent Stocks The constituents and the criteria for the selection judge the effectiveness of the index. Selection of the index set is based on the following criteria : · All the stocks, which constitute more than 5% market capitalization of the universe (after sorting the securities in descending order of market capitalization), shall be excluded in order to reduce the skewness in the weightages of the stocks in the universe. · After step (a), the weightages of the remaining stocks in the universe is determined again. · After step (b), the cumulative weightage is calculated. · After step (c) companies which form part of the cumulative percentage in ascending order unto first 75 percent (i.e. up to 74.99 percent) of the revised universe shall be ignored. · After, step (d), all the constituents of S&P CNX Nifty shall be ignored. · From the universe of companies remaining after step (e) i.e. 75th percent and above, first 100 companies in terms of highest market capitalization, shall constitute the CNX Midcap Index subject to fulfillment of the criteria mentioned below.

Trading Interest All constituents of the CNX Midcap Index must have a minimum listing record of 6 months. In addition, all candidates for the Index are also evaluated for trading interest, in terms of volumes and trading frequency.

Financial Performance All companies in the CNX Midcap Index have a minimum track record of three years of operations with a positiv net worth. * Source: www.nseindia.com

80

Option Trading

Others A company which comes out with a IPO will be eligible for inclusion in the index, if it fulfills the normal eligibility criteria for the index for a three-month period instead of a six-month period. Table 4.8 List of Stocks in CNX Midcap Company Name

Industry

Symbol

Akruti City Ltd.

CONSTRUCTION

AKRUTI

Allahabad Bank

BANKS

ALBK

Alstom Projects India Ltd.

POWER

APIL

Amtek Auto Ltd.

AUTO ANCILLARIES

AMTEKAUTO

Anant Raj Industries Ltd.

CONSTRUCTION

ANANTRAJ

Andhra Bank

BANKS

ANDHRABANK

Apollo Hospitals Enterprises Ltd.

MISCELLANEOUS

APOLLOHOSP

Areva T&D India Ltd.

ELECTRICAL EQUIPMENT

AREVAT&D

Ashok Leyland Ltd.

AUTOMOBILES - 4 WHEELERS

ASHOKLEY

Asian Paints Ltd.

PAINTS

ASIANPAINT

Aurobindo Pharma Ltd.

PHARMACEUTICALS

AUROPHARMA

Aventis Pharma Ltd.

PHARMACEUTICALS

AVENTIS

BEML Ltd.

ENGINEERING

BEML

Bajaj Hindusthan Ltd.

SUGAR

BAJAJHIND

Balrampur Chini Mills Ltd.

SUGAR

BALRAMCHIN

Bank of Maharashtra.

BANKS

MAHABANK

Bharat Forge Ltd.

CASTINGS/FORGINGS

BHARATFORG

Biocon Ltd.

PHARMACEUTICALS

BIOCON

Bombay Dyeing & Manufacturing Co. Ltd.

TEXTILES - SYNTHETIC

BOMDYEING

Britannia Industries Ltd.

FOOD AND FOOD PROCESSING

BRITANNIA

CESC Ltd.

POWER

CESC

Century Textile & Industries Ltd.

DIVERSIFIED

CENTURYTEX

Chennai Petroleum Corporation Ltd.

REFINERIES

CHENNPETRO

Corporation Bank

BANKS

CORPBANK

Crompton Greaves Ltd.

ELECTRICAL EQUIPMENT

CROMPGREAV

Price Index Company Name

Industry

Symbol

Cummins India Ltd. Colgate Palmolive (India) Ltd.

DIESEL ENGINES PERSONAL CARE

CUMMINSIND COLPAL

Deccan Chronicle Holdings Ltd.

PRINTING AND PUBLISHING

DCHL

Divi’s Laboratories Ltd.

PHARMACEUTICALS

DIVISLAB

EIH Ltd.

HOTELS

EIHOTEL

Engineers India Ltd.

ENGINEERING

ENGINERSIN

Exide Industries Ltd.

AUTO ANCILLARIES

EXIDEIND

GVK Power & Infrastructures Ltd.

POWER

GVKPIL

Gammon India Ltd.

CONSTRUCTION

GAMMONIND

Gillette India Ltd.

PERSONAL CARE

GILLETTE

GlaxoSmithkline . Consumer Healthcare Ltd

FOOD AND FOOD PROCESSING

GSKCONS

Glenmark Pharmaceuticals Ltd.

PHARMACEUTICALS

GLENMARK

Godrej Consumer Products Ltd.

PERSONAL CARE

GODREJCP

Godrej Industries Ltd.

CHEMICALS -

GODREJIND

Great Eastern Shipping Co. Ltd.

SHIPPING

GESHIP

HCL Infosystems Ltd.

COMPUTERS HARDWARE

HCL-INSYS

HMT Ltd.

AUTOMOBILES - 4 WHEELERS

HMT

HT Media Ltd.

PRINTING AND PUBLISHING

HTMEDIA

Hindustan Petroleum Corporation Ltd.

REFINERIES

HINDPETRO

IDBI Bank Ltd.

BANKS

IDBI

ING Vysya Bank Ltd.

BANKS

INGVYSYABK

IVRCL Infrastructures & Projects Ltd.

CONSTRUCTION

IVRCLINFRA

India Cements Ltd.

CEMENT AND CEMENT PRODUCTS

INDIACEM

Indian Bank

BANKS

INDIANB

81

82

Option Trading

Company Name Indian Hotels Co. Ltd. Indian Overseas Bank

Industry HOTELS BANKS

Symbol INDHOTEL IOB

Indraprastha Gas Ltd.

GAS

IGL

Jammu & Kashmir Bank Ltd.

BANKS

J&KBANK

Jet Airways (India) Ltd.

TRAVEL AND TRANSPORT

JETAIRWAYS

Jindal Saw Ltd.

STEEL AND STEEL PRODUCTS

JINDALSAW

Jubilant Organosys Ltd.

CHEMICALS - ORGANIC

JUBILANT

KSK Energy Ventures Ltd

POWER

KSK

Kansai Nerolac Paints Ltd.

PAINTS

KANSAINER

Kirloskar Brothers Ltd

COMPRESSORS / PUMPS

KBL

Lanco Infratech Ltd.

CONSTRUCTION

LITL

Lupin Ltd.

PHARMACEUTICALS

LUPIN

Madras Cements Ltd.

CEMENT AND CEMENT PRODUCTS

MADRASCEM

Mahanagar Telephone Nigam Ltd.

TELECOMMUNICATION - SERVICES

MTNL

Marico Ltd.

PERSONAL CARE

MARICO

Matrix Laboratories Ltd.

PHARMACEUTICALS

MATRIXLABS

Max India Ltd.

PACKAGING

MAX

Moser Baer India Ltd.

COMPUTERS HARDWARE

MOSERBAER

Motherson Sumi Systems Ltd.

AUTO ANCILLARIES

MOTHERSUMI

Mphasis Ltd.

COMPUTERS SOFTWARE

MPHASIS

Nirma Ltd.

DETERGENTS

NIRMA

Omaxe Ltd.

CONSTRUCTION

OMAXE

Oriental Bank of Commerce

BANKS

ORIENTBANK

Pantaloon Retail (India) Ltd.

MISCELLANEOUS

PANTALOONR

Parsvnath Developser Ltd.

CONSTRUCTION

PARSVNATH

Price Index Company Name

Industry

Symbol

Patni Computer Systems Ltd. Petronet LNG Ltd.

COMPUTERS SOFTWARE GAS

PATNI PETRONET

Pfizer Ltd.

PHARMACEUTICALS

PFIZER

Piramal Healthcare Ltd.

PHARMACEUTICALS

PIRHEALTH

Procter & Gamble Hygiene & Health Care Ltd.

PERSONAL CARE

PGHH

Punj Lloyd Ltd.

CONSTRUCTION

PUNJLLOYD

Rashtriya Chemicals and Fertilizers Ltd.

FERTILISERS

RCF

Sesa Goa Ltd.

MINING

SESAGOA

Shipping Corporation of India Ltd.

SHIPPING

SCI

Shree Cement Ltd.

CEMENT AND CEMENT PRODUCTS

SHREECEM

Shriram Transport Finance Co. Ltd.

FINANCE

SRTRANSFIN

Sobha Developers Ltd.

CONSTRUCTION

SOBHA

Sterling Biotech Ltd.

PHARMACEUTICALS

STERLINBIO

Syndicate Bank

BANKS

SYNDIBANK

Tata Chemicals Ltd.

CHEMICALS INORGANIC

TATACHEM

Tata Tea Ltd.

TEA AND COFFEE

TATATEA

Thermax Ltd.

ELECTRICAL EQUIPMENT

THERMAX

Titan Industries Ltd.

GEMS JEWELLERY AND WATCHES

TITAN

UCO Bank

BANKS

UCOBANK

Ultra Tech Cement Ltd.

CEMENT AND CEMENT PRODUCTS

ULTRACEMCO

Union Bank of India

BANKS

UNIONBANK

United Phosphorous Ltd.

PESTICIDES AND AGROCHEMICALS

UNIPHOS

Vijaya Bank

BANKS

VIJAYABANK

Welspun Gujarat Stahl Rohren Ltd.

STEEL AND STEEL PRODUCTS

WELGUJ

Wockhardt Ltd.

PHARMACEUTICALS

WOCKPHARMA

Yes Bank Ltd.

BANKS

YESBANK

83

84

Option Trading

Nifty Midcap 50* The medium capitalized segment of the stock market is being increasingly perceived as an attractive investment segment with high growth potential. The primary objective of the Nifty Midcap 50 Index is to capture the movement of the midcap segment of the market. It can also be used for index-based derivatives trading.

Method of Computation Nifty Midcap 50 is computed using market capitalization weighted method, wherein the level of the index reflects the total market value of all the stocks in the index relative to a particular base period. The method also takes into account constituent changes in the index and importantly corporate actions such as stock splits, rights, etc without affecting the index value

Base Date and Value The Nifty Midcap 50 Index has a base date of 1 January 2004 and a base value of 1000.

Criteria for Selection of Constituent Stocks The constituents and the criteria for the selection judge the effectiveness of the index. Selection of the index set is, inter alia, based on the following criteria: · Stocks with average market capitalization ranging from Rs.1000 Crore to Rs.5000 Crore at the time of selection. · Stocks which are not part of the derivatives segment are excluded. · Stocks which are forming part of the S&P CNX NIFTY index are excluded.

Other Statistics · Nifty Midcap 50 stocks represent about 3.78 % of the total market capitalization as on January 30, 2009. · The average traded volume for the last six months of all Nifty Midcap 50 stocks is approximately 6.27 % of the traded volume of all stocks on the NSE. Table 4.9 List of Stocks in Nifty Midcap 50 Company Name

Industry

Symbol

Allahabad Bank

BANKS

ALBK

Alstom Projects India Ltd.

POWER

APIL

Source: www.nseindia.com

Price Index Company Name

Industry

Symbol

Andhra Bank Ashok Leyland Ltd.

BANKS AUTOMOBILES - 4 WHEELERS

ANDHRABANK ASHOKLEY

Aurobindo Pharma Ltd.

PHARMACEUTICALS

AUROPHARMA

BEML Ltd.

ENGINEERING

BEML

Bajaj Hindusthan Ltd.

SUGAR

BAJAJHIND

CESC Ltd.

POWER

CESC

Chennai Petroleum Corporation Ltd.

REFINERIES

CHENNPETRO

Corporation Bank

BANKS

CORPBANK.

Cummins India Ltd.

DIESEL ENGINES

CUMMINSIND

Divi’s Laboratories Ltd.

PHARMACEUTICALS

DIVISLAB

Edelweiss Capital Ltd.

FINANCE

EDELWEISS

Educomp Solutions Ltd.

COMPUTERS-SOFTWARE

EDUCOMP

Great Eastern Shipping Co. Ltd.

SHIPPING

GESHIP

GVK Power & Infrastructures Ltd.

POWER

GVKPIL

Hindustan Construction Co. Ltd.

CONSTRUCTION

HCC

Hotel Leelaventure Ltd.

HOTELS

HOTELEELA

IVRCL Infrastructures & Projects Ltd.

CONSTRUCTION

IVRCLINFRA

India Cements Ltd.

CEMENT AND CEMENT PRODUCTS

INDIACEM

Indian Bank

BANKS

INDIANB

Indian Hotels Co. Ltd

HOTELS

INDHOTEL

IDBI Bank Ltd.

BANKS

IDBI

JSW Steel Ltd.

STEEL AND STEEL PRODUCTS

JSWSTEEL

Lanco Infratech Ltd.

CONSTRUCTION

LITL

Lupin Ltd.

PHARMACEUTICALS

LUPIN

85

86

Option Trading

Company Name

Industry

Symbol

Mahanagar Telephone Nigam Ltd.

TELECOMMUNICATION SERVICE

MTNL

Moser Baer India Ltd.

COMPUTERS HARDWARE

MOSERBAER

Mphasis Ltd.

COMPUTERS SOFTWARE

MPHASIS

Nagarjuna Construction Co. Ltd.

CONSTRUCTION

NAGARCONST

Oracle Financial Services Software Ltd.

COMPUTERS SOFTWARE

OFSS

Patel Engineering Ltd.

CONSTRUCTION

PATELENG

Petronet LNG Ltd.

GAS

PETRONET

Piramal Healthcare Ltd.

PHARMACEUTICALS

PIRHEALTH

Praj Industries Ltd.

ENGINEERING

PRAJIND

Punj Lloyd Ltd.

CONSTRUCTION

PUNJLLOYD

Reliance Natural Resources Ltd.

GAS

RNRL

Rolta India Ltd.

COMPUTERS SOFTWARE

ROLTA

Shipping Corporation of India Ltd.

SHIPPING

SCI

Sintex Industries Ltd.

PLASTIC AND PLASTIC PRODUCTS

SINTEX

Sterling Biotech Ltd.

PHARMACEUTICALS

STERLINBIO

Syndicate Bank

BANKS

SYNDIBANK

Tata Chemicals Ltd.

CHEMICALS INORGANIC

TATACHEM

Tata Tea Ltd.

TEA AND COFFEE

TATATEA

Tata Teleservices (Maharashtra) Ltd.

TELECOMMUNICATION SERVICES

TTML

Titan Industries Ltd.

GEMS JEWELLERY AND WATCHES

TITAN

UltraTech Cement Ltd.

CEMENT AND CEMENT PRODUCTS

ULTRACEMCO

United Phosphorous Ltd.

PESTICIDES AND AGROCHEMICALS

UNIPHOS

Vijaya Bank

BANKS

VIJAYABANK

Voltas Ltd.

AIRCONDITIONERS

VOLTAS

Welspun Gujarat Stahl Rohren Ltd.

STEEL AND STEEL PRODUCTS

WELGUJ

Price Index

87

S&P CNX Defty* Almost every institutional investor and off-shore fund enterprise with an equity exposure in India would like to have an instrument for measuring returns on their equity investment in dollar terms. To facilitate this, a new index the S&P CNX Defty-Dollar Denominated S&P CNX Nifty has been developed. S&P CNX Defty is S&P CNX Nifty, measured in dollars.

Salient Features · Performance indicator to foreign institutional investors, off-shore funds, etc. · Provides an effective tool for hedging Indian equity exposure. · Impact cost of the S&P CNX Nifty for a portfolio size of Rs. 2 crore is 0.16%. · Provides fund managers an instrument for measuring returns on their equity investment in dollar terms.

Calculation of S&P CNX Defty* Computations are done using the S&P CNX Nifty index calculated on the NEAT trading system of NSE and INR-USD exchange rate that is based on the real time polled data feed. S&P CNX Nifty at time t * Exchange rate as on base date S&P CNX Defty = Exchange rate at time t

Calculation of Closing Value of S&P CNX Defty Closing value of S&P CNX Defty is computed by considering average of INRUSD polled data values (exchange rate) of the last 30 minutes of the market. Closing value of S&P CNX Defty

=

Closing value of S&P CNX Nifty * Exchange rate as on base date Average of exchange rate of last 30 minutes of the market

Specifications of S&P CNX Defty: Base date: 03 November 1995 Base S&P CNX Defty Index Value: 1000 S&P CNX Nifty Value as on Base date: 1000 Exchange rate as on base date: 34.65 Adjustment factor as on Base date:1.00

Summary An index represents the value of items included in a basket of items such as share prices, wholesale prices etc. The movement of index represents the movement in prices of items included in the basket. Stock market indices are * Source: www.nseindia.com

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Option Trading

used to measure the price movements in the market. In the F&O segment, indexbased derivatives are used as a hedging tool. Futures and options are available with index as the underlying asset. The two important indices traded in the F&O segment of the Indian Capital Market are S&P CNX Nifty of NSE and Sensex of BSE. These indices have sub-indices representing major sectors. As an underlying asset, indices also form one of the factors in option pricing. The mechanism of option pricing is discussed in detail in the next chapter.

Keywords Index Futures CNX 500

Nifty CNX IT index CNX Midcap

Sensex CNX Bank Index Nifty Midcap 50

Impact cost CNX 100 Index CNX Defty

CHAPTER

05

PRICING OF OPTIONS

5.1

OBJECTIVES

In the second chapter, we found that an option trader has to pay an upfront fee as premium or price for buying an option. The option price may change according to various reasons like time decay, change in implied volatility, change in underlying asset price and change in interest rate. The readers would be now interested to know how the options are priced. In this chapter, we shall discuss the option pricing technique. Though there are numerous methods of calculating option prices, we are discussing only Black-Scholes formula and Binomial Model.

5.2

INTRODUCTION

Option premiums are classified into two: intrinsic value and time value. Intrinsic value is the difference between the strike price of the option and the spot price, while the time value is the difference between the option value and the intrinsic value. Assume a stock is trading at Rs. 100 and its 100-strike price call option is trading at Rs. 3, then Rs. 3 is the time value. The stock K trading at Rs. 100 and its 80-strike price call option trading at Rs. 22 indicates Rs. 20 as intrinsic value and Rs. 2 as its time value. At-the-money, out-of-themoney call options and put options do not carry any intrinsic value but have time value. In-the-money and deep-in-the money options have lower time value.

5.3 BLACK–SCHOLES OPTION PRICING MODEL The option price is the upfront fee paid by the option buyer to the option writer (Fig. 5.1). The pricing of options has been attempted by many experts like Sprenkle (1961), Ayres (1963), Boness (1964), Samuelson (1965), Baumol, Maikiel and Quandt (1966) Thorp and Kssouf (1967), Samuelson and Merton (1969), Chen (1970), Black and Scholes (1973). Subsequently, Merton and Black had attempted to modify the formula to suit the requirement of capital market where the question of payment of dividend also arises. The Black-Scholes formula is constructed on the basis of the following assumptions referred to by them as ‘ideal conditions’.

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Option Trading

5.3.1

Ideal Conditions for Black-Scholes Formula for Option Pricing (F. Black and M. Scholes, 1973)1

(a) The short-term risk-free interest rate is known and is constant throughout the lifetime of option. (b) The stock price follows a random walk in continuous time with a variance rate proportional to the square of the stock price. Thus, the distribution of possible stock prices at the end of any finite interval is lognormal. The variance rate of the return on stock is constant. (c) There should not be any takeovers or other events that prematurely end the life of the option. (d) Volatility of the asset is constant throughout the lifetime of option. (e) The stock pays no dividend or other distributions. (f) The option is ‘European’ i.e., it can only be exercised upon maturity. (g) There are no transaction costs in buying or selling the stock or the option. (h) It is possible to borrow any fraction of the price of a security to buy it or to hold it, at the short-term interest rate. (i) Short selling in securities is permitted. (j) For the same risk-free rate, borrowing and lending of securities should be available. Option price for a call, C = [S * N (d1)] – [X * e–r (T–t) * N (d2)] Option price for a put, P = [X * e–r (T–t) * N(– d2) – [S * N (– d1)] Where, d1 = {[ln(S/X) + (r + s 2/2) * (T – t)]/[s * (T – t) ]} d 2 = {[ln(S/X) + (r + s 2/2) * (T – t)]/[s * = d1 – [s * C – Price of call option P – Price of put option S – Underlying asset price X – Option strike price r – Rate of interest (T– t) – Time to expiry s – Volatility of underlying N – Normal distribution e – Exponential function ln – Logarithmic function

(T – t) ]}

(T – t) ]

Fig. 5.1 Black–Sholes Option Pricing Model

1. F. Black and M. Scholes, The Pricing of Options and Corporate Liabilities, Strategic Issues in Finance, (Ed.) Keith Ward. Butterworth-Heinemann, 1994, pp. 288-289.

Pricing of Options 91

5.3.2

Factors Affecting Option Price

The factors affecting the option price under the Black-Scholes formula are the spot price of the underlying asset, the exercise price, volatility of the underlying asset, risk-free interest rate and time for maturity. The difference between the spot price and the exercise price determines whether the option is in-themoney, at-the-money or out-of-the-money. Volatility is the tendency of the asset price to move upwards or downwards. The frequency of such movements decides how much the asset is volatile. Volatility is represented by the annualized standard deviation of the continuously compounded returns and is measured by using the natural logarithm of the asset/price relative. We shall learn the art of calculating option price with the help of examples. Example 1: Call Option An investor is holding one share of Infosys. The spot price of Infosys shares as on 20 June is Rs. 3500. S/he decides to buy a call option at a strike price of Rs. 3600 for delivery on 19 June next year. The annual volatility in the stock market is 57.96%. What would be the price he may have to pay if the risk-free interest rate is 7.5 % p.a.? Call Premium, C = [S* N – (d1)] – [X * e – r (T– t) N– (d2)] S = 3500

(1)

X = 3600 r = 0.0750 s = 0.5796 (57.96 %) s 2/2 = 0.16796 T-t = 1 ( June 20, to June 19, next year = 1 year) d1 = ln(S/X)+(r+s 2/2)*(T– t)/s Ö(T – t) = {[ln(3500/3600) + (0.0750+0.16796)*1] / [0.5796* Ö1]} = [-0.0282 + 0.2429] / [0.5796] = 0.3704 N(d1) = 0.6443 (Value to be selected from the appendix table) S * N(d1) = 3500 * 0.6443 = 2255.05 d2 = d1 – [s Ö(T – t)] = 0.3704 – [0.5796*Ö1] = 0.3704 – 0.5796 = – 0.2092 (Rounded off to – 0.210) N(d2) = N(– 0.2092) = (1 – 0.5832) (Value to be selected from the appendix table) = 0.4168

(2)

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Xe-r (T-t) * N(d2) = 3600* e-0.075 * 1 * 0.4168 = 3600*0.9277 * 0.4168 = 1391.99 Substituting the values of (2) and (3) in (1), we get Call Premium, C = 2255.05 – 1391.99 = 863.06

(3)

Example 2: Calculation of Put Price (August Call) Price of Infosys shares as on 27 July (S) Strike Price (X) Risk-free interest rate (r) Daily volatility (s) Time to expiry (20 June 2002 to 19 June 2003)

Rs. 3500 Rs. 3100 7.50% (0.075) 57.96% (0.5796) 1 year

In order to calculate the put premium, first we have to find out the various values in the formula. The Black-Scholes Model for calculation of put option is: Put premium, P = [X * e-r (T-t) *N(– d2)] – [S *N(– d1)]

(1)

Following the same steps as in the case of call premium, the values for different variables in the formula will be as follows: X = 3100 Xe– r (T– t)= 3100* e–.0750 * 1 = 3100 * 0.9277 = 2875.87 Next step is calculating the value of d2. The value of d2 = d1 – s *Ö(T – t). Therefore, we have to calculate the value of d1 first. d 1 = ln(S/X) +(r+s 2/2)*(T – t)/s Ö(T – t) ln (S/X) = 0.1214 r = 0.0750 2

s /2 = (0.57962/2) = 0.1679 2

(r + s /2)*(T – t) = (0.0750+0.1679)*1.00 = 0.2429 s Ö(T – t) = 0.5796* Ö1.00 = 0.5796 d 1 = ln(S/X) +(r+s 2/2)*(T – t)/s Ö(T – t) = (0.1214 + 0.2429)/0.5796 = 0.6285 d 2 = d1– [s Ö(T– t)] = 0.6285 – 0.5796 = 0.0489

Pricing of Options 93

In order to find out the value of N(d1) and N(d2), the value of d1 and d2 should be multiplied by the standardized normal distribution factor. These factors are given in the appendix. N(d1) = 0.7357 (Value to be selected from the appendix table) N(d2) = 0.5199 (Value to be selected from the appendix table) N(–d1) = 1– N(d1) = 1 – 0.7357 = 0.2643 N(–d2) = 1 – N(d2) = 1 – 0.5199 = 0.4801 X * e-r (T-t) *N(–d2) =2875.87 * 0.4801 =1380.705 S* N(–d1) = 3500 * 0.2643 = 925.05

(2) (3)

Substituting (2) and (3) in (1), we get Put premium, P = 1380.705 -925.05 = 455.65 The put premium is 455.65 Example 3: Calculation of Call Premium where the Stock is purchased 1 month prior to Maturity In the previous example, let us assume that a July option is purchased on 28 June. Asset price (S) Exercise price (X)

3500 3600

Annual volatility (s) Risk-free interest rate (r)

57.96% 7.50%

Time to maturity 1 month (0.08) Call premium = [S *N(d1)] – [X * e – r (T – t) N(d2)]

(1)

d1 = [ln(S/X) +(r+ (s2/2) (T – t)] / [s Ö(T – t)] = [(ln (3500/3600) + (0.0750 + (0.57962/2)*0.08)] /[0.5796* Ö0.08] = – 0.0532 N(d1) = (1– 0.5199) = 0.4801 (* Value to be selected from the appendix table) SN (d1) = 3500*0.4801 = 1680.35 d2 = d1 – s * Ö(T – t)

(2)

= – 0.0532 – (0.5796* Ö0.08) = – 0.2171 N(d2) = 1 – 0.5871 = 0.4129 (* Value to be selected from the appendix table) Xe – r (T-t) N(d2) = (3600 * e– 0.0750*0.08)* 0.4129 = 1477.55 By substituting (2) and (3) in (1), we get Call premium = 1680.35 – 1477.55 = 202.80

(3)

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Option Trading

Calculation of Time to Expiry The time to expiry in this case is different from that in the previous example. The July option expires on 28 July, whereas the option was purchased on 28 June. Therefore, the buyer will hold the option for a period of 30 days only. If we convert 30 days into day count factor (30/360), we get 0.08.

5.4 PRICING OF EQUITY OPTIONS Pricing of equity options is different from other options since equity involves payment of dividend periodically. All equity options are European options. Black–Scholes formula is normally used to calculate the price of equity options. One of the basic assumptions under which the Black–Scholes formula is that there is no payment of dividend. However, the realities of stock market are different. We may recapitulate the basic assumptions relating to the equity market: 1. There are no dividend payments against the underlying assets. However, most often, the stock traded in the equity market are exdividend during the life of the option. Generally, the underlying equities are listed in the stock exchanges and are actively traded in the market. 2. The options are of European type and therefore could not be exercised during the life of the option. However, the options traded in the equity market are generally of American Type. 3. The Black–Scholes formula assumes that the asset price follows a continuous diffusion process. In reality, the prices of shares jump on announcement of dividends or announcements of quarterly/half yearly/annual profits or a major turn of event in the company; this ultimately may result into an increase in profits. 4. The volatility was known and constant during the life of the option. But, in practice, the volatility in stock market is not constant due to the changes in share prices according to the bull/bear behaviour of the investors. 5. The natural logs of the price relatives are normally distributed. 6. The borrowing and lending in the market takes place at the same rate and the risk-free rate of interest remains constant during the life of the option. In reality, the interest rate is decided by the demand for and supply of funds in the market. Therefore, the lender is always benefited by the spread between the borrowing and lending and provides him arbitrage opportunities. 7. There are no transaction costs in equity transactions. However, the equity transactions involve brokerage, spread between buying and selling rates, financing costs etc. 8. In the case of binomial model, the asset price follows a binomial process, and when the time gap between two price changes is very small, the binomial distribution approximately equals the normal distribution.

Pricing of Options 95

5.5 PRICING OF OPTIONS ON DIVIDEND PAYING SCRIPS The general assumption of Black–Scholes model pricing of options is that the asset does not produce any yield. However, in reality, it is not correct as far as the equity is concerned. The equity options are listed on major equities, which are likely to pay dividend. However, the amount of dividend may be uncertain. The pricing of options on such stocks, where dividend is paid periodically, has to be done by considering the dividend yield also. In order to give effect for the dividend, the spot price of the equity is adjusted with the discounted value of the dividend. The companies abroad pay dividend more than one time in a year. In India, though some companies pay interim dividend, most of them pay only annual dividend. Generally, the prices come down when the dividend is declared and moves up after the dividend date. Therefore, the dividend dates have some impact on calculating the option price. The Black–Scholes formula can be adjusted to accommodate the price volatility on account of dividend. Example 4: Call Price Where Dividend is Paid on the Scrip Price of Infosys shares on 25 July 2003 was Rs. 3570. The annual volatility of the stock was 57.96%. A customer buys an August call for Rs. 3600. The riskfree interest rate was 7.5%. The company was expected to declare a dividend of 40% on 30 September 2003. What would be the option price the customer may have to pay? Spot price of Infosys shares (S) 3570 Strike price (X) Annual volatility (s)

3600 57.96 %

Risk-free interest rate (r) Time to maturity (T – t)

7.5% 33days (25.07.2003 to 25.08.2003)

Dividend Ex-dividend days

40% 33 days (25.07.2003 to 28.08.2003 or 25.07.2003 to 30.09.2003, whichever is earlier)

Step 1: Calculate the present value of the dividend The present value of the dividend = De– r(Td-t), where D is the dividend, r is the risk-free interest rate, Td – t is the number of days from the date on which the option was purchased to the ex-dividend date or the date on which the option expires, whichever is earlier. Present value of the dividend = 0.40 e – 0.0750 * 33 = 0.03 Step 2: Calculate the spot price ex-dividend (Sd) The spot price after adjusting the dividend = 3570-0.03 = 3569.97

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Option Trading

Step 3: Apply the Black–Scholes formula by replacing S with Sd C = Sd* N (d1)-X * e-r (T – t) * N (d2) d1 = [ln (Sd/X) + {(r+ (s 2/2) (T – t)}] / [s Ö(T – t)] d2 = d1 – s Ö(T – t) d1 = ln (3569.97/3600)+{(0.075+(0.57962/2)(33/365)}/ 0.5796 * Ö(33/365) = 0.07797 N(d1) = 0.5279 (* Value to be selected from the appendix table) d2 = 0.07799 – [0.5279* Ö(33/365)] = –0.081 N(d2) = 1 – 0.5279 = 0.4721 Call premium, C = (3569.97*0.5279) – ((3600 e–0.075 (33/365))*0.4721) = Rs. 196.58 Example 5: Put Price Where Dividend is paid on the Stock Put price also can be calculated by applying the Black–Scholes formula, wherein we use the dividend-adjusted spot price as in calculating the call price. Taking the same example where the strike price is assumed as Rs. 3400, the put price will be: Put premium = {Xe-r (T-t) [1-N(d2)]} – {S [1-N(d1)]} Here, the value of d1 changes consequent to loading the present value factor of dividend. d1 = [ln (Sd/X) + {(r+ (s2/2) (T – t)}] / [s Ö(T – t)] d1 = [ln (3569.97/3400) + {(0.075+ (0.57962/2) (33/365)}] / [0.5796 Ö(33/365)] = 0.4068 Nd1 = 0.6591 ( Value to be selected from the appendix table) 1 – N(d1) = 0.3409 d2 = 0.4068 – 0.5796 Ö(33/365) = 0.2325 N(d2) = 0.5910 ( Value to be selected from the appendix table) 1– N(d2) = 1 – 0.5910 = 0.409 Put premium = [3400 * e-0.0750 (33/365) * 0.409] – [3569.97*0.3409] = 1381.20 – 1217.00 = 164.20

5.6

BINOMIAL MODEL OF OPTION PRICING

Another method of calculation of option price is the Binomial Model. The Binomial Model starts from the current spot price of the asset from which the future spot price of the asset is estimated on the basis of market volatility. The

Pricing of Options 97

option pay off under Binomial Model is the difference between the strike price and the spot price. The binomial price represents the present value of the future pay off calculated using the risk-free interest rate. The following parameters are used for calculating the binomial price: S = Spot price at the beginning, u = the upper movement in percentage terms and Su = the upper level of the future spot price, d = the downward movement of the price in percentage terms and Sd = the lower level future spot price of the asset, X = the strike price, R = the risk-free interest r, (T – t) is the time to maturity, N = the number of options, c = call options and p = put options. The Binomial Model also requires that d < R < u because if d and u are less than the risk-free interest rate, the risk-free asset would always show higher return than the risky asset. If d and u are greater than the risk-free interest rate, the risky asset would show higher return than the risk-free asset. In binomial calculation a tree is created having two nodes, one upper and the other lower, each node converging at the current spot price and the other end resting at the upper and lower prices. The binomial tree can be calculated covering more than one period, say four quarters in a year. In such cases, there will be 20 nodes (10 sets). The first one is known as one period binomial tree and the second one is known as multi-period binomial tree. The option price using the Binomial Model can be calculated on the basis of the following assumptions: Calculation of Call Premium, One Period Example 4 Price of Infosys shares as on July 27, 2003 (S) Strike price (X)

Rs. 3500 Rs. 3100

Risk-free interest rate (r) Daily volatility (s )

7.50% 57.96%

Time to expiry (20 June 2002 to 19 June 2003)

1 year

An investor holds one Infosys share with the spot price (S) = Rs. 3500. He buys a call option with a strike price (X) of Rs. 3600. The annual volatility (s)of Infosys shares is 20%. Risk-free interest rate (r) is 7.5 %. The formula used for calculating the values in the Binomial Model is: c = {cu [(R – d)/ (u – d)/(u – d )] + cd [(u – R)/(u – d)]}/R The value of all parameters except R is available in the diagram. The value of R = 1.08% (continuously compounded risk-free interest rate). By substituting the values in the formula: c = {600 [( 1.08 – 0.80)/ (1.20 – 0.80 )] + 0 [(1.20 – 1.08 / 1.20 – 0.80 )]}/1.08 = [600 (0.28/0.40) + 0)]/1.08 = [600*0.69 + 0]/1.08 = 388.88

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Option Trading

u= +1.20%

Su = 4200 (Pay off (Cu) = 0, Su – X ) = (0, 4200 – 3600) = 600

S = 3500 d = – 0.80%

Sd = 2800 (Pay off (Cd) = 0, X – Sd )

In the above calculation, the price of Infosys shares is estimated to go up by 1.20% or go down by 0.80% if the market volatility is 20%. This means, the price would go up to 4200 (Su) or go down to 2800 (Sd). The pay off when the price moves up works out to be 700 whereas the pay off when the price goes down is negative. The option buyer will exercise the option only if the spot price of the underlying asset (Su) is greater than the exercise price (X) and the pay off (Su – X) will be 600. On the contrary, if the price goes down the option buyer will abandon the option, since it would be more beneficial to him if he buys the asset from the spot market and gives delivery as the spot price is less than the exercise price. Therefore, the pay off will be 0.

5.7

PRICING OF BINOMIAL PUT OPTION

In a put option, the put option buyer will abandon the option if the spot price is more than the exercise price because s/he can sell his holding at a higher price to the market than selling it at the exercise price. The binomial price for option as per the previous example can be calculated considering the exercise price as Rs. 3300. The formula for calculating put option is: p = Ppu + Pd (1 – p) R where

P = The spot price when the volatility comes down pu = P(R – u)/(u – d) Pd = The spot price when the spot price goes up

In the case of put option, the buyer will not exercise the option if the spot price goes up since it would beneficial for him/her to sell in the market than exercising the option. However, if the price comes down, s/he will exercise the option since s/he can buy the asset from the market at the lower price, give delivery under the option contract, and earn the profit. So, the option price represents the present value of the pay off at the time of maturity. Example 5 u = + 1.20%

Su = 4200 (Pay off (Pd ) = 0, X – Su) = (0, 3300 – 4200) = – 900 = 0

S = 3500 d = – 0.80%

Sd = 2800 (Pay off (Pu) = 0, X – Sd )

Pricing of Options 99

p = Ppu + (Ppd(1-p))/R = {500 [(1.08 – 0.80)/(1.20 – 0.80)]+ 0 [(1.20 – 1.08)/1.20 – 0.80)]}/1.08 = (500*0.70+0)/1.08 = 324.07

5.8 BINOMIAL MULTIPLE PERIOD MODEL In the case of Binomial Multiple Period Model, the option period is divided into a number of times in a year. Under this approach, the spot price at the end of one period will become the spot price for the next period on which the spot period for the next period is calculated based on the volatility. The multiperiod binomial approach fine-tunes the price calculation since the volatility is recalculated at the end of each period. The division of period is known as the binomial trials. The more trials over a given period (i.e. the smaller the time interval represented by each trial), the more accurate the option valuation will be (Terry J. Watson, 1998)2. The same principles are used for valuation of option under each trial. Using the same example of Infosys shares, the multiperiod binomial model can be worked out for both call and put if the option period is divided into four quarters and all the other factors remaining the same. Before we increase the number of binomial trials, the one-year risk-free interest rate should be adjusted to reflect the shorter time between the trials. The risk-free interest rate r in the example is 7.5%. The quarterly continuously compounded interest rate (R) can be calculated by applying the following formula: R = er (T – t)*0.25 = Exponential value of 7.25 * 0.25. (By multiplying with 0.25 we get the quarterly value. Alternatively, we can divide by 4 also) = 1.018927. Similarly, the value of u, d, p and (1 – p) also are to be calculated splitting them into quarterly basis. u = es Ö(T-t)/n = e0.20 Ö 0.25 d =e

–s Ö(T-t)/n

=e

–0.20 Ö 0.25

= 1.105171 = 0.904837

R = 1.018927 (R – d) = 1.018927 – 0.904837 = 0.114090 (u – d) = 1.105171-0.904837 = 0.200334 (u – R) = 1.105171 – 1.018927 = 0.086244 p = (R – d)/(u – d)

= (0.114090/0.200334) = 0.569498

(1 – p) = (u – R)/(u – d)

= (0.086244/0.904837) = 0.430502

Asset price (S) = 3500 Exercise price = 3400 2

. Terry J.Watson, Futures and Option in Risk Management.

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Option Trading

Accordingly a multi-period binomial tree can be constructed as in Fig. 5.2. Call Price Put Price Su3 4724.51 1387.66 0 Su2d 3868.10 489.00 0

Su2 4274.9 Su1 3868.10 3500 435.91 406.96

664.20 0

664.45 208.83

Sud 3500 273.31 373.63

3166.93 Sd1 152.76 Sd2 570.27

Fig. 5.2

Table 5.1

2865.56 0 737.86

Sud2 3166.93 0 668.48

Sd3

2592.86 0 814.84

Su4 5221.39 0.00

Pay off 1821.39

Su3d 4274.9 0

874.91

Su2d2 3500 100

0

Sud3 2865.56 –534.44 0 534.44 Sd4 2346.1 1053.9

–1053.88

Multi-period binomial tree

Calculation of Binomial Option Pricing

Asset price (S) Strike price (X) Risk-free interest rate Annual volatility Period Number of binomial trials Life of option (fractions of year) (n) Spot price at the upper level (u) Spot price at the lower level (d) Continuously compounded risk-free interest rate Call price Put price P (1-P)

es Ö(T-t)/n e– s Ö(T-t)/n er*(T-t)*n

3500 3400 7.50% 20% 1 year 4 0.25 1.105171 0.904837 1.018927

Pcu + (1 – P)Cud)/R Ppu + (1 – p)Ppd)/R (R – d)/(u – d) (u – R)/(u – d)

0.569498 0.430502

0

Pricing of Options 101

Table 5.2

Option Premium at Various Stages

STAGE 1 STAGE 2 STAGE 3 STAGE 4 STAGE 5 STAGE 6 STAGE 7

Put

Call

814.84 668.48 737.86 373.63 570.27 208.83 406.96

1387.66 489.00 982.20 273.31 664.45 152.76 435.91

It can be observed from Tables 5.1 and 5.2 that the multi-period binomial tree approach in option pricing is a more refined process. However, Black– Scholes is more widely used for calculation of equity-related option prices.

Summary In this chapter, we have explained two models of option pricing. The Black– Scholes model is based more on mathematical derivation model, whereas the Binomial model is more of an arithmetical process. In India we use Black– Scholes model for index-related option pricing. Due to complexity of Black– Scholes, investors often use Binomial method, put call party etc. They also use strategies like PC ratio, rollover etc. to find market sentiments. These strategies are covered in the next chapter.

Keywords Black–Scholes Put premium

Binomial Dividend

Call premium Lognormal distribution

Appendix Finding out Lognormal Value from the Table The lognormal distribution value can be found out from the appended table. Take for example a value of 0.37. This can be split into 0.30 + 0.07. Look for the number 0.3 in the left column (value of z) and find out the corresponding value in the column for 0.07. This value is 0.6443. In this way the value for the other variable also can be found out. For negative values such as –0.37, the answer is found by subtracting 0.6443 from 1, or equally, 1 – N(0.37). Alternatively, instead of using tables, one can use the NORMSDIST function in Microsoft Excel. In such a case, the input would be NORMSDIST (0.37) and NORMSDIST (-0.37), respectively.

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Option Trading

Standardized Normal Distribution Table Z

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0.5000 0.5398 0.5793 0.6179 0.6554 0.6915 0.7257 0.7580 0.7881 0.8159 0.8413

0.5040 0.5438 0.5832 0.6217 0.6591 0.6950 0.7291 0.7611 0.7910 0.8186 0.8438

0.5080 0.5478 0.5871 0.6255 0.6628 0.6985 0.7324 0.7642 0.7939 0.8212 0.8461

0.5120 0.5517 0.5910 0.6293 0.6664 0.7019 0.7357 0.7673 0.7967 0.8238 0.8485

0.5160 0.5557 0.5948 0.6331 0.6700 0.7054 0.7389 0.7704 0.7995 0.8264 0.8508

0.5199 0.5596 0.5987 0.6368 0.6736 0.7088 0.7422 0.7734 0.8023 0.8289 0.8531

0.5239 0.5636 0.6026 0.6406 0.6772 0.7123 0.7454 0.7764 0.8051 0.8315 0.8554

0.5279 0.5675 0.6064 0.6443 0.6808 0.7157 0.7486 0.7794 0.8078 0.8340 0.8577

0.5279 0.5675 0.6064 0.6443 0.6808 0.7157 0.7486 0.7794 0.8078 0.8340 0.8577

0.5359 0.5753 0.6141 0.6517 0.6879 0.7224 0.7549 0.7852 0.8133 0.8389 0.8621

1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8

0.8643 0.8849 0.9032 0.9192 0.9332 0.9452 0.9554 0.9641

0.8665 0.8869 0.9049 0.9207 0.9345 0.9463 0.9564 0.9649

0.8686 0.8888 0.9066 0.9222 0.9357 0.9474 0.9573 0.9656

0.8708 0.8907 0.9082 0.9236 0.9370 0.9484 0.9582 0.9664

0.8729 0.8925 0.9099 0.9251 0.9382 0.9495 0.9591 0.9671

0.8749 0.8944 0.9115 0.9265 0.9394 0.9505 0.9599 0.9678

0.8770 0.8962 0.9131 0.9279 0.9406 0.9515 0.9608 0.9686

0.8790 0.8980 0.9147 0.9292 0.9418 0.9525 0.9616 0.9693

0.8810 0.8997 0.9162 0.9306 0.9429 0.9535 0.9625 0.9699

0.8830 0.9015 0.9177 0.9319 0.9441 0.9545 0.9633 0.9706

1.9 0.9713 0.9719 0.9726 0.9732 0.9738 0.9744 0.9750 0.9756 0.9761 0.9767 2 0.9772 0.9778 0.9783 0.9788 0.9793 0.9798 0.9803 0.9808 0.9812 0.9817 2.1 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3

0.9821 0.9893 0.9918 0.9938 0.9953 0.9965 0.9974 0.9981 0.9987

0.9826 0.9896 0.9920 0.9940 0.9955 0.9966 0.9975 0.9982 0.9987

0.9830 0.9898 0.9922 0.9941 0.9956 0.9967 0.9976 0.9982 0.9987

0.9834 0.9901 0.9925 0.9943 0.9957 0.9968 0.9977 0.9983 0.9988

0.9838 0.9904 0.9927 0.9945 0.9959 0.9969 0.9977 0.9984 0.9988

0.9842 0.9906 0.9929 0.9946 0.9960 0.9970 0.9978 0.9984 0.9989

0.9846 0.9909 0.9931 0.9948 0.9961 0.9971 0.9979 0.9985 0.9989

0.9850 0.9911 0.9932 0.9949 0.9962 0.9972 0.9979 0.9985 0.9989

0.9854 0.9913 0.9934 0.9951 0.9963 0.9973 0.9980 0.9986 0.9990

0.9857 0.9916 0.9936 0.9952 0.9964 0.9974 0.9981 0.9986 0.9990

CHAPTER

06

STRATEGIC DERIVATIVE TOOLS

6.1

OBJECTIVES

In the last chapter, we found that the option prices can be calculated using Black-Scholes Model and Binomial Model. However, in practice, the dealers may not get the time to apply the formula and arrive at the call price or put price. Therefore, they work out the put price from the call price and vice versa on the basis of put–call parity. In this chapter, we will explain the concept of put–call parity and how the PC ratio is used in option trading.

6.2 INTRODUCTION Strategic derivative tools are generally used to find market outlook. Put–call parity is a strong indicator for determining market outlook. An arbitrage opportunity due to high call price or put price due to good demand is an indicator of market sentiment. With put–call parity, one can find out the put– call premiums. Arbitrageurs always monitor put–call parity. There are other technical indicators in the option segment such as open interest PC ratio, weighted PC ratio, volume PC ratio etc.

6.3

PUT–CALL PARITY

Arbitrage is an important area in the capital market in general and the derivatives market in particular. In theory, arbitrage is a position where the arbitrageur earns riskless profit due to mispricing in the market. In an efficient market, the occurrence of arbitrage is very rare and even if it does occur, the returns from the arbitrage would be negligible. Indian markets have been seeing frequent arbitrage opportunities which stay for a reasonably long time and which give decent returns. Then comes the question: why does an arbitrage opportunity occur? The first reason is the lack of institutional participation with deep pockets to finance the required capital (cash or stock) to take advantage of the opportunity. Next is the slew of advanced technologies that are required to take full advantage of the opportunities. Right from identifying the opportunity to executing it, the same speed and accuracy is inevitable and this

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Option Trading

can be done only with advanced technology. If the market looses its equilibrium, then mispricing of options will occur. In a bear market, investors pay more money to buy put options and they will offer less money to buy call options and vice versa. This is an early indication of a market trend. Last but not least is the lack of awareness among market participants. Common examples of arbitrage opportunities are put–call parity, cash and carry and reverse cash and carry, early exercise of American options, price differentials in calendar spreads, mispricing between exchanges etc. Let us examine the put-call parity: Most put–call parity propositions apply to European put and call or American options wherein stocks pay no dividends before the options expire. PUT CALL PARITY: C – P = S – K (1 + r)(–t) where C = call premium, P = put premium, S = spot price, K = strike price, R = rate of interest, T = time to expiry This proposition states that the difference between the price of a call and the price of a put on the same stock/index with the same strike price and time to expiration equals the price of the underlying asset minus the present value of the strike price. If it is not equal, there is a mispricing. Let us assume that the Nifty futures are trading at 1114.10 on 1 January, 2005. The 1080 calls are trading at a premium of Rs. 49.30 and 1080 puts are trading at a premium of Rs. 12.75. Here, an arbitrageur can exploit the mispricing by buying the discounted Nifty futures at 1114.10 and 1080 put at 12.75; at the same time he will write the 1080 call for Rs. 49.30 and wait for automatic expiration. In this case, s/he will make an arbitrage profit of Rs. 490 (without considering transactions cost, cost of borrowing funds etc). On the other hand, if Nifty futures are at a premium, s/he can sell Nifty futures, buy calls and sell puts and wait till the expiration day. For example, Nifty futures are trading at 1085.85 on 10 February , while 1090 call premium is at Rs. 11.30 and the 1080 puts are available for Rs. 15.90. The arbitrageur will buy the 1080 calls for 11.30 and sell the 1080 puts for Rs. 15.90; s/he will also sell the Nifty futures at 1085.85. On expiry, he will make a net profit of Rs. 90. When the price of the same asset is different in two markets, there will be operators who will buy in the market where it is cheaper and sell in the market where it is costly. This activity is termed as arbitrage. This buying cheap and selling expensive activity continues till the prices in the two markets reach equilibrium. Hence, arbitrage activities facilitate to equalize prices and to restore market efficiency. A commonly used arbitrage strategy in India is the reverse cash and carry model/lend stock to the markets. In this strategy if the future price of an underlying asset is trading at a discount to the spot; an arbitrageur can deliver the stocks in hand to the spot market and at the same time buy the discounted futures. On the day of expiry, both futures and spot will converge (both prices will become the same). On the day of expiry, s/he has to reverse his/her positions by selling futures and buying back his/her sold shares.

Strategic Derivative Tools 105

Generally, stock futures are traded at a premium to the spot price due to the cost of carry. But there are times when futures of an underlying asset trade at the discount to spot due to dividend announcement, demand–supply constraints and future price projections. We shall now examine how an arbitrageur can make riskless profit by lending stocks to the market. For example, on 1 May, Reliance Industries was trading at Rs. 670 in the spot market, but the May future was trading at a discount at Rs. 663 due to future price projections. Here, an arbitrageur could earn riskless profit by lending his Reliance stock to the market for Rs. 670 and at the same time buying the discounted futures at Rs. 663. On the day of expiry of May contract, the trader has to reverse his positions. Let us assume that on the day of expiry Reliance spot and futures were trading at Rs. 665. S/he had to buy back the stocks at Rs. 665, which s/he sold at Rs. 670. At the same time, he should sell the futures at Rs. 665, which s/he bought at Rs. 663. Thereby s/he could earn a profit of Rs. 7, without considering brokerage, commission etc. One question arises here whether this arbitrage opportunity is more frequently available in India. The answer is yes. If we examine spot Nifty and Nifty futures from April 2004 to April 2005, it has been noted that on 188 out of 272 business days, Nifty futures were trading at a discount to spot Nifty, whereas for 84 days Nifty futures were at a premium. This clearly indicates that stock futures are trading more frequently at a discount to spot price. Even if we consider the dividends and other corporate announcements, the opportunity for arbitrageurs is very high with returns ranging from 4% to 114% per annum available in the Indian derivatives market (Table 6.1). PUT CALL PARITY: C – P = S – K (1 + r)(–t) Table 6.1

DATE

Put Call Parity for Non-Dividend Stock

14-Jul-08

STRIKE

920

CALL PREMIUM

16.6

PUT PREMIUM

FUTURE

DATE OF EXPIRATION

31-Jul-08

2.2

934.05 (Contd.)

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Option Trading

RISK FREE RATE LEFT HAND SIDE

RIGHT HAND SIDE

0.6

0.046 6

14.4 RESULT

NO PARITY

13.74577928

STRIKE

920

CALL OPTION PREMIUM

16.6

PUT OPTION PREMIUM

NUMBER OF DAYS TO EXPIRATION (YEARS)

2.2

PAYOFF TABLE

SPOT

SELL CALL

BUY PUT

BUY FUTURE

INDEX

PAY OFF

PAY OFF

PAY OFF PAY OFF

TOTAL

PAY OFF INRs

850

16.6

67.8

– 84.05

0.35

17.5

860

16.6

57.8

– 74.05

0.35

17.5

870

16.6

47.8

– 64.05

0.35

17.5

880

16.6

37.8

– 54.05

0.35

17.5

890

16.6

27.8

– 44.05

0.35

17.5

900

16.6

17.8

– 34.05

0.35

17.5

910

16.6

7.8

– 24.05

0.35

17.5

920

16.6

– 2.2

– 14.05

0.35

17.5

930

6.6

– 2.2

– 4.05

0.35

17.5

940

– 3.4

– 2.2

5.95

0.35

17.5 (Contd.)

Strategic Derivative Tools 107 950

– 13.4

– 2.2

15.95

0.35

17.5

960

– 23.4

– 2.2

25.95

0.35

17.5

970

– 33.4

– 2.2

35.95

0.35

17.5

980

– 43.4

– 2.2

45.95

0.35

17.5

990

– 53.4

– 2.2

55.95

0.35

17.5

1000

– 63.4

– 2.2

65.95

0.35

17.5

1010

– 73.4

– 2.2

75.95

0.35

17.5

1020

– 83.4

– 2.2

85.95

0.35

17.5

1030

– 93.4

– 2.2

95.95

0.35

17.5

1040

–103.4

– 2.2

105.95

0.35

17.5

1050

–113.4

– 2.2

115.95

0.35

17.5

1060

–123.4

– 2.2

125.95

0.35

17.5

1070

–133.4

– 2.2

135.95

0.35

17.5

1080

–143.4

– 2.2

145.95

0.35

17.5

1090

–153.4

– 2.2

155.95

0.35

17.5

1100

–163.4

– 2.2

165.95

0.35

17.5

Table 6.2

Put–Call Parity for Dividend Stock

PUT–CALL PARITY USING STOCK OPTIONS STOCK PRICE STRIKE RATE BUY CALL PREMIUM PAID SELL PUT PREMIUM RECEIVED

RATE OF INTEREST

TIME TO EXPIRATION

126.87 DIVIDEND FOR FIRST YEAR 125 TIME OF DIVIDEND

9.5 DIVIDEND FOR SECOND YEAR 4.25 TIME TAKEN FOR PAYMENT 0.10

0.4795

1.25 0.0274

1.35

0.2712

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Option Trading

LEFT HAND SIDE

7.45

MIDDLE PORTION

5.25

RIGHT HAND SIDE

– 0.436864631

NET RESULT STRIKE

NO PARITY 125

BUY CALL PREMIUM

9.5

SELL PUT

4.25

SPOT

CALL PAY-OFF

PUT PAY-OFF

SELL STOCK PAY-OFF

TOTAL PAY-OFF

96.87

– 9.5

– 23.88

30

– 3.38

106.87

– 9.5

– 13.88

20

– 3.38

116.87

– 9.5

– 3.88

10

– 3.38

126.87

– 7.63

9.5

0

1.87

136.87

2.37

9.5

– 10

1.87

146.87

12.37

9.5

– 20

1.87

156.87

22.37

9.5

– 30

1.87

166.87

32.37

9.5

– 40

1.87

176.87

42.37

9.5

– 50

1.87

186.87

52.37

9.5

– 60

1.87

196.87

62.37

9.5

– 70

1.87

206.87

72.37

9.5

– 80

1.87

216.87

82.37

9.5

– 90

1.87

226.87

92.37

9.5

– 100

1.87

236.87

102.37

9.5

– 110

1.87

246.87

112.37

9.5

– 120

1.87

Strategic Derivative Tools 109

6.4

PC RATIO

PC ratio is the sum of all put options’ open interest of an underlying asset divided by the sum of all call options’ open interest at various strike prices of the same underlying on same maturity date. It is used to find out overbought and over-sold situation of a stock. If the PC ratio of Nifty is above 1.2 and is steadily increasing, then market outlook is over-bought, that is, investors are expecting a fall in indices and thereby they are buying the Nifty put options. On the other hand, if the Nifty PC ratio suddenly declines then one can assume that the market is over-sold and a recovery is expected (Fig. 6.1). But, there are investors who use this tool as a contrarian; when the PC ratio steadily declines then they may buy put options than call options. On the other hand, if the PC ratio inches above 1.2 and steadily moves up, then one has to buy calls than put options. The other important factor which gives a market direction is the Nifty options’ implied volatility. If both call options and put options are expensive (buying calls and puts of the Nifty at the same expiry and same strike), then one can assume the market is poised for a correction. If long straddles of Nifty (buying long call and long puts of the Nifty at the same strike and same expiry) are less expensive, then assume that the market may trade firm. The rationale behind this is simple. The seller of the call asks for money when implied volatility trades higher. In the same way, the seller of the put option also will ask for more premiums in a highly volatile market because of the uncertainty of the market. In a less volatile market, option writers will write options at lower premiums. In a bull market, the volatility will always remain low; on the other hand, if the volatility is high, then one has to assume that there can be uncertainties in the markets. In a typical bear market, there will not be many sellers for Nifty put options, but at the same time the call writers will ask huge premiums. In other words, the impact cost of Nifty options will increase faster. In a falling market, open interest of Nifty call options (out of the money) tend to remain low. On the other hand, the out-of-the-money put options may witness high open interest. Nifty support and resistance can be gauged through the open interest built-up of Nifty put options and call options. If the out-of-the-money puts (3500 Nifty put) attract high volume and high open interest in a falling market, then one can assume 3500 is the key Support for Nifty. On the other hand, a firm buying in-out-of-the-money call with high open interest suggests major resistance for Nifty.

Strategic Derivative Tools 113

call volumes, bullish conditions exist as potential buying power increases. When index call volumes reaches high levels compared to put volumes, the ratio becomes very low, signifying excess market optimism which will give an early warning signal for impending bearishness. Apart from PC ratio, open interest configuration is also a good tool to find out the market trend. The open interest configuration of a stock is simply the number of open puts or calls at various strike rates. The study will also help one to find out supports and resistance for an individual stock or index. The open interest configuration can be constructed by plotting a chart with adjacent call–put bars representing open interest at every strike rate.

6.7.1

Out-of-the-money Options: A Market Indicator

Generally, derivative analysts depend on PC ratio and open interest to predict the future direction of the market. PC ratio can be calculated by the following formula: PC ratio = No. of puts/No. of calls Other reliable indicators for market analysis are activities in both out-ofthe-money calls and puts. For example, if ONGC PC ratio goes above 1.2 with rise in prices and if rise in open interest is bullish, a knowledgeable option analyst will find out-of-the-money (OTM) options activity. After examination, it is found that 920 and 950 strikes are actively traded. The number of contracts traded are almost equal or even more than the ATM options. It clearly indicates that there are people who anticipate that the stock may test 920 or even higher. Hence, new long positions on ONGC can be created. On the other hand, TISCO is weak and the current price is Rs. 370. PC ratio and the open interest too confirms the weak trends in TISCO. While examining the most active OTM put options of TISCO, below 360 put options were not traded actively but activity was very high in 360 put options. This sends a clear message to the option trader that the stock will get support at Rs. 360. Any activity in call options above the current price indicates the underlying trends. One should take care of the number of OTM contracts traded. Volume is the key factor that determines the fate of a stock. There are times when both OTM calls and puts are equally traded. It sends a clear message that the stock will not show higher volatility during the expiry. So, it is advisable not to trade in that stock. Sometimes, stocks like HDFC have lower liquidity in the options segment. This indicates that the stock normally shows abnormal moves in either direction and that investors are afraid to trade in options. Another simple indicator that can be used for option trading is implied volatility of an option. If the price of an option seems excessively priced (above the theoretical premium), it is better to avoid that option for buying purposes. On the other hand, if the options are under-priced, one can think of buying it, provided that all other indicators show a buy signal.

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Option Trading

6.7.1.1

Rising PC ratio in a weak market and its impacts

PC ratio is nothing but a total of all put options’ open interest of an underlying divided by the total number of call options’ open interest. If the figure is less than 0.4, then one can say that the outlook for the underlying is weak. As majority of the market participants take long positions, one has to take short positions – the contrarian view is advisable. On the other hand, if it is higher than 1.4, it denotes majority of the market players are having negative view on that particular underlying and one should take long positions. This is the general theory applicable to normal circumstances. On the other hand if the PC ratio continuously rising above 1.4 along with the underlying then the applicability of the theory has not much relevance. Since 15.08.05, Nifty PC ratio was steadily rising along with the Nifty Futures. On 21.09.05 Nifty‘s PC ratio tested the all time high of 2.2 and it was well above 1.4 continuously. On 22.09.05 market crashed by more than 3 % and the PCR came down to 1.7. Now, one has to think about the reliability of PC ratio. If the PC ratio is continuously above 1.4 for quite some time, and again if it rises along with the underlying then at any time if the underlying falls one can expect a major sell off. How does it happen? The answer is the hedgers. Hedgers normally buy stocks from the cash market or from futures market, and they will hedge their portfolio using index futures or with index puts. If the market is rising then they make money from the rising underlying. If the market falls, they make money from short index or long puts. If the fall is more than expected, then the hedgers normally sell their underlying in the cash market and may hold on the short index positions or long puts. This will create panic situations in the market. The fall in U.S on 19 October 1987 was a classic example of over-hedging by the hedgers. Before concluding, we have four things to keep in mind while entering into the market. (1) If PC ratio is above 1.4, one should buy the underlying all the time. (2) If PC ratio is below 0.4, in normal conditions, we should take short positions. (3) When PC ratio is continuously rising with rise in underlying then caution should be taken. (4) When PC ratio is more than 2 and the market starts falling, then expect a major fall in the underlying; therefore, short positions are advisable.

6.7.2 Open Interest and Volume Analysis Open interest in the number of outstanding positions (both short and long) at a particular time, followed by the net change from the previous day on an underlying in the F&O segment. Open interest analysis along with the volumes will give a clear picture of underlying assets’ strengths and weaknesses.

Strategic Derivative Tools 115

Let us understand how open interest changes: · Create a new long position and create a new short position, then open interest will increase by two. · Square the old short position and square the old long position, then open interest will decrease by two. · Create a new long position and square the old long position, then open interest will remain same. Rising open interest in an uptrend is bullish. Rising open interest in a weak market is negative. Declining open interest in a weak market is bullish. Declining open interest in a bullish market is weak. However, sometimes we have seen abnormally high open interest in stock futures and option segment; most of the time if the underlying opens slightly at a higher price, then the long holders liquidate their long causing higher level sell off. Moderate open interest increase coupled with slightly higher closing is a good sign for fresh long holdings. Open interest increase and decrease is measured along with Nifty open interest. If Nifty open interest indicate a weak trend, and at the same time the underlying stocks futures open interest are higher with higher closing, it suggests a strong outlook; however, due to Nifty’s weak trend, the stock may not perform well and sometimes we may see bull liquidation also. It is advisable to first check the Nifty’s open interest behavior, prior to predicting the trends of the stock futures. The high correlation of many of the F&O stocks with Nifty has made it a compulsion to study the Nifty outlook before studying stock futures. While analyzing the options, especially Nifty’s options, will give us a clear indication of Nifty. For example, if Nifty futures are trading at 3900 and Nifty put options of 3800, 3700 and 3600 strikes open interests are declining, it gives us a clear indication that Nifty may reverse its direction because put buyers have already booked the profit on the assumption that downward correction is over. Same way, if the call options’ open interests of 3900, 4000, 4100 are in a declining mode, then it suggests bull liquidation to be followed by a weakness.

6.7.3

Impact Cost*

Impact cost is another way of finding the market direction. Normally, Nifty impact cost remains Rs. 0.05 in normal market conditions, whereas if the market volatility remains high then the Nifty impact cost may move up towards 0.11–0.16. Higher the impact cost, higher the risk; lower the impact cost, stable the market (Table 6.3; Fig. 6.4). The monthly impact cost of Nifty can be checked from NSE’s website. * Source: www.nseindia.com

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Option Trading

Table 6.3

Year 2003

2004

2005

Impact Cost Change on Nifty During the Two Market Crashes in 2004 and 2008* Date

Impact Cost

Year 2005

Date

Impact Cost

JAN

0.09

JAN

0.11

FEB

0.09

OCT

0.08

MAR

0.09

NOV

0.07

APR

0.1

DEC

0.07

MAY

0.1

JAN

0.07

2006

JUN

0.1

FEB

0.08

JUL

0.11

MAR

0.08

AUG

0.12

APR

0.1

SEP

0.12

MAY

0.13

OCT

0.1

JUN

0.12

NOV

0.1

JUL

0.08

DEC

0.09

AUG

0.06

JAN

0.12

SEP

0.06

FEB

0.11

OCT

0.06

MAR

0.09

NOV

0.06

DEC

0.08

JAN

0.07

APR

0.09

MAY

0.17

2007

JUN

0.09

FEB

0.08

JUL

0.09

MAR

0.08

AUG

0.07

APR

0.07

SEP

0.07

MAY

0.07

OCT

0.08

JUN

0.07

NOV

0.07

JUL

0.07

DEC

0.08

AUG

0.08

JAN

0.11

SEP

0.08

FEB

0.07

OCT

0.11

MAR

0.07

NOV

0.11

DEC

0.09

JAN

0.15

APR

0.07

MAY

0.06

JUN

0.07

FEB

0.11

JUL

0.08

MAR

0.09

AUG

0.08

APR

0.08

SEP

0.09

MAY

0.07

* Source: www.nseindia.com

2008

Impact cost

Strategic Derivative Tools 117

0.18 0.16 0.14 0.12 0.1 0.08 0.06 0.04 0.02

Series1 Series2

R SE P FE B JU L D EC M ay O C T M ar AU G JU N

ov

AP

N

N

JU

JU

N

0

Months (2003–2008)

Fig. 6.4 Impact cost

6.7.4

Rollover

Rollover simply means that open positions in the current month’s expiry has been rolled over to the next month contract by squaring off the existing position, whether it is of long or short positions and then taking the same position for the next month contract. Usually, they begin a week before the current month’s expiry or sometimes much earlier. There can be long and short rollovers. Long rollover happens when long positions in the existing contracts have been squared off in the current month, by simultaneously taking long positions in the next month contract. Likewise, short rollover refers to squaring of short positions in the current month, along with opening a new short position in the next month contract. Rollover for futures contract is calculated by dividing the open interest in the middle month of the available series by the sum of open interests of current, middle and distant months. It is expressed in percentage terms. Open interest taken for the calculation purpose is the open interest at the end of the day or at the moment of calculation (Fig. 6.5).

Open interest in the middle month Rollover = Total open interest in current, middle and distant months ´ 100 The above method is the most common method for calculating the rollover of equity index future contracts.

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Option Trading

Fig. 6.5 Rollover

6.7.4.1

Rollover: Its relevance in F&O expiry

During the expiry of F&O segment, market volatility reaches its highest point. Both short sellers and buyers in the futures market are busy closing out their futures positions ahead of expiry of the contract. That makes the closing sessions highly volatile. If the total long positions are higher than short positions then the market will fall during the last days of expiry. On the other hand, if the total short positions are higher than the long positions, then it will remain firm at the closing of the futures. One who knows this ahead of the expiry can predict the future direction. Then the question arises: how can one quantify the total outstanding short positions and long positions ahead of the expiry? The total rollover data will provide the futures direction. Rollover is nothing but exiting one’s position from the near month and simultaneously creating a new position in the far month. For example, Mr. A is long (bought) on TISCO for one lot at Rs. 380. After he made his long position in TISCO, the stock didnt move up above Rs. 380. But he anticipated a price rise in the near future. He then closes out his position in August futures before the expiry and creates a new position in September futures. Shifting of a position from near month to far month is known as rollover of positions. Like long positions, short positions can also be rolled over. For example, Mr. B has a short position in Telco August futures at Rs. 450 and he wants to carry over his short position from August to September. He simply covers his short position in August and then creates a new short position in September. The F&O analysts keep track of the rollover positions ahead of F&O segment expiry. It is assumed that higher rollover before the expiry keeps the market on

Strategic Derivative Tools 119

the positive side. On the other hand, if the rollover figure is lower, the market may encounter selling pressure. How can we calculate rollover position? Compare the near month’s total open interest with the far month’s open interest; the net change will give the rollover figure. Open interest is nothing but the total of all unsquared positions on that particular stock/index future. (Open interest data is available on the NSE terminal and most newspapers.) For a smooth settlement, at least 50% open interest positions should be rolled over ahead of expiry. If the figure is more than 80%, the market will remain positive during the days of expiry. But if the rollover positions are lower than 25%, then one can expect a sharp drop in prices. One should always correlate rollover positions with market conditions for better prediction of the market trend during expiry.

6.7.4.2

Rollover and its impact on futures expiry

Shifting of open interest (either long or short) from near month expiry to the far month expiry is known as rollover in F&O segment. In India expiry of futures take place on the last Thursday of each month. Take one-month contract on November that expired on 24 November. Rollover would require the open interest in the November contracts to be transferred to the December contracts. For example, Mr. A is bullish on the outlook of TISCO; therefore, he bought one lot of TISCO at Rs. 340 in November expiry on 21st. The November futures expiry is on 24 November (last Thursday of the month). Here Mr. A has two options. Firstly, he can hold TISCO futures till Thursday and sell the TISCO futures at a profit or loss. The second option in front of him will be to sell TISCO futures at a profit or loss and buy new TISCO futures in December expiry thereby getting more holding period. The rollover however will not happen on a single day. Normally, rollover of contracts starts to happen days before the expiry. Changes in open interest in different expiry periods show the level of rollover. The rollover figure, will give a better picture of the stock/index closing during the expiry. Higher the rollover figure, smoother is the settlement. If the rollover figure is low, then high volatility can be seen during the settlement. In order to find out the rollover figure, one has to calculate the pending open interest figure in the near month and compare it with the far month’s open interest. For example in order to find out the 24 November rollover figure of a stock future, we require open interests of 23 November and 24 November of the same stock future. On 23 November we can get 23 November open interest and 23 December open interest. After that we have to find out the 24 November futures’ open interest and 24 December futures’ open interest on 24 November. Then compare the net change in open interest. Normally, the open interest of 24 November contract will have a decline and 24th Dec. contract will have an increase interest. Now we have to find out the net change in open interest. In many instances, if the open interest of a stock or index has lesser rollover figure, then one should expect highly volatile movements in that counter. Like stock futures, options can also be rolled over from a near month contract to the

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Option Trading

far month contract. It will give higher holding period for the investor. Generally speaking, the rollover figures do not have any impact on the spot market.

6.7.5

Other Bear Market Indicators

The discount between Nifty spot and Nifty futures may get widened in a bear market. In our market, normal average discount of Nifty varies between 12 and 20 points, but in a bear market the Nifty futures discount is more than 40 points. Nifty index heavyweight stocks such as Reliance Industries, ICICI Bank, SBI, TISCO, ONGC and Reliance Infra are hammered down by the bear operators in a bear market. In a bull market, these stocks will definitely move up continuously because Nifty arbitrageurs normally create a replica of Nifty through these stocks. In a bear market, midcap and smallcap F&O stocks with high beta will start falling first. In a bullish market these are the ones which act as speculators’ paradise. If the underlying, either the Nifty or stock falls below its weekly low, monthly low, 52- week low or all-time low, then one should assume the market is bearish; so he is advised to buy Nifty or stocks put options. On the other hand, if the Nifty or the stocks are trading above their weekly highs, monthly high, 52-week high or all-time high, one should not write calls but should write put options or buy calls. If one of the Nifty 50 stock is trading below its 52-week low due to various reasons, it is advisable to buy put option of that particular stock and can buy Nifty call options. In a classical bear market, one can see selling happening in Nifty deep-inthe-money call options. This is mainly from institutions as they prefer not to buy put options of Nifty in a falling market because of cash outflows. On the other hand, by selling Nifty deep-in-the-money calls, they can earn both time value and intrinsic value.

Summary In this chapter we have discussed the use of put–call parity in option trading. We have seen how PC ratio is calculated and how this is applied in practical terms. We have also discussed arbitrage opportunities on account of PC parity issues and also the impact cost on account of put–call parity. Besides, we have also discussed about open interest and its impact on volume. Another important factor in option trading is volatility because an option trader’s strategy depends mainly on the volatility of the market. The concept of volatility, its computation and its application are discussed in the next chapter. The majority of the strategies discussed in this chapter are our

Strategic Derivative Tools 121

experiences based on the market conditions at that point of time. Readers who take positions based on these experiences should make their own analysis drawing the spirit from our experiences and using their wisdom while making investments in the F&O segment.

Keywords Put–call parity Volume analysis ratio Rollover

PC ratio Open interest

Market sentiment Riskless profit

Impact cost Weighted PC

CHAPTER

07

VOLATILITY

7.1

OBJECTIVES

In the previous two chapters, we used the term volatility several times. We used volatility for calculating the option prices. In this chapter, we will discuss the concept of volatility, types of volatility, measuring volatility, impact of events on volatility etc. The objective of this chapter is to provide a better idea about volatility and its uses to the readers.

7.2

INTRODUCTION

While calculating option prices, we normally come across the term volatility. What is volatility? Volatility is a term associated with liquids. The characteristic of a liquid is that it is highly volatile. The dictionary meaning of the word 'volatile' is 'moving lightly and rapidly about' (Chambers Twentieth Century Dictionary). Another meaning for the same is 'evaporating'. In the context of stock market, the term volatility is used to describe the uncertainty of the future price of the scrip or index. The frequent movement of the stock prices up and down makes it really difficult for one to predict the future price (Fig. 7.1). Consequently, the investor has the chance of gaining as well as losing in a volatile market. It depends on how he reads the market sentiments and finetunes his investments, which in turn decides his profit or loss. Volatility is an important factor in determining the option prices. Volatility is measured in terms of annualized standard deviation (represented by the symbol s) of the continuously compounded returns of the asset. Usually, it is used to quantify the risk involved while trading in an instrument over a particular time period.

7.2.1 Annualized Volatility The annualized volatility (s) is the standard deviation (s) of the instrument's logarithmic returns in a year. The generalized volatility (sT ) for time horizon T in years is expresse d as: sT = s T

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Option Trading

Fig. 7.1

Daily volatility

For example, if the daily returns of a stock has a standard deviation of 0.01 and there are 252 trading days in a year, then the time period of returns is 1/252 and annualized volatility is calculated as s =

s =

s

SD

P

0.01 = 0.1587 1/252

The monthly volatility (i.e. T = 1/12 of a year) would be s month = 0.1587

1/12 = 0.0458[mps1]

7.3 TYPES OF VOLATILITY Volatility can be historical volatility or implied volatility or realized volatility. Historical volatility, as the name implies, is a measure of the movement of price of the asset over a given period in the past. Historical volatility is something that has happened already. Implied volatility is an embedded measure in Black–Scholes model of option pricing. It is something, that is happening in the present and is highly influenced by the market forces. As a result, the

Volatility

125

implied volatility changes every time the option price changes consequent to the demand-supply factors. Realized volatility is the movement of the price of the underlying asset over the time period between the day the option is traded and its expiry. Precisely, this also represents a trade that has already taken place; hence, it can be considered as another form of historical volatility with a difference that the realized volatility is calculated for traded options. Black–Scholes model of option pricing assumes that volatility will remain constant throughout the life of the option. In practice, this never happens. Option prices change in response to the new information received by the market. Thus, we cannot really observe volatility. We can only estimate the volatility from the data provided by the market.

7.4

ESTIMATING VOLATILITY

The historical volatility is used to measure the amount of volatility experienced over a given period of time. It is the standard deviation of the 'price returns' over a period of time multiplied by the number of trading sessions, which will give us the annualized volatility level. Historical volatility is estimated from the past data using mathematical calculations. Implied volatility, on the other hand, is derived from the Black–Scholes method using an iterative search procedure. There are a number of ways to calculate historical volatility. The first thing to determine is the timeframe. Generally, traders observe volatility over a long time, at least 10 years. This allows them to identify short-term changes from normal activity. If a commodity has averaged 25% volatility over the last year, but only 15% over the past 30 days, you may want to adjust the volatility estimates to accommodate the most recent data. Rather than using a figure of 25%, adjusting the figure to 20% as the midpoint may prove more accurate.

7.5

ESTIMATING HISTORICAL VOLATILITY

Two alternative processes are generally used in estimating the historical volatility. The first method is estimating the standard deviation as a fixed parameter, assuming that it is a proxy of historical movement of returns and can describe the future probability distribution of the underlying asset. The second alternative is estimation of the historical volatility considering it a time-varying process and use econometric models like GARCH. Statistically, any differences in the measured volatility are functions of statistical error. This is the reason for assuming volatility as a constant factor. Hence, if we are estimating volatility from historical data, say 90 days or 360 days, the result would be the same. Therefore, we may have to increase the size of the sample until we get the desired degree of accuracy. However, as already seen, the new

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Option Trading

information arrived at the market can change the underlying asset prices. Consequently, the volatility also changes, thereby making the assumption that the volatility is constant an illogical one. These controversies normally raise the following questions: 1. What should be the frequency of data? Should it be daily, intra-day, weekly or any other period? 2. What should be the sampling period? Should it be opening, closing, high, low, average etc.? 3. What should be the period to be covered by the historical return data, i.e. annual, half-yearly etc.? (Normally, volatility is quoted as annualized percentage) 4. Which days are to be included? Is it the calendar days, trading days etc.? The following example will try to find answers to these questions. As already explained, historical volatility is measured by the annualized simple standard deviation of the continuously compounded return on the underlying asset. Accordingly, if we are using daily data, we will get daily historical volatility, whereas weekly data will provide weekly historical data. The following formula is used for calculating historical volatility. s = where s n ri µ

æ 1 ö n 2 ç ÷ * å (ri - m ) è (n - 1) ø i =1

= Volatility = Number of price observations = Individual continuously compounded return = Arithmetic mean of returns

The result of this equation is the daily volatility. In order to get the annualized volatility, multiply the above equation by the square root of the number of data observation days per year, say number of trading days. Table 7.1 shows the price movement for a period of one month. Table 7.1 Value of Scrip

Price Relative

Log of Price Relative

Mean Deviation or Price Relative (ri-µ)

Square of Mean Deviation (ri - µ)2.

181 183

1.01105

0.004773

0.004693

2.20219E-05

187

1.021858

0.009391

0.009311

8.66902E-05 (Contd.)

Volatility Value of Scrip

Price Relative

Log of Price Relative

188 186

1.005348 0.989362

0.002316 – 0.00464

Mean Deviation or Price Relative (ri – µ) 0.002236 – 0.00472

187

1.005376

127

Square of Mean Deviation (ri – µ)2 5.00185E-06 2.23225E-05

0.002329

0.002249

5.05756E-06

187

1

0

– 8E-05

6.36173E-09

187

1

0

– 8E-05

6.36173E-09

184

0.983957

– 0.00702

– 0.0071

5.04603E-05

186

1.01087

0.004695

0.004615

2.13016E-05

184

0.989247

– 0.0047

– 0.00477

2.27995E-05

182

0.98913

– 0.00475

– 0.00483

2.32922E-05

176

0.967033

– 0.01456

– 0.01464

0.000214285

177

1.005682

0.002461

0.002381

5.66839E-06

179

1.011299

0.00488

0.0048

2.304E-05

181

1.011173

0.004826

0.004746

2.25225E-05

183

1.01105

0.004773

0.004693

2.20219E-05

181

0.989071

– 0.00477

– 0.00485

2.35446E-05

186

1.027624

0.011834

0.011755

0.000138171

185

0.994624

– 0.00234

– 0.00242

5.86113E-06

184

0.994595

– 0.00235

– 0.00243

5.92273E-06

183

0.994565

– 0.00237

– 0.00245

5.98533E-06

182

0.994536

– 0.00238

– 0.00246

6.04895E-06

180

0.989011

– 0.0048

– 0.00488

2.38012E-05

180

1

0

– 8E-05

6.36173E-09

180

1

0

– 8E-05

6.36173E-09

179

0.994444

– 0.00242

– 0.0025

6.24617E-06

177

0.988827

– 0.00488

– 0.00496

2.45969E-05

180

1.016949

0.007299

0.007219

5.21209E-05

181

1.005556

0.002406

0.002326

5.41172E-06

Total of mean deviation of price relative (SMD) = 0.000849572 With this information, we shall apply the formula to find out the volatility when n = 30.

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Option Trading n

å (ri – m )2

= 0.000849572

i =1

æ 1 ö ç ÷ * (0.000849572 * 250) = 0.8558 (8.56% ) è ( 30 - 1) ø The historical volatility on annual basis works out to be 8.56%, assuming that there were 250 trading days in the year. The first column of the table shows the value of the scrip. The second column shows the price relative. Price relative is the change of price over the previous day's price. This is calculated by dividing the current price by the previous day's price. Log value of the price relatives are calculated and shown in the third column. The arithmetic mean of the log value is computed and the mean deviation (ri - µ ) is calculated. The mean deviation is shown in the fourth column. The square of mean deviations is computed and is shown in the last column. The mean deviation square column is summated and the aggregate value is arrived at.

7.6 FACTORS AFFECTING THE COMPUTATION OF HISTORICAL VOLATILITY 1. Dividends: Black-Scholes formula assumes that no dividend is paid during the period. This assumption is not correct. Practically every company will give dividend at the end of every year. Some companies pay interim dividend also. Normally, the share price goes up prior to the declaration of dividend and falls after the dividend is declared. Therefore, the value of the scrip will increase as the dividend date comes closer and may hover around till the dividend is declared and the price declines after a few days. It is difficult to predict the price changes on the ex-dividend date due to two reasons. Firstly, the amount of dividend received by each individual shareholder varies. Secondly, the tax rates vary from person to person on account of a differential income pattern. A viable solution to this issue is to exempt the ex-dividend data for the purpose of calculation of volatility.

2. Frequency of Data: Another factor affecting the estimation of volatility is the frequency of the data used for the estimation. The risk-free interest plays an important role in determining the fair price of an option. A hedger who is long in asset will go short in his options portfolio in order to manage the risk. He has to review his position and re-balance the portfolios periodically. Whether he does this re-balancing intra-day, daily, weekly etc. is important in deciding on the frequency. The best possible method is to select the sample data that match the frequency hedge re-balancing.

Volatility

129

3. Price Observation: Since volatility is a measure of price changes, the type of price observed is very important in calculating volatility. It depends on the behaviour of the hedgers. Some hedgers re-balance their portfolios based on the opening price, whereas some others do this exercise based on the closing balance. Hence, it is better to select the sample based on the hedger's choice of price for re-balancing their portfolios.

4. Length of Sample Period: The sampling error can adversely affect the calculation of historical volatility. The sampling error depends on the length of the sample period, which is in effect to the sample size. Therefore, in order to ensure accuracy we have to find out the standard error as well as the sample size. The standard error can be calculated as follows: SE = s /Ö2N where SE = Standard error s = Volatility estimate Ö2N = Size of the sample used to estimate the volatility If we apply this formula to the previous example, the standard error works out to: SE = 8.56/Ö2*30 = 1.11 The second requirement in the determination of the volatility is the sample size. The following statistical formula can be used for estimating the sample size: n = ((z * s) /e)2 where n = The required sample size z = The critical value in the distribution table for the required level of confidence (For example, the critical value for a 99% confidence level is 2.85) e = The acceptable standard error s = The standard error of the volatility distribution Example: Suppose the acceptable standard error is 0.5 with 99% confidence level, sample size for the above example can be calculated as follows: n = ((2.85*1.11)/0.5)2 » 40 Thus, in order to give required degree of accuracy, the required sample size is 40. However, since volatility is considered to be a constant factor for the purpose of calculation of option price under Black-Scholes model, this calculation is not fully correct. Hence, the best method is to choose a longer sample period.

130

7.7

Option Trading

IMPLIED VOLATILITY

The implied volatility of an option is the volatility expressed indirectly by the price of the option in the market based on an option pricing model. It gives a theoretical value for an option equal to price of the option in the current market when used in a pricing model. It is directly influenced by the demand and supply of the underlying option and the expectation of the market about the direction of the share price. When the expectation rises, demand as well as the implied volatility increases along with it. The expensiveness of an option is determined to a great extent by the rise or fall in the implied volatility of the option. The options that are near to the money are seen most sensitive to implied volatility fluctuations whereas options which are deep-in-the-money (DITM) and out-of-the-money (OTM) are seen less sensitive to the changes or fluctuations in implied volatility.

7.8 VOLATILITY SMILE

Implied volatility

In finance, the volatility smile is a long-observed pattern in which at-themoney (ATM) options tend to have lower implied volatilities than other options. The pattern displays different characteristics for different markets and results from the probability of extreme moves (Fig. 7.2). Equity options traded in American markets did not show a volatility smile before the crash of 1987, but began showing one afterwards.

Strike price

Fig. 7.2 Volatility smile Modelling the volatility smile is an active area of research in quantitative finance. Typically, a quantitative analyst will calculate the implied volatility from liquid vanilla options and use models of the smile to calculate the price of more exotic options. A closely related concept is that of term structure of volatility, which refers to how implied volatility differs for related options with different maturities. An implied volatility surface is a 3-D plot that combines volatility smile and term structure of volatility into a consolidated view of all options for an underlying.

Volatility

131

For example, for volatility smile using Reliance Industries call option and its implied volatility, see Table 7.2 and Fig. 7.3.* Table 7.2

Implied Volatility of Reliance Options at Various Strikes

Call Strike

Implied Volatility

1950

53.01

1980

54.05

2010

48.03

2040

42.5

2070

39.045

2100

35.23

2160

32

2190

34.58

2220

36.6

2250

39.47

2280

41.15

2310

45.5

2400

50.8

2500

58.53

2600

60.1 Volatility smile

70

Implied volatility

60 50 40 30 20 10

Reliance call option strike

26 00

24 00

22 80

22 20

21 60

20 70

20 10

19 50

0

Smile

Fig. 7.3 Volatility smile of Reliance Industries * Source: Terry J. Watson, Futures and Options in Risk Management, 1998.

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Option Trading

Implied volatility is a measure embedded in the option price. Although the option price calculated using Black–Scholes Model has implied volatility as one of the factors, it is difficult to segregate this factor from the formula directly. However, after extensive research in this area two models have been developed. The first model was developed by Corrado and Miller in 1996 and the second one was developed by Newton–Raphson. According to the first method, the implied volatility can be calculated in the following manner: s =1/ÖT – t[Ö2Õ/(S + Xe-r(T-t)) (c– (S – Xe-r(T-t)))/2+Ö(c – S – Xe-r(T-t))/2)2 – S – Xe–r(T-t)2/Õ)] Example: Assume that the asset price is Rs. 150, strike price is Rs. 150, riskfree interest rate 6%, annual volatility 37.20%, time to maturity of 3 months and call option premium Rs. 12.09. The implied volatility can be calculated in the following manner by applying the above formula:

1/Ö(T-t) Value of Õ SQRT 2Õ/(S+xe -rt) c-(S-xe -rt)/2 c-(S+xe -rt)/2)2 (S - xe -rt)2/Õ

2.01 3.141593 0.008417 10.99176 120.8189 1.544604

In this example, we have used 365 days for the purpose of calculation. s = 2.01[(0.008417*10.99176) + Ö(120.8189 –1.544604)] = 22.18% It can be observed from the above calculation that the implied volatility used for calculating the premium for the same option was 37.20% whereas the implied volatility extracted works out at 22.18% only. The Black–Scholes formula is used to calculate the implied volatility; we can't invert the formula to arrive at the implied volatility with a given option price. What we can do is to use the Newton–Raphson method to arrive at the implied volatility quickly. We may use the option's Vega* to arrive at the true implied volatility after making a guess on the option's implied volatility. In this method, the volatility parameter is changed keeping the other parameters fixed so as to make the difference between the modelled price and the market price zero. Here, the option price calculated using the option pricing model and the actual price prevailing in the market is considered for arriving at the implied volatility. Since this calculation is beyond the scope of this book, we are not giving the formula and calculation here.

*

Refer to Chapter 8 in this book.

Volatility

7.9

133

GARCH

Volatility can also be calculated using GARCH Model that is based on the assumption that stock returns are heteroskedastic, which means the returns are not scattered evenly or homogenously during the observed period. The model was initially developed as autoregressive conditional heteroskedasticity (GARCH). Traditionally, we presumed that the mean of the expected value outcomes of the random variable is unconditional. Alternatively, the unconditional mean is the weighted mean of the expected outcomes of the random variable. Consequently, the unconditional variance of a random variable represents the difference between the expected outcome and the unconditional mean. However, practically this is not true because the outcome of the random variable is highly responsive to the new information received at the market, which makes it conditional. The difference between the conditional mean and the random variable constitutes the conditional variance, which is better known as function of the squared residuals of the conditional mean equation. The process of ARCH is the modeling of the conditional mean using autoregressive model through the autoregressive process of the squared residuals. Subsequently, the process was more refined by taking into consideration some value of the previous conditional variance as well. Thus, the generalized autoregressive conditional heteroskedasticity (GARCH) came into existence. GARCH is a constant as the current value of the conditional variance, some value of the squared residuals from the conditional mean equation (conditional variance) plus some value of the previous conditional variance. GARCH is now used for estimation of implied volatility not only in stock market, but also in currency market as well.

Beta Beta is a measure of a stock's volatility in relation to the market. Beta can be referred to as a measure assets sensitivity of the asset's returns to market returns. Beta value of stocks give much idea on how much a stock is related to the market movement. The Beta of S&P CNX Nifty comprising of 50 stocks is 1. Beta of a stock = Covariance of stock return with respect to market return/ Variance of market return

Increase Your Portfolio Betas: Beta is a measure of risk. In a bullish market, high beta stocks give high returns and while markets are on the downtrend, high beta stocks tend to fall more than that of low beta stocks. Let us assume that the beta of stock ACC is 1.2. It indicates that if Nifty has given a return of 10%, then the investments in ACC can give a return of 12%. Stocks with high beta are good for investment in a bullish market, but if the markets are on the downturn then these stocks will give you higher losses. Generally, we can call stocks which are having beta less than one as old economy stocks, which may move very slowly and returns will also be very low. On the other hand, stocks having more than one beta are known as

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Option Trading

aggressive stocks because their performance on the bourses will be far better than the indices. Stocks of sectors such as capital goods, wind energy, electricity, infrastructure etc. come under aggressive stocks, whereas steel, cement, automobiles, etc., normally fall under the category of below 1 beta, because these are cyclical stocks, which may move occasionally. A combination of leveraged positions and investments can give higher returns on the stock markets. Punters and fund managers are using various combinations. Creating a portfolio with high beta stocks will fetch you higher returns. Some aggressive fund managers are even creating portfolios having beta of 1.75. It means that if Nifty has given a 20% return; these fund managers will make a windfall profit of 35%. There are fund managers who try to reduce beta to one. These fund managers are looking at decent returns, but do not want to take high risk. Traditional fund managers are not very keen on beta; they may select midcap stocks with good fundamentals and buy the stocks continuously, which itself will push up the prices. The danger in this old method is that once the market falls there will not be many takers for these stocks because midcap stocks will have liquidity problems. Modern researchers are using the support of technical analysis to create high beta portfolios. For example, when the stock is above 200, 100, 50 and 10 day simple moving averages, they create highly leveraged positions in order to get more beta for the portfolios. If the stock falls below 10, 50, 100 and 200 day simple moving averages, then they reduce the portfolio beta to 1 or even below 1.

Portfolio Beta: Portfolio beta is defined as the weighted sum of individual asset betas according to the weightage of each asset's investment in the portfolio, that is, portfolio beta is the aggregate of each asset's beta times proportion of each asset's proportion (amount) in the portfolio. It is the relative volatility of returns earned from holding specific portfolio of securities. Portfolio having higher beta is more vulnerable to the Nifty movements. To understand portfolio beta is important for portfolio hedging by the investors in the derivatives segment. For example, consider a portfolio of three securities, A, B and C included in Nifty with stock beta of 0.80, 1.50 and 1.2 respectively, each having an investment of Rs. 50,000, Rs. 25,000 and Rs. 100,000 totaling Rs. 175,000. Portfolio beta = 0.80 (50000/175000) + 1.5 (25000/175000) + 1.2 (100000/175000) = 0.23 + 0.21 + 0.69 = 1.13 So, if Nifty moves up by 2%, the above portfolio is expected to move up by 2.26% (i.e. 1.13 ´ 2%). Let us take another example on a real-time basis to find out portfolio beta. The total portfolio is worth Rs. 50 lakhs. Stocks included in the portfolio in Table 7.3 are given in the order of their proportion in the portfolio. They are taken from S&P CNX Nifty, which include the most liquid stocks with least impact cost in terms of market capitalization.

Volatility

Table 7.3 Sl No.

Model Portfolio and its Weightage

Stock Name

Sector

Weightage (%)

1.

RELIANCE

Refineries

25

2.

ICICI BANK

Banks

19

3.

L&T

Engineering

15

4.

ONGC

Oil Exploration

11

5.

INFOSYS

IT

9

6.

SBI

Banks

7

7.

NTPC

Power

5

8.

HDFC

Banks

4

9.

RELIANCE COMMUNICATIONS

Telecommunication

3

10.

TATA STEEL

Steel

2

Table 7.4 Sl No.

135

Stock Name Weightage of Stocks in the Portfolio (in Rs. lakhs) (A)

Calculation of Portfolio Beta Total Profolio Value (in Rs. lakhs)

Proportion Stock Product of of Stock Beta Stock Beta in the and Portfolio Proportion

Portfolio Beta Stock (F) = Sum of (D) (E) = C × D (E)

(B) (C) = A/B

1

Reliance

1250000

5000000

0.25

1.12

0.28

2

ICICI Bank

950000

5000000

0.19

1.14

0.2166

3

L&T

750000

5000000

0.15

1.05

0.1575

4

ONGC

550000

5000000

0.11

1.04

0.1144

5

Infosys

450000

5000000

0.09

0.65

0.0585

6

SBI

350000

5000000

0.07

0.99

0.0693

7

NTPC

250000

5000000

0.05

1.19

0.0595

8

HDFC

200000

5000000

0.04

0.92

0.0368

9

Reliance Communications

150000

5000000

0.03

1.15

0.0345

10

Tata Steel

100000

5000000

0.02

1.11

0.0222

1.05

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Option Trading

7.10 IMPACT OF IMPLIED VOLATILITY AND UNDERLYING ASSET PRICE ON PURCHASE OF OPTIONS Let us look the option premium movements after the purchase of an option in relation to the underlying asset price and its implied volatility. Underlying Asset

Volatility of the Asset

Option Price

Moves in the expected way

­

­­

Moves in the expected way

¾

­

Moves in the expected way

¯

­

No change in price

­

­

No change in price

¾

­ slowly

No change in price

­

­

Moves the opposite way

­

¯

Moves the opposite way

¾

¯

Moves the opposite way

¯

¯¯

Impact of implied volatility and underlying asset price on sale of options. Let us look the option premiums movements after the sale of an option in relation to the underlying asset price and its implied volatility. Underlying Asset

Volatility of the Asset

Option Price

Moves in the expected way

­

¯ slowly

Moves in the expected way

¾

¯

Moves in the expected way

¯

¯¯

No change in price

­

­ slowly

No change in price

¾

¯

No change in price

¯

¯¯

Moves the opposite way

­

­

Moves the opposite way

¾

­­

Moves the opposite way

¯

¯ slowly

Volatility

137

OPTION CALCULATOR BASED ON BLACK–SCHOLES

Strike price Share price Time to expiry

80

Volatility Annual interest rate

35

OPTION VALUE

Fig. 7.4

82 30 9.5 CALL

PUT

4.703

2.082

Option premium of a stock if the volatility remains at 35%

OPTION CALCULATOR BASED ON BLACK–SCHOLES

Strike price Share price Time to expiry Volatility Annual interest rate

OPTION VALUE

80 82 30 50 9.5 CALL

PUT

6.033

3.412

Fig. 7.5 Option premium of the same stocks with increase in volatility from 35% to 50%

7.11

VOLATILITY TRADING

Volatility represents the actual annualized standard deviation of the underlying between the evaluation date and the expiration date. Of course, no one knows exactly what the future volatility will be—we can only guess. Volatility is defined as 'the degree to which the price of an underlying tends to fluctuate over time'. This is generally calculated using a standard deviation of

138

Option Trading

price movements over a period of time. For example, if the volatility of an underlying market is 20, it implies that the market can be expected to fluctuate over the next 12 months with a range of ± 20% from the current levels, with 68% degree of probability. Generally, the more the underlying price fluctuates, the higher the volatility. In turn, the higher the volatility level, the higher the general level of option premiums. Generally, higher the volatility, higher the premiums; a decrease in volatility means lower premiums. This is due to the fact that an option writer will demand to receive a higher premium to write an option when the underlying volatility is high. There are several good benchmarks that traders used to estimate the volatility variable. Such estimation methods involve calculating the recent volatility of the underlying itself and looking at the volatility 'implied' by the actual option market price. These estimation methods are usually effective. Volatility is always changing, and sometimes the changes are quite sudden and powerful. In October 1987, the implied volatility of OEX options had been about 25, but on one day (Black Monday), volatilities actually climbed to 200 or higher. Normally, stock index and volatility are inversely correlated. If the underlying asset price rises, the volatility drops. On the other hand, when the underlying falls, the volatility moves up. Generally speaking, when the volatility of an underlying is falling, the call option premiums tend to remain cheaper and investors should buy call options. On the other hand, if volatility is rising, investors can expect a fall in price of underlying and can buy put options. There are volatility traders who plot the volatility over a period of time and calculate the mean volatility. Whenever daily volatility falls below the mean level, they will buy 'straddles' (buying both call options and put options) in anticipation that volatility may move back above the mean. On the other hand, if the volatility moves above the mean level, they may sell 'straddles' in anticipation of a fall in premiums. Before entering into a volatility trading strategy, one should have a clear picture on the following factors: 1. What is the long-term mean volatility of the underlying contract? 2. What has been the recent historical volatility in relation to the mean volatility? 3. What is the trend in the recent historical volatility? 4. What is implied volatility and its trend? 5. Are we dealing with options of shorter or longer duration? If a trader has the answers to these questions, then s/he can play the game of volatility trading in a successful manner.

7.12

NSE VOLATILITY INDEX

Volatility is the standard deviation of the continuously compounded returns of a financial instrument with a specific time horizon. It is often used to quantify the risk of the instrument over that time period.

Volatility

139

Volatility is related to, but not exactly the same as, risk. Risk is associated with an undesirable outcome, whereas volatility as a strict measure of uncertainty could be due to a positive outcome. The first volatility index (VIX) was introduced by the CBOE in 1993. It was the weighted measure of the implied volatilities of S&P 500 ATM put and call options. There are different volatility indices in the US. VIX tracks the S&P 500, the VXN tracks the NASDAQ 100 and the VXD tracks the Dow Jones industrial average. India VIX is a volatility index based on the Nifty 50 index options price. From the best bid-ask price of Nifty 50 options contracts, a volatility figure (%) is calculated, which indicates the expected market volatility over the next 30 calendar days. Volatility index is a good indicator of investors' confidence in the market in the near term. If the volatility index rises considerably, then it is an early indication of an uncertain market. If it is steadily decreasing then it is an indication of an impending uptrend in the market. If the volatility index is below 15-20 levels, then buying of call options is advised. If the volatility index is above 30, then one has to reduce naked positions or engage in portfolio hedging. If the volatility index is above 40, then investors should take utmost care, and they have to reduce portfolio size and accumulate index put options because they can expect uncertain market movements. It was interesting to note that on 1 January 2008 the volatility index of NSE was at 25.38, moved towards 31.17 on 16 January and even tested a high of 54.41 on 29 January. We witnessed massive sell off in Indian equities market during that period. It is very interesting to note that both Nifty index and volatility index of Nifty are inversely correlated. Whenever Nifty rises, volatility falls and vice versa. If the international volatility indices are on the higher side (above 30) and the NSE volatility is also rising, it is an early indication of a fall in index in India due to global factors. On the other hand, if the international volatility indices are lower than 20 and the Indian volatility index is rising steadily, it is an indication of a fall in the market due to domestic issues. The regular monitoring of NSE VIX will help investors to be aware of the market uncertainties. Traders who create high leverage positions must track the NSE VIX on a daily basis. The volatility index study will also help the option traders in a great way because option premiums are fully dependent on volatility.

7.13

BEHAVIORAL STUDY OF NIFTY OPTIONS DURING DISTRESS

May 17, 2004 regarded as the 'black Monday' when the markets fell down drastically, causing huge distress among the investors. Taking this incident and index option data into consideration from March to July, it could be examined as to what actually happened and how market participants as well

140

Option Trading

as investors behaved on 17 May, pre- and post-17 May? This will give an insight into how such distress can be tackled efficiently and what necessary steps should be taken. Implied volatility and its behavior at the time of distress: During the time of distress it is interesting to note that mispricing of call options starts a few days before the event, but when market declines further, mispricing becomes nil. Consequently, after the event, mispricing starts again. When IV increases generally above 25% in a bear phase, mispricing reduces, but during the fall of IV, mispricing of call options starts again. When volatility increases, investor's expectation about anticipated volatility is revised upwards, giving rise to higher risk premium. As a result, discount rate increases and Nifty index falls. Arbitrage opportunities in call options arise during a bear phase few days prior to the event and a few days after the day of the event. Mispricing of put options: During a bear phase, the mispricing of call and put options will decrease substantially but the volatility will increases. Relationship between risk premium and implied volatility: During a bear rally, Risk premium exhibits a positive relationship with put option's implied volatility and a negative relationship with call option's implied volatility. Option premium of put options increases with the increase in implied volatility just prior to and after the event day (14 and 18 May) but not on the event day (17 May). The event taken for the study was the period preceding and following the general elections in May 2004. The unprecedented fluctuation in the market on 17 May was taken as a basic distressful event for the study. Put options during a bear phase: In a bear phase, implied volatility rises, but volume traded drops. Post the event, IV declines but options trading volume increases. It can be summarized that there exists an inverse relationship between implied volatility and liquidity for put options during a distress. In the pre-event phase, as IV increases the open interest increases, but the relationship ceases on the event day and post-event. Call option during a bear phase: Pre-event, ATM call option is most actively traded, while during the event, OTM call is traded most actively, and postevent, in-the-money (ITM) call options are traded most. During a market distress, open interest is always more in deep-out-of-money (DOTM) calls compared to other calls. At the time of distress, there exists an inverse relationship between the implied volatility and the Volumes traded. Comparison: The implied volatility curve exhibits the same pattern for both calls and puts. But the interesting thing to be noted is that the IV of calls is less than the IV of puts. The correlation between IV of calls and IV of puts during an event is 0.89 signifying that if IV of put option increases by 1, then IV of call option increases by 0.89. Liquidation of call option is more than that of put option just prior to the event, while at the time of the event and postevent liquidation of put option is more than that of call option.

Volatility

141

Relationship between open interest, volumes and volatility: In a normal healthy market, open interest and volume are high during the days when there is slight fluctuation in implied volatility, but during the market distress, even though the fluctuation in implied volatility is very high, open interest and volumes are low. Open interest started increasing in the pre-event phase and reached the maximum on 14 May, the day prior to the event. On and after the event, open interest started declining. To conclude, the study has attempted to chalk out the distinct characteristics of the market in general, and the components of options market in particular, during a period of distress.

7.14

IMPACT OF EVENTS ON VOLATILITY—A CASE STUDY

An efficient market is the one that rapidly absorbs information and adjusts the price swiftly. Therefore, any information that affects the demand and supply of the stock is valuable as far as the participants of the trading operations are concerned. Generally, the stock prices go down by an amount reflected by the dividend date. This happens because the demand for the shares comes down once dividend is paid. Dividend is the reward given by the company to the shareholders and the rate of dividend depends on the profitability of the company. Therefore, any economic activity that affects the performance of the company, which will ultimately either deny the dividend or reduce the dividend rate, is valuable information for the stock market. Such news will pull down the demand for the shares, resulting a fall in the spot price. A study conducted on selected scrips in the BSE on the responses of announcement of quarterly results revealed that stock returns fell during the period between tenth and fifth day and started rising four days before the announcement of the quarterly earnings and continued till the previous day of announcement. The stock prices rising four days before the event day is an indication of the good news from the quarterly earnings. Methodology: Seven days prior to and five days after the event are taken for observing the impact of events on the implied volatility based on the findings in the previous studies. The event selected for the study is the announcement of quarterly results by selected entities and the announcement of the Union budget. We have taken 10 scrips, which contributed 80% of Nifty's total traded volume. Also, we have selected Nifty for studying the impact of events on the implied volatility. We observed the movement of implied volatility prior to and after declaration of quarterly results in case of selected scrips. In the case of Nifty, we have taken the announcement of the Union Budget as the event. The scrips selected are Infosys, Satyam, TCS, SBI, ICICI bank, TISCO, TELCO, Maruti, ONGC and Reliance Industries. The implied volatility is generally calculated on the futures prices of the underlying stocks, considering the higher trading volume in the future market than that in the cash market. We have considered five different scenarios viz., DITM, ATM, OTM and DOTM scenarios.

142

Option Trading

Nifty's relevant data during the interim budget on 8 July 2004 was taken for analysis. The volatility of the call and put prices during the pre-budget, post budget period and the event day were taken for analysis. The event taken is the budget presented in July 2004. Volatility is measured for different options. The analysis showed that the volatility peaked during the event and then came down gradually. The correlations were measured for the period before the event (the budget) and post-event (after 8 July 2004) to establish the relation between the call's implied volatility and the call price, put's implied volatility and put price. In the case of ATM options, the call's implied volatility has more correlation with the price rather than the put Implied volatility and price. The call is more sensitive to change in the implied volatility than the put. In general, six to seven days before the event, the Nifty's IV starts to gradually rise. The day before the event, the IV peaks, then starts to fall on the event day and then after four to five days, the IV slightly rises. The same pattern is observed in all the five cases that are ATM options, ITM options, out-of-the money options, DITM options and DOTM options. In all the cases mentioned above, post-event, the IV falls to a level lower than the IV level preevent. The rise and fall of IV are common for both call and put options. The correlation analysis found that the call IV and call price have higher degree of direct correlation than the put options. The post-event correlation is higher than the pre-event correlation. The analysis indicates that the call price tends to rise a week before the event, attains the peak a day before the event and then gradually comes down. In the case of Nifty, volatility trading will fetch better returns in the case of call options rather than that in the put options. Pre-event correlation of puts suggests that in most of the cases, though there was a positive correlation the relationship was weak. The best available option for volatility trading can be ATM options in the case of post-event. In the case of calls, ITM options can be used for trading in volatility both in the case of whole period and before the event. Infosys: Infosys showed a distinct pattern in the case of volatility. The IV of ATM calls and puts showed 14% and 40% increases respectively during the pre-event phase and around 50% fall in the post-event. The magnitude of rise and fall in the IV was approximately same in all the five categories of options taken for the test. DOTM options did not show such a drastic rise or fall in the IV either pre-event or post-event. To put it in other words, DOTM options in the case of Infosys are less receptive to events like Q2 results. There was high degree of positive correlation in all the five categories of options. ATM call options and ITM put options had the highest amount of direct correlation. The pre-event correlation for puts in all the categories was comparatively less. It can be inferred that the put options behave in the direction of IV more frequently in the post-event than in the pre-event. In case of calls, the DOTM options showed a negative correlation in the postevent.

Volatility

143

Volatility trading will be effective for both calls and puts in the case of postevent if OTM options are chosen. On the whole, if an investor wants to take advantage of the volatility during an event, his best choice would be ITM options. The unique feature with Infosys was that the DOTM option showed very high correlation in the case of puts for post-event and negative correlation in the case of calls for the same post-event. TCS: In case of TCS, ATM call and put IV gained around 10 points in the preevent period. They peaked just two days before the result and started to decline in the post-event phase. In the post-event the call IV lost around 15 points and the put IV lost around 6 points. Except in the case of ATM options, the put IV showed a slight rise in the post-event phase. This may be due to the fact that the actual Q2 results were lower than the anticipated results. ATM calls, ITM calls and OTM calls showed high correlation between call IV and call price. The correlation was very less in the case of DITM and DOTM calls. In the case of puts except ITM options, the correlation was very less. DOTM options in the post-event showed inverse relationship in the case of calls and highly positive relationship in the case of puts. Satyam: Satyam announced its quarterly result on 20 October 2004. ATM calls peaked much before the event day (8 October 2004) and came down to a lower level by 15 October. From there the call IV gained just 2.38 points till the day before the event. The event day saw the IV declining. But the IV again gained 14 points by the second day after the event. ATM puts gained 5 points before the event day, but the drop in the IV after the event day was more significant in puts rather than in calls. The general pattern of rising IV in the pre-event period and falling IV in the post-event period was seen, though with less degree of precision. The same trend was also seen in the other scenarios of ITM, OTM, DITM and DOTM options. SBI: SBI declared its Q2 results on the 30 October 2004. The set pattern of rising volatility in the pre-event phase and falling volatility in the post-event phase was followed with higher degree of precision in the case, SBI in general and call IV of SBI in particular. ATM calls of SBI gained 9 points in its call IV and 6 points in its put IV in the pre-event phase. The gain in the ITM options was higher in the case of calls. Post-event, the ATM calls saw a fall of about 10 points while the put options in the same category fell by around 3 points. The relationship between call IV and call price was high only in the case of DITM options. Otherwise there was a positive moderate relationship in most of the other categories. Negative correlation between options price and IV was observed in around five cases. Thus, it can be inferred that the pre-event volatility trading should be approached with care. Post-event, ITM puts and DITM puts can be ideal for volatility trading. Pre-event OTM calls can be a better choice.

144

Option Trading

ICICI Bank: The Q2 result was declared on 20 October 2004. Both call and put showed a rising trend in the pre-event phase and a fall in IV in the post-event phase. The IV rose from a lower level in all the cases of calls and puts for all categories and then showed wave patterns suggesting volatile movements. The relationship was generally positive both in the calls and puts. The call and put prices generally followed the direction of IV. The IV and option price relationship was less in the case of ITM options. Maruti Udyog Ltd: Maruti declared Q2 results on 27 October 2004. The IV of Maruti showed a wave pattern during the event. The call IV and put IV gained around 10 points in the pre-event phase. The post-event phase was not calculated as the contract ended on 28 October while the event day was 27. A distinct volatile wave pattern of high rise and falls was witnessed in the puts in all the five categories. DOTM call can be best described as not having any impact for the event. There existed a high degree of direct relationship between the IV and option prices in all the categories. Tata Motors: The event was declaration of Q2 results by Tata Motors on 29 October 2004. In this case, the set pattern was distinct in DITM and DOTM options. In both these cases, the IV gained around 6 points in the pre-event phase. In the other entire category, the IV rose but its magnitude was varied. High degree of correlation existed in the pre-event phase and for the whole period. Post-event, the results were mixed. The relationship exhibited was weak to inverse. This can be because of the fact that the November month contract after the expiry of October month contract was traded more heavily and this was the post-event period. Regardless of the volatility, the option prices increased due to the higher demand. Reliance Industries: The established pattern of pre-event rises, peaking of IV and decline in the post-event was not established with high degree of volatility change. In all the cases, the put IV showed a rise with varying magnitude immediately after the event day. In the case of call options except in the case of DITM call, the IV declined after the event. On the whole, it can be generalized that a significant pattern of IV was not established for Reliance. This can be justified by the varying degree of correlation for different scenarios, which does not suggest a pattern. ONGC: The oil major ONGC announced its Q2 results on 29October 2004. The pre-event rise of IV in the call option was not very distinct. The ATM call gained 2 points while the puts did not show any significant rise. Significant rise was seen in ITM call and DITM calls. Except in the case of deep-in-the money and DOTM options, there existed a post-event slide in IV of both calls and puts. High positive correlation was seen between the IV and volatility in all the five categories for the whole event. Post-event relation in the case of DITM and deep-out-of-he money puts did not signify that the IV factor was not taken into account in these categories. Pre-OTM correlation was high in all the five categories.

Volatility

7.15

145

COMPARATIVE STUDY OF THE BEHAVIOR OF NIFTY AND IT STOCKS DURING AN EVENT

In this analysis, the relationship and the impact of events in the IT sector on the index are compared. For this, ATM call IV and put IV for seven days before the event and five days after the events of Infosys, Satyam and TCS are taken (Table 7.5). The respective IV of Nifty on the above-mentioned days are taken. Assigning equal weights, average IV for IT and Nifty are constructed. Table 7.5 Day Preevent 7 Preevent 6 Preevent 5 Preevent 4 Preevent 3 Preevent 2 Preevent 1 EVENT Postevent 1 Postevent 2 Postevent 3 Postevent 4 Postevent 5

Behavioral Study of Nifty and IT Stocks

Infy 24.06

TCS 23.72

Satyam 34.09

Average 27.29

Nifty 1 Nifty 2 23.42556 21.29

Nifty 3 20.62

Average 21.77852

26.97

19.5

38.04

38.27

21.29

21.14

20.73

21.05333

28.34

25.84

31.11

28.43

21.14

22.45

19.51

21.0333

32.13

25.67

33.97

30.59

22.45

22.57

16.7

20.57333

31.69

25.36

29.78

28.94333 22.57

22.53

16.63

20.57667

29.94

27.44

36.09

31.15667 22.53

24.18

17.47

21.39333

35.22

23.67

33.63

30.84

24.18

21.62

19.77

21.85667

23.22

24.88

21.62

20.76

21.19

26.54 31.22

26.53

20.18

25.97667 20.62

20.626

20.11

20.45

26.29

27.31

32

28.53333 20.73

20.73

19.12

20.19333

21.52

20.36

33.59

25.15667 19.51

19.51

19.3

19.44

18.37

24.52

31.06

24.65

16.63

20

17.7767

20.45

20.21

32.02

24.22667 16.63

17.47

19.73

17.94333

16.7

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Option Trading

50 40 30 20 10

Average IT Put IV

Fig. 7.6

Postevent 1 Postevent 2 Postevent 3 Postevent 4 Postevent 5

t

t

Po s

t

Po s

t

t

Po s

Po s

T

Average Nifty Put

Implied volatility movement during pre-event and post-event

Table 7.6

Day Pre -event 7 Preevent 6 Preevent 5 Preevent 4 Preevent 3 Preevent 2 Preevent 1 EVENT

Po s

EN

Pr e

EV

Pr e

Pr e

Pr e

Pr e

Pr e

Pr e

0

Implied Volatility of IT Stocks and Nifty on Pre-event and Post-event

Infy 25.64

TCS 26.7

Satyam 43.5

Average Nifty 1 31.94667 19.6

Nifty 2 19.2

Nifty 3 20

Average 19.6

31.6

30

42.97

34.85667 19.2

19.89

21.11

20.06667

31.14

28.34

42.36

33.94667 19.89

20.18

20

20.02333

36.81

29.69

35.81

34.10333 20.18

20.64

18.7

19.84

34.7

34.18

34.5

34.46

20

16.97

19.20333

40.27

35.38

36.54

37.39667 20

21.11

19.11

20.07333

40.22

34.49

36.88

37.19667 21.11

20

16.32

19.14333

34.4

32.52

16.79

18.395

30.64

20.64

20

19.34

29.98

30.62

26.64667 18.7

18.7

16.19

17.86333

19.04

20.92

44.86

28.27333 16.97

16.97

16.86

16.93333

27.11

22.11

35.11

28.11

19.11

19.11

16.62

18.28

26.29

19.7

30.48

25.49

16.32

16.79

16.78

16.63

23.95

19.19

31.71

24.95

16.79

16.19

18.42

17.13333

Volatility

147

40 35 30 25

Average IT IV

20

Average Nifty IV

15 10 5 e

e

e

e

Pr

Pr

Pr

Pr

T Pr e Pr e Pr e Pr e Pr e

e Pr

EN

e Pr

EV

e Pr

0

Fig. 7.7

IT and Nifty ATM calls

The charts indicate that the index has similar pattern during the event. The index peaks during an event and falls after the event. The same relation is seen in IT stocks. The relationship between index and IT stocks can be termed as positive. The ATM calls and puts carry higher degree of positive relationship. Pre-event of the Nifty generally remains dominant and does not respond to the volatile movements, but in the post-event, Nifty's volatility also falls along with the IT stocks volatility. Chi square test was done to find out whether there is significant relation between call option with respect to its sensitivity towards IV. Correlation Below 0.5 Above 0.5 66 83 149 83 78 161 149 161 310 X2 = 0.40698 P [ (X2) (r – 1) (c – 1)] @ 950 degree freedom = 0.00393 Call Put

The test shows that there is no significant relation between calls and puts in regard to correlation. In other words, it can be said that both call and put prices respect implied volatility and there is no significant relation between call and put and their reaction to implied volatility. The study reveals that most of the option prices have a tendency to move towards the direction of option volatility, which moves in a set pattern during an event. The IV rises in the pre-event phase, peaks a day or two before the event, and then falls to lower levels. In most of the cases, the IV falls to a level lower than the IV level in the pre-event phase.

Call

0.87101

0.0576

0.45879

Whole

Pre-event

Post-event

ATM

0.59370

0.00282

0.69649

ITM

0.27043

0.52731

0.87462

OTM

0.02954

0.01419

0.51500

DITM

0.55858

0.69501

0.50051

DOTM

0.74943

0.01591

0.66469

ATM

Put

Table 7.7 Correlation

ITM

0.36957

0.47777

0.73898

OTM

0.50929

0.15624

0.70745

DITM

0.61121

0.08114

0.59632

0.62893

0.1445

0.29525

DOTM

148 Option Trading

Volatility

149

The best strategy that can be adopted is to enter long straddles seven or eight days before the event (quarterly numbers, budget, dividend declaration, bonus declaration) and liquidate the long straddle a day or two before the event or atleast before the announcement of the event. Alternatively, short straddles can be created one day before the event and can be liquidated on the same day after the announcement of the event. Table 7.8

Call Option Premium at Different Strike Prices

1500 strike

1560

CALL 1620

0 0 0 0 0 0 0 96 89 76 36

0 0 0 0 0 0 69.61 84.46 62.98 58.14 49.1

0 0 0 0 0 0 65.59 65.66 60.72 56.22 54.48

4 7 8 9 10 11 14 15 16 17 18

1770

1860

1920

69.34 52.92 57.57 51.78 108.56 59.12 62 67.2 60.73 55.6 56.38

47.18 47.15 56.64 52.13 54.51 58.41 69.91 72.67 62.66 60.61 68.53

48.99 45.87 54.26 53.84 53.94 59.52 70.46 73.34 69.41 63.77 67.66

120 100 1500 strike

80

1560 1620

60

1770 1860

40

1920 20 0

4

7

8

9

10

11

14

15

16

17

18

Fig. 7.8 Graphical presentation of Infosys strikes

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Option Trading

Table 7.9

4 7 8 9 10 11 14 15 16 17 18

7.16

Put Option Premiums of Infosys at Various Strike Prices

1500 0 0 0 0 0 0 0 20.69 0 0 0

1560 0 0 0 0 0 0 59 76.05 60.73 31.49 59.06

PUT 1620 0 0 0 0 0 0 65.59 102.92 131.03 116.4 120.81

1770 63.06 46.44 70.93 0 73.6 126.45 245.59 289.58 293.98 295.4 324.44

1860 3.18 1.66 3.91 73.14 80.62 129.03 169.48 182.54 184.12 184.09 230.41

1920 5.16 3.42 5.73 2.8 3.51 8.72 13.42 15.88 15.2 13.9 17

IMPACT OF QUARTERLY RESULTS ON STOCK FUTURES

According to efficient market hypothesis, the stock price of a security will reflect the clear image of the corporate. All information regarding the corporate, both past and recent happenings rapidly adjusts to the stock price preventing from an arbitrage opportunity in the future. As no stock market in the world is efficient in the absolute sense, it is important to find out under which category is our market a strong, semi-strong or weak form of efficient market hypothesis (EMH). The researchers on market efficiency have conducted various studies and have shown that the Indian capital markets are inefficient. In an efficient market, the average abnormal return (AAR) can tend to be zero and cumulative average abnormal return (CAAR) can rise before the event day (quarterly results/any corporate announcements etc.) and taper off after the event. But in Indian market such things don't happen; the trend of AAR and CAAR after the event day shows that they are increasing more than decreasing. (The average returns are averaged over the number of securities to get AAR and this is further added to find out the CAAR.) We had conducted a study on the stock futures' behaviour before and after the quarterly announcement of results. The year 2005 was selected for the study because this year had experienced a dream run in the stock market as the Sensex touched the all-time high 9443 mark. Moreover, more fresh funds were targeted to the emerging market in the following months. Therefore it was interesting to understand the trends. For the purpose of study, October 2005 quarterly results announcements were taken as the event day and the respective future price before and after 30 days were taken into account. The study was based on Nifty-based companies of the stock exchange (NSE) which are available on stock futures segment (46 stocks).

Volatility

151

350 300 1500

250

1560 200

1620

150

1770 1860

100

1920

50 0 4

7

8

Fig. 7.9

9

10

11

14

15

16

17

18

Graphical presentation of Infosys put options at various strike prices

A residual analysis technique was used to find out the returns of the future price before and after 30 days surrounding the event day. The results indicate the stock returns were highly volatile before and after the event day. It was observed that 10 days prior to the event, the returns were negative indicating the market expectation of forthcoming results was not satisfactory. But, soon after the quarterly results, the CAAR of stocks were found to be rising. (The stock prices had a net change in price of 50%.) If we analyse this phenomenon we can come to a conclusion that future price of stocks track the cash segment. The CAAR in both cases (cash segment and futures segment) are high because Indian capital markets are still inefficient and fall under the category of weak form according to the EMH. The main reason for the inefficiency is primarily due to the lack of information available to all the investors at the same time. This is subjected to the frequency in which it ultimately reaches out to the investor. Moreover, in the present scenario, the market is highly volatile. It is very difficult to judge the future of a firm that why large standardized unexpected earnings result is abnormal.

7.17

VOLATILITY SKEW

Volatility skew is one of two curve shapes formed by charting the implied volatility of options across the various strike prices. Basically, what the volatility skew shows is that implied volatility is higher as the options go more and more (ITM), forming a right skewed curve, hence the name volatility skew. It can also skew to the left, indicating higher implied volatilities for OTM options.

Option Trading

Implied volatility

152

Skew

Strike

Fig. 7.10

Volatility skew

For example, Fig. 7.11 shows a volatility skew using the Reliance Industries put option and its implied volatility (IV) on the basis of data in Table 7.10. A Strike

B Put IV

C Call IV

D (B-C)

1950 1980 2010 2040 2070 2100 2130 2160 2190 2220 2250

53.55 53.09 60 65 73 80 109.04 93.33 120.33 147.25 174.11

53.01 54.05 48.03 42.5 39.045 35.23 32 34.58 36.6 39.47 41.15

0.54 -0.96 11.97 22.5 33.955 44.77 77.04 58.75 83.73 107.78 132.96

7.18 STOCHASTIC VOLATILITY Stochastic volatility models are used to evaluate derivative securities such as options. The name derives from the model's treatment of the underlying security's volatility as a random process, governed by state variables such as the price level of the underlying, the tendency of volatility to change to some long-run mean value, and the variance of the volatility process itself, among others. Stochastic volatility models are used to resolve a shortcoming of the BlackScholes model. Specifically, these models assume that the underlying volatility is constant over the lifetime of the derivative product, and remain unaffected

Volatility

153

Volatility skew 140 120 100 80 60 40 20 0

Reliance put/call option strike

50 22

20 22

90 21

60 21

30 21

00 21

70 20

40 20

10 20

80 19

19

50

–20

SKEW

Fig. 7.11 Volatility skew of Reliance Industries by the changes in the price level of the underlying. However, these models cannot explain the long-observed features of the implied volatility surface such as volatility smile and skew, which indicate that implied volatility does tend to vary with respect to strike price and expiration.

7.19 VOLATILITY ARBITRAGE Volatility arbitrage is a type of statistical arbitrage that is implemented by trading a delta neutral portfolio of an option and its underlying. The objective is to take advantage of the differences between the implied volatility of the option and a forecast of future realized volatility of the option's underlying. In volatility arbitrage, volatility is used as the unit of relative measure rather than price, that is, traders attempt to buy volatility when it is low, and sell volatility when it is high.

7.20

VOLATILITY CHANGE

We know that volatility is expressed as the standard deviation of the percentage change in the daily spot price of the underlying asset. If suppose the annual volatility is 15%, single-day volatility can be worked out in the following manner: 15% ÷v365 = 15% ÷ 19.105 = 0.785%

154

Option Trading

The larger the volatility, the larger the chance for the spot price moving into the ITM zone and at the same time the value of the option will be great. The impact of volatility on the option's value is expressed as: Vega = Change in premium or volatility If suppose the value of Vega is 0.5 and the volatility changes from 20% to 25%, then the value of the option will increase by 0.5(0.25-0.20) = 0.025. This means an increase in volatility will normally lead to an increase in option value. One thing to keep in mind is that the forecast volatility done on the basis of historical data may not always be correct because of the changes in the spot rate are influenced by a number of economic and non-economic factors that may or may not occur in the future.

Summary In this chapter we discussed about the concept of volatility and found that volatility can be either historical or implied. We also discussed ways of measuring historical volatility, and factors affecting historical volatility. Further, we discussed about stock beta and portfolio beta and analysed some of the study results connected with volatility, impact of events on volatility etc. Concepts like volatility smile, volatility skew, stochastic volatility and volatility arbitrage were also explained. While discussing about volatility we have presented the Vega which is the impact of volatility on the option price. Vega is otherwise known as Greek in option-trading parlance. There are other option Greeks also which will explain in detail in the next chapter.

Keywords Volatility Volatility smile Volatility change

Historical volatility Volatility skew GARCH

Implied volatility Volatility arbitrage Volatility

CHAPTER

08

OPTION GREEKS

8.1

OBJECTIVES

While discussing volatility in the previous chapter, we found that Option Greeks form a part of volatility. This chapter is aimed at familiarizing the readers with the Option Greeks used in option trading. We are explaining only the important ones: delta, gamma, theta, vega and rho.

8.2

INTRODUCTION

The Greeks quantifies the sensitivities of derivatives market, which include options. They actually calculate the various aspects of risk in an option position and at the same time show a parameter on which the value of an option is dependent. They can be used to measure the risk in owning an option, and the portfolio can be adjusted to achieve the desired exposure. Each risk variable is the result of a faulty assumption or is due to the sophisticated hedging strategies which are used to neutralize the risk effect. In order to neutralize the effect of the risk variable, we require good amount of buying and selling, which involves high transaction cost. There are five kinds of Greeks commonly used, which are explained in the following sections.

8.3

DELTA

It measures the sensitivity to changes in the price of the underlying asset. The delta of a call option ranges from 0 to 1 and that of a put option ranges from 0 to -1. We can add, subtract and multiply deltas to calculate the delta of a position of options and stock. The position delta is a way to see the risk/ reward characteristics of your positions in terms of shares. Delta is sensitive to changes in volatility and time to expiration. The delta of an option mainly depends on the price of the stock in relation to the strike price. For example, if we talk in respect to a call option, a delta value of 0.7 means that for every Rs. 10 increase in the underlying stock, the option value increases by Rs. 7.

156

Option Trading

Figure 8.1 shows the movement of delta and call premium in relation to the underlying asset price.

30

1.2

25

1

20

0.8

15

0.6

10

0.4

5

0.2

0 100

105

110

115

120

125

DELTA

Premium

Movement of delta of call option in relation to underlying asset price

Premium DELTA

0 130

Underlying asset price

Fig. 8.1

Impact of movements of underlying asset price on Delta and call option premiums

From the graph we can see that both delta and premium of the call option increases when the underlying asset price increases. When the underlying asset price increase from 100 to 105 the delta increases from 0.5478 to 0.7291 and it becomes 0.9906 when the underlying asset price is 125. Delta reaches close to one when the call option is deep in the money. The delta values of put option will be negative.

4 3.5 3

0.05

2.5

0.03

2 1.5

0.02

0.04

1 0.5 0 100

DELTA

Premium

Movement of delta and premium of put option in relation to underlying asset price

Premium DELTA

0.01

105

110

115

120

125

0 130

Underlying asset price

Fig. 8.2 Impact of movement of underlying asset price on Delta and put option premiums The graph shows the movement of delta and premium of put option in relation to the underlying asset price. From the graph we can see that delta of the put option increases when the underlying asset price increases while the

Option Greeks 157

premium of the put option decreases. When the underlying asset price increases from 100 to 105 the delta increases from -0.4522 to -0.2709 and it becomes -0.0094 when the underlying asset price is Rs.125. Delta reaches close to zero when the put option is deep out the money.

8.3.1

Delta Hedging on Expiration Dates

Option premiums are mainly determined by their volatility and time value. The latter plays a key role. Due to time decay, many of the options end as out-of-the-money. Especially, when writers buy options at the beginning of the month, they may ask for high premiums. On the other hand, on the last days, options are available at throwaway prices. Imagine that on the last Thursday of the month (expiry day), put options of 350 Satyam were trading at a premium of Rs. 1.10 and Satyam December futures were trading at Rs. 351, while the spot was at Rs. 351.50. Here an investor who expects a volatile movement on the scrip can buy one lot of Satyam Computer stock futures at Rs. 351, and at the same time he can buy two lots of Satyam put options at Rs. 1.10. The upside breakeven can be attained at Rs. 353.20 [351 + (2 × 1.10)] and downside breakeven at Rs. 347.80 [350 – (1.10 × 2)]. Being the last day, a sharp volatile movement is expected in most of the stocks. Suppose the stock moves up from Rs. 351 to Rs. 359; the investor has to book profits by selling Satyam futures. On the other hand, if it falls below the breakeven point, he may have to book loss in Satyam stock futures and can make profits in long put options. Generally, investors may have doubts why one lot of Satyam futures and two lots of Satyam puts? The answer is simple:here, we are creating a delta hedge position. An at-the-money option delta should be very close to 0.05, and hence, by buying two put options we are creating a net –1200 delta [0.5 × 2 × 1200], and buying one lot of stock futures translates into +1200 delta [1 × 1200]. Hence, the position is neutral. (Assume Satyam's lot size is 1200). According to the rule, instead of buying puts, one can sell stocks futures and can buy two lots of call options. This should be done in volatile stocks where the strikes are at-the-money. Buying puts or calls purely depends on their relative premiums.

8.3.2

Delta Neutral

When the market outlook is bearish, it is advisable to sell naked Nifty futures of the stock. The seller of the naked Nifty futures makes profit to the extent of the fall in the value of Nifty. But if Nifty moves up drastically, the seller incurs loss. If the seller's stop loss gets triggered, he will lose on the particular trade. If the trader is a swing trader and he expects some bad news on Nifty, he will tend to hold the short position for days. And in the worst case scenario, if Nifty bounces back in the opening hour of trade, the trader will incur huge losses. To reduce the downside risk, instead of holding a naked

158

Option Trading

short position on Nifty, the trader can buy a call option of Nifty at the strike price close to the price at which he had sold Nifty futures. So even if the script bounces back the next day, the long call will act as a buffer to the losses incurred on Nifty's futures. A trader can use this strategy to reduce the risk involved in selling naked futures and in the meantime he can make profits if Nifty moves down. Here the trader starts making profit if Nifty moves below the value of the premium paid for the long call. Any loss arising due to any upside move in the index prices by holding the naked futures is limited by the long call. Thereby, the trader can just reduce his risk of holding a naked short futures position with a protective call. Sometimes investors convert this strategy to a delta neutral by buying equal delta call options. For example, Mr. Thomas is bearish on markets and sells 100 March 2009 Nifty futures at 3000, and he buys two 3000 strike Nifty call options with 0.5 deltas. Selling Nifty futures will give him –100 deltas. Buying call options of two lots with 0.5 deltas will give him +100 deltas. Here, the net delta position becomes 0. Delta-neutral strategies are extensively used one or two days prior to the expiry.

8.3.3

Thumb Rule on Deltas

Buying call generates positive deltas. Selling put generates positive deltas. Buying stock generates positive deltas. Selling stock generates negative deltas. Selling call generates negative deltas. Buying put generates negative deltas.

8.3.4

Illustration to Find Delta

An investor holds one share of Infosys. The spot price of Infosys shares as on 20 June is Rs. 3500. S/he decides to buy a call option at a strike price of Rs. 3600 for delivery on 19 June, next year. The annual volatility in the stock market is 57.96%. Risk-free interest rate is 7.5%. Calculate delta for call and put. S X T–t r s

= 3500 = 3600 = 1 (June 20 to June 19 next year = 1 year) = 0.075 (7.5%) = 0.5796 (57.96%)

Delta for a call = N(d1) Delta for a put = N(d1) – 1 where d1 = [ln(S/X) + (r + s 2/2) × t]/[s × Ö (T – t)] d1 = [ln(3500/3600) + (0.075+(0.57962)/2 × 1]/[0.5796 × 1] = [ln(0.9722) + 0.075 + 0.1679]/0.5796

Option Greeks 159

= (– 0.0282 + 0.075 + 0.1679)/0.5796 = 0.2147/0.5796 = 0.3704 Delta for a call = N(d1) = N(0.3704) = 0.6443 Delta for a put = N(d1) – 1 = 0.6443 – 1 = – 0.3557

8.4 GAMMA It measures the rate of change in delta and shows how the price will react to a significant change in the price. A large gamma value shows that your delta can change significantly when there is a small move in the stock price. Suppose the delta of a call option is 0.45 and the delta of a put option is – 0.55 when the price of the underlying asset is Rs. 99 and the gamma value for both call and put options is 0.07. If the underlying asset moves up from Rs. 1 to Rs. 100, then the delta value of the call option is 0.52 [0.42 + (Rs. 1 × 0.07)], and the delta value of the put option is – 0.48 [– 0.55 + (Rs. 1 × 0.07)]. When the price of Changes in Delta the underlying asset comes down to Rs. 1, then the delta value of call option becomes 0.38 and that of put option becomes – 0.62. So, here the gamma value shows the rate of change of delta value when there is a change in the underlying asset price. For an option trader, a position with a positive gamma is much safer because it gives deltas from an upward or a downward move in the stock price. At the same time, a position with a negative gamma can be equally dangerous.

30

0.05

25

0.04

20 0.03

15 10

0.02

5

0.01

0 100

Gamma

Premium

Movement of gamma and premium of call option in relation to underlying asset price

Premium

Gamma

0 105

110

115

120

125

130

Underlying asset price

Fig. 8.3

Impact of movement of underlying asset price on Gamma and call option premiums

160

Option Trading

The graph shows the movement of Gamma and premium of call option in relation to the underlying asset price. From the graph we can see that Gamma of the call option decreases when the underlying asset price increases while the premium of the call option increases. When the underlying asset price increase from 100 to 105 the Gamma decreases from 0.0395 to 0.0314 and it becomes 0.0020 when the underlying asset price is 125. Gamma reaches close to 0 when the call option is deep in the money.

3.5

0.045 0.04

3

0.035

2.5

0.03 0.025

Premium

4

2

0.02

1.5 1

0.015 0.01

0.5

0.005

0 100

105

110

115

120

125

Gamma

Movement of gamma and premium of put option in relation to underlying asset price

Premium Gamma

0 130

Underlying asset price

Fig. 8.4

Impact of movement of underlying asset price on Gamma and put option premiums

The graph shows the movement of gamma and premium of put option in relation to the underlying asset price. From the graph we can see that both Gamma and premium of the put option decreases when the underlying asset price increases. When the underlying asset price increase from 100 to 105 the Gamma decreases from 0.0395 to 0.0314 and it becomes 0.0020 when the underlying asset price is 125. Gamma reaches close to zero when the put option is deep out of the money.

8.4.1

Illustration to Find Gamma

An investor holds one share of Infosys. The spot price of Infosys shares as on 20 June is Rs. 3500. S/he decides to buy a call option at a strike price of Rs. 3600 for delivery on 19 June, next year. The annual volatility in the stock market is 57.96%. Risk-free interest rate is 7.5%. Calculate gamma for call and put. S = 3500 X = 3600 T – t = 1 (June 20 to June 19 next year = 1 year) r = 0.075 (7.5%) s = 0.5796 (57.96%)

Option Greeks 161

where d1 = [ln(S/X) + (r + s 2/2) × t]/[s × Ö (T – t)] d1 = [ln(3500/3600) + (0.075 + 0.5796 2/2 × 1]/[0.5796 × 1] = [ln(0.9722) + 0.075 + 0.1679]/0.5796 = (– 0.0282 + 0.075 + 0.1679)/0.5796 = 0.2147/0.5796 = 0.3704 So, gamma for a call and put = N(0.3704)/3500 × (0.5796 × Ö 1) = 0.6443/2028.60 = 0.0003176

8.5

VEGA

Vega shows the sensitiveness to volatility. It is the estimate of how much the theoretical value of an option changes when the change in volatility is 1.00%. Higher volatility means higher option prices. Positive vega means that the value of an option increases when volatility increases and vice versa. Suppose the value of a call option is Rs. 20 and the vega of the option is 0.2 with volatility at 30%. If the volatility of the option increases from 30% to 31%, then the value of the option rises to Rs. 22. On the other hand, when the volatility falls from 30% to 29%, the value of the call also falls to Rs. 18.

25

0.1

20

0.08

15

0.06

10

0.04

VEGA

Premium

Movement of Vega and premium of call option in relation to underlying asset price 30 0.12

Premium

Vega

0.02

5 0 100

105

110

115

120

125

0 130

Underlying asset price

Fig. 8.5

Impact of movement of underlying asset price on Vega and put option premiums

The graph shows the movement of Vega and premium of call option in relation to the underlying asset price. From the graph we can see that Vega of the call option decreases when the underlying asset price increases along with the call option premium. When the underlying asset price increases

162

Option Trading

from 100 to 105 the Vega decreases from 0.1135 to 0.0997 and it becomes 0.0091 when the underlying asset price is 125. Vega reaches close to zero when the call option is deep in the money.

4 3.5 3 2.5 2 1.5 1 0.5 0 100

0.12 0.1 0.08 0.06 0.04

Vega

Premium

Movement of Vega and premium of put option in relation to underlying asset price

Premium Vega

0.02 105

110

115

120

125

0 130

Underlying asset price

Fig. 8.6

Impact of movement of underlying asset price on Vega and put option premium

The graph shows the movement of Vega and premium of put option in relation to the underlying asset price. From the graph we can see that both Vega and premium of the put option decreases when the underlying asset price increases. When the underlying asset price increase from 100 to 105 the Vega decreases from 0.1135 to 0.0997 and it becomes 0.0091 when the underlying asset price is 125. Vega reaches close to zero when the put option is deep out of the money.

8.5.1

Impact of Vega on Option Trading

Options are characterized by a very important factor known as volatility. Vega is a sensitivity factor which attempts to measure the rate of change of the value of the portfolio for a change in volatility of the underlying assets. Vega neutrality protects against variance in volatility Volatility is merely a term used to describe how fast a stock, futures or index changes with respect to change in the price. Implied volatility (IV) is the option market prediction of volatility of the underlying instrument over the life of the option. Vega quantifies the impact of volatility changes on the price of an option. It tries to predict the extent of rise/fall in option premium for a rise or fall in IV. For example, a vega of 0.9 means that option premium would increase by 0.9 if IV increases by one percentage point.

Option Greeks 163

8.5.2

Hedging Volatility

Volatility exposure is conceptually distinct from price exposure. But in practice, increase in volatility tends to be associated with large price changes. The underlying futures or stocks have zero vega, so taking other options positions can only offset volatility exposure. A vega-neutral portfolio requires a short position in one option to be offset by a long position in another. This is the same as in attaining gamma neutrality. Options writing therefore requires traders to balance price and volatility exposure.

8.5.3

Application of Vega

Vega trade refers to buying cheap options or selling expensive options and holding the position until it returns to a fair valuation level. This is referred to as volatility trading. The IV level of the options helps in determining what strategies are to be used. When IV is very high, the market price of the options will be greater than their theoretical price. Such options are considered 'expensive'. Many traders favor premium-selling strategies. This means that selling long-term options would be a better idea while selling volatility. Long-term options tend to have higher vega. If an option contract with higher vega is sold, this means an expensive option is sold. The period available for the volatility levels to move closer to the normal level is large. So one can wait for it to return to normal levels, by when the option price would have reduced. It can, therefore, be said that option prices are very sensitive to volatility; trading options on this basis can be attractive. However, there is substantial risk of loss in trading if the forecast of the direction is wrong. IV can increase or decrease even without price changes in the underlying security. This is because IV is the level of expected volatility; that is, it is based not on actual prices of the security, but on expected price trends. Generally IV declines as the option gets closer to expiration. The change in volatility becomes less significant with fewer trading days.

8.5.4

Illustration to Find Vega

An investor holds one share of Infosys. The spot price of Infosys shares as on 20 June is Rs. 3500. S/he decides to buy a call option at a strike price of Rs. 3600 for delivery on 19 June, next year. The annual volatility in the stock market is 57.96%. Risk-free interest rate is 7.5%. Calculate vega for call and put. S = 3500 X = 3600 T – t = 1 (June 20 to June 19 next year = 1 year) r = 0.075 (7.5%) s = 0.5796 (57.96%) Vega for call and put = S × N(d1) × Ö (T – t)

164

Option Trading

where d1 = [ln(S/X) + (r + s 2/2) × t]/[s × Ö (T – t)] – d1 = [ln(3500/3600) + (0.075 + 0.57962/2 × 1]/[0.5796 × 1] = [ln(0.9722) + 0.075 + 0.1679]/0.5796 = (– 0.0282 + 0.075 + 0.1679)/0.5796 = 0.2147/0.5796 = 0.3704 Vega = S × N(d1) × Ö (T – t) = 3500 × N(0.3704) × Ö 1 = 3500 × 0.6443 = 2255.05

8.6

THETA

It measures the sensitiveness to the passage of time or the option time value. Theta is represented by the symbol ' È ' and is the negative of the derivative of the option value with respect to the amount of time to expiry of the option instrument. It is an estimate of how much the theoretical value of an option decreases when a day passes and there is no movement in either the stock price or the volatility.

Put/call premium

250 200 150

Call premium Put premium

100 50

29

da 26 y da 23 y d 20 ay da 17 y da 14 y da 11 y da y 8 da y 5 da y 2 da y

0

Expiration

Fig. 8.7 Impact of time value on option premium Theta value for a call option and a put option would not be the same at the same strike and same expiration period. Theta values of call and put options also depend on the cost of carry for the underlying stock. From Fig. 8.7, we can understand that when the days to expiration is more, then the theoretical value of the put option would be higher, but when the expiry comes near, the put option will lose its value.

Option Greeks 165

Table 8.1 Days to expiry

Impact of Time Value on Option Premium

Call premium

Put premium

Call premium change

Put premium change

29

234.99

211.05

28

231.7

207.62

3.29

3.43

27

227.34

204.11

4.36

3.51

26

222.9

200.54

4.44

3.57

25

218.39

196.88

4.51

3.66

24

213.79

193.14

4.6

3.74

23

209.1

189.31

4.69

3.83

22

204.32

185.38

4.78

3.93

21

199.43

181.36

4.89

4.02

20

194.44

177.22

4.99

4.14

19

189.33

172.97

5.11

4.25

18

184.09

168.59

5.24

4.38

17

178.71

164.07

5.38

4.52

16

173.19

159.4

5.52

4.67

15

167.49

154.57

5.7

4.83

14

161.62

149.56

5.87

5.01

13

155.55

144.35

6.07

5.21

12

149.26

138.92

6.29

5.43

11

142.71

133.23

6.55

5.69

10

135.88

127.26

6.83

5.97

9

128.71

120.95

7.17

6.31

8

121.15

114.26

7.56

6.69

7

113.13

107.1

8.02

7.16

6

104.55

99.37

8.58

7.73

5

95.24

90.93

9.31

8.44

4

85

81.55

10.24

9.38

3

73.42

70.83

11.58

10.72

2

59.76

58.03

13.66

12.8

1 day

42.08

41.22

17.68

16.81

0

0

42.08

41.22

On Expiry

166

Option Trading

20 18 16 14 12 10 8 6 4 2 0

29

da 27 ys da ys 25 da y 23 s da y 21 s da 19 ys da y 17 s da 15 ys da y 13 s da y 11 s da y 9 s da ys 7 da ys 5 da ys 3 da ys

Put/call premium change

Table 8.1 shows the changes of 3500 strike call and put options premiums of an underlying stock with 57% IV, 9% interest rate and Rs. 3500 stock price from the first day of the contract month till the last day of expiry, on the assumption that the stock price and IV remain same during this period (Figs. 8.5 and 8.7). The intensity of the time decay is very high when options are close to expiry.

Expiration Call premium change Put premium change

Fig 8.8

Put/Call premium change

Put/call premium

250 200 150

Call premium

100

Put premium

50

26

29

da

ys da y 23 s da y 20 s da y 17 s da y 14 s da y 11 s da ys 8d ay s 5d ay s 2d ay s

0

Expiration

Fig. 8.9

8.6.1

Call and put premium change

Illustration to Find Theta

An investor holds one share of Infosys. The spot price of Infosys shares as on 20 June is Rs. 3500. S/he decides to buy a call option at a strike price of Rs. 3600 for delivery on 19 June, next year. The annual volatility in the stock market is 57.96%. Risk-free interest rate is 7.5%. Calculate theta for call and put.

Option Greeks 167

S = 3500 X = 3600 T – t = 1 (June 20 to June 19 next year = 1 year) r = 0.075 (7.5%) s = 0.5796 (57.96%) d1 = 0.3704 d2 = d1 – [s × Ö (T – t)] = 0.3704 – (0.5796 × 1) = –0.2090 Theta for call = – {[S × N(d1) × s]/[2 × Ö (T – t)]} – [r × X × e–r(T–t) × N(d2)] = – {[3500 × N(0.3704) × 0.5796]/2 × Ö 1} – [0.075 × 3600 × e –0.075×1 × N(–0.2090)] = –[(3500 × 0.6443 × 0.5796)/2] – (0.075 × 3600 × 0.9277 × 0.4168) = – 653.51 – 104.39 = –757.90 Theta for a put = [–S × N(d1) × s] + [r × X × e–r(T–t) × N(d2)] 2 × Ö (T – t) = –[3500 × N(0.3704) × 0.5796] + [0.075 × 3600 × e–0.075×1 × N(0.2090) 2 × Ö1 = –[3500 × 0.6443 × 0.5796] + [0.0750 × 3600 × 0.9277 × 0.5832] 2 = – 653.51 + 146.08 = –507.43

8.7

RHO

It measures the sensitiveness to the applicable interest rate. Rho is the least used Greeks. In an economy when the interest rates are stable, the chance of option value changing dramatically because of a rise in interest rates is low. For example, suppose we expect stock A to rise; we could either buy 100 shares of A for Rs. 5000 or buy two call options of the same stock A for Rs. 500. Here we need to spend 10 times the money that we spend on the stock. It means that we would need to borrow money out of the interest bearing account to buy the stock. This interest cost is built into the call option's value. An increase in interest rates increases the value of calls and decreases the value of puts and vice versa.

168

Option Trading

Suppose the value of call option is Rs. 20 and a rho of 0.02, with value of share A at Rs. 50 and interest rates at 5%. If the interest rate increases to 6%, the value of the stock's call option would increase to Rs. 20.2, and if the interest rate decreases to 4%, then the value of the stock option would decrease to Rs. 21.98. 0.045 0.0445 0.044 0.0435 0.043

Call

0.0425 0.042 0.0415 0.041 0%

5%

10%

15%

20%

25%

Interest rate

Fig. 8.10

Impact of interest rate change on Rho of call option

If the interest rate increases the value of the Rho of both call option and put option will increase. Interest rate 0%

5%

10%

15%

20%

25%

–0.0355 –0.036 –0.0365 –0.037 Rho

–0.0375 –0.038 –0.0385 –0.039 –0.0395 –0.04 –0.0405 Put

Fig. 8.11

Impact of interest rate change on Rho of put option

Option Greeks 169

The basic principle is that when the underlying price of the asset increases, the rho value also rises alongside, and when the underlying price comes down, the rho value also follows suit. One more thing to keep in mind is that when the number of days to expiration is more, then the rho value will also be more.

8.7.1

Illustration to Find Rho

An investor holds one share of Infosys. The spot price of Infosys shares as on June 20 is Rs. 3500. S/he decides to buy a call option at a strike price of Rs. 3600 for delivery on June 19, next year. The annual volatility in the stock market is 57.96%. Risk-free interest rate is 7.5%. Calculate rho for call and put. S = 3500 X = 3600 T – t = 1 (June 20 to June 19 next year = 1 year) r = 0.075 (7.5%) s = 0.5796 (57.96%) d1 = 0.3704 d2 = d1 – [s × Ö (T – t)] = 0.3704 – (0.5796 × 1) = – 0.2090 Rho for a call = X × (T – t) × e–r(T– t) × N(d2) = 3600 × 1 × e–0.075×1 × N(– 0.2090) = 13.91% (rho is expressed in percentage terms) Rho for a put = –[X × (T – t) × e–r(T–t) × N(– d2)] = – [3600 × 1 × e– 0.075×1 × N(0.2090)] = 19.47% (rho is expressed in percentage terms)

Summary Option Greeks form a part of volatility and assumes great importance in formulating trading strategies. The major Option Greeks which are discussed in this chapter are delta, gamma, theta, vega and rho. The other Greeks are Itto's lemma, lambda, kappa, epsilon and so on, which are not discussed in this chapter because they are not widely used in India. We have also found how these Greeks are used in option trading. We will move to the most interesting part of option trading strategies in the next chapter.

Keywords Greeks

Delta

Gamma

Vega

Theta

Rho

170

Option Trading

Appendix Standardized Normal Distribution Table Z

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0.5000 0.5398 0.5793 0.6179 0.6554 0.6915 0.7257 0.7580 0.7881 0.8159 0.8413

0.5040 0.5438 0.5832 0.6217 0.6591 0.6950 0.7291 0.7611 0.7910 0.8186 0.8438

0.5080 0.5478 0.5871 0.6255 0.6628 0.6985 0.7324 0.7642 0.7939 0.8212 0.8461

0.5120 0.5517 0.5910 0.6293 0.6664 0.7019 0.7357 0.7673 0.7967 0.8238 0.8485

0.5160 0.5557 0.5948 0.6331 0.6700 0.7054 0.7389 0.7704 0.7995 0.8264 0.8508

0.5199 0.5596 0.5987 0.6368 0.6736 0.7088 0.7422 0.7734 0.8023 0.8289 0.8531

0.5239 0.5636 0.6026 0.6406 0.6772 0.7123 0.7454 0.7764 0.8051 0.8315 0.8554

0.5279 0.5675 0.6064 0.6443 0.6808 0.7157 0.7486 0.7794 0.8078 0.8340 0.8577

0.5279 0.5675 0.6064 0.6443 0.6808 0.7157 0.7486 0.7794 0.8078 0.8340 0.8577

0.5359 0.5753 0.6141 0.6517 0.6879 0.7224 0.7549 0.7852 0.8133 0.8389 0.8621

1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2

0.8643 0.8849 0.9032 0.9192 0.9332 0.9452 0.9554 0.9641 0.9713 0.9772

0.8665 0.8869 0.9049 0.9207 0.9345 0.9463 0.9564 0.9649 0.9719 0.9778

0.8686 0.8888 0.9066 0.9222 0.9357 0.9474 0.9573 0.9656 0.9726 0.9783

0.8708 0.8907 0.9082 0.9236 0.9370 0.9484 0.9582 0.9664 0.9732 0.9788

0.8729 0.8925 0.9099 0.9251 0.9382 0.9495 0.9591 0.9671 0.9738 0.9793

0.8749 0.8944 0.9115 0.9265 0.9394 0.9505 0.9599 0.9678 0.9744 0.9798

0.8770 0.8962 0.9131 0.9279 0.9406 0.9515 0.9608 0.9686 0.9750 0.9803

0.8790 0.8980 0.9147 0.9292 0.9418 0.9525 0.9616 0.9693 0.9756 0.9808

0.8810 0.8997 0.9162 0.9306 0.9429 0.9535 0.9625 0.9699 0.9761 0.9812

0.8830 0.9015 0.9177 0.9319 0.9441 0.9545 0.9633 0.9706 0.9767 0.9817

2.1 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3

0.9821 0.9893 0.9918 0.9938 0.9953 0.9965 0.9974 0.9981 0.9987

0.9826 0.9896 0.9920 0.9940 0.9955 0.9966 0.9975 0.9982 0.9987

0.9830 0.9898 0.9922 0.9941 0.9956 0.9967 0.9976 0.9982 0.9987

0.9834 0.9901 0.9925 0.9943 0.9957 0.9968 0.9977 0.9983 0.9988

0.9838 0.9904 0.9927 0.9945 0.9959 0.9969 0.9977 0.9984 0.9988

0.9842 0.9906 0.9929 0.9946 0.9960 0.9970 0.9978 0.9984 0.9989

0.9846 0.9909 0.9931 0.9948 0.9961 0.9971 0.9979 0.9985 0.9989

0.9850 0.9911 0.9932 0.9949 0.9962 0.9972 0.9979 0.9985 0.9989

0.9854 0.9913 0.9934 0.9951 0.9963 0.9973 0.9980 0.9986 0.9990

0.9857 0.9916 0.9936 0.9952 0.9964 0.9974 0.9981 0.9986 0.9990

CHAPTER

09

OPTION TRADING STRATEGIES

9.1

OBJECTIVES

In Chapter 2, we discussed about writing options, and in the previous two chapters, we discussed about volatility and Greek letters which are used in option trading. Having understood these essential aspects of trading in options, we are now taking the readers to the most important and interesting part of this book, that is option trading strategies. These strategies have been found to be highly effective from our trading experiences. The objective of this chapter is to introduce various strategies to the readers so that they will be enthused to develop their own strategies.

9.2

INTRODUCTION

Simple trading strategies can be created by option purchases or by option sales. Purchases and sales of different options at different strikes and different maturities enable us to create complex strategies. Strategies help us to predict the future profits and losses. It also helps to convert from one strategy to other strategies according to different market conditions. For example, buying a call option can be riskier because of time-sensitive nature of option and that can be partially eliminated by selling a call option of the same underlying asset at a higher strike price.

9.3

ADVANTAGES OF STRATEGIES

1. Creating strategies for directional view (a) Maximizing return (b) Reducing risk 2. Trading on volatility 3. Exploiting arbitrage opportunities 4. Creating cash inflow strategies by writing options 5. Hedging risk

172

Option Trading

9.4 BUYING PUT OPTION Buying put option is the simplest strategy one can adopt in a bear market. The maximum loss in this strategy is the premium paid for the purchase of the put option. For example, Mr. B is bearish on market and buys Nifty put option at a strike price of 3000 for a premium of Rs. 100. Mr. B will make profit, if Nifty falls below 2900 (3000 – 100). If Nifty does not move below 2900, he will incur a loss of Rs. 100.

9.4.1

Long Put

In the following example, we will examine how the put option purchase will be profitable. Here, Mr. B buys a put option of Infosys at a strike price of 1060 for a premium of Rs. 25.90 and a spot at 1060. In Table 9.1, one can find out profits and losses on various closing prices on expiry. For example, if Infosys closes at 980 on expiry, the investor will make a profit of Rs. 54.10. His break even point (BEP) will be Rs. 1034.10. Maximum loss is limited to Rs. 25.90 (Fig. 9.1). Table 9.1 Pay off of Long put Long put

1060

Premium

25.90

Price at expiration 980 990 1000 1010 1020 1030 1040 1050 1060 1070 1080 1090 1100

Pay off long put 54.1 44.1 34.1 24.1 14.1 4.1 – 5.9 – 15.9 – 25.9 – 25.9 – 25.9 – 25.9 – 25.9

The major issues confronted in the purchase of put options are high volatility, time decay and liquidity. Sometimes, in-the-money put options are fairly illiquid due to higher premiums. In a highly volatile market, premiums of put options tend to remain very high, thereby causing illiquidity.

Option Trading Strategies 173

Long put 200

1140

1040

1020

1000

–50

980

0

1120

1060

50

1100

100

1080

Profit

150

Loss

–100 –150 –200

Strike price Long put

Fig. 9.1

9.4.2

Long Put

Short Call

If the investor is bearish, then instead of buying the put option of Nifty, he can write the call option of Nifty. (In the following example (Table 9.2], we have included the Nifty strike prices and its premiums as on 11 July 2008 for your reference. The spot Nifty trades at 4162.) Table 9.2 Strikes and Premium Strike

Premium

3700 3800 3900 4000 4050 4100 4150 4200 4250 4300 4350 4400 4450 4500

471 360 312 240 199 175 146 119 95 74 58 42 22 18

174

Option Trading

Assume that you are bearish on Nifty and according to your estimate, Nifty may find support only at the 3700 level. In this case, instead of buying the put option, you can sell 3700 call options at Rs. 471. If Nifty falls below 3700, you don’t gain more than the premium of Rs. 471. There is unlimited risk above 4171 (3700 + 471) (Table 9.3; Fig. 9.2). Table 9.3 Index at expiration

Short Call Short call pay off (3700)

3300 3400 3500 3600 3700 3800 3900 4000 4100 4200 4300 4400 4500 4600 4700 4800 4900

471 471 471 471 471 371 271 171 71 – 29 – 129 – 229 – 329 – 429 – 529 – 629 – 729

Short call pay off (3700) 600

Profit/Loss

400 200 0 –200

0

1000

2000

3000

–400 –600 –800

Nifty

Fig. 9.2

4000

5000

6000

Option Trading Strategies 175

9.5 BEAR SPREAD WITH PUTS This strategy is good when the market remains in a range with downward bias. For example, Nifty is expected to remain at the 4000–3800 level. The current Nifty is at 4020. One can buy Nifty 4000 put options and can sell 3800 put options. This strategy is suited to save the time value. If you are buying the 4000 puts at Rs. 122 on the first day of the contract and the Nifty falls down below 4000 after the 10th day, your put option can lose some value due to time decay. If you are writing a simultaneous put at 3800 at Rs. 83, then loss in time value can be reduced up to a certain extent (Table 9.4; Fig. 9.3). Table 9.4 Pay off of Bear Spread with Puts

Long put Written put Index at expiration

Strike

Premium

4000 3800

122.00 83.00

Pay off long put (4000)

3300 3400 3500 3600 3700 3800 3900 4000 4100 4200 4300 4400

578.00 478.00 378.00 278.00 178.00 78.00 – 22.00 – 122.00 – 122.00 – 122.00 – 122.00 – 122.00

Pay off sell put (3800)

Total pay off

– – – –

161.00 161.00 161.00 161.00 161.00 161.00 61.00 – 39.00 – 39.00 – 39.00 – 39.00 – 39.00

417.00 317.00 217.00 117.00 – 17.00 83.00 83.00 83.00 83.00 83.00 83.00 83.00

250.00 200.00 150.00 100.00 50.00 0.00 –50.00

3600

3700

3800

3900

4000

4100

4200

–100.00 –150.00

Sell Put

Net Pay off

Fig. 9.3 Bear spread with puts

Buy Put

4300

176

Option Trading

After creating the bear spread with put strategy, your maximum loss has decreased from Rs. 122 to Rs. 39. Again, you are attaining the BEP at 3961. On the other hand, if you are holding the single put, then you may attain the breakeven only below 4000 – 122 = 3878.

9.5.1

Bear Spread with Puts: Its Importance

As days pass by, more and more market players are participating in the options segment. They are attracted because of its cost-effectiveness. When we examine carefully, we observe that most of the market players are still reluctant to adopt simple strategies by which they can reduce the cost of acquisition of options. An investor who is bullish on an underlying stock normally buys a call option. In the same way, if he is bearish he will buy a put option. If he buys these in the early days of the contract month, then he has to pay high premiums for its time value. Buying a single call or put option is riskier due to time sensitiveness of options. If the underlying stock is not moving according to the investor’s calculations at the earliest, then the investor may lose the entire premium. Instead of buying an expensive put option, a ‘bear spread with puts’ can be created by buying put options at a higher strike rate and writing put options at lower strike rate having same maturity of the underlying stock. Hence, limited profits and limited losses can be realized. Let us take an example of Mr. B, who is bearish on TISCO, when the spot price of TISCO is trading at Rs. 300 on 1 March, and who expects a price fall towards Rs. 280 by the end of March. In normal case, Mr. B may buy a put option at the strike rate of 300 at a premium of Rs. 8, and he can make a profit of Rs. 12 (i.e. 300 – (280 + 8)) if the underlying stock closes at Rs. 280 on the expiry. But if it stays flat, as time passes, the put option premium will also fall and Mr. B will lose the entire premium of Rs. 4800 (Rs. 8 × 600), where 600 is the lot size. To avoid this, Mr. B can buy the put option with a strike rate of 300 for Rs. 8 and at the same time he can write (sell) a put option with a strike rate of 280 at a premium of Rs. 3, and there he can reduce the cost to Rs. 5 (8 – 3). Thus, he will make a maximum profit of Rs. 9000 ([(300 – 280) – 5] × 600). On the other hand, if TISCO closes at around Rs. 300, Mr. B’s loss is limited to Rs. 3000 ((8 – 3) × 600). Why are investors reluctant to apply this strategy in the day-to-day market conditions? The answer is simple: additional margin requirements. In the previous example, if an investor buys a single put option, then the margin requirement is only the premium, that is Rs. 4800. If the investor is creating a bear spread with puts, then the margin requirement will be around Rs. 15,000 (i.e., an additional amount of Rs. 10,200). An investor who thinks various ways to reduce risk can adopt this strategy, whereas a risk taker can buy single put options and can try his luck.

Option Trading Strategies 177

9.6

LONG PUT RATIO SPREAD

This is a bearish strategy implemented through selling one lot of Nifty put option (probably selling one lot of out-of-the-money put and buying two lots of deep out-of-the-money put at the same strike and same expiration). Selling a put and buying two lots of puts normally ends with a small premium credit, without considering the direction of the market (Table 9.5; Fig. 9.4). Table 9.5

Pay off of 2 :1 Long Put Ratio Spread Strike

Premium

Sell put

250

12.86

Buy two puts

230

5.35

Spot at expiration

Sell put (250) pay off

Buy two puts (230) pay off

Net pay off

190

– 47.14

69.3

22.16

195

– 42.14

59.3

17.16

200

– 37.14

49.3

12.16

205

– 32.14

39.3

7.16

210

– 27.14

29.3

2.16

215

– 22.14

19.3

– 2.84

220

– 17.14

9.3

– 7.84

225

– 12.14

– 0.7

– 12.84

230

– 7.14

– 10.7

– 17.84

235

– 2.14

– 10.7

– 12.84

240

2.86

– 10.7

– 7.84

245

7.86

–10.7

–2.84

250

12.86

–10.7

2.16

255

12.86

–10.7

2.16

260

12.86

–10.7

2.16

265

12.86

–10.7

2.16

270

12.86

–10.7

2.16

275

12.86

–10.7

2.16

178

Option Trading

80

60

40

285

280

575

270

265

260

255

245

250

235

230

220

225

210

215

205

200

195

0

190

Pay off

20

–20

–40

–60 Buy 2 puts 230

Sell put 250

Fig. 9.4

9.7

Net pay off

Long 2/1 put ratio spread

BEAR SPREAD WITH CALL

This is another trading strategy for persons who have bearish attitude. Earlier, we had discussed the naked call writing in a bearish market. Here, one has to sell an in-the-money call option and simultaneously buy an outof-the-money call option of an underlying stock with the same maturity. Writing call option is riskier, because if the market moves up sharply against your expectation, you may incur huge loss. On the other hand, buying outof-the-money calls gives the seller protection. In fact, this trading strategy is the extension of writing call option. Here, the risk appetite of an investor is less (Table 9.6; Fig. 9.5). Table 9.6

Sell call Sell call

Pay off of Bear Spread with Call Strike

Premium

260 240

8 20 (Contd.)

Option Trading Strategies 179 Spot at expiration 190 200 210 220 230 240 250 260 270 280 290 300 310 320 330

Long (260) call –8 –8 –8 –8 –8 –8 –8 –8 2 12 22 32 42 52 62

Short (240) call 20 20 20 20 20 20 10 0 –10 –20 –30 –40 –50 –60 –70

Pay off 12.00 12.00 12.00 12.00 12.00 12.00 2.00 –8.00 –8.00 –8.00 –8.00 –8.00 –8.00 –8.00 –8.00

50 40

20

1130

1120

1110

1100

1090

1080

1070

1060

1050

1040

-10

1030

0

1020

10 1010

LOSS & PROFIT

30

-20 -30 -40 -50 Long 1080 Call

Short 1070

Pay off

Fig. 9.5 Bear spread with calls

9.8

SYNTHETIC SHORT

Traders with strong bearish mentality adopt this strategy. Both buying a put and selling a call are bearish strategies. If an investor uses this combination of buying puts and selling calls, he can get synthetic short. This is as good as shorting the futures. Buying put gives a breakeven only after the asset’s price

180

Option Trading

falls at least by the premium. On the other hand, writing a call at the same strike and same maturity allows the trader to get an early breakeven through getting premium of the written call. Both buying puts and selling calls generate negative deltas. Investors will select various strike prices according to their risk appetite (Table 9.7; Fig. 9.6). Table 9.7

Buy put Sell call Stock at expiration 480 530 580 630 680 730 780 830 880 930 980 1030 1080 1130

Strike

Premium

980 980

7.2 9.45

Short call (980) 9.45 9.45 9.45 9.45 9.45 9.45 9.45 9.45 9.45 9.45 9.45 – 40.55 – 90.55 –140.55

Long put (980) 492.80 442.80 392.80 342.80 292.80 242.80 192.80 142.80 92.80 42.80 – 7.20 – 7.20 – 7.20 – 7.20

Net Pay off 502.25 452.25 402.25 352.25 302.25 252.25 202.25 152.25 102.25 52.25 2.25 – 47.75 – 97.75 – 147.75

80 Profit

60 40 20 0

Short call 980

Long put 980

Net pay off

Fig. 9.6

Synthetic short futures

Loss

1130

– 80

1080

– 60

1030

– 40

980

930

880

830

780

730

680

630

580

530

480

– 20

Option Trading Strategies 181

9.9

SHORT PUT LADDER

Short put ladder can be created by buying a put option at a higher strike price and again one more put option at an equally higher strike price and selling a put option at an equally higher strike price. The buyer is anticipating a sharp fall in the underlying.

9.9.1

Relevance of ‘Short Put Ladder’ in a Bearish Market Scenario

An investor who is cautious and also thinks that the market may fall from the current level can buy put options. Both call options and put options are now being traded at higher premiums due to high volatility persisting in the market. On 20 October, Nifty closed at 1537 and a 1530 put option was priced at a premium of Rs. 17.40. An investor who buys a 1530 put option will get profit if Nifty closes below 1512.60 at month end. On the other hand, if Nifty closes above 1530, he may lose his entire premium. At this point, creating a short put ladder on Nifty is advisable. A short put ladder position can be created in the following way: writing a put option at a higher strike rate and simultaneously buying two put options at equally distant lower strike rates. For example, writing October 1530 put options for Rs. 17.40 and buying two put options of 1520 and 1510 for a premium of Rs. 14.80 and Rs. 11.70, respectively, has advantage, because even if the market closes above the 1530 level, the investor may lose only Rs. 9.10. On the other hand, downside breakeven can be attained at the level of 1489.10. But, if the market closes at 1520, the investor may suffer a loss of Rs. 19.10 (Table 9.8; Fig. 9.7). Table 9.8 Strike

Premium

Buy put Buy put Sell put

1510 1520 1530

11.7 14.8 17.4

Nifty at expiration 1450 1460 1470 1480 1490 1500 1510

Buy put (1510) 48.30 38.30 28.30 18.30 8.30 –1.70 –11.70

Buy put (1520) 55.20 45.20 35.20 25.20 15.20 5.20 –4.80

Sell put (1530) –62.60 –52.60 –42.60 –32.60 –22.60 –12.60 –2.60

Pay off

1520 1530 1540 1550

–11.70 –11.70 –11.70 –11.70

–14.80 –14.80 –14.80 –14.80

7.40 17.40 17.40 17.40

–19.10 –9.10 –9.10 –9.10

40.90 30.90 20.90 10.90 0.90 –9.10 –19.10

182

Option Trading Short put ladder 70 60 50 40 20 10 1550

1530

1520

1510

1500

1490

1480

1470

1460

0 –10

1450

Loss Profit

30

–20 –30 –40 –50 –60 –70 Buy put 1510

Buy put 1520

Sell put

Payoff

Fig. 9.7 Short put ladder Investors can select the strike prices according to prevailing market situations and put option premiums.

9.10

LONG COMBO

This strategy can be created by selling the call (out-of-the-money) at 1100 for Rs. 30.75 and buying a 1080 put option (at-the-money) at Rs. 11 (Table 9.9; Fig. 9.8). Table 9.9 Strike Sell call Buy put Strike at expiration 1050 1055 1060 1065 1070 1075 1080

Sell call (1100) 30.75 30.75 30.75 30.75 30.75 30.75 30.75

1100 11080

Premium 30.75 11.00 Buy put (1080) 19 14 9 4 –1 –6 – 11

Pay off 49.75 44.75 39.75 34.75 29.75 24.75 19.75

Option Trading Strategies 183 Strike at expiration 1085 1090 1095 1100 1105 1110 1115 1120 1125 1130 1135 1140 1145 1150

Sell call (1100) 30.75 30.75 30.75 30.75 25.75 20.75 15.75 10.75 5.75 0.75 – 4.25 – 9.25 – 14.25 – 19.25

Buy put (1080) – 11 – 11 – 11 – 11 – 11 – 11 – 11 – 11 – 11 – 11 – 11 – 11 – 11 – 11

60

Pay off 19.75 19.75 19.75 19.75 14.75 9.75 4.75 – 0.25 – 5.25 – 10.25 – 15.25 – 20.25 – 25.25 – 30.25

Sell call Buy put Net cash flow

50 40 30 20

1150

1140

1130

1120

1110

1100

1090

1080

1070

–10

1060

0

1050

10

–20 –30 –40

Fig. 9.8

9.11

Long combo

LONG CALL CHRISTMAS TREES

This strategy will give an investor unlimited loss once it has breached the BEP levels. On the other hand, profit will be very low. The out-of-the-money calls will have high implied volatility due to various reasons. One can buy at-the-money call and can sell two calls at a higher strike price with equal intervals on the same expiry and same underlying stock.

184

Option Trading

Generally, investors create a delta hedge strategy with long call Christmas tree. Buying an in-the-money call option (0.75 delta) and selling two lots of out-of-the-money call (0.25 × 2), the net delta position can be positive (0.25). This type of reduction in deltas is normally risk-free up to a certain extent, but one should be very careful about the net gamma positions. Buy one lot of call at 1030 for Rs. 27.30 and sell 1040 calls at Rs. 21.65 and sell 1050 calls at Rs. 16.65 (Table 9.10; Fig. 9.9). Table 9.10 Strike Buzy call Sell call Sell call

Spot at expiration

Long call (1030)

960

1030 1040 1050

Premium 27.3 21.65 16.65

Sell call (1040)

Sell call (1050)

Net pay off

– 27.3

21.65

16.65

11

970

– 27.3

21.65

16.65

11

980

– 27.3

21.65

16.65

11

990

– 27.3

21.65

16.65

11

1000

– 27.3

21.65

16.65

11

1010

– 27.3

21.65

16.65

11

1020

– 27.3

21.65

16.65

11

1030

– 27.3

21.65

16.65

11

1040

– 17.3

21.65

16.65

21

1050

– 7.3

11.65

16.65

21

1060

2.7

1.65

6.65

11

1070

12.7

– 8.35

–3.35

1

1080

22.7

– 18.35

– 13.35

–9

1090

32.7

– 28.35

– 23.35

– 19

1100

42.7

– 38.35

– 33.35

– 29

1110

52.7

– 48.35

– 43.35

– 39

1120

62.7

– 58.35

– 53.35

– 49

1130

72.7

– 68.35

– 63.35

– 59

1140

82.7

– 78.35

– 73.35

– 69

Option Trading Strategies 185

50 40

20

1140

1130

1120

1110

1100

1080

1090

1070

1060

1050

1040

1030

1020

1010

990

980

970

0 –10

1000

10 960

Loss & Profit

30

–20 –30 –40 –50 Long 1030

Fig. 9.9

9.12

Sell 1040

Sell 1050

Net pay off

Long call Christmas tree

SHORT PUT ALBATROSS

Limited risk and limited profits are the main attractions of this strategy. The investor here expects a sharp movement, in either direction. But if the movement is flat during the expiry, then one may incur minimum loss. This strategy is commonly adopted in European options. Sell 1150 puts at Rs. 1.2 and buy 1160 puts at Rs. 2.95 and buy 1180 puts at Rs. 9.05 and sell 1190 puts at Rs. 16.05 (Table 9.11; Fig. 9.10). Table 9.10 Strike 1150 1160 1180 1190

Sell put Buy put Buy put Sell put Spot at expiration 1100 1105 1110 1115 1120

Sell put (1150) – 48.8 – 43.8 – 38.8 – 33.8 – 28.8

Buy put (1160) 57.05 52.05 47.05 42.05 37.05

Premium 1.20 2.95 9.05 16.05

Buy put (1180) 70.95 65.95 60.95 55.95 50.95

Sell put (1190) –73.95 –68.95 –63.95 –58.95 – 53.95

Net pay off 5.25 5.25 5.25 5.25 5.25

186

Option Trading

Spot at expiration 1125 1130 1135 1140 1145 1150 1155 1160 1165 1170 1175 1180 1185 1190 1195 1200 1205 1210

Sell put (1150) – 23.8 – 18.8 – 13.8 – 8.8 – 3.8 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

Buy put (1160) 32.05 27.05 22.05 17.05 12.05 7.05 2.05 – 2.95 – 2.95 – 2.95 – 2.95 – 2.95 – 2.95 – 2.95 – 2.95 – 2.95 – 2.95 – 2.95

Buy put (1180) 45.95 40.95 35.95 30.95 25.95 20.95 15.95 10.95 5.95 0.95 – 4.05 – 9.05 – 9.05 – 9.05 – 9.05 – 9.05 – 9.05 – 9.05

Sell put (1190) – 48.95 – 43.95 – 38.95 – 33.95 – 28.95 – 23.95 – 18.95 – 13.95 – 8.95 – 3.95 1.05 6.05 11.05 16.05 16.05 16.05 16.05 16.05

Net pay off 5.25 5.25 5.25 5.25 5.25 5.25 0.25 – 4.75 – 4.75 – 4.75 – 4.75 – 4.75 0.25 5.25 5.25 5.25 5.25 5.25

60

40

Payoff

20

0 1120

1140

1160

1180

1200

1220

–20

–40

–60 Sell put 1150

Buy put 1160

Sell put 1190

Net pay off

Fig. 9.10

Short put albatross

Buy put 1180

Option Trading Strategies 187

9.13

SHORT STRADDLE VERSUS PUT

Here, the investor expects a stable market where volatility is expected to decrease. But he fears a possible fall in the market below a certain extent. It is a combination of short straddle (selling both call option and put option at the same strike rate and at the same maturity of a same underlying stock) and a long put. The profit is limited below a certain extent. But there is always a risk of shorting the call. This risk can be eliminated by going long in Nifty futures when the underlying moves above the upside BEP (Table 9.12; Fig. 9.11). Table 9.12 Strike

Stock at expiration

Sell call Sell put Buy put

220 220 200

Sell call (220)

Sell put (220)

Premium 26.65 6.92 2.75

Buy put (200)

Net pay off

– 130

26.65

– 343.08

327.25

10.82

– 80

26.65

– 293.08

277.25

10.82

– 30

26.65

– 243.08

227.25

10.82

20

26.65

– 193.08

177.25

10.82

70

26.65

– 143.08

127.25

10.82

120

26.65

– 93.08

77.25

10.82

170

26.65

– 43.08

27.25

10.82

220

26.65

6.92

– 2.75

30.82

270

– 23.35

6.92

– 2.75

– 19.18

320

– 73.35

6.92

– 2.75

– 69.18

370

– 123.35

6.92

– 2.75

– 119.18

420

– 173.35

6.92

– 2.75

– 169.18

470

– 223.35

6.92

– 2.75

– 219.18

520

– 273.35

6.92

– 2.75

– 269.18

570

– 323.35

6.92

– 2.75

– 319.18

620

– 373.35

6.92

– 2.75

– 369.18

188

Option Trading

400 300 200

620

570

520

470

420

370

0

270

220

170

70

20

30

120

–100

80

0 130

Pay off

100

–200 –300 –400 –500 Sell call 220

Sell put 220

Buy put 200

Total pay off

Fig. 9.11 Short straddle vs put

9.14

SHORT STRIP WITH CALLS

This is a high-risk strategy with limited profit and unlimited loss. This bearish strategy is normally used in a bearish market, or it can be used just few days prior to the expiry. Here, the investor is confident that the underlying stock will not move above the strike prices. Normally, calls are to be sold at higher strike price with equal intervals with the same expiry and with the same underlying stock (Table 9.13; Fig. 9.12). Table 9.13 Strike Sell call Sell call Sell call

200 210 220

Premium 41.5 32.25 24.55 (Contd.)

Option Trading Strategies 189 Stock at expiration 140 150 160 170 180 190

Sell call (200) 41.5 41.5 41.5 41.5 41.5 41.5

Sell call (210) 32.25 32.25 32.25 32.25 32.25 32.25

Buy call (220) 24.55 24.55 24.55 24.55 24.55 24.55

200 210 220 230 240 250 260 270 280 290 300

41.5 31.5 21.5 11.5 1.5 – 8.5 – 18.5 – 28.5 – 38.5 – 48.5 – 58.5

32.25 32.25 22.25 12.25 2.25 – 7.75 – 17.75 – 27.75 – 37.75 – 47.75 – 57.75

24.55 24.55 24.55 14.55 4.55 – 5.45 –15.45 – 25.45 – 35.45 – 45.45 – 55.45

Net pay off 98.3 98.3 98.3 98.3 98.3 98.3 98.3 88.3 68.3 38.3 8.3 – 21.7 – 51.7 – 81.7 – 111.7 – 141.7 – 171.7

150 100 50

300

290

270

280

260

250

240

230

220

210

200

190

180

170

150

160

–50

140

Payoff

0

–100 –150 –200

Sell call 200

Sell call 220

Sell call 210

Total pay off

Fig. 9.12

Short strip with calls

9.15 LONG GUTS This strategy is similar to long strangles strategy. The only difference is that long guts strategy is constructed by the purchase of a long call and a long put at different strike prices. Risk is limited, but a sudden surge in the underlying asset price is required, along with a rise in implied volatility (Table 9.14; Fig. 9.13).

Option Trading

Table 9.14

Buy call Buy put Spot at expiration 180 185 190 195 200 205 210 215 220 225 230 235 240 245 250 255 260 265 270 275 280

Strike

Premium

220 240

22.1 16.85

Buy call (220) – 22.1 – 22.1 – 22.1 – 22.1 – 22.1 – 22.1 – 22.1 – 22.1 – 22.1 – 17.1 – 12.1 – 7.1 – 2.1 2.9 7.9 12.9 17.9 22.9 27.9 32.9 37.9

Buy put (240) 43.15 38.15 33.15 28.15 23.15 18.15 13.15 8.15 3.15 – 1.85 – 6.85 – 11.85 – 16.85 – 16.85 – 16.85 – 16.85 – 16.85 – 16.85 – 16.85 – 16.85 – 16.85

Net pay off 21.05 16.05 11.05 6.05 1.05 – 3.95 – 8.95 – 13.95 – 18.95 – 18.95 – 18.95 – 18.95 – 18.95 – 13.95 – 8.95 – 3.95 1.05 6.05 11.05 16.05 21.05

50 40 30 20 10

–20 –30 Buy call 220

Fig. 9.13

Buy put 240

Long Puts

Total pay off

280

270

275

260

265

250

255

245

235

240

225

230

215

220

205

210

195

200

185

–10

190

0 180

Pay off

190

Option Trading Strategies 191

9.16

LONG CALL LADDER

Buying in-the-money call option and selling two lots of call options at higher strike prices of equal intervals is known as long call ladder. This is a bearish strategy (Table 9.15; Fig. 9.14). Table 9.15 Strike

Spot at expiration

Sell call Sell call Buy call

1080 1070 1060

Sell call (1080)

Sell call (1070)

Premium 4.05 6.25 10.15

Buy call (1060)

Net pay off

1045

4.05

6.25

– 10.15

0.15

1050

4.05

6.25

– 10.15

0.15

1055

4.05

6.25

– 10.15

0.15

1060

4.05

6.25

– 10.15

0.15

1065

4.05

6.25

– 5.15

5.15

1070

4.05

6.25

– 0.15

10.15

1075

4.05

1.25

4.85

10.15

1080

4.05

– 3.75

9.85

10.15

1085

–0.95

– 8.75

14.85

5.15

1090

– 5.95

– 13.75

19.85

0.15

1095

– 10.95

– 18.75

24.85

– 4.85

1100

– 15.95

– 23.75

29.85

– 9.85

1105

– 20.95

– 28.75

34.85

– 14.85

1110

– 25.95

– 33.75

39.85

– 19.85

1115

– 30.95

– 38.75

44.85

– 24.85

1120

– 35.95

– 43.75

49.85

– 29.85

9.17 LONG IRON BUTTERFLY The strategy is a combination of synthetic long and a synthetic short. The holder of this position expects a sharp move in either direction and benefits from an increase in volatility (Table 9.16; Fig. 9.15).

192

Option Trading 40 30 20

1120

1115

1110

1105

1095

1090

1085

1080

1075

1070

1070

1065

1060

–10

1055

0 1050

Loss/Profit

10

–20 –30 –40 Sell call 1080

Sell call 1070

Fig. 9.14

Buy call 1060

Pay off

Long call ladder

Table 9.16 Strike 1040 1050 1050 1060

Short put Long call Long put Short call Spot at expiration 970 980 990 1000 1010 1020 1030 1040 1050 1060 1070 1080

Long call (1050) – 18.65 – 18.65 – 18.65 – 18.65 – 18.65 – 18.65 – 18.65 – 18.65 – 18.65 – 8.65 1.35 11.35

Long put (1050) 62.95 52.95 42.95 32.95 22.95 12.95 2.95 – 7.05 – 17.05 – 17.05 – 17.05 – 17.05

Short put (1040) –56.00 –46.00 –36.00 –26.00 –16.00 –6.00 4.00 14.00 14.00 14.00 14.00 14.00

Premium 14.00 18.65 17.05 15.00 Short call (1060) 15 15 15 15 15 15 15 15 15 15 5 –5

Net pay off 3.30 3.30 3.30 3.30 3.30 3.30 3.30 3.30 – 6.70 3.30 3.30 3.30 (Contd.)

Option Trading Strategies 193 Spot at expiration 1090 1100 1110 1120 1130 1140

Long call (1050) 21.35 31.35 41.35 51.35 61.35 71.35

Long put (1050) – 17.05 – 17.05 – 17.05 – 17.05 – 17.05 – 17.05

Short put (1040) 14.00 14.00 14.00 14.00 14.00 14.00

Short call (1060) – 15 – 25 – 35 – 45 – 55 – 65

Net pay off 3.30 3.30 3.30 3.30 3.30 3.30

80

60

40

1140

1130

1120

1110

1100

1090

1080

1070

1060

1040

1030

1020

1010

1000

990

980

0 970

Loss & Profit

20

–20

–40

–60

–80 Long call

Long put

Short put

Short call

Net pay off

Fig. 9.15 Long iron butterfly

9.18

LONG PUT SPREAD VERSUS SHORT CALL

Here, a trader takes a bear spread with puts and in addition s/he sells a call option. This is called long put spread versus short call. S/he expects the market to fall and also expects a decline in volatility (Table 9.17; Fig. 9.16).

194

Option Trading

Table 9.17 Strike

Spot at expiration 200 205 210 215 220 225 230 235 240 245 250 255 260 265 270 275

Buy put Sell put Sell call

240 230 250

Buy put (240) 28.17 23.17 18.17 13.17 8.17 3.17 – 1.83 – 6.83 – 11.83 – 11.83 – 11.83 – 11.83 – 11.83 – 11.83 – 11.83 – 11.83

Sell put (230) – 21.97 – 16.97 – 11.97 – 6.97 – 1.97 3.03 8.03 8.03 8.03 8.03 8.03 8.03 8.03 8.03 8.03 8.03

Premium 11.83 8.03 14.57 Sell call (250) 14.57 14.57 14.57 14.57 14.57 14.57 14.57 14.57 14.57 14.57 14.57 9.57 4.57 – 0.43 – 5.43 – 10.43

Net pay off 20.77 20.77 20.77 20.77 20.77 20.77 20.77 15.77 10.77 10.77 10.77 5.77 0.77 – 4.23 – 9.23 – 14.23

40 30

–10 –20 –30

Buy put 240

Sell put 230

Sell call 250

Net pay off

Fig. 9.16 Long put spread vs call

275

270

265

255 260

250

245

235 240

230

225

215 220

210

0

205

10

200

Pay off

20

Option Trading Strategies 195

9.19 9.19.1

BASIC OPTION STRATEGIES Long Strangles

Long strangle is normally used when the market volatility is expected to increase and when a large move on either direction is expected. Smart traders normally buy call options above the resistance level and put options below the support level. A rise or fall below the support and resistance levels can cause one-sided profit, which in turn gives the trader a handful of profit. Maximum loss is limited to the premium paid (Table 9.18; Fig. 9.17). Table 9.18

Buy call Buy put Spot at expiration 950.00 960.00 970.00 980.00 990.00 1000.00 1010.00 1020.00 1030.00 1040.00 1050.00 1060.00 1070.00 1080.00

9.19.2

Buy call (1050) – 20 – 20 – 20 – 20 – 20 – 20 – 20 – 20 – 20 – 20 – 20 – 10 0 10

Strike

Premium

1050 1000

20 21 Buy put (1000) 29 19 9 –1 – 11 – 21 – 21 – 21 – 21 – 21 – 21 – 21 – 21 – 21

Net pay off 9.00 – 1.00 – 11.00 – 21.00 – 31.00 – 41.00 – 41.00 – 41.00 – 41.00 – 41.00 – 41.00 – 31.00 – 21.00 – 11.00

Short Strangles

This strategy is exactly the opposite of long strangles. If a trader believes that the market may not break the support and resistance points, but will remain in a range till expiry, then short strangle is the most suitable strategy. Sometimes volatility traders sell both the call and put options in anticipation of lower volatility. The risk is unlimited, but gains are limited to the extent of the premium of both the call and the put (Table 9.19; Fig. 9.18).

Option Trading

60

40

120.00

1 110.00

1 100.00

1 090.00

1 080.00

1 070.00

050.00

1 060.00

1 040.00

1 030.00

1 020.00

1 010.00

990.00

1 000.00

980.00

970.00

–20

960.00

0

950.00

20

Pay off

196

–40

–60 Buy call =

Fig. 9.17

Buy put =

Net pay off

Long strangle

Table 9.19 Short Strangle

Short call Short put Spot at expiration 4250 4300 4350 4400 4450 4500 4550 4600 4650 4700 4750 4800

Short call (4500) 50 50 50 50 50 50 0 –50 –100 –150 –200 –250

Strike

Premium

4500 4400

50 10 Short put (4400) -140 -90 -40 10 10 10 10 10 10 10 10 10

Net pay off –90 –40 10 60 60 60 10 –40 –90 –140 –190 –240

Option Trading Strategies 197

100

Profit & Loss

50 0 4200 –50

4300

4400

4500

4600

4700

4800

4900

–100 –150 –200 –250 –300 Strike price Short call

Fig. 9.18

Short put

Net pay off

Short strangle

9.20 TRADING STRATEGY ADOPTED BY NICK LEESON 27 February 1995 was a black day for the 233-year-old London-based Bearings Bank. Bearings were the personal bank of Her Majesty Queen Elizabeth and were performing well. Chairman Peter Bearing, still holding the confession note written to him by the ‘Rogue Trader’ Nick Leeson, could not still believe that the bank which was founded by his forefather Francis Bearing in 1762 became bankrupt. Over 1200 employees of the bank became jobless consequent to the winding up. He recollected how Leeson became the blue-eyed boy of the top brasses of Bearings. Born in a working-class family on 29 February 1967, Nicholas William Leeson was a poor performer in his school. His father was a plasterer. He failed in his final mathematics examination and left the school with a mere handful of qualifications. He started his banking career as a clerk with Royal Bank Coutts in 1980 and also worked with some other banks before taking up his employment with Bearings. Leeson could very easily win the favour of his bosses in Bearings and was soon promoted to the trading floor. In 1992, Leeson was appointed the manager of the Singapore arm of Bearings and was in charge of a new operation in futures markets on the Singapore International Monetary Exchange (SIMEX). He was the chief trader as well as was responsible for settling his trades. Normally, a trader is not allowed to place his hands on the back-office operations, but Leeson enjoyed this privilege because of the confidence he instilled among his superiors. This placed Leeson in the position of reporting to an office inside Bearings Bank

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which he himself held. His bosses back in London viewed with glee the millions of profits brought to the bank by Leeson by betting on future direction of the Nikkei 225 Index. Leeson and his wife Liza enjoyed their exotic life with a fat salary of £50,000, bonuses up to £150,000, a smart flat, frequent parties and weekends in exotic places—all gracefully given to him by his employer. Leeson started by buying and selling the simplest kind of derivatives futures pegged to Nikkei 225, the Japanese equivalent of UK’s FTSE 100. At the time the trader only had to put down a small percentage of the amount that was being traded; it was therefore easily possible for the money on the table to exceed the losses by many times. However, to Bearings chief executives, Leeson seemed to be infallible. By the end of 1993, he had made more than £10 million—about 10% of total profit that year. Leeson took advantage of the overconfidence reposed by his superiors in him and opened a secret account 88888, better known as ‘five-eights account’ to hide his losses. This account was initially opened to cover up a mistake made by an inexperienced team member, which led to a loss of £20,000. He also succeeded in making his bosses believe that he was executing purchase orders on behalf of a client. By December 1994, the balance in the account 88888 swelled to $512 million. Leeson never thought that the Nikkei Index would fall below 19,000 points, and he continued to buy Nikkei Index Futures against SIMEX. His strategy was to arbitrage between Osaka Stock Exchange (OSE) and SIMEX to take advantage of the temporary price difference between the two exchanges. Leeson continued to build up substantial positions in Japanese equities, interest rates, Nikkei 225, government bonds (JGB) and Euroyen contracts. On 17 January 1995, Japan was shocked by the devastating earthquake measuring 7.2 on the Richter scale in Kobe City. Consequently, Nikkei 225 crashed by 7% in a week. Before the earthquake, Nikkei was trading within a range of 19,000-19,500. In order to cover up the losses, Leeson requested for additional funds to Bearings headquarters, and the bosses pumped funds to Singapore without asking a second question. Leeson was hopeful of a rebound of Nikkei and expected that it would stabilize at 19,000. Before the earthquake, he had a long position of approximately 3000 contracts on the OSE. And the equivalent number of contracts on SIMEX was 6000 because the SIMEX contracts were half the size of OSE. The aggressive buying to which he resorted to move the market took Leeson’s position over 20,000 futures contracts worth $180,000 each. But his efforts proved to be futile as the market did not move as expected. Figure 9.19 explains the movement of the Nikkei Index and Leeson’s position before and after the earthquake. OSE publishes the trading positions weekly for public information, and Leeson’s position at OSE reflected only half of his sanctioned trades. In principle, Leeson had to go short by twice the number of contracts on SIMEX if he was on OSE for long, because his official trading strategy was to take advantage of temporary price differences between OSE Nikkei 225 contracts

Option Trading Strategies 199 Nikkei 225 average 19 600

Kobe earthquake

20

19 400 19 200 Nikkei 225 average 19 000

Thousand contracts

15

18 800 Bearing’s net long 10 futures positions

18 600 18 400 18 200 18 000

5

17 800 17 600 17 400

0 6

13 20 27 January

3

10 17 24 February

1995

Fig. 9.19 Source: Data Stream and Osaka Securities Exchange

and SIMEX. In Bearings’ parlance, this arbitrage strategy was known as ‘switching’, which required the trader to buy the cheaper contract and simultaneously sell the expensive one. The trade would be reversed when the price difference narrowed or disappeared. This strategy carried lesser market risk because the positions were always matched. But, Leeson broke the rules and was long by approximately the same number of contracts he was supposed to be short. He routed all these unauthorized trading transactions through his secret account 88888. Besides, he used this account to route his trades in JGB, Euroyen Futures and Nikkei 225 options. Table 9.20 shows Leeson’s actual trade positions versus reported positions. For the rest of this case, the contracts will be discussed as converted into SIMEX contract sizes. The most striking fact was that Leeson sold 70,892 Nikkei 225 options worth $6.68 billion without the knowledge of his headquarters. Leeson’s option strategy was to sell puts and calls simultaneously. In option parlance, this strategy is straddle. The advantage of straddle is that the trader earns premium at both ends. Lesson sold over 37,000 straddles over a period of 14 months. Such trades could have been very profitable, provided Nikkei 225 was trading at the options’ strike price on expiry date since both the puts and calls would expire worthless. The seller then enjoys the full premium earned from selling the options. If Nikkei was trading near the options’ strike on expiry, it could still have been profitable because the earned premium more than offsets the small loss experienced on either the call (if the Tokyo market had risen) or the put (if Nikkei had fallen). An example of the straddle strategy is given in Fig. 9.20.

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Table 9.20 Leeson’s Actual Trading Position versus Reported Position at the End of February 1995 Reported1

Actual2

Number Nominal of value contracts4 (amount in millions of US$)

Number Nominal of value contracts5 (amount in millions of US$)

Actual position in terms of open interest of relevant contract3

Futures Nikkei 225

30,112

2809

JGB

15,940

8980

Euroyen 601

26.5

Long 61,039 (Amount 7000) Short 28,034

7000

49% of March 1995 contract and 24% of June 1995 contract.

19,650

Short 6845

350

85% of March 1995 contract and 88% of June 1995 contract. 5% of June 1995 contract, 1% of September 1995 contract and 1% of December 1995 contract

Options Nikkei 225

Nil

Calls 37,925 Puts 32,967

3580

3100

1. Leeson's reported futures positions were supposedly matched because they were part of Bearings' switching activity, that is the number of contracts on the OSE or the SIMEX or the TSE. 2. Actual positions refer to those unauthorized trades held in the error account 88888. 3. Open interest figures for each contract month of each listed contract. For Nikkei 225, JGB and Euroyen contracts, the contract months are March, June, September and December. 4. Expressed in terms of SIMEX contract sizes, which are half the size of those of the OSE and the TSE. For Euroyen, SIMEX and TIFFE contracts are of similar size. 5. Expressed in terms of SIMEX contract sizes which are half the size of those of the OSE and the TSE. For Euroyen, SIMEX and TIFFE contracts are of similar size. Source: The Report of the Board of Banking Supervision Inquiry into the Circumstances of the Collapse of Bearings, Ordered by the House of Commons, Her Majesty’s Stationery Office, 1995.

Option Trading Strategies 201

Short Straddle If an investor believes that the market will remain in a tight range, then the most suitable strategy can be Short Straddles. Short Straddle position can be created by selling both call options and put options of the same expiry and same strake price. For example, if the trader has taken short on both call and put options at the strike rate of 100,for a call premium of Rs. 3 and put premium of Rs. 2.10. If the market/stock remains in the range of Rs. 105.10-Rs. 94.90, the investor will make profit. On the other hand, above and below the prescribed range, the trader may incur unlimited loss. The maximum profit for this strategy is limited and the maximum loss for this strategy is unlimited. The maximum profit the investor can gain is Rs. 5.10 if the market/stock closes at Rs. 100. Pay off short call 3 3 3 3 3 –2 –7 – 12 – 17 – 22

Pay off short put – 17.90 – 12.90 – 7.90 – 2.90 2.10 2.10 2.10 2.10 2.10 2.10

Net pay off – 14.90 – 9.90 – 4.90 0.10 5.10 0.10 – 4.90 – 9.90 –14.90 – 19.90

125

120

115

110

105

100

95

90

85

10.00 8.00 6.00 4.00 2.00 0.00 –2.00 –4.00 –6.00 –8.00 –10.00

80

Loss & Profit

Stock at expiration 80 85 90 95 100 105 110 115 120 125

Strike price Short put

Net pay off

Fig. 9.20

Short call

Short straddle

The strike prices of most of Leeson’s straddle positions ranged from 18,500 to 20,000. He thus needed Nikkei 225 to continue to trade in its pre-Kobe earthquake range of 19,000-19,500 if he was to make money on his option

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trades. The Kobe earthquake shattered Leeson’s options strategy. On 17 January, the day of the quake, Nikkei 225 was at 19,350. It ended that week slightly lower, at 18,950, so Leeson’s straddle positions were starting to look shaky. The call options Leeson had sold were beginning to look worthless, but the put options would become very valuable to their buyers if Nikkei continued to decline. Leeson’s losses on these puts were unlimited and totally dependent on the level of Nikkei at expiry, while the profits on the calls were limited to the premium earned. Prior to the Kobe earthquake, his unauthorized book, that is account 88888, showed a flat position in Nikkei 225 futures. Yet on Friday, 20 January, three days after the earthquake, Leeson bought 10,814 March 1995 contracts. When Nikkei dropped by 1000 points to 17,950 on Monday, 23 January 1995, Leeson found himself showing losses on his two-day-old long futures position and facing unlimited damage from selling put options. Leeson tried single-handedly to reverse the negative post-Kobe sentiments that swamped the Japanese stock market. On 27 January, account 88888 showed a long position of 27,158 March 1995 contracts. Over the next three weeks, Leeson doubled his long position to reach as high as 55,206 March 1995 contracts and 5640 June 1995 contracts on 22 February. Leeson started unauthorized activities almost as soon as he opened trading in Singapore in 1992. He took proprietary positions on SIMEX on both futures and options contracts against the mandate from London, which allowed him to take positions only if they were part of ‘switching’ and to execute client orders. He was never allowed to sell options. Leeson lost money from his unauthorized trades almost from day one. Yet he was perceived in London as the wonder boy and turbo-arbitrageur who singlehandedly contributed to half of Bearings Singapore’s 1993 profits and half of the entire firm’s 1994 profits. In 1994 alone, Leeson lost Bearings US$296 million; his bosses thought he made them a profit of US$46 million, so they proposed paying him a bonus of US$720,000. In January and February 1995, Bearings Tokyo and London transferred US$835 million to its Singapore office to enable the latter meet its margin obligations on the SIMEX. Table 9.21 shows the facts and fantasy of Leeson’s trade. Table 9.21

Facts and Fantasy of Leeson’s Trade

Period 1 January to 31 December 1993 1 January to 31 December 1994 1 January to 31 December 1995

Reported (million) + GBP 8.83 + GBP 28.529 + GBP 18.567

Actual (million) – GBP 21 – GBP 185 – GBP 619

Cumulative actual* (million) – GBP 23 – GBP 208 – GBP 827

*Leeson’s cumulative losses carried forward. Source: The Report of the Board of Banking Supervision Inquiry into the Circumstances of the Collapse of Bearings, Ordered by the House of Commons, Her Majesty’s Stationery Office, 1995.

Option Trading Strategies 203

The Board of Banking Supervision (BoBS) of the Bank of England, which conducted an investigation into the collapse of Bearings, believes that ‘the vehicle used to effect this deception was the cross trade’. A cross trade is a transaction executed on the floor of an exchange by just one member who is both buyer and seller. If a member has matching buy and sell orders from two different customer accounts for the same contract and at the same price, he is allowed to cross the transaction (execute the deal) by matching both his client accounts. However, he can do this only after he has declared the bid and offer price in the pit and no other member has taken it up. Under SIMEX rules, the member must declare the prices three times. A cross trade must be executed at market price. Leeson entered into a significant volume of crosstransactions between account ‘88888’ and account ‘92000’ (Bearings Securities Japan—Nikkei and JGB arbitrage), account ‘98007’ (Bearings London—JGB arbitrage) and account ‘98008’ (Bearings London—Euro-yen arbitrage). After executing these cross trades, Leeson instructed the settlements staff to break down the total number of contracts into several different trades and to change the trade prices thereon to cause profits to be credited to ‘switching’ accounts referred to above and losses to be charged to account ‘88888’. Thus, while the cross trades on the exchange appeared on the face of it to be genuine and within the rules of the exchange, the books and records of BFS, maintained in the Contac system—a settlement system used extensively by SIMEX members—reflected pairs of transactions adding up to the same number of lots at prices bearing no relation to those executed on the floor. Alternatively, Leeson could enter into cross trades of smaller size than that discussed above, but when these were entered into the Contac system, he could arrange for the price to be amended, again enabling profit to be credited to the ‘switching’ account and losses to be charged to account ‘88888’. The BoBS report notes: ‘In each instance, the entries in the Contac system reflected a number of spurious contract amounts at prices different to those transacted on the floor, reconciling to the total lot size originally traded. This had the effect of giving the impression from a review of the reported trades in account “92000” that these had taken place at different times during the day. This was necessary to deceive Bearings Securities Japan into believing the reported profitability in account “92000” was a result of authorised arbitrage activity. The effect of this manipulation was to inflate reported profits in account “92000” at the expense of account “88888,” which was also incurring substantial losses from the unauthorised trading positions taken by Leeson. In addition to crossing trades on SIMEX between account “88888” and the switching accounts, Leeson also entered into fictitious trades between these accounts which were never crossed on the floor of the Exchange. The effect of these off-market trades, which were not permitted by SIMEX, was again to credit the “switching” accounts with profits whilst charging account “88888” with losses’.

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The bottom line of all these cross trades was that Bearings was counterparty to many of its own trades. Leeson bought from one hand and sold to the other, and in so doing did not lay off any of the firm’s market risk. Bearings was thus not arbitraging between SIMEX and the Japanese exchanges, but taking open (and very substantial) positions, which were buried in account ‘88888’. It was the profit and loss statement of this account which correctly represented the revenue earned (or not earned) by Leeson. Details of this account were never transmitted to the treasury or risk control offices in London, an omission which ultimately had catastrophic consequences for Bearings’ shareholders and bondholders. Table 9.22 shows how Leeson manipulated the accounts. Table 9.22 Dated

20 January 23 January 23 January 25 January 26 January

Number of contracts in account 88888*

Examples of Leeson’s Manipulations

Price per SIMEX

Average price per contract

Value per SIMEX JPY millions

18,950

19,019

66,173

66,413

240

17,810

18,815

26,715

28,223

1508

17,810

18,147

(71,970)

(73,332)

(1362)

10,047

18,220

18,318

91,528

92,020

420

16,276

18,210

18,378

148,193

149,560

1367 2245

Buy 6984

Value per Profit/ contract (loss) to JPY ‘92000’ JPY millions millions

Sell

3000 8082

The size of Nikkei 225 cross-trades on the floor of SIMEX for the dates shown, with the other side being in account ‘92000’ Source: The Report of the Board of Banking Supervision Inquiry into the Circumstances of the Collapse of Bearings, Ordered by the House of Commons, Her Majesty’s Stationery Office, 1995. The BoBS in its report to the House of Commons identified five major areas of failures on the part of Bearings’ management. These areas are as follows: (a) segregation of front and back offices, (b) senior management involvement, (c) adequacy of capital, (d) poor control procedures and (e) lack of supervision.

9.20.1

Segregation of Front and Back Offices

The management of Bearings broke a cardinal rule of any trading operation and effectively let Leeson settle his own trades by making him in charge of both the dealing desk and the back office. Abusing his position as head of the

Option Trading Strategies 205

back office, Leeson suppressed information on account ‘88888’. But, Bearings London did not know of its existence since Leeson had asked a systems consultant, Dr. Edmund Wong, to remove error account ‘88888’ from the daily reports which BFS sent electronically to London. This state of affairs existed from on or around 8 July 1992 to the collapse of Bearings on 26 February 1995. (Information on account ‘88888’ was, however, still contained in the margin file sent to London.) Error accounts are set up to accommodate trades that cannot be reconciled immediately. A compliance officer investigates the trade, records them on the firm’s books and analyses how it affects the firm’s market risk and profit and loss. Reports of error accounts are normally sent to senior officers of the firm. Bearings’ management compounded their initial mistake of not segregating Leeson’s duties by ignoring warnings that prolonging the status quo would be dangerous. An internal auditor’s report in August 1994 concluded that his dual responsibility for both front and back offices was ‘an excessive concentration of powers’. The report warned that there was a significant general risk that the general manager (Mr. Nick Leeson) could override the controls. The audit team recommended that Leeson be relieved of four duties: supervision of the back-office team, cheque signing and signing-off SIMEX reconciliations and bank reconciliations. Leeson never gave up any of these duties even though Simon Jones, regional operations manager of South Asia and chief operating officer of Bearings Securities Singapore, had told the internal audit team that Leeson will ‘with immediate effect cease to perform these functions’.

9.20.2

Senior Management Involvement

Every major report on managing derivative risks has stressed the need for senior management to understand the risks of the business, to help articulate the firm’s risk appetite and draft strategies and to control procedures needed to achieve these objectives. But, Bearings’ senior managers had only a very superficial knowledge of derivatives. They also miserably failed to implement proper risk management system in that branch. Though the internal audit report recommended hiring of a suitably experienced person to take charge of back office, the senior managers did not consider this recommendation, stating that there was not adequate work for a full-time treasury and risk manager. No senior managers in London checked on whether key internal audit recommendations had been followed up in the Singapore back office. While the senior managers at Bearings enjoyed the fruits of success of the Singapore branch, they did not try to find out how the profit was made and never analysed or properly assessed this at Management Committee meetings. They did not even know the breakdown of Leeson’s reported profits. They erroneously assumed that most of the switching profit came from Nikkei 225 arbitrage, which actually only generated profits of US$7.36 million for 1994, compared with US$37.5 million for JGB arbitrage.

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Peter Bearing remembered having told the BoBS that he found the earnings ‘pleasantly surprising’. He did not even know the breakdown. Andrew Tuckey, ex-deputy chairman, when asked whether there had ever been any discussion about the long-term sustainability of the business, told about the same investigation, ‘Yes ... in very general terms. We seemed to be making money out of this business and if we can do it, can’t somebody else do it? How can we protect our position? ....’ Senior management naively accepted that this business was a goldmine with little risk. Ron Baker (head of the Financial Products Group) and Mary Walz (global head of Equity Financial Products), two of Bearings’ most senior derivatives staff and Leeson’s bosses, got the following adverse remarks by BoBS: ‘Neither were familiar with the operations of the SIMEX floor. Both claim that they thought that the significant and large profits were possible from a competitive advantage that BFS had arising out of its good inter-office communications and its large client order flow. As the exchanges were open and competitive markets, this suggests a lack of understanding of the nature of the business and the risks (including compliance risks) inherent in combining agency and proprietary trading’. Given the huge amounts of cash that Bearings had to borrow to meet the margin demands of SIMEX, senior managers were almost negligent in their duties when they did not press Leeson for more details of his positions or/ and the credit department for client details. Members of the Asset and Liability Committee (ALCO), which monitored the bank’s market risk, expressed concern at the size of the position, but took comfort in the thought that the firm’s exposure to directional moves in Nikkei was negligible since they were arbitrage (and hedged) positions. This same misplaced belief led management to ignore market concerns about Bearings’ large positions, even when queries came from high-level and reputable sources, including a query on 27 January 1995 from the Bank for International Settlements in Basle. The bank was haemorrhaging cash and still London took no steps to investigate Singapore’s requests for funds—partly because senior management assumed that a proportion of these funds represented advances to clients. Even then the complacency is still baffling. BFS had only one thirdparty client of its own—Banque Nationale de Paris in Tokyo. The rest were clients of the London and Tokyo offices. Either London’s or Tokyo’s existing customers had suddenly become very active or Leeson had recently gone out and won some very lucrative accounts or Tokyo or London had a new supersalesman who had brought new business with him. Yet no enquiries were made on this front, which displays a blasé attitude to a potentially important source of revenue.

9.20.3

Adequacy of Capital

An institution must have sufficient capital to withstand the impact of adverse market moves on its outstanding positions as well as enough money to keep the positions going. Bearings’ management thought that Leeson’s

Option Trading Strategies 207

positions were market-neutral and happily funded the margin requirements till the contracts expired, without knowing that the positions were unhedged. In fact, the collateral calls received from the two exchanges, SIMEX and OSE, were very much larger than Bearings’ capital base. Bearings was highly exposed to market risk from its naked position. Therefore, even if it had borrowed money to fund the margin calls, it would have been impossible for them to withstand the substantial losses on expiry of the contracts. The agent appointed Bearings administrators closed out the contracts at losses totalling US$1.4 billion, and Bearings was unable to meet this obligation, which ultimately resulted in pulling down of its shutters.

9.20.4

Poor Control Procedures

Apart from separation of operational duties between front and back offices, many trading houses provide some additional checks and balances such as funding, credit risk, market risk and imposition of exposure limits.

9.20.4.1

Funding

The London office was automatically remitting to Leeson the sum of money he asked for, without asking for justification of such demands. Some of the operating staff even expressed their apprehensions about the accuracy of the data which was not considered while making these remittances. In fact, the London office could have calculated the margins using Standard Portfolio Analysis of Risk (SPAN) margining programme and could have crosschecked Leeson’s demands, which would have revealed that the money Leeson was requesting was substantially higher than that called for under SIMEX margining rules. The cumulative funding by Bearings London and Tokyo at the end of December 1994 amounted to US$354 million, and in the first 2 months of 1995, this figure went up by US$835 million to US$1.2 billion. The BoBS inquiry team made the following observations in their report to HM: We described ... how [Tony] Railton [futures and option settlements senior clerk] discovered in February 1995 that the breakdown of the total US Dollar request was meaningless, and that the BFS clerk knew the total funding requirement for that day and made up the individual figures in the breakdown to add up to the required total. From November 1994, BFS usually requested a round sum number split equally between US dollars for client accounts and proprietary positions. Tony Hawes [group treasurer] confirmed that he identified this feature of the requests: ‘That was one of the main reasons why during February 1995 I paid two visits to Singapore.’ If the US Dollar requests had been in relation to genuine positions taken by clients and house [Bearings itself], on any one day we consider it unlikely for the margin requests for these two sets of positions to be identical; as for having the requests split 50:50 most days, this is in our view beyond all possibility. Tony Hawes appears to agree with this

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view. He told us that: ‘It was just one of the factors that made me distrust this information ... It was quite too much of a coincidence. ... Throughout I put it down to poor book-keeping and sloppy treasury management in Bearings Futures [BFS]. David Hughes [treasury department manager] also told us that the 50:50 split: ‘was a cause for concern ... we said, this cannot be right.’ He explained that: ‘I do not think we could have house positions and client positions running totally in tandem.’ [Brenda] Granger [manager, futures and options settlements] confirmed that she would have spoken to Hughes about the split. She added: ‘We would joke about Singapore. Why don’t we send somebody’s mother [anyone] out there to run the department since Nick is so busy now? Staff in London could not reconcile funds remitted to Singapore to both proprietary in-house and individual client positions. Had it been done, they would have discovered that it was sending out far too much money just to cover the margin calls of clients.

9.20.4.2

Credit Risk

The Credit Risk Department did not question Bearings on lending over US$500 million to its clients to trade on SIMEX, collecting only 10% in return and without enquiring about their profile. According to the head of credit committee, George Maclean, it was Bearings’ policy to finance client margins until they could be collected. The credit committee never considered the credit aspects of the top-up balances although they could see the growth of these advances as recorded on the balance sheets. No limit neither on per client nor on the total ‘top-up’ funds was set. Besides, the system of credit approval process was totally absent. In short, an effective credit risk management system was totally absent in Bearings.

9.20.4.3

Market Risk

Leeson reported only what he wanted and what actually took place, and there was no system of independently checking the accuracy of his reports. As a result, the market risk reports generated by Bearings’ risk management unit and passed on to ALCO were inaccurate. Leeson’s futures positions did not show any market risk since trades were supposedly offset by opposite transactions on another exchange.

9.20.4.4

Exposure Limit

Bearings did not impose any gross position limit on Leeson’s proprietary trading activities because it felt that there was little price (directional) risk attached to arbitrage operations. As per rule, the position at the close of business must be flat. However, they did not consider the basis and settlement risk. The former arises because the prices in two markets do not always move in tandem, and the latter occurs because different markets have different settlement systems which create liquidity and funding risks.

Option Trading Strategies 209

9.20.5

Lack of Supervision

Theoretically, Leeson had lots of supervisors; in reality, none exercised any real control over him. Bearings operated a ‘matrix’ management system, where managers who are based overseas report to local administrators and to a product head (usually based at head office or the regional headquarters). Leeson’s Singapore supervisors were James Bax, regional manager of South Asia and a director of BFS, and Simon Jones, regional operations manager of South Asia, also a director of BFS and chief operating officer of Bearings Securities Singapore. Jones and the heads of the support functions in Singapore also had reporting lines to the group-wide support functions in London. Yet, both Bax and Jones told the BoBS inquiry that they did not feel operationally responsible for Leeson. Bax felt Leeson reported directly to Baker or Walz on trading matters and to settlements/treasury in London for back-office matters. Jones felt his role in BFS was limited only to administrative matters and concentrated on the securities side of Bearings’ activities in South Asia. Leeson’s ultimate boss was Ron Baker, head of the Financial Products Group. But who had day-to-day control over him? Mary Walz, global head of equity financial products, insists that she thought Fernando Gueler, head of equity derivatives proprietary trading in Tokyo, was in charge of Leeson’s intra-day activities since the latter’s switching activities were booked in Tokyo. However, Gueler insists that in October 1994, Baker told him that Leeson would report to London and not Tokyo. He thus assumed that Walz would be in charge of Leeson. Walz herself still disputes this claim. Tapes of telephone conversations show that Leeson spoke frequently to both Gueler and Walz. (The bottom line, however, is that Gueler reported to Walz.) The following two incidents illustrate the recalcitrant attitude of Bearings’ management. The first involves two letters to BFS from SIMEX. In a letter dated 11 January 1995, SIMEX senior vice president for audit and compliance, Yu Chuan Soo, complained about a margin shortfall of about US$116 million in account ‘88888’ and that Bearings had appeared to break SIMEX rule 822 by previously financing the margin requirements of this account (which appeared in SIMEX’s system as a customer account). SIMEX also noted that the initial margin requirement of this account was in excess of US$342 million. BFS was asked to provide a written explanation of the margin difference on account ‘88888’ and of its inability to account for the problem in the absence of Leeson. No warning lights went off in Singapore. No one investigated who this customer really was and why he was having difficulties in meeting margin payments or why he had such a huge position or the credit risk Bearings faced if this ‘customer’ defaulted on the margins that Bearings had paid on its behalf. A copy of the letter was not sent to

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operational heads in London. Simon Jones did not press Leeson for an explanation; indeed, he dealt with the matter by allowing Leeson to draft Bearings’ response to SIMEX. The second incident did come to the attention of London, but again was dealt with unsatisfactorily, perhaps because Bearings’ personnel themselves were unsure about what really happened. At the beginning of February 1995, Coopers & Lybrand brought to the attention of London and Simon Jones the fact that US$83 million apparently due from Spear, Leeds & Kellogg (SLK), a US investment group, had not been received. No one is sure how this multimillion dollar receivable came about. One version of events is that BFS, through Leeson, had traded or broked an over-the-counter deal between SLK and BNP, Tokyo. The transaction involved 20,050,000 call options, resulting in a premium of 7.778 billion (US$83 million). The second version was that an ‘operational error’ had occurred; that is a payment had been made to a wrong third party in December 1994. Both versions had very serious control implications for Bearings. If Leeson had sold or broked an OTC option, it should have been considered as an unauthorized activity. Yet he was not admonished for doing so; nor is there any record of Bearings’ management taking any steps to ensure that it did not happen again. If the SLK receivable was an operational error, Bearings had to tighten up its back-office procedures.*

9.21 SHORT STRADDLE If an investor believes that the market will remain in a tight range, then the most suitable strategy can be short straddle. Short straddle position is created by selling both call options and put options of the same expiry and same strike price. For example, if the trader has taken short on both call and put options at the strike rate of 100, for a call premium of Rs. 3 and a put premium of Rs. 2.10, and if the market/stock remains in the range of Rs. 105.1–Rs. 94.90, the investor will make profit. But on the other hand, above and below the prescribed range, the trader may incur unlimited losses. For this strategy, the maximum profit is limited to the premium received and the maximum loss is unlimited. The maximum profit the investor can gain is Rs. 5.10 if the market/stock closes at Rs. 100 (Table 9.23; Fig. 9.21). Table 9.23

Sell call Sell put

Strike

Premium

100 100

3 2.10 (Contd.)

*Source: Financial Services and System, Dr. Sasidharan and Alex K Mathews.

Option Trading Strategies 211 Stock at expiration

Pay off short call (100)

80 85 90 95 100 105 110 115 120 125

Net pay off

–17.90 –12.90 –7.90 –2.90 2.10 2.10 2.10 2.10 2.10 2.10

–14.90 –9.90 –4.90 0.10 5.10 0.10 –4.90 –9.90 –14.90 –19.90

3 3 3 3 3 –2 –7 –12 –17 –22

120

125

120

125

115

110

105

Strike price

Short put

Short call

Net pay off

Short call

Short straddle

115

Fig. 9.21

110

Net pay off

105

100

95

Short put

90

85

80

Loss

Profit

10.00 8.00 6.00 4.00 2.00 0.00 –2.00 –4.00 –6.00 –8.00 –10.00

100

95

90

85

80

Loss

Profit

10.00 8.00 6.00 4.00 2.00 0.00 –2.00 –4.00 –6.00 –8.00 –10.00

Pay off short put (100)

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9.22

LONG STRADDLE

If the underlying volatility is expected to rise and a single directional move is also expected, then one can buy both call options of an underlying asset at a fixed strike and a fixed maturity. The risk is limited to the extend of the premiums paid to purchase the options. In the following example Rs. 3,630 will be the maximum loss if the trader buys 100 call option and 100 put option at the strike price of 970 (Table 9.24; Fig. 9.22). Table 9.24

Long call Long put Index at expiration 910 920 930 940 950 960 970 980 990 1000 1010 1020 1030

Strike

Premium

970 970

20.80 15.50

Pay off long call (970)

Pay off long put (970)

Net pay off

– 20.8 – 20.8 – 20.8 – 20.8 – 20.8 – 20.8 – 20.8 – 10.8 – 0.8 9.2 19.2 29.2 39.2

44.50 34.50 24.50 14.50 4.50 – 5.50 – 15.50 – 15.50 – 15.50 – 15.50 – 15.50 – 15.50 – 15.50

23.70 13.70 3.70 – 6.30 – 16.30 – 26.30 – 36.30 – 26.30 – 16.30 – 6.30 3.70 13.70 23.70

60.00

Profit

45.00 30.00 15.00

1030

1020

1010

990

Long put Net pay off

– 30.00 – 45.00

1000

980

970

950

960

940

930

920

910

Loss

0.00 – 15.00

Long call Strike price

– 60.00

(Contd.)

Option Trading Strategies 213

Profit

60.00 45.00 30.00 15.00

–30.00

1030

1010

990

970

950

930

910

Loss

0.00 –15.00

Long put Net pay off

–45.00

Long call

–60.00

Fig. 9.22 Long straddle

9.23

COVERED CALL WRITING

Call option writing (option selling) is of two types, namely ‘covered call writing’ and ‘naked call writing’. The former strategy is built by buying stock from the spot and writing its call in the option market, or holders of a stock can also write its call options. It is a hedge position and one of the popular strategies for serious option traders. On the other hand, selling call option alone is called ‘naked call writing’, which involves high risk because if the underlying stock increases, the writer of the option will lose substantially. The purpose of the covered call strategy is to generate income from the sale of the call, and also some of the upside potential of the stock. Usually, the covered call writer believes that the stock is substantially priced and offers some upside potential for price appreciation. Thus, an outof-the-money call is written. If his calculation is right, then he will receive the call premiums and also some price increase (up to the strike price). On the other hand, if he is wrong, and the stock is down, the loss on the stock is cushioned by the retention of the option premium. For example, Mr. A, who buys TELCO 3300 stocks at a price of Rs. 165, assumes that during the month the stock has a potential to move above Rs. 168. Thus, he sells its call with strike at 170 (out-of-the-money) for a premium of Rs. 3 (maturing on 29 May, lot size 3300). While writing the call for Rs. 3, he earns Rs. 9900, which, in turn, reduces the cost of acquisition from Rs. 165 to Rs. 162. After writing the call, three situations may arise: 1. The stock moves above the strike of Rs. 170. 2. The stock trades around Rs. 165. 3. The stock falls below Rs. 162. In the first situation, as soon as the stock moves above Rs. 170, Mr. A covers the short call position in the option at a loss, and at the same time he sells the stock for a profit at Rs. 170.

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In the second scenario, if the stock stays flat during the lifetime of the option, Mr. A can pocket the whole premium of Rs. 9900, and he may sell his stock at Rs. 165. In the third situation, if the stock starts trading below Rs. 162, then Mr. A can liquidate the stocks in the spot market and cover the short position in the option market, which will be less than his sold premium. Buying highdividend-yielding stocks and writing its calls is an attractive cash management tool. The disadvantage of the covered call strategy is that if the call is exercised, delivery of the stock will not take place because now in India it is settled in cash. If the holder exercises his option, the writer of the option will come to know the obligation only on the next day morning, when the stock can open even at a lower side gap.

9.23.1

Covered Call Writer

Buying a stock in the cash market and selling its call is called covered call strategy. An investor can buy stock futures instead of stocks and sell its call options. The call premium received will act as a cushion for a downward movement in the stock prices. Consistent call writing is an art, especially in a falling market. Normally, investors write out-of-the-money calls in a bullish market. Sometimes in-the-money options are also written to get maximum protection (Table 9.25). In India, options are settled in cash, whereas in many developed countries it is settled in stock. In India option writers will have to pay the difference, i.e., strike price at which we sell call option + premium received while writing call – exercise price. Even though it can be a loss while early exercise, as call writer owns his stock future which can be sold (if he wants) at around exercise price, it will reduce initial capital.

9.24

PROBABILITY

Probability, in simple terms, is the chance of occurrence of an event. Statisticians generally quote throwing a dice as an example. Dice is used in gambling where we throw the dice to get a specific number. The dice has six sides, and the player throws the dice to get the specific number he desires. The number required by the player is only on one side of the dice. By using probability, we can find out the pattern of occurrences. For example, if we want to find the probability of getting an even number, how many times should we throw the dice? The numbers marked on the sides of the dice are 1, 2, 3, 4, 5 and 6, and the even numbers are 2, 4 and 6. This means, out of six numbers, three are even and the other three are odd. Hence, the probability of getting an even number can be 3/6 or ½. So, we can assume that there is a probability of getting an even number on every second throw.

Option Trading Strategies 215

Probability is only an estimation. It may or may not happen. Hence, the risk of non-happening is also present. Probability can be a continuous probability or a discrete probability. In the case of continuous probability, the events appear in a continuous sample space, for example the points in a line. The discrete probability events occur in a countable sample space, for example throwing a dice. The two important theorems of probability are the additional theorem or total probability and the multiplication theorem or compound probability. The additional theorem or total probability states that if the events are mutually exclusive, then the probability of happening of any one of them is equal to the sum of the probabilities of happening of the separate events. The second theorem of compound probability states that the probability of simultaneous occurrence of two events, A and B, is equal to the probability of one of the events multiplied by the conditional probability of the other, given the occurrence of the first.

9.24.1

Probability of Stock Price Moving Downward

Buying or holding stocks and selling call option at higher strike price is known as covered call writing. Covered call writing gives some protection to the writer at lower levels. For example, if Mr. A buys 200 Infosys at Rs. 1400 and writes one month of 1500 Infosys call at Rs. 50, the call premium will act as a protection at the lower level. Here, Mr. A will get downward protection up to Rs. 1350 (1400 – 50). He can calculate the probability of the asset price being below the protected level at expiry in one-month time, with the help of implied volatility of Infosys. Implied volatility is the best predictor of the future volatility. The option contracts are based on the assumption that either the underlying asset price will go up or the underlying asset price will go down. A call buyer will always hope for an upward movement of price so that he can increase his pay off, whereas a put buyer will be happy if the underlying asset price goes below the strike. The probability of the future value of an underlying asset moving down to a desired lower level can be estimated using the probability distribution as follows:

æ ln(Q/P ) ö P (below Q) = N ç è s T – t ÷ø where P (below Q) = Probability of asset price going below Q on expiry Q = Price level of the downside protection (asset price – premium) ln = Natural logarithm P = Current asset price

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s = Volatility of the asset for the period of the option N = Cumulative normal distribution (T – t)/365 = Life of option expressed in the decimal of the year Example Asset price Rs. 1150, strike price Rs. 1200, implied volatility 56%, premium Rs. 50 and contract period from 28 March 2008 to 30 April 2008. A trader can find out the probability of the asset price going down to Rs. 1100 by applying this formula. Q = 1100 (1150 – 50) P = 1150 s = 0.56 (56%) Ö(T – t)/365 = 0.2867 (Ö(30/365))

æ ln(Q/P ) ö P (below Q) = N ç è s T – t ÷ø æ ln(1100/1150) ö P (below 1100)= N ç è 0.56 ´ 0.2867 ÷ø æ ln(0.9565) ö = Nç è 0.1605 ÷ø = N (– 0.2771) = 1– N (0.2771) = 1 – 0.6064 = 0.3936 = 39.36% This indicates that there is 39.36% probability of the price of the underlying asset moving down to 1100.

9.24.2

Probability of Stock Price Moving up

Writing call options is riskier, because of unlimited loss, if the underlying asset moves above the protection level. For example, spot Nifty is at 3000 and Nifty one month 3100 calls is at Rs. 120. If you want to write the call at Rs. 120, your upside protection is at 3220 (3100 + 120). If Nifty moves up above 3220, you may stand to lose. Here, you can calculate the probability of Nifty being above 3220 using Nifty call’s implied volatility. The probability of the future value of an asset reaching a desired upper level can be estimated by applying the same formula, but replacing the values.

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P (above Q) = 1 – P (below Q)

æ ln(Q/P ) ö P (above Q) =1 – N ç è s T - t ÷ø where P (above Q) = Probability of asset price going above Q on expiry Q = Price level of the upside protection (strike price + premium) ln = Natural logarithm P = Current asset price s = Volatility of the asset for the period of the option N = Cumulative normal distribution (T – t)/365 = Life of option expressed in the decimal of the year Example Asset price Rs. 100, strike price Rs. 110, option premium Rs. 5, implied volatility of the asset 35%, contract period from 27 January 2008 to 27 March 2008. The breakeven level of the trader in this case is Rs. 115 (strike price Rs. 110 + option premium Rs. 5). Any amount above this level would be his profit. The probability of the price going above this level can be verified by applying the above formula as follows: Q = 115 (110 + 5) P = 100 s = 0.35 (35%) Ö(T – t)/365 = 0.4054

æ ln(115/100) ö P (above 115) = 1 – N ç è 0.35 ´ 0.4054 ÷ø æ -0.1398 ö = 1 – Nç è -0.1419 ÷ø = 1 – N(0.9844) = 1 – 0.8340 = 0.1660 = 16.60 % The result indicates that given the present conditions, the probability of the asset price moving up above the breakeven level of Rs. 115 is 16.60%.

9.25 SPREAD TRADING Spread trading is a strategy usually adopted in commodity futures and options. Here, one buys an asset in one expiry and sells the same quantity in

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another expiry. In India, natural rubber output remains very high in the month of December, January and February, whereas shortage is expected in August and September. A spread trader exploits this situation and sells the December/January/February natural rubber futures and buys the August/ September futures. The major attraction is the low margins and low risks. There are traders who exploit inter-exchange arbitrage by buying an underlying stock in one exchange and selling a far-month contract in another exchange of the same commodity and vice versa.

9.25.1

Collapse of Amaranth: A Classic Example of Spread Trading

Amaranth is the name of an imaginary flower that never fades. But Amaranth Advisors LLC, the US-based hedge fund manager with $9.5 billion asset base, lost heavily in their investments in natural gas futures. The First Post, the online daily magazine, published a report on 23 September that Amaranth had admitted a loss of $5 billion so far in the trade. According to an estimate by the Energy Hedge Fund Center, nearly $60 billion had been invested in often volatile energy markets by hedge funds. No wonder, Brian Hunter, a 32-year-old trader at the Amaranth’s trading desk, preferred to bet extensively on natural gas trades. After 2000, more capital came to the equity and commodity derivative markets to exploit the arbitrage opportunities. As the capital flown to the market was high, the return from the adoption of arbitrage strategies came down. This forced many of the hedge fund managers to create highly risky strategies with highly leveraged positions. They were creating positions without much consideration of weather conditions, which normally controls the price, other than demand and supply. Amaranth’s energy desk was run by Brian Hunter, who placed spreads in the natural gas futures. The fund had made huge profits in the year 2005 while trading in natural gas, because of Hurricane Katrina’s devastation. In the year 2006, they had created a spread position by taking long natural gas futures March 2007 contracts, and April 2008 shorts were created against the long futures . Their view on natural gas was bullish on the short term and bearish on the long term. Each natural gas future contract is 10,000 million BTUs or 10 million cubic feet of gas, which means for each 1 point movement in price, the contract value changes by $10,000. Natural gas futures have plunged 17% during end of September. ‘The losses may have been exacerbated by Amaranth’s attempt to exit bets on rising prices’, said Robert Van Batenburg, head of research at Louis Capital Markets LP in New York. The near-month long futures dropped sharply, but

Option Trading Strategies 219

the far-month contract remained weak, and the fall was comparatively low. The lowest risky trading strategy gave a whopping $6 billion dollar loss to the firm. Analysts estimate that in order to fund his positions, Hunter borrowed $8 for every $1 of Amaranth’s own funds. When the bets went in his favour, he could pay back the debt and keep the rest of the profit for Amaranth. However, the inverse happened. The bets went against him, and his borrowing amplified his losses. To quote The Wall Street Journal, ‘Much of the blame is being put on a single trader, Brian Hunter, 32, a Canadian. Hunter’s bold bets and deep understanding of the natural gas market had propelled him through Wall Street and into Amaranth, which is based in Connecticut. Such was his success in trading natural gas futures, or bets on the future price of the commodity, that Amaranth allowed him to work from his home in Calgary, where he drove a Ferrari in summer and a Bentley in winter’. Traders in the natural gas market referred to Mr. Hunter of Amaranth as a ‘bully’, in reference to not his personality but his ability to move the price of natural gas artificially, because of the huge positions he was taking. Energy trading has its own distinct qualities. ‘Energy trades a bit differently from most other commodities, in that the volatilities are quite high and liquidity can be varied and or poor’, said Michael Denton, an energy risk expert at Towers Perrin Risk Capital. Both issues present ‘risk control challenges’, Mr. Denton added, which can be managed by limiting concentration and providing adequate capital to support trading. Still, energy trading will always entail ‘significant risk’, he said, ‘because storage and transportation are limited and demand is stochastic and highly inelastic’. ‘In addition, data used to build quantitative models to control risk are also more difficult to obtain or of poorer quality in the energy markets’, Denton added. Most people investing in commodities are ‘investing on the sustainability of the cycle, on things going higher’, said Louis Gargour, a former RAB Capital fund manager who recently founded his own fund, LNG Capital. Because so many people are buying and not selling, the short-term volatility has increased, which can particularly hurt people who are highly leveraged, as commodity traders are. ‘When the market retreats, it is vicious’, Mr. Gargour said. He added, ‘No one listens to the risk managers until it is too late’. Especially the younger traders, said fund managers and long-time traders. They say the commodity markets are full of Brian Hunters, traders in their late 20s or early 30s, who have never traded through severe conditions like

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the plummet in crude oil prices in the 1980s. Instead, they have watched as natural gas prices, as well as prices of many other commodities, rose since 2001—unevenly, but with clear annual gains. (The trading desk Mr. Hunter ran at Deutsche Bank suffered losses in 2003, but he contended in a lawsuit that he personally made money for the firm that year.) Where more experienced traders in commodities have pulled out of the market in recent months, or have made long-term bets that these historically cyclic investments would fall, younger traders may have been convinced the market could keep going up, for example their peers. Emerging-market demand for commodities and fears about petroleum supplies have created what traders refer to as a supercycle, one that has driven prices higher, for longer, than ever. In a bid to stem losses, Amaranth gave up its energy trades to Citadel Investment Group LLC, a $12 billion hedge fund in Chicago, and to New York-based bank JPMorgan Chase & Co. The market speculated that Citigroup Inc., the largest US bank by assets, may buy a stake in Amaranth. Amaranth has managed money for Wall Street banks Morgan Stanley, Credit Suisse Group, Deutsche Bank AG and Bank of New York Co., according to US Securities and Exchange Commission filings. A $2.3 billion Morgan Stanley pool that invests in other hedge funds had about $126 million in Amaranth as of 30 June, according to regulatory filings. Bank of New York allocated $10 million of a $165 million fund to the firm. MotherRock LP, a $400 million fund run by former Nymex President Robert ‘Bo’ Collins, shut down last August after unsuccessful bets on the direction of natural gas. Both funds attempted to profit from spreads, or discrepancies in price, between different gas futures contracts. Amaranth used loans to expand its bets, increasing its losses. Hedge fund investors should take Amaranth as a warning to do better homework before trusting money with a fund.

9.26

CONTANGO AND BACKWARDATION

In the futures markets, sometimes the far-month contracts are traded at a premium to the near-month contract. The far-month futures premium attributed by higher cost of carry is known as contango. Sometimes the nearmonth contracts trade at a premium to the far-month contracts because of the anticipated supply–demand mismatch. This kind of price decline in which far month is compared with near month is known as backwardation.

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9.26.1

Ranbaxy–Daiichi Deal: A Case Study on Backwardation

On 11 June 2008, Daiichi Sankyo, a Japanese drug manufacturer and founder of Ranbaxy Lab, agreed to buy about 130 million shares from the Ranbaxy’s promoter Malvinder Singh and his family at Rs. 737 per share and also decided to buy 46.26 million new shares from the company at Rs. 737 per share. It also decided to buy 23.83 million options that could be converted into shares at Rs. 737 per share, sometime in the next 6-18 months, and to make an open offer to the general public at a price of Rs. 737 per share up to 20% of the emerging capital, as per the SEBI regulations. This is somewhere around 462.6 million shares. Malvinder Singh and his family get to sell all their shares at Rs. 737, and all other shareholders get to sell only one out of three shares they own at Rs. 737. The share buyback that opens on 8 August is to close on 27 August. The balance two out of three shares that the investors have in Ranbaxy will fluctuate with the market price after 27 August. Ranbaxy futures as on 18 July 2008 Ranbaxy closing price in the spot market on 18 July 2008 (Rs. 432) Table 9.25 Ranbaxy Futures on Different Maturity Month

Closing

Net Change

Open

High

Low

July 433.05 August 420.20 September 388.30

–20.10 –24.85 –17.20

449.95 441.10 400.00

455.00 442.25 403.00

425.00 417.00 382.05

Volume

Open Interest

7,841,600 1,021,680 1,072,800 2,170,400 734,400 17,896

9.27 TRADING STRATEGIES WITH LONG-TERM OPTIONS After the introduction of LEAPS in India, the fascination for the calendar spread has increased in the trading community. First, let us explain what calendar spread is. Buying an underlying asset in the far month and selling the same underlying asset in the near month or vice versa is called the calendar spread. Buying December 2008 call option at the strike price of 4500 and selling October 2008, 4500 calls is said to be a calendar spread. The theta of the short-term options will vanish faster than that of the long-term options and will give traders immense trading opportunities. But, if the underlying asset price changes very fast and the volatility of the long-term options diminishes faster then the calendar spread, positions will give troubles to the option traders.

Fig. 9.24 Nifty July option spread sheet as on July 08

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9.27.1 Long-term Call/Put Options Expiring on June 2010 Spot Nifty on 14 August 2008 = 4039 Call options and put options strike price and their premiums as on 14 July 2008 (Table 9.26). Table 9.26 Call

Premium (Rs.)

Put

Premium (Rs.)

5000

985.20

5000

1056.05

4900

1015.65

4900

1004.30

4800

1047.10

4800

953.50

4700

1079.60

4700

903.80

4600

1113.15

4600

855.15

4500

1147.75

4500

807.55

4400

1183.50

4400

761.10

4300

1220.45

4300

715.80

4200

1258.55

4200

671.70

4100

1297.85

4100

628.80

4000

1338.45

4000

587.20

9.27.2 Short-Term Call/Put Options Expiring on 31 July 2008 Spot Nifty on 14 August 2008 = 4039 Call options and put options strike price and their premiums as on 14 July 2008 (Table 9.27). Table 9.27 Call

Premium (Rs.)

Put

Premium (Rs.)

5000

1.8

5000

975

4900

2.15

4900

880

4800

2.85

4800

784.55

4700

4.40

4700

680

4600

7.05

4600

570

4500

12.25

4500

481.1

4400

21.3

4400

386.45

4300

38.3

4300

310.2

4200

66.6

4200

238.25

4100

106.2

4100

177.85

4000

156

4000

132.25

Option Trading Strategies 225

9.27.3

Short-Term Call/Put Options Expiring on 28 August 2008

Spot Nifty on 14 August 2008 = 4039 Call options and put options strike price and their premiums as on 14 July 2008 (Table 9.28). Table 9.28 Call 5000 4900 4800 4700 4600 4500 4400 4300 4200 4100 4000

Nifty Call and Put Option Premium on July 14th Premium (Rs.) 13 18.5 20 30.45 40 50.95 70.00 94.4 133.3 172.6 227

Put 5000 4900 4800 4700 4600 4500 4400 4300 4200 4100 4000

Premium (Rs.) 934 846 760 715 596 452 450 375 330 241 216.15

We have already mentioned about the spread traders activities, such as buying options in the far month and selling them in the near month. We have given the various Nifty options with premiums and expiries. Let us assume that on 14 July 2008 a spread trader buys both call and put options at the strike price of 4000 expiring on June 2010 and sells August put and calls at the strike price of 4000. While buying 4000 calls and puts (both one lot each) expiring on June 2010, the investor invests nearly Rs. 48,141.25 (1338.45 + 587.20/2 × 50), and he sells the July or August Nifty 4000 calls and puts one lot each. Imagine that the investor here writes both call and put options at 4000 August series; thus, he gets Rs. 11,078.75 (227 + 216.15/2 × 50). As long as the investor holds the long put and long call, he can write the near-month call options and put options because of low risk. This process can be continued till the expiry of the June 2010 contract. In this strategy, the investor gets 5 expiries in 2008, 12 expiries in 2009 and 6 expiries in 2010. Altogether the investor gets nearly 23 contract expiries. If everything is fine, he looks for a positive cash flow of above Rs. 2093.10 per month (48,141.25/23 months) while writing the calls and puts on each expiry. If historical volatility rises, then the implied volatility of the assets also tends to rise. In the same way, if the historical volatility falls, then the implied volatility will also fall. Rise and fall of the implied volatility of the long-term options happen slowly, whereas changes in implied volatility have much

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impact on the short-term maturities. A spread trader should take utmost care of implied volatility changes, which is the one and only main risk associated with spreading.

9.28 PORTFOLIO HEDGING BY CALL WRITING If you are holding a portfolio worth Rs. 1,000,000 and the portfolio beta is 1.5 and you are expecting a 10% market fall, then your portfolio value will decline by 15% and your loss on portfolio will be Rs. 150,000. Hence, the portfolio would be equal to Rs. 850,000. In this case if you want to sell call option in anticipation of a 10% fall, how many lots of call should be written? Assume that the current Nifty futures are trading at 5000, and you are expecting a decline of 500 points (10%) before the contract expiry. The Nifty 4500 call option is trading at a premium of Rs. 540. One lot of Nifty is 50. Number of calls to be written = 150,000/(540/50) That is, portfolio loss anticipated/(premium of Nifty 4500 calls/50) Premium of Nifty 4500 calls is 540, and the lot size is 50 Number of calls to be written = 5.55 lots = 6 lots (approx.) That is, 6 lots × 50 × 540 premiums = Rs. 162,000 While selling the 4500 call options, you get Rs. 40 [(4500 + 540) – 5000] as extra money (the time value). Instead, if you are selling the Nifty 5000 futures and Nifty closed at 4500, then you will make only Rs. 500 as profit. If you are buying the put, then you have to pay the premium, so you will attain the breakeven slightly at lower levels. Portfolio hedging through call writing can be used if you know the intensity of the fall of the market. Writing American calls (stock call in-themoney) are dangerous, so one should avoid writing the American call options of the stocks.

9.29 PORTFOLIO HEDGING THROUGH DELTA HEDGE Like portfolio hedging through call writing, one can hedge the risks associated with shorting the securities in the market. Suppose you are holding 200 shares of Infosys and you want to create a hedge against the market risks, then you have to calculate your portfolio deltas. Stocks have a delta of one, and different strike prices of options have different deltas. According to the rule of thumb, at-the-money options have 0.5 delta (if the underlying stock increases or decreases, the option premium will increase or decrease by Rs. 0.5). Out-of-the money options have a delta of 0.25, and deep out-of-the-money options have a delta of less than 0.01. In-the-money options have a delta of 0.75. Deep in-the-money options have a delta of almost one. In the above example, you are holding 200 shares of Infosys, so you have 200 positive deltas. In order to hedge your Infosys risks, you need to create

Option Trading Strategies 227

200 negative deltas, either by selling 200 shares in stock futures market or the Infosys call or by buying the Infosys put option. If you have decided to hedge the risks in Infosys by selling the at-themoney call option with 0.50 delta, you have to sell 400 call options (two lots of Infosys calls). If you want to write the out-of-the-money call options (0.25 deltas), then you have to write eight lots of Infosys calls. Writing the deep inthe-money call of Infosys is not advisable, because a deep in the call options can attract early exercise. Buying Infosys put options in the money (0.75 delta) is not advisable, because the position may be either over-hedged or under-hedged. So, deep in-the-money put (1 delta) or at-the-money put (0.5 delta) is advisable. In order to create a delta neutral position, out-of-themoney calls and puts are used.

9.30 DIAGONAL SPREAD Spreads are created by selling a contract in one expiry and buying a similar contract in another expiry. Diagonal spreads are spreads, but the strike prices in the different maturities will differ from one another. Advanced option traders use diagonal spread on the basis of implied volatility changes.

9.31 SCALPING If a trader can buy a commodity or futures or even an option at bid price and can sell the same underlying contract at ask price, then the investor may make a small profit risklessly. A scalper here looks into the theoretical price, but always traders on bid ask differentials. In an illiquid counter, the scalpers will take the advantage of this kind.

Summary In the foregoing paragraphs, we have discussed extensively about various option strategies. These strategies were mainly aimed at a bear market. We also discussed about the traditional strategies like straddles and strangles and how the Bearings Bank lost in option trading due to the speculative trading done by its dealer Nick Leeson. We have explained the concept of spread trading with examples from the Indian capital market, quoting the cases of Ranbaxy–Daiichi Deal, and have presented the case of Amaranth as an example of how spread trading can lead to losses also. The option strategies discussed here are developed considering the Indian market conditions. However, the investors, traders and professionals using these strategies are advised to analyse the market and examine the suitability of these strategies for their portfolio. The market analysis has to be done from drawing data from various sources. Readers may have a doubt as to where the reliable data can be obtained. In the next chapter, we present some of the sources of data.

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Keywords Long put Delta hedge Synthetic short Long call Christmas trees Short strip Long strangles Spread trading

Short call Bear spread Synthetic index futures Albatross Long guts Long straddles Contango

Portfolio hedging Put ratio spread Long combo Short straddle Long call ladder Covered call Backwardation

CHAPTER

10

MARKET INFORMATION

10.1 OBJECTIVES Having understood the basics of options, techniques of writing options and option strategies, readers may raise a question about the source of the price information. In this chapter we have provided information about the sources of data relating to stock prices and indices.

10.2

INTRODUCTION

For analysis and interpretation, we need current and historical data. Historical data can be collected from newspapers and from NSE website (www.nseindia.com). Current and historical data for Index options, Stock options and for Index futures and Stock futures and spot markets are available in NSE website. Some of the typical news paper quotes taken from www.economictimes. com. Figures 10.1–10.5 show some of the typical newspaper quotes taken from www.economictimes.com.

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Fig. 10.1

Market Information 231

Fig. 10.2

232

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Fig. 10.3

Market Information 233

Fig. 10.4

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Fig. 10.5

Market Information 235

Database from NSE Website NSE offers an extensive data base to investors on various segments like Equity, Derivative, Debt and IPO market. We shall look in detail how we can get details on equities and derivatives segments from NSE website.

Derivatives: Just like data retrieving for equities, we can access information on derivatives too. It can be obtained from Market Today, Charting and Historical Data directly.

Fig. 10.6

Market Today: Trade statistics for the day, with number of contracts, turnover and PC ratio of Index & Stock Options, with aggregate of F&O can be obtained from this page. Cumulative FII positions on the derivative segment as a percentage of total gross market position is also obtained from this page.

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Fig. 10.7

Get Quote: Quotes of selected instruments like Index Futures, Stock Futures, Index Options and Stock Options can be obtained for the respective expiry dates, strike prices, and option types which are available. Following print screens on various instruments will give a clear picture on data collection.

Source: www.economictimes.com

Fig. 10.8

Market Information 237

Fig. 10.9 To find out the spot interest rates, we use N-S (Nielson-Siegel) parameters using the traded bonds. From this page, you will get the N-S parameters for the day, along with historical data of all trades and trades up to 3:00 pm. Next in the Market Today is the Bhavcopy of Derivatives daily that gives you the information on Futures and Options price, Net Change, % Change, Open Interest at the end of the day, Traded Quantity, Traded number of Contracts and Traded Value.

Fig. 10.10 Also, Archives of derivatives data can be obtained for various dates and its print screen is Daily Settlement prices are obtained from the Market Today by downloading from the page. Next to that, in the Daily Volatility link, we can obtain volatility of underlying and futures in terms of daily and annualized, with respect to applicable daily volatility and applicable annualized volatility. Archives can also be obtained for daily volatility of futures segment. Client wise position limits in the derivatives segment on a daily basis, along with Archives, is published by NSE and is obtained from the Market Today page. Base prices for illiquid contracts of stock options are released on a daily basis.

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Historical Data: Historical data on derivatives are obtained from the Historical Data page given below.

Fig. 10.11

Archives: Lot of information on derivatives trades are included in Archives segment.

Fig. 10.12

Market Information 239

Historical Contract-wise Price Volume Data: In this page, historical data on various ‘F&O’ instruments for the selected expiry dates having a data range of 90 days can be obtained. The expiry dates and strike price are optional fields.

Fig. 10.13

Facts and Figures In the Facts and Figures segment, a complete data on derivatives segment, particularly for analysis purpose is provided by NSE.

Business Growth in Derivatives Segment: In this section, F&O details on number of contracts traded and their corresponding turnover for each days are available in terms of daily, monthly and on an yearly basis. At present, data is available from 2000 onwards.

250000000

Index futures

200000000 150000000 100000000 50000000

Stock futures Index options Stock options 2

3

4

20

01

-0

-0

-0

02

03 20

20

5

6 20

04

-0

-0

7

05 20

-0

-0

06 20

07

08

20

-0

9

8

0 20

Amount (in Rs. crores)

Business growth in derivatives segments (NSE)

Years

Fig. 10.14 Business growth in derivatives segment

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Monthly Derivatives Update: A tri-monthly derivatives update is issued from NSE with overview of F&O segment for three months and previous month’s update on Futures and Options segment with proper analysis is obtained. Also, derivatives update of previous months can be obtained from Archives file.

Nifty Close on Expiry: An excel sheet giving Nifty closing values of expiry dates for the past one year, with % change from previous close is obtained.

Quantity Freeze: Volume Freeze quantity for those stocks in the F&O segment is obtained on a daily basis in excel sheet.

Average Quarter Sigma: Average Quarter Sigma is calculated for those securities that satisfies the Top 500 securities criteria for the past six months along with their corresponding market wide position limits. Source: - www.nseindia.com

Summary In this chapter we have seen how to gather information pertaining to markets from the daily business news papers, where to get different information, how to interpret the information available from the NSE website and put ourselves in a position to check the positions of the stocks in our portfolio. The option trading is aimed at managing risks in the portfolio of investments. It would be interesting to know what are the risks in option trading. We are throwing light on this aspect in the next chapter.

Keywords Market Today Archives

Get Quote Bhavcopy

Historical Data

CHAPTER

11

RISK IN DERIVATIVES

11.1 OBJECTIVES Derivatives are tools for managing risk. In the previous few chapters we discussed how derivatives can be used for risk management. But derivatives themselves can be risky. In this chapter we have explained various types of risks to which trading in option market is exposed.

11.2

INTRODUCTION

Warren Buffett, the financial Guru, says that ‘derivatives are like hell… easy to enter and it is almost impossible to exit’. According to him, derivatives pose dangerous incentives for false accounting, profits and losses from derivative deals are booked straight away, even though no actual money changes hands. In many cases the real costs hit companies only many years later.

11.3

RISK IN OPTIONS

Options are highly complex in nature; options in India are settled in cash and thus pose high risks. Options’ time sensitivity is the most dangerous element in trading. As each day passes, the time value tends to come down, and it becomes difficult for traders to make profit. Writing naked calls and puts carries high risk. Options are extremely risky investments that need a lot of attention and knowledge especially for preparing strategies, or otherwise you may end up with massive losses.

11.4

IS WRITING OPTIONS A HIGH RISKY STRATEGY?

This question always arises when we discuss the risks associated with the writing of an option to our clients. First of all, the writer of the option gets only a nominal amount as premium. When the reward is too low, then the risk associated with writing the options is also low. The probability of losing money while writing options is too low. In exceptional conditions the risk

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can be very high, but in a trending market, the risk in writing an option is low. Secondly, the time decay factor always supports the writers of the option than the buyers of the option. Many knowledgeable option writers are using various ways and strategies to protect their risks associated with writing of both call and put options. A knowledgeable call option writer should always have a long position in the cash market or in the futures market. A put option buyer is always ready to buy the underlying assets which he might have sold at a higher rate previously, or he should always calculate the probability of the underlying asset falling below a certain level. This has been explained in the previous chapters.

11.5

CLASSIFICATION OF RISKS*

Liquidity risk: Liquidity risk arises when one party wants to trade in an asset but cannot do it because of the reason that there are no perspective takers for that particular offer. Liquidity risk becomes particularly important to parties who are about to hold or currently hold an asset, since it affects their ability to trade. In the case of index options, once the liquidity dries up (deep-inthe-money call and put option becomes illiquid), option traders particularly find it difficult to exit from the trade. Due to the high premiums, demand for deep-in-the-money calls and puts will fall making them illiquid. Also, as the index options are European options, the holders of options can’t exercise their option until the expiry.

Market risk: Market risk is the risk that the value of an investment will decrease due to movements in market factors. The Reserve Bank of India has defined market risk as ‘the possibility of loss to a bank caused by changes in the market variables’. According to the Bank for International Settlement (BIS), market risk is ‘the risk that the value of ‘on’ or ‘off’ balance sheet positions will be adversely affected by movements in equity and interest rate markets, currency exchange rates and commodity prices’. Market risk is typically measured using a value-at-risk methodology. The price of a stock depends on fundamental values, but sometimes the prices can fluctuate due to various other reasons like interest rate variations, inflation, geo-political tensions etc. For example, an investor is expecting very strong quarterly numbers from Infosys, which is expected to announce the quarterly number on 13 April 2007, and expecting 10% price appreciation from the current levels. So s/he bought 100 shares of Infosys at a price of Rs. 2000 on 10 April 2007. As expected, the company came with strong quarterly numbers on 13 April 2007, but due to RBI’s decision to hike the CRR on the same day, the stock market (the Nifty) crashed by 150 points. Even though the quarterly numbers of Infosys were good, the stock closed at Rs. 1920 due to Infy’s beta relationship with Nifty. The four standard market risks are equity risk, interest rate risk, currency risk and commodity risk. *

Sasidharan K and Alex K Mathews, Financial Services and System.

Risk in Derivatives

243

Financial analysts are using various methods to reduce the market risk of a security or an underlying. The most preferred method is known as hedging. Holding the financial asset and selling the indices or selling an underlying which has highest correlation with the underlying asset held by you, that is holding SBI stocks in hand (SBI is an index heavy weight) and selling PNB (PNB is also a heavy weight but has low weightage comparing with SBI.) Futures and options are said to be the best products to hedge the market risks.

11.6

ELIMINATION OF MARKET RISK THROUGH HEDGING

The major risk associated with the financial market is market risks. The spillover of volatility, political uncertainties, geo-political tension and inflationary trends all come under the roof of market risk. If an investor is able to reduce the market risk to a certain extent, he can make profits easily. Market risk can be eliminated by buying put options of Nifty or of stocks. We can hedge the market risk by shorting the Nifty futures and stock futures or by buying the put options of the stock options or by buying the index options. If the investor is looking for 100% hedge, then one has to apply the delta neutral strategies. Stock futures, index futures and stocks-in-hand are all having delta of one. Suppose, you are holding 250 shares of ABB stock (one lot), it means that you are holding 250 positive deltas. If you want to hedge your risk by buying the put option, then you have to bring down the +100 deltas to neutral, by buying ABB put options or Nifty put options. Generally speaking, all at-the-money put options are having delta of 0.5; all in-the-money put options are having delta very close to 0.75 and all deep-inthe-money put options are having delta of one. On the other hand, if you are buying the out-of-the-money put option, then your delta position will only be 0.25, and deep-out-of-the-money put options are having almost 0.1 deltas. To elaborate the same, assume that you are holding 250 ABB shares (one lot), and you want to hedge the stock’s risk at least 99%, then you have to buy at least 2 lots of 0.5 delta at-the-money put options of ABB. So your total delta position will come down to zero. On the other hand, if you are holding 250 shares of ABB and would like to buy a deep-in-the-money put option, then purchase of 1 lot of deep-in-the-money put option is sufficient to protect your risks in the portfolio. There are groups of traders in the US and other stock exchanges known as market makers; they always recalculate their portfolio deltas on regular basis in order to reduce the market risk. In India options are so complex that investors are reluctant to trade freely in the options market.

Interest rate risk: Interest rate is the one of the key factors affecting price movements of stocks, bonds and currencies. If the interest rates are moving up, then the stock prices may tend to stay lower because the purchaser of the stock, who uses the funds borrowed for the investment, has to pay a high rate of interest for the borrowed fund. For example, Mr. Alok borrowed four lakh rupees from a financier at the rate of 13% per annum, in anticipation of marking a return of 25% while investing in equities. Assume that the interest

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rate has gone up from 13% to 18%. In this case, Mr. A may not borrow funds because it is less rewarding to him. It means that higher the interest rate, lower the demand for stocks. When the interest rate moves up, the call option premium will move up and the premium of put option will come down (Figs. 11.1 and 11.2). OPTION CALCULATOR BASED ON BLACK– SCHOLES STRIKE PRICE

100

SHARE PRICE

120

TIME TO EXPIRY

23

VOLATILITY% ANNUAL INTEREST RATE

47

OPTION VALUE

Fig. 11.1

6.5 CALL

PUT

20.75

0.334

Option calculator

OPTION CALCULATOR BASED ON BLACK– SCHOLES STRIKE PRICE

100

SHARE PRICE

120

TIME TO EXPIRY

23

VOLATILITY% ANNUAL INTEREST RATE

47

OPTION VALUE

Fig. 11.2

11 CALL

PUT

20.89

0.20

Option calculator

Risk in Derivatives

245

Model risk: Different models are used in financial operations for valuation of assets, pricing of products, measuring risk, etc. The risk associated is very high with complex derivatives models, due to wrong values. For example, LTCM suffered substantial losses due to the failure of the model they selected.

Volatility risk: One of the key factors affecting option premium is implied volatility. If volatility increases, the implied volatility also increases alongside. Option writing is very dangerous if the volatility is on the rise. Even if the underlying asset price remains the same, the option premium can rise if the implied volatility increases. Figures 11.3 and 11.4 show that when the implied volatility of the option increases, even if the underlying asset price remains the same, the option premium will also rise. In Fig. 11.3, we can see that the strike price and the share price are at Rs. 200. The volatility is 25, the time to expiry is 17 days and the annual interest rate is 7%. The option premium that we see in the first diagram is Rs. 7.49 for the call option and Rs. 6.4 for the put option. In Fig. 11.4, we can see that the strike price, the share price and the interest rate remain the same. The things that have changed are the volatility, which is at 40, and the expiration day, which has reduced to 13. Now, we can see that both the call option premium and the put premium have come down to Rs. 5.82 and 5.33 respectively. OPTION CALCULATOR BASED ON BLACK– SCHOLES STRIKE PRICE

200

SHARE PRICE

200

TIME TO EXPIRY

17

VOLATILITY% ANNUAL INTEREST RATE

25

OPTION VALUE

Fig. 11.3

7 CALL

PUT

7.49

6.4

Option calculator

246

Option Trading OPTION CALCULATOR BASED ON BLACKSCHOLES STRIKE PRICE

200

SHARE PRICE

200 13

VOLATILITY% ANNUAL INTEREST RATE

OPTION VALUE

Fig. 11.4

40 7 CALL

PUT

5.82

5.33

Option calculator

Time risk: One of the key risks associated with option trading is the decay of time value. As the time passes, the time value of an option quickly decreases. So, switching from one expiry to another is the easiest way of protecting the time value. Another way of protection can be availed by selling lower strike options in the case of a put option and higher strike call in the case of a call option. Sometimes, changing the option’s strike price also protects the time value to a certain extent. Instead of selling the existing out-of-the-money put (the time value will be eroded in out-of-the-money option very fast) which we bought earlier, we can buy a new put option at at-the-money strike price (where time value will be high). Time decay is the only major reason why traders are constructing spreads. Buying at-the-money call and selling an out-of-the-money call is a spread. In the same way, one can create spread position by buying at-the-money put and selling an out-of-the-money put option.

Summary We have seen that though derivatives are tools for managing risks, they themselves are exposed to different types of risks. These risks can be liquidity risk, interest rate risk, model risk, volatility risk and time risk. The list is exhaustive. As time passes, the market may bring in more complex risks

Risk in Derivatives

247

necessitating sophisticated risk management plans to mitigate these risks. The risks also may arise due to accounting and taxation issues. In the next chapter we are discussing about certain accounting and taxation issues regarding derivatives.

Keywords Risk Model risk

Interest rate risk Volatility risk

Liquidity risk Risk of time

CHAPTER

12

ACCOUNTING AND TAXATION OF OPTION TRADING 12.1 OBJECTIVES Having discussed about trading in F&O segment and the risk associated with the F&O transactions, one should know the accounting treatment of these transactions and the taxation issues involved. We will explain these two vital issues in this chapter.

12.2

INTRODUCTION

Guidance notes on accounting of equity and stock index and on equity and stock options have been issued by the Institute of Chartered Accountants of India (ICAI) for the buyers and sellers in the F&O segment. Accounting norms at various stages of positions right from the inception to the final settlement for equity index and stock options are given in the following sections.

12.3

ACCOUNTING NORMS FOR EQUITY AND INDEX OPTIONS*

Given below are guidelines for accounting treatment in the case of cashsettled index and stock options:

Accounting at the time of inception of a contract: An initial margin is required for the seller or writer of an option to enter into the contract. This margin is debited to the 'Equity Index Option Margin Account' or in the 'Equity Stock Option Margin Account'. Such account is shown under the head, 'Current Assets'. On the other hand, the buyer or holder of an option need not be paid any margin, but only the premium amount. So, in the buyer's book, premium is debited to 'Equity Index Option Premium Account' or 'Equity Stock Option Premium Account'. The premium received by the seller/writer is credited to 'Equity Index Option Premium Account' or Equity Stock Option Premium Account'. * Source: www.nseindia.com (NCFM Derivative Module)

250

Option Trading

Accounting at the time of payment/receipt of margin: Payments received by the seller for the margin is credited to the client's account and the payments to be made by the seller is debited to the client's bank account. If client has already deposited lump sum amount with the trading member for margin purpose, then the amount of margin from such account is debited or credited, as the case may be.

Accounting for open positions: Equity Index and Equity Stock option premium account is shown under the head Current Assets and Current Liabilities. In the case of a buyer/holder, a provision is made for the amount by which the premium paid for the option increases or decreases for the premium prevailing on the balance sheet date. This provision is shown as deduction from Equity Index or Equity Stock Option Premium and is shown under 'Current Assets'. In the case of option seller/writer, provision is made for the amount by which premium prevailing on the balance sheet date exceeds the premium received for that option. This provision is credited to provision for loss on 'Equity Index option or Equity Stock Option Account' as the case may be.

Accounting at the time of final settlement: The buyer or holder of an option will incur premium as an expense and will debit the profit and loss account by crediting 'Equity Index or Equity Stock Option Premium Account'. In addition, the buyer/holder will receive a difference of the amount between the final settlement price and the strike price on the expiry/ exercise date and is considered as an income. On the exercise of the option, the seller or writer will get premium as the income and credit the profit and loss account by debiting Equity Index Option Premium Account or Equity Stock Option Premium Account. In addition, seller or writer has to pay adverse difference, if any, between final settlement price and strike price. Stock exchange will credit the margin paid towards option and is credited to the Equity Index or Equity Stock Option Margin Account.

Accounting at the time of squaring off of an option contract: On the squaring off of option contracts, a difference between premium paid and received is transferred to the profit and loss account of the client. If an option expires unexercised at the time of settlement, then the accounting entries will be same for cash settled options. If options are exercised, then shares are transferred in cash at the strike price. In the case of a call option being exercised, buyer or holder will receive equity shares at the strike price, for which the call option was entered and should debit relevant equity shares account and have to credit cash. In the case of a put option being exercised, the buyer or holder of put option has to deliver equity shares for the strike price at which put option was entered into. The buyer or holder must credit the relevant equity shares account and debit cash. For the seller or writer of a call option, equity shares have to be delivered for the strike price at which the call option was entered into, thereby crediting relevant equity shares account and debit cash. The seller or writer of a put option will receive equity

Accounting and Taxation of Option Trading 251

shares for the strike price at which put option was entered into and must debit relevant equity shares account and debit cash. Also, premium paid or received is transferred to the profit and loss account with accounting entries as same as those in cash settled options.

12.4

CHARGES IN F&O SEGMENT

We already mentioned that the maximum brokerage that is chargeable by a trading member for the transactions executed in F&O by clients is fixed as 2.5% of the contract price, exclusive of statutory levies like SEBI turnover fee, service tax and stamp duty. The service tax charge is about 12% of the brokerage, as mentioned by the exchange. Education cess to be paid is 2% of the service tax. Stamp duty for the F&O segment is 0.002% of the transaction value of the contract. Exchange levy is 0.0021% for the F&O segment. Securities transaction tax (STT) (needs to be paid by the sellers of the transaction only) is 0.017% in the F&O segment.

12.5 TAXATION OF DERIVATIVES It is mandatory to pay STT on all derivative transactions which are traded in a recognized stock exchange. The seller of the transaction is liable to pay tax on the derivative contract, in the case of both futures and options. In the case of options, the taxable amount is calculated based on the option premium of stock or index options times' market lot of the contract. Consider the case of options. If a client sells Infosys with a strike price of 1260 call for Rs. 80 (lot size of 132), STT is calculated in the following manner: 1. 1620 call option's value = Rs. 10,560 (Rs. 80 x 132) 2. STT payable = 0.017% x 10,560 = Rs. 1.79

12.6

INCOME TAX

Security transactions are subject to the capital gains tax payable for shortterm and long-term gains. According to Section 73 (1) and 43 (5) of the Income Tax Act, any loss, computed in respect of a speculative business carried on by the assessee, shall not be set off except against profits and gains, if any, of speculative business. Section 43 (5) of the Act defines speculation as a transaction in which a contract for purchase or sale of any commodity, including stocks and shares, is periodically or ultimately settled otherwise than by actual delivery or transfer of the commodity or scrip. However, the following transactions are exempted from the purview of speculation: - A contract in respect of stocks and shares entered into by a dealer or investor therein to guard against loss in his holding of stocks and shares through price fluctuations.

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- A contract entered into by a member of a forward market or a stock exchange in the course of any transaction in the nature of jobbing or arbitrage to guard against loss, which may arise in ordinary course of business as such member. These provisions necessitate derivatives to be considered as speculative transactions where actual delivery does not take place except in the above two exceptions. As a result, the hedging and arbitrage will have to be considered normal securities transactions and accordingly the tax rules applicable for securities transactions will have to be made applicable to these transactions also. But, the problem arises in the case of index options and index futures where physical delivery of underlying is not possible and accordingly the hedger or arbitrageur loses the facility of set-off as available in the case of shares and securities. This being the case with Income Tax Law, the Securities Contracts (Regulations) Act 1956, as amended up to 2004, permits derivative trading in recognized stock exchanges. Section 18A of the Act reads as 'not withstanding any thing contained in any other law for the time being in force, contracts in derivatives shall be legal and valid if such contracts are (a) traded on a recognized stock exchange and (b) settled on the clearing house of the recognized stock exchange in accordance with the rules and bye-laws of such stock exchange. Section 2 (ac) of the above Act defines derivatives as (A) a security derived from a debt instrument, share, loan, whether secured or unsecured, risk instrument or contract for differences or any other form of security and (B) a contract which derives its value from the prices, or index of prices, of underlying securities'. Again Sub-section (h) (ia) of the same Section includes derivatives under securities. All these indicate that derivatives are legal form of securities transactions and therefore there is no reason for any doubt regarding treatment of derivatives also as securities for taxation purpose.

Summary We have seen that the ICAI has issued extensive guidelines regarding accounting of option transactions, and how these liabilities under these heads are to be shown in the balance sheet. We have also discussed the taxation issues and found that ambiguity still prevails with regard to carry forward of losses and set off of losses on derivative trading. Before we conclude this chapter, two other important areas to be covered are the technical terms used in F&O operations and clarifying some of the general doubts on F&O trading. We will cover these areas in the next two chapters.

Keywords Securities transaction tax Income tax

Brokerage charges Carry forward

Educational cess Set off

CHAPTER

13

FAQs ON OPTIONS

13.1 OBJECTIVES In this chapter, we will clarify some of the doubts on F&O transactions raised by various people at different points. Under option trading, who is in a relatively safe position: the buyer or the seller? In the world of options, the buyers are in a better position than the sellers as far as risk and rewards are concerned. As mentioned earlier, the buyer enjoys only the right either to buy (call option) or sell (put option) and he is no way obliged to exercise his right. If the situation is not favorable to the buyer, he can simply walk out of the contract and the maximum loss he has to incur is the premium paid for buying the contract. Thus, the risk or loss is certain as well as limited in the case of an option buyer. On the other side, the return to the buyer may be fairly large and no limit can be placed in advance. In the case of a call option buyer, profit emerges when the market value of the contracted asset exceeds the strike price, and it goes on increasing so long as the price of the asset is moving up. Similarly, a put option holder makes profit when the market value of the asset in question decreases below the strike price, and every fall in the value of the asset will add up to his profit. From the option seller’s point of view, the profit is fixed while the risk or loss is unlimited. This is due to the fact that the seller is obliged to honour the rights of the option buyer. Take the case of a call option writer. If the price of the asset is going up and surpassing the strike price, the option buyer will exercise his/her right to buy and the option seller may have to buy the security from the market at a higher price in order to honour his/her commitment. No limit can be placed to such losses in advance. On the other hand, his/her income from this deal is limited to the premium he/she received earlier. The case of a put option writer too is not different in case the value of the contracted asset is falling. As the market price of the asset falls below the strike price, the put option buyer will exercise his/her right to sell, and this

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right has to be honoured by the option writer by purchasing the asset at the strike price. The loss will continue to widen with every fall in the value of the asset in relation to the strike price. On the other hand, the maximum income to the option seller is limited to the extent of the premium received. Is there a way out to minimise losses of option sellers? Yes. As in the case of option buyers, option writers too have the freedom to square up their positions by entering into an opposite transaction. For example, a call option seller can square up his sale position by purchasing a call option on the same asset. Similarly, a sale position on a put contract can be settled by purchasing a put option on the same asset. In both the cases, care should be taken to ensure that the options bought are identical to the options sold in all respects like strike price, expiry date etc. Needless to say, options are bought or sold on the basis of the expectation of the trader regarding the probable price movements of the underlying asset in future. For example, a call option is bought when the buyer expects an increase in the price of the asset before the contract expires, and he/she hopes to make a profit by executing his/her right (purchase at the strike price) and then selling the asset at the market price which is expected to be higher than the strike price. On the other hand, the seller of the call expects that an upward movement in the price of the asset is quite unlikely and hence the contract buyer would not come forward to exercise his/her right. Similarly, a put option is bought on the anticipation that the price of the asset would fall, whereas the put option is written on the expectation that the asset price may go up or remain steady during the tenure of the contract. When any of these expectations is belied, the person (buyer or seller) who is likely to be affected will square up his/her position by entering into an opposite transaction instead of waiting for the expiry day of the contract. What is meant by European and American options? In the European model of options, contract buyers are allowed to exercise their right to buy or sell (the asset) on the settlement day alone, which may probably be the expiry day of the contract. However, buying and selling positions could be squared up at any time in the market by entering into a reverse transaction. In American model of options, call or put option holders can settle their claims by exercising the right to buy or sell on any day that falls between the date of entry and the date of expiry. At what price are the options settled? All outstanding contracts on the settlement day or the date of expiry are settled at the settlement price. The settlement price is arrived at on the basis of the market value of the underlying asset on the settlement/expiry day.

FAQs on Options

255

What are covered and naked calls? A call option written with the possession of the underlying asset is a covered call. Possession means the asset is either in hand or the option writer is having a purchase position of the asset in the cash market. Naked call is one where the seller of the call option does not have the possession of the underlying asset. In the case of covered calls, the risk to the option writer is lower as compared to naked calls. Even if the price of the asset is shooting up in the cash market, the covered call option writer will not be affected much since he has already acquired the asset from the market to fulfil his obligation of delivering it to the option buyer. Who can buy put options? Any investor who is ready to pay the put option premium upfront to the exchange can buy put options. Normally, investors with a bearish attitude buy put options. The maximum risk is limited to the extent of premium he pays upfront. The maximum profit is unlimited in the case of a buyer of the put option. What are the risks for an option writer? An option writer’s risk is unlimited, while his/her gains are limited to the premium received. When a physical delivery uncovered call is exercised, the writer will have the obligation to deliver the underlying stock, at the strike price. In the case of cash settlement the writer of the call option has the obligation to pay the difference between exercise price minus strike price of the call reduced by the premium received for writing the call. The writer of a put option will face the risk of loss if the value of the underlying asset falls below the strike price. If it is settled in stock, the writer of the put has the obligation to buy the stock at the strike price, when underlying asset price is below the strike price. Who can be the seller of put options? Any investor who is ready to take the unlimited risk of writing put options can sell put options. The risk in writing a put option is unlimited, so the exchange will collect sufficient collaterals initially. The writer has to pay mark-to-market margins also. If there is deficiency of margins, then the broker will advise the investor to liquidate the position. Generally, institutional investors are writing put options. How is the strike price selected for buying puts? Option premium contains both intrinsic and extrinsic values. Out-of-themoney put options attract high extrinsic value and these strike prices will retain this quality till expiry. But, if you are buying some in-the-money put options, you may pay less for the extrinsic value because very soon these strike prices can become in-the-money options.

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What is the logic behind writing the put options? Investors who find value in stocks at lower levels write the put options. For example, Infosys is trading at Rs. 1800 and an investor is ready to buy Infosys option below Rs. 1700. Here, the investor can sell Infosys put option of 1700 at a premium of Rs. 40. During the period of expiry, if Infosys falls below Rs. 1700, the investor can buy Infosys stock at Rs. 1700 and get Rs. 40 as put premium. If Infosys doesn’t fall below 1700 during the period, the investor can gain Rs. 40 per share. Are writing Nifty puts safer than writing stocks put options? You are aware that Nifty options are European, which means you can trade on premiums, but cannot exercise your options during the period of the contract. As stock options are American, an investor can exercise his option during the maturity. European put option holders have to hold till the expiry for exercising their put option. How do we exercise American put options? Imagine that you are holding Infosys Rs. 1700 put option and your cost of acquisition is Rs. 40 per stock and the lot size is 200. Hence you pay Rs. 8000 as premium. Before the expiry of the contract, the Infosys stock falls below Rs. 1700 and tests Rs. 1500. In this case, you are supposed to get a profit of Rs. 200 per share, and a total of Rs. 40,000 for 200 shares. Unfortunately, due to liquidity problems, the buyer was available only at Rs. 175. That means if you are selling your option you will make a profit of only Rs. 35,000. In this case you can approach your broker and can ask him/her to exercise your put option; if he/she does then you will get the intrinsic value of the option, that is Rs. 200 per share. After getting the intimation from the broker, NSE will find out the perspective put writers of Infosys and inform the assignment. The writer of the Infosys put option has the obligation to provide the intrinsic value to the NSE in exchange of the premium he/she received while writing the put option. Both put options and call options will be exercised only after the closing session, and exercise price will be determined on the weighted closing price, rather than the closing price at 3.30 pm. How do we exercise European put options? American options can be exercised earlier, but European put options cannot be exercised earlier; its exercise will take place only on the expiry date. Both American and European options can be traded on premiums. For example, you bought a Nifty put option at Rs. 120, and after the purchase Nifty falls down and the put option premium increases to Rs. 160. Here the investor will have the freedom to sell the put option at a profit of Rs. 2000 (Rs. 160 – Rs. 120 = 40 ´ 50 (lot size) = Rs. 2000).

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How should one manage purchased put option positions? It is advisable to manage the purchased put option positions. Several times, we have received calls from investors asking what they would do with their purchased put positions though they are at profits. The simple answer for this question is book profits, in another words sell the long put and realize the profit. The other way to manage the profitable long put position is to liquidate existing long and buying a new put at slightly lower strike price. Sometimes, holding the long position of put option and simultaneously selling an out-of-the-money put is also advisable. If you are holding a purchased put position and still you are not in profit, it is advisable to roll over your long put to another preceding month. In other words, exit the long put position and buy a new put position in the far month before the expiry of the contract. Please keep in mind that the intensity of the fall in the premium due to time decay will be higher when the expiry comes closer. What is meant by synthetic put options? Earlier in the US, only call options were traded. At that time, the investors used call options as put options; in other words, they were creating synthetic put options. If you are selling your stocks in the market, simultaneously selling its put options and buying call option is known as synthetic put option. In a bearish market, investors prefer to buy put options than call options. They are even ready to pay more premiums for the purchases of put options than that for calls. In this situation, the call premium tends to stay lower due to lower demand and put premium will stay very high due to high demand. Whenever the situation persists, investors do not buy the expensive put options, but they will sell them. Also, they sell their stocks and buy the cheap calls. Tomorrow if the stock rises above your sale value, there would be no problems as you are holding the call option of the stock and are going to benefit out of it. On the other hand, if the stock falls, there is no need to worry as you have already sold your stock to buy at lower levels, so buy it back. Why derivative strategies? Derivative strategies are tailormade to various situations and different market scenarios. But most of the derivative strategies are well suited for index options because they are European in nature. Simple trading strategies are adopted (bulls spread, bear spread, long straddles, long call, long put, short futures with protective calls and long future with long put) commonly by the investors. Strategy can be a single position or a combination of multiple positions one strategy can be easily converted to another. Risk and reward can be easily estimated, so an investor can create various strategies according to his risk taking capacities. Risk-reward ratios and its probability for success can easily be estimated in the initial phase. Derivative strategies can be created to mitigate market risk, interest rate risk, volatility risk and time value risk. .

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What is the difference between futures and options? Futures contracts have similar risk profile for both the buyer as well as the seller, but in options the risk profile is not the same. In case of options, for a buyer, the downside risk is limited to the premium he has paid while the profits may be unlimited. For a seller of an option, the downside is unlimited while profits are limited to the premium he has received from the buyer. It costs nothing to enter into a futures contract, whereas there is a cost of entering into an options contract known as premium. Futures are contracts to buy or sell specified quantity of the assets at an agreed-upon price by the buyer and seller, on or before a specified time. Both the buyer and the seller are obliged to buy/sell the underlying asset. In case of options the buyer has the right and not the obligation to buy or sell the underlying asset. The futures contract prices are influenced mainly by the prices of the underlying asset. The prices of options are affected by prices of the underlying asset, time remaining for expiry of the contract and volatility of the underlying asset. What is in-the-money option? A call option is said to be in the money when the strike price is lesser than the current price of the underlying asset. For example, a stock option of strike 220 is said to be in the money when the current price of the underlying asset is at Rs. 250 which is greater than the strike price. What is the intrinsic value of an option? Intrinsic value is the value by which the option is said to be in the money and the value can only be positive. Call option intrinsic value = Spot price – Strike price Put option intrinsic value = Strike price – Spot price What is time value of an option? Time value is the amount that the option buyer is ready to pay the seller hoping that the option price will grow, making it profitable when it nears its expiration. The time value of an option cannot be a negative digit. Who decides the option premium? The premium of the option is not fixed by the stock exchange but calculated using different option pricing models, market conditions and of course by competitive bids and offers on the trading platform. The option premium is the sum of the time value and the intrinsic value. What is the need for investing in options? It provides high leverage even as small amount of capital is invested; one can take exposure in the underlying asset of much greater value. The maximum risk involved in the trade is known to the investor in advance. It can also be

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used as a hedge to protect ones equity portfolio from a decline in the market. Hence, by paying a relatively small premium, an investor knows that no matter how far the stock drops, it can be sold at the strike price of the put anytime until the put expires. How can the option be put to use? When an investor hopes the price of an underlying to rise or fall in near future, the investor can get hold of an option which gives him/her the right to buy or sell the stock at a pre-determined price. If the investor expects the underlying asset to rise, then s/he can go for a call option, but if his expectation is a fall in the price of the stock, then s/he may go for a put option which will earn him profit if the stock price falls. How does the settlement of an option take place? The settlement of an option takes place in two ways: one in which the investor can sell an option of the same series which the investor is currently holding and close the position in that option any time before expiration; the other way is to exercise the option on or before the expiration. Who would use index options? Index options are effective enough to a broad spectrum of users, from conservative investors to more aggressive stock market traders. Individual investors may use index options to have an upper hand on market opinions by acting on their views of the broad market or any of its sectors. The more sophisticated market participants may find variety of index option contracts as excellent tools for enhancing market timing decisions and adjusting asset mixes for asset allocation. To market participants, managing the risk involved in large equity positions may mean using index options to mitigate their exposed risk or to increase market exposure. What is SPAN? Standard portfolio analysis (SPAN) of risk is a globally acknowledged risk management system developed by Chicago Mercantile Exchange. It is a portfolio-based margin calculating system adopted by all major derivatives exchanges. It identifies overall risk in a complete portfolio of futures and options, and at the same time recognizes the unique exposures associated with both inter-month and inter-commodity risk relationships. It determines the largest loss that a portfolio might suffer within the period specified by the exchange. What are KYC norms? Know your customer (KYC) norms are mandatory details of customers required by banks and financial institutions before opening of customer’s account. These norms were issued by RBI and came into effect from the

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second half of 2002. This was an attempt to prevent identity theft, identity fraud, money laundering, terrorist financing, etc. from the customer’s side. Mandatory details include proof of identity and residence. Quite often, passport, voter’s ID card, PAN card, driving licence, ration card, electricity or telephone bill, organization letter etc. are proofs of identity. It’s main objective is to restrict money laundering and terrorist financing. The objectives of the KYC framework are of two–fold: (i) to ensure appropriate customer identification and (ii) to monitor transactions of a suspicious nature. What are the account opening procedures with a broker/sub-broker? After selecting a SEBI-approved broker/sub-broker, the first step is to open a trading account with the sub-broker. Firstly, clients have to fill in a client registration form with the broker/sub-broker. Every client should read and understand the Risk Disclosure document, which is issued by the stock exchange, before trading in equities or derivatives segment. The trading member will obtain a signed copy of the same from all clients. Secondly, every client needs to enter into the broker/sub-broker agreement and he/ she has to read carefully the terms and conditions of the agreement, before executing them on a valid stamp paper. The client or his/her authorized signatory should sign on all pages of the agreement. The agreement has also to be signed by the witnesses along with their names and addresses. The client has to give in details such as name, address, copy of client’s PAN card, photo identity documents, details of bank account, proof of residence etc. What is the maximum brokerage chargeable by a broker/sub-broker? The maximum brokerage that can be charged by a broker/sub-broker is 2.5% of the contract price, and there is no stipulation on minimum brokerage that can be charged to the clients. The trade price should be shown separately from the brokerage charged. The maximum brokerage that can be charged is Rs. 0.25 per share/debenture or 2.5% of the contract price per share/ debenture, whichever is higher. Any additional charges that the trading member can charge are securities transaction tax, service tax on brokerage, stamp duty etc. as may be applicable from time to time. The brokerage and service tax are required to be indicated separately in the contract note. What are the different taxes to be paid by the customer? The different taxes payable by the customer includes: · Service tax (charged as 12% of the brokerage) · Education cess on service tax (3% including secondary and higher educational cess at 1% of service tax from May 11, 2007) · Security transaction tax (STT), which is charged based on the volume traded by the client - for cash delivery it is 0.125% - for cash speculation it is 0.025% (sell side only) - for F&O, it is 0.017% (sell side only)

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· Education cess on STT is nil · Exchange levy, which is charged on volume - for cash market, it is 0.0035% - for F&O, it is 0.0021% · Stamp duty, which is charged on volume - for cash delivery, it is 0.01% - for cash speculation, it is 0.002% - for F&O, it is 0.002% What is contract note and trade confirmation? It is mandatory that a stock broker should give trade confirmation and contract notes to the client for every transactions done. The main details that the contract note contains are: à The name, address and the registration number of the member broker with the SEBI. à Dealing office address, telephone number and the code number of the member given by the exchange. à Name of the authorised signatory or partner or the proprietor. à Client name and code number. à The number and kind of security bought or sold by the client. à Trade number and the time at which the trade was executed. à Time of entering the order and the order number. à The purchase/sale rate and the brokerage. à Securities Transaction Tax (STT), Service Tax rates and other charges that are levied by the broker is shown on the contract note. à Appropriate stamps have to be affixed on the contract note or it is mentioned that the consolidated stamp duty is paid. à Signature of the stock broker or the authorized signatory. You have different strategies in option trading. Can we use all these strategies in index and stock options? No. All these strategies are not good in the case of stock options, because these options are American in nature. When should we exit from the strategy? One can hold till date of expiry or can send it for automatic expiration, especially strategies like butterflies. Strategies like long call, long put, etc. can be exited before expiry if there is a good profit.

Summary We have answered frequently asked queries raised by our clients as well as investors. We will answer the questions from investors on a continuous basis through our website www.derivativeforum.com.

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Keywords Covered and Naked calls Time value Tax Levy Trade confirmation

Synthetic Put option KYC Education Cess Stamp Duty

SPAN Intrinsic Value Securities Transaction Service Tax Exchange Contract Note

CHAPTER

14

DERIVATIVE GLOSSARY

14.1 OBJECTIVES The objective of this chapter is to familiarize the investors and practitioners with the terminologies used in options and futures trading.

Alpha The amount an investment’s average rate of return exceeds the riskless rate, adjusted for the inherent systematic risk is known as Alpha. One way to compute alpha is to regress an investment’s excess rate of return against the market portfolio’s excess rate of return. The intercept in this regression is an estimate of the risk-adjusted excess rate of return.

American Depository Receipt (ADR) A receipt showing a claim on certain number of shares in a foreign corporation that a depository bank holds for the U.S. investors.

Arbitrage The art of taking advantage of the price differential of two markets.

Arbitrageur A person who engages in arbitrage activity.

Atlantic Spread Option strategy in which a trader holds long (or short) on an American option and short (or long) on the otherwise identical European option— hence, long (short) on the value of early exercise.

Asset Class A broadly defined generic group of financial assets which includes stocks or bonds.

Ask (Asked) The price at which a dealer is ready to sell. Ordinarily, the ask exceeds the bid and the bid–ask spread is what the dealer stands to make by quickly turning around one unit of product. It is also known as offer, offered or offering price.

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Asset-Backed Security (ABS) An asset-backed security is a type of debt security that is based on pools of assets, or collateralized by the cash flows from a specified pool of underlying assets. Assets are pooled to make otherwise minor and uneconomical investments worthwhile, while also reducing the risks by diversifying the underlying assets.

Asset Swap A swap that converts a fixed (or floating) coupon asset into a floating (or fixed) coupon asset. This is in contrast to the more familiar (liability) swap that converts a fixed (or floating) coupon liability into a floating (or fixed) coupon liability.

At-the-money Forward Having a strike price which equals the forward price.

At-the-money Having a strike price that which equals the spot price.

Basis The difference between spot price of an asset and the futures price of the same asset.

Basis Point A basis point is a unit that is equal to 1/100th of a percentage point.

Basis Risk Risk arising out of widening and narrowing the difference between spot and future price.

Back Months Futures contracts with delivery dates in the more distant future.

Benchmark Portfolio A portfolio against which the investment performance of an investor can be compared for the purpose of determining investment skill. A benchmark portfolio represents a relevant and feasible alternative to the investor’s actual portfolio and is similar in terms of risk exposure.

Best-of-two Option A pay off which equals the maximum of two option payoffs, such as the maximum of a call on asset 1 and a put on asset 2.

Benchmark Notes Agency notes aimed at filling the partial vacuum in the Treasury note market, now that the deficit appears somewhat under control. Fannie Mae began issuing benchmark notes, and Freddie Mac and other agencies have followed. Apparently, the U.S. Treasury is considering halting its auction of two-, three- or five-year notes.

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Bid The price at which a dealer (market maker) is ready to buy. Ordinarily, the bid is less than the ask (q.v.), and the bid–ask spread is what the dealer stands to make by quickly turning around one unit of product.

Bid–Ask Spread The difference between the price that a market-maker is willing to pay for a security and the price at which the market-maker is willing to sell the same security.

Bidder In the context of a corporate takeover, the firm making a tender offer to the target firm.

Bid Price The price at which a market-maker is willing to purchase a specified quantity of a particular security.

Bet Option A binary option.

Binary Option An option with a pay off function that has two levels, such as zero dollars or one million dollars.

Binary Call (Put) Option Typically, a binary call (put) option (q.v.) that pays off nothing if the underlying risk factor is below (above) the strike, and a constant amount if the risk factor exceeds (is below) the strike.

BOBL Futures Option An American option that settles into a BOBL futures (q.v.) contract. Payment of the option premium is ‘futures-style’, which means none of it occurs immediately, and a piece of it occurs with each daily mark-to-market. An implication of this is that the ‘buyer’ (really, the ‘long’) may pay no premium and the ‘seller’ (really, the ‘short’) may pay all the premium!

Book Value of Equity The sum of the cumulative retained earnings and other balance sheet entries classified under stockholder’s equity, such as common stock and capital contributed in excess of par value.

Bowie Bond A specific, $55-million issue of 10-year asset-backed bonds (q.v.) that British rock star David Bowie issued and Prudential Insurance Co. bought. The specific collateral consists of royalties from 25 of Mr. Bowie’s albums that he recorded before 1990.

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Bullet Bond A bond that amortizes fully on a single date. Its cash flows consist of regular coupon payments of interest and a final repayment of principal.

BUND Futures The DTB futures contract on a notional long-term debt security of the German Federal Government or the Treuhandanstalt, with a notional interest rate of 6%. The BUND (q.v.) and other instruments qualify.

Bundle A strip of consecutive, quarterly Eurodollar or Euroyen futures contracts. Markets, such as Simex, offer a bundle as a convenient package of futures contracts, without the execution risk inherent in building up the strip, contract by contract. A trader can use bundles and packs to implement bets on the change in shape of the forwards curve.

Buy-Write An investment strategy that consists of buying an asset and selling a call on it. Thus, the investor sells upside potential to elevate the rest of his/her pay off function.

Callable Bond A (no callable) bullet bond, minus a call option on the bond. The call price as a function of calendar time is the call schedule.

Call Option The right, but not the obligation, to buy the underlying asset at the previously agreed-upon price on (European) or anytime through (American) the expiration date.

Call Market A security market in which trading is allowed only at certain specified times. At those times, persons interested in trading a particular security are physically brought together and a market clearing is established.

Call Money Rate The interest paid by brokerage firms to banks on loans used to finance margin purchases by the brokerage firm’s customers.

Capital Gain (Loss) The difference between the current market value of an asset and the original cost of an asset, with the cost adjusted for any improvement or depreciation in the asset.

Catastrophe Bond A bond that promises a coupon that starts out high, but drops after a suitable catastrophe occurs. A suitable catastrophe might be an earthquake or hurricane of sufficient magnitude and within a particular region.

Clean Price The quoted bond price without the accrued interest.

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Clearing House A cooperative venture among banks, brokerage firms and other financial intermediaries that maintains records of transactions made by member firms during a trading day. At the end of the trading day, the clearing house calculates net amounts of securities and cash to be delivered among the members, permitting each member to settle once with the clearing house.

Commission The fee an investor pays to a brokerage firm for services rendered in the trading of securities.

Common Factor A factor that affects the return on virtually all securities to a certain extent.

Constant Growth Model A type of dividend discount model in which dividends are assumed to exhibit a constant growth rate.

Constant Price Index A cost of living index representative of the goods and services purchased by U.S. consumers.

Contrarian An investor who has opinions opposite to most other investors, leading to actions such as buying recent losers and selling recent winners.

Cost of Carry The differential between the futures and spot prices of a particular asset. It equals the interest foregone less the benefits plus the costs of ownership.

Common Share A sort of call option on the assets of the corporation, because the common shareholder get those assets if he pays off everyone else with a claim against the assets. The common share represents a fractional ownership interest in the corporation; it has voting rights and may receive a dividend.

Concentration Risk According to ‘Risk Concentrations Principles’, which the BIS released in 12/ 99, risk concentrations in financial conglomerates come in seven categories of exposures to: individual counterparties, groups of individual counterparties, counterparties in specified geographical locations, counterparties in industries, counterparties in products, key business services (such as back-office services), and natural disasters.

Contract for Difference An OTC currency forward contract that settles for a cash amount, perhaps in a third currency, without requiring the exchange of the two underlying currencies.

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‘Costless’ Collar A collar in which the proceeds of the sale of the short call option exactly finance the purchase of the long put option.

Coupon Payments The periodic payment of interest on a bond.

Coupon Rate The annual coupon payments in dollar terms made by a bond expressed as a percentage of the bond’s par value.

Covariance A statistical measure of the relationship between two random variables. It measures the extent of mutual variation between two random variables.

Credit Default Swap A swap in which B pays C the periodic fee, and C pays B the floating payment that depends on whether a pre-defined credit has occurred or not. The fee might be quarterly, semiannual or annual. The floating payment would likely occur only once, and might be proportional to the discount of the reference loan below par.

Credit Option on Brady Bonds (COBRA) A credit spread option with a payoff that depends on the yield spread between a Brady bond and another bond—usually, a comparable maturity Treasury.

Currency Swap The exchange of specified amounts of currencies on one (nearby) date, exchange of specified amounts of currencies in opposite directions on a future date, and (possibly) exchange of specified coupons in between. A currency swap is like the exchange of bills, notes or bonds in different currencies.

Defensive Stocks Stocks that have betas-less than one.

Dirty Price The dirty price of a bond represents the value of a bond, exclusive of any commissions or fees. The dirty price is also called the ‘full price’.

Dividends Cash payments made to stockholders by the corporation.

Efficient Market A market for securities in which every security’s price equals its investment value at all times, implying that a specified set of information is fully and immediately reflected in market prices.

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Equal-Weighted Market Index A market index in which all the component securities contribute equally to the value of the index, regardless of the various attributes of those securities.

Equity Swap A swap in which one of the payment streams derives from an equity instrument. For example, in one sort of ordinary equity swap, each period, Party A receives (and Party B pays) the capital gains on an equity investment of a given notional amount, while Party B receives (and Party A pays) a floating interest based on LIBOR and the same notional amount. This swap is practically equivalent to buying the underlying equity with 100% borrowing and realizing the gain or loss in each period.

Eurobond A bond that is offered outside of the country of the borrower and usually outside of the country in whose currency the security is denominated.

Euro LIBOR The British Bankers Association’s Euro-denominated analog to dollar LIBOR. As of January 1999, the European Banking Federation’s Euribor (q.v.) seems to be winning its battle for acceptance over Euro LIBOR, but London still hopes to win the war for the financial business. On 1 July, 1999 LIFFE announced plans for new contracts, based on 5- and 10-year Euribor swaps.

Exotic Option In finance, an exotic option is a derivative which has features that make it more complex than commonly traded products (vanilla options). These products are usually traded over-the-counter (OTC), or are embedded in structured notes.

Expiration Date The date on which the right to buy or sell a security under an option contract ceases.

Financial Leverage The use of debt to fund a portion of an investment.

Forward Rate The interest rate that links the current spot interest rate over one holding period. Equivalently, the interest rate agreed upon at a point in time where the associated loan will be made at a future date.

Flex Option Exchange-traded options that do not have the standard terms of listed options. The customer and the market-maker can negotiate various terms, such as strike price and expiration date.

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Forward Contract** A contract to exchange (buy or sell) an underlying instrument for a fixed forward price at a specific future delivery date. In certain cases—but not always—the forward price exceeds the spot price by the cost of carrying the underlying asset from the spot delivery date to the forward delivery date. The cost of carry is an increasing function of the rate of interest and storage costs, and a decreasing function of the rate of dividends, interest or other cash flows from the underlying instrument (cf. Futures Contract).

Futures Option A listed option that settles into a futures contract.

Greenshoe Option A greenshoe option can provide additional price stability to a security issue because the underwriter has the ability to increase supply and smooth out price fluctuations if demand surges. Greenshoe options typically allow underwriters to sell up to 15% more shares than the original number set by the issuer, if demand conditions warrant such action.

Hedging To offset the potential risks and returns of one position by taking out an opposing position to create an outcome of greater certainty.

Hedge Ratio A ratio comprising the value of a position protected via hedge with the size of the entire position itself.

Indexation A method of linking the payments associated with a bond to the price level in order to provide a certain real return on the bond.

Index Arbitrage An investment strategy that involves buying a stock index futures contract and selling the individual stocks in the index, or selling a stock index futures contract and buying the individual stocks in the index. The strategy is designed to take advantage of a mispricing between the stock index futures contract and the underlying.

Inflation Hedge An asset that preserves the value of its purchasing power over time despite changes in the price level.

Interest Rate Risk The uncertainty in the return on a fixed income security caused by unanticipated fluctuations in the value of the asset owing to changes in interest rates.

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Intrinsic Value of an Option The value of an option if it was exercised immediately. It is same as the market price of the asset upon which a call option is written less the exercise price of the option, or the exercise price less the market price of an asset in the case of put option.

Initial Margin It is the margin which is paid by a investor for the purchase and sale of futures and option of any security. Initial margin is refundable to the investor after the close out of the prevailing contracts.

Jamming Executing a large sell (or buy) order in stages by asking for a market on a small size, hitting the bid (offer) and then repeating the process with a different market-maker, ultimately driving the price considerably lower (higher).

Knock-in Option An option that ‘comes to life’ when a trigger event occurs. Typically when a price crosses a particular barrier, it pulls the trigger (cf. Knock-out Option).

Knock-out Option An option that ‘dies’ when a trigger event occurs. Typically when a price crosses a particular barrier, it pulls the trigger (cf. Knock-in Option).

Ladder Option An option somewhere between a lookback (q.v.) and a European option. A ladder call option has one or more ‘rungs’ (price levels) above the initial spot level. The call’s payoff equals the greater of the European call’s payoff or the excess over strike (q.v.) of the highest rung that the underlying price reaches.

Lambda The expected return premium (above the risk-free rate of interest) per unit of sensitivity to a particular common factor. It is also the sensitivity of the price of an option to changes in its volatility.

Long-term Equity Anticipation Securities (LEAPS) Listed call and put options on shares and indexes with expiration dates as many as two years in future. Ordinary listed calls and puts expire within 9 months. LEAPS permit investors to express longer-term views, without buying the underlying instruments.

Limit Order A trading order that specifies a limit price at which the broker is to execute the order. The trade will be executed only if the broker can meet or better the limit price.

Limit Price The price specified when a limit order is placed with a broker, defining the maximum purchase price or minimum selling price at which the order can be executed.

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Liquidity The ability of investors to convert securities to cash at a price similar to the price of the previous trade in the security, assuming that no significant new information has arrived since the previous trade; in other words, the ability to sell an asset quickly without having to make a substantial price concession.

Margin Account An account maintained by an investor with a brokerage firm in which securities may be purchased by borrowing a portion of the purchase price from the brokerage firm, or may be sold short by borrowing the securities from the brokerage firm.

Margin Call A demand upon an investor by a brokerage firm to increase the equity in the investor’s margin account. The margin call is initiated when the investor’s actual margin falls below the maintenance margin requirement.

Market Risk The risk of loss from being on the wrong side of a bet about a market move.

Modified Duration A measure of the sensitivity of a financial instrument’s value to a change in its yield.

Mark-to-market The process of determining the present market value of a security or derivative position (cf. market contingent credit derivative, mark-to-market swap, mark-to-market cap, swap guarantee).

Money market rates Interest rates on short-term instruments, including bankers’ acceptances, commercial paper, LIBOR and U.S. Treasury bills. The accrual rate to maturity equals the quoted rate times a day count fraction that has 360 in the denominator. The days in the numerator might be actual days or days according to a 30/360 calendar.

Naked Call Writing The process of writing a call option on a stock that the option writer does not own.

Naked Put Writing The process of writing a put option on a stock when the writer does not have sufficient cash (or securities) in his or her brokerage account to purchase the stock.

National Association of Securities Dealers (NASD) NASD operates an automated nationwide communications network that connects dealers and brokers in the over-the-counter market. Nasdaq provides current market-maker bid–asked price quotes to market participants.

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No Deliverable Forward A cash-settled forward contract, typically on a non-convertible or thinly traded foreign currency (probably from an emerging or submerging (q.v.) market) or two such currencies, that settles into a convertible currency (typically the USD). The cash value is a function of the contract’s reference rate(s) on the fixing date, typically two business days before the value date. Its main attraction is avoiding currency controls.

Normal Backwardation A relationship between the futures price of an asset and the expected spot price of the asset on the delivery date of the contract. It states that the futures price will be greater than the expected spot price.

Normal Contango A relationship between the futures price of an asset and the expected spot price of the asset on the delivery date of the contract. Normal contango states that the futures price will be greater than the expected spot price.

Notional Amount Am stated amount in a derivatives contract on which the derivative payments depend. The notional amount is most analogous to the principal amount of a bond.

One-Touch Option An option that pays off as soon as the trigger price touches the barrier. Often, it is a binary option (q.v.).

Option The right, but not the obligation, to buy (call, q.v.) or sell (put, q.v.) an underlying asset at a pre-determined and fixed price, to enter into a long or short futures position, or to receive a payoff that simulates a purchase or a sale.

Par Value The nominal value of shares of common stock as legally carried onto the books of a corporation.

PCS Options The CBOT’s option contracts with the underlying Property Claims Service (PCS) index. Apparently, they operate more or less as a call option on the underlying index, which could be any one of the nine indexes.

Put Option The right, but not the obligation, to sell the underlying asset at the strike price (cf. Call Option).

Rainbow Option An option that has several risk factors of the same type, for example two stock prices or three exchange rates.

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Option Trading

Range Binary Option An option that pays off a fixed amount at expiration if and only if the underlying price remains in the range of the option’s entire life.

Replicating Portfolio A portfolio of securities that either mimics the returns on a derivative security or is part of a trading strategy that mimics those returns.

Sharpe Ratio A measure of investment performance, namely the investment’s average excess rate of return (investment’s rate of return minus riskless rate of return) divided by standard deviation of its rate of return. Thus, the Sharpe ratio measures how many standard deviations the average rate of return is from the riskless rate of return. If the distribution of rate of return was normal and we knew its mean and variance exactly, the Sharpe ratio would provide an idea of the probability that the risky investment would beat a riskless investment.

Short Hedger A hedger who offsets risk by selling futures contracts is called a Short Hedger..

Span Margin Standardized portfolio analysis of risk (span) margin system determines risk on the basis of an entire portfolio. It provides a method to integrate both futures and option contracts and assess one-day risk for a traders account.

Speculator An investor in futures contracts whose primary objective is to make a profit from buying and selling these contracts.

Spot Date The date from which interest starts accruing in a fixed income transaction, in the USD swap market (1999), typically two business days after the transaction date.

Spread Trade A trade that profits from a positive move in one risk factor and a negative move in another.

Step-payment Option A ‘free’ ordinary European option, minus a portfolio of binary options with successively higher or lower strikes. For example, for no premium paid up front, Party A receives a European call option struck at 100 in return for making one payment if the underlying price goes to 98, another if the price goes to 96, etc.

Straddle An option portfolio consisting of one call option and one put option, both with the same underlying, direction (long or short), strike and expiration date.

Derivative Glossary

275

Strap A straddle plus another one of the call options.

Strip A straddle plus another one of the put options.

Structured Product Essentially a portfolio of securities and other (often, Vanilla) derivative products, although the dealer that creates it hopes the customer doesn’t realize this.

Up-and-in Option An option that pays off nothing unless the underlying price rises to an upper barrier (cf. Up-and-out Option).

Up-and-out Option An option that pays off as the corresponding ordinary option unless the underlying price rises to an upper barrier.

Vega It measures the risk exposure to changes in implied volatility and tells option traders how much will an option’s price will increase or decrease as the volatility of the option varies.

Value At Risk (VaR) A measure of the maximum potential change in the value of a portfolio of financial instruments with a given probability over a specified time period.

Vol-Vol The volatility of volatility. This presupposes that volatility is a random market risk factor, which is a lot more reasonable than the original assumption of the incredibly robust Black-Scholes model that it is known and constant.

Volatility The annualized standard deviation of the percentage change in a risk factor.

Warrant A warrant is a call option issued by the company whose securities

Weather Derivatives Derivative products whose values depend on risky weather variables, such as temperature, precipitation or dollar damage from extreme weather.

Summary Though we have discussed many terms, the list not exhaustive. As the market develops further, newer terms will be used in derivative trades.

276

Option Trading

Keywords Alpha Arbitrageur Ask (asked) Asset Swap At-the-money forward Basis point Benchmark Portfolio Big dogs Bidder Binary Option BOBL Futures Option Bullet Bond

Call Option Capital Gain (Loss) Clearing House Constant Growth Model Cost of Carry Common Share Concentration risk Covariance “Costless” Collar

ADR Arbitrage Atlantic spread Asset Class Asset-Backed Security Average Price Call/ Put Option At-the-money Basis Basis Risk Back Mont Best-of-Two Option Benchmark notes Bid Bid-Ask Spread Bid Price Bet Option Binary Call /Put Option Book Value of Equity Bowie Bond BUND Futures Bundle Option Buy-Write Callable Bond Call Market Call Money Rate Catastrophe Bond Commission Constant Price Index

Coupon Payments Credit Default Swap Credit Option on Brady Bonds

Currency swap Defensive Stocks Dividends Equal – Weighted Market Index Equity Swap Eurobond Exotic Option Expiration Date Forward Rate Flex Option Futures Option Green Shoe option Hedge Ratio Indexation Inflation Hedge Interest Rate Risk Intrinsic Value of an option Jamming Knock out Option Ladder Option LEAPS Limit Order Liquidity

Clean price Common Factor Contrarian Contract for Difference Coupon Rate

DAXFutures Option Dirty price

Efficient Market

Euro LIBOR Financial Leverage Forward Contract Hedging Index Arbitrage Knock in Option Lambda Limit Price

Derivative Glossary

Margin Account Mark-to-market Naked Call writing Naked Put writing Normal Contango Option Put Option Replicating Portfolio Short Hedger Spread trade Strap Up-and-in Option Vol-Vol Warrant Weather Derivatives

Margin Call Modified Duration National Association of Securities Dealers No deliverable forward Notional Amount Par Value Rainbow Option

Speculator Step-Payment Option Strip Up-and-out Option Volatility

277

Market Risk Money market rates

Normal Backwardation One-Touch Option PCS Options Range Binary Option Option Sharpe ratio Spot date Straddle Structured product Value at Risk (VaR)

INDEX A A day order 21 Account 88888 199, 202 Account 88888 203, 204, 205, 209 Accounting norms 249 Adesi-Whaley 20 Albatross 228 Alpha 263 Amaranth 218 American 6 American and European options 16 American call options 226 American Depository Receipt (ADR) 263 American option 23 Amortizes 266 An immediate or cancel order 21 Annual volatility 153 Annualized volatility 124 Arbitrage 252, 263 Arbitrage funds 2 Arbitrageur 5, 8, 252, 263 Archives 240 Ask (Asked) 263 Asset class 263 Asset swap 264 Asset-Backed Security (ABS) 264 At the money 15, 23, 157, 264 At-the-money forward 264 Atlantic spread 263 ATM 140 Average abnormal return 150 Ayres 89

B Back months 264 Backwardation 228 Bank of England 203 Banque Nationale de Paris in Tokyo 206

Basic option strategies 195 Basis 264 Basis point 264 Basis risk 264 Baumol 89 Bear spread 228 Bear spread with call 178 Bear spread with puts 175 Bearings 197, 198, 204 Bearings securities 205 Bearings Securities Japan 203 Bearish 157 Behavioural study of nifty options during distress 139 Benchmark notes 264 Benchmark portfolio 264 Best-of-two option 264 Bet option 265 Beta 133, 268 Bhavcopy 110, 240 Bid 265 Bid price 265 Bid–ask spread 265 Bidder 265 Binary call (Put) option 265 Binary option 265 Binomial 20, 101 Binomial model of option pricing 96 Binomial multiple period model 99 Black and Scholes 89 Black–Scholes option pricing model 89 Black–Scholes 20, 101, 125 Black–Scholes model 152 BOBL futures option 265 Bombay Cotton Trade Association 3 Bombay Stock Exchange 8 Bonds 243 Boness 89 Bonus 39 Bonus, stock splits and consolidations 30

280

Index

Book value of equity 265 Bowie bond 265 Breakeven point 157 Brokerage 260 Brokerage charges 252 Bullet bond 266 BUND Futures 266 Bundle 266 Buy-Write 266

C Calculation of Quarter–Sigma order size of stock 34 Calculation of S&P CNX Defty 87 Calendar spread 221 Calendar spread charge 21 Calendar spreads 20 Call market 266 Call money rate 266 Call option 10, 23, 266 Call premium 101 Callable bond 266 Capital gain (loss) 266 Capital gains tax 251 Caplets 8 Caps 8 Caps, floors and collars 8 Carry forward 252 Cash 32 Cash settled options 250 Catastrophe bond 266 CBOE 139 Charges 29 Chen 89 Clean price 266 Clearing house 267 Client level position limits 26 Close out 12 Close out closing buy 23 Closing buy (buy close) 12 Closing sell 23 Closing sell (sell close) 13 CM 18, 22, 23 CNX 100 index 52, 88 CNX 500 88 CNX bank index 51, 88 CNX defty 88

CNX IT index 88 CNX midcap 79, 88 CNX Nifty junior 46 Collars 8 Collateral for margins 32, 39 Collateral limits for trading members 26 Commercial paper 272 Commission 267 Commodity derivatives 5 Commodity exchanges 8 Commodity futures and options 217 Commodity markets 219 Common factor 267 Common share 267 Compound options 6 Compound probability 215 Concentration risk 267 Constant growth model 267 Constant price index 267 Construction of index 42 Consumer price index 41 Contac system 203 Contango 228 Contango and backwardation 220 Contract cycle 26, 39 Contract for difference 267 Contract month 176 Contract note 262 Contrarian 267 Corporate action adjustments 29 Corrado and Miller 132 Correlation 147 Cost of carry 267 Cost-effectiveness 176 ‘Costless’ collar 268 Coupon payments 268 Coupon rate 268 Covariance 268 Covered and naked calls 262 Covered call 228 Covered call writer 214 Covered call writing 213 Credit default swap 268 Credit derivatives 5 Credit option on brady bonds (COBRA) 268 Credit risk 208

Index

CRISIL 42 Cumulative average abnormal return 150 Cumulative normal distribution Currency derivatives 5 Currency swap 268 Cyclical stocks 134

281

Exotic option 6, 269 Expiration date 13, 269 Exposure limit 208 216

D Dealers 4, 8 Deep in the money 15, 23, 226 Deep out of the money 15, 23 Defensive stocks 268 Deficit 264 Delta 155, 158, 169 Delta hedge 228 Delta hedging 157 Delta neutral 157 Derivatives 8 Desirable attributes of an index 43 Diagonal spread 227 Dirty price 268 Dividend 39, 101 Dividend discount model 267 Dividends 30, 268 DOTM 140

E Economic Times 100 41 Economic Times midcap 41 Educational cess 252, 262 Efficient market 268 Eligibility criteria for securities in options trading 33 Equal-weighted market index 269 Equity derivatives 5 Equity index option premium account 249 Equity swap 269 Estimating historical volatility 125 Estimating volatility 125 ET automobiles 41 Euro LIBOR 269 Eurobond 269 European 6 European options 23, 185 Exchange rates 273

F Factors affecting option price 91 Factors affecting the computation of historical volatillity 128 FAQs on options 253 Far-month 220 Final exercise settlement 22 Financial derivatives 5 Financial leverage 269 Fixed deposit receipts (FDRs) 32 Flex option 269 Floating stock 44, 47 Floorlets 8 FMCG 41 Forward contract 270 Forward price 264 Forward rate 269 Forward rate agreements 7 Forwards 6, 8 FRA 8 Fund managers 134 Futures 6, 8, 88 Futures contracts 19 Futures option 270

G Gamma 159, 160, 169 GARCH 125, 133 Generalized autoregressive conditional heteroskeda 133 Generation of strikes 28 Get quote 240 Greek letters 171 Greeks 169 Greenshoe option 270

H Hedge fund 218 Hedge ratio 270 Hedgers 4, 8, 114, 252 Hedging 252, 270 Hedging volatility 163

282

Index

High-dividend-yielding stocks 214 Historical data 240 Historical volatility 16, 154, 225

I ICAI 252 ICICI Bank 144 Ideal conditions for Black–Scholes formula for option pricing 90 IISL 42 Illiquid 172 Impact cost 88, 115, 121 Impacts of events on volatility—A case study 141 Impacts of implied volatility and underlying asset price on purchase of options 136 Implied volatility 16, 130, 142, 154 In the money 15, 23 In-the-money options 214 Income tax 251, 252 Index 88 Index arbitrage 270 Index derivatives 5 Index options, stock options 229 Indexation 270 India VIX 139 Inflation hedge 270 Infosys 142 Initial margin 271 inter-exchange arbitrage 218 Interest rate derivatives 5 Interest rate risk 243, 247, 270 Interim exercise settlement 22 Intrinsic value 14, 256 Intrinsic value of an option 271 Intrinsic value premium 23 IPO 44 IRDs 4 ITM 140

J Jamming 271 JGB 205 JGB arbitrage 203 JGB, Euroyen futures 199

K Knock-in option 271 Knock-out option 271 Kobe 202 Kobe city 198 KYC 262 KYC norms 259

L Lack of supervision 209 Ladder option 271 Lambda 271 Leveraged positions 134 Levy 262 LIBOR 272 Limit order 271 Limit price 271 Liquidity 172, 272 Liquidity (impact cost) 43 Liquidity risk 242, 247 Lognormal distribution 101 Lognormal value 101 Long 12 Long call Christmas trees 183, 228 Long call ladder 191, 228 Long combo 182, 228 Long guts 189, 228 Long iron butterfly 191 Long position 23 Long put 172, 228 Long put ratio spread 177 Long put spread versus short call 193 Long rollover 117 Long straddle 212, 228 Long strangles 195, 228 Long-term Equity Anticipation Securities (LEAPS) 271 Low margin requirement 12 Low risk and high returns 11

M Maikiel and Quandt 89 Margin 23, 209 Margin account 272

Index

Margin call 272 Margin on purchases of options 16 Margin on selling of options 17 Margin requirement 176 Margin requirements for investors 16 Margins for option trading 17 Margins for trading members 17 Mark-to-market 272 Market capitalization 58 Market risk 208, 272, 242 Market sentiment 121 Market Today 240 Market-wide limits 25, 39 Maruti Udyog 144 Matrix management system, 209 Mergers 31, 39 Merton 20, 89 Method of computation 53, 57, 79, 84 Methodology 52, 141 Methodology for adjustment 30 Model risk 245, 247 Modified duration 272 Money 23 Money market rates 272 Multi-period binomial tree 97

N Naked call writing 272 Naked Nifty futures 157 Naked put writing 272 National Association of Securities Dealers (NASD) 272 National Stock Exchange 8 NEAT-F&O trading system 26 Newton-Raphson 132 Nick Leeson 197, 205 Nifty 88 Nifty midcap 50 84, 88 Nikkei 225 198, 199 Nikkei 225 options 199 Nikkei index 198 No deliverable forward 273 Normal backwardation 273 Normal contango 273 Notional amount 273

283

NSCCL 18, 22, 32, 33 NSCCL-SPAN 18 NSE VIX 139 NSE volatility index 138

O OEX options 138 One-touch option 273 ONGC 144 Open interest 13, 23, 121 Open interest and volume analysis 114 Opening buy 23 Opening buy (buy open) 12 Opening sell 23 Opening sell (sell open) 12 Option 273 Option class 13 Option contracts 19 Option Greeks 155 Option price 144 Option series 13 Option volatility 147 Options 6, 8 OSE 200 OSE for long 198 OSE Nikkei 225 198 OTC (over-the-counter) 10 OTC derivatives 6 Other bear market indicators 120 OTM 140 OTM options 143 Out of the money 15, 23 Out-of-the-money calls 214 Out-of-the-money options: A market indicator 113

P Par value 273 Payoff function 265 PC ratio 109, 121 PCS options 273 Plain vanilla options 6 Portfolio beta 134 Portfolio deltas 226 Portfolio hedgers 2

284

Index

Portfolio hedging 134, 226, 228 Portfolio hedging by call writing 226 Portfolio hedging through delta hedge 226 Premium 14 Premium settlement 22 Price bands 16 Price condition 21 Pricing of binomial put option 98 Pricing of equity options 94 Pricing of options on dividend paying scrips 95 Probability 214 Probability of stock price moving up 216 Procedure for margin collection 18 Protective call 158 Punters 134 Put option 10, 273 Put option strike price 23 Put premium 101 Put ratio spread 228 Put–call Parity 103, 121

Q Quarter sigma

34, 39

R Rainbow option 273 Ranbaxy–Daiichi deal 221 Range binary option 274 Ratio rollover 121 Realized volatility 153 Regression 263 Relationship between open interest, volumes and volatility 141 Reliance Industries 144 Replicating portfolio 274 Rho 167, 169 Right skewed curve 151 Rights 31, 39 Risk 247 Risk disclosure document 260 Risk factor 275 Risk of time 247 Risk/reward 155

Riskless profit 121 Rollover 117 Rollover and its impact on futures expiry 119

S S&P CNX 500 57 S&P CNX Defty 87 S&P CNX IT index 50 S&P CNX NIFTY 43 S&P CNX Nifty 41, 47 Samuelson 89 Satyam 143 SBI 143 Scalping 227 Schaeffer’s investment research 112 SEBI regulations 221 Securities 32 Securities contracts regulation act of 1956 3 Securities transaction 262 Securities transaction Tax 252, 261 Sensex 88 service tax 251 Service tax exchange 262 Set off 252 Settlement mechanism 22 Settlement schedule for option contracts 22 Sharpe ratio 274 Short call 173, 228 Short hedger 274 Short option minimum charge 18, 19, 23 Short position 23 Short put albatross 185 Short put ladder 181 Short rollover 117 Short straddle 210, 228 Short straddle versus Put 187 Short strangles 195 Short strip with calls 188 Sigma 34 SIMEX 197, 198, 200, 204, 205, 206, 209 Singapore 205

Index

SPAN intrinsic value 262 Span margin 274 Speculator 274 Speculators 4, 8 Spot date 274 Spot price 153, 264 Spread trade 274 Spread trader 225 Spread trading 217, 228 Sprenkle 89 Stamp duty 251, 262 Standard deviation 153 Standard Portfolio Analysis of Risk 207 Standard Portfolio Analysis of Risk (SPAN) 17, 19 Standardized Normal Distribution Table 102 Step-payment option 274 Stochastic volatility 152 Stock futures 150 Stock options 229 Stock split 39 Straddle 274 Strap 275 Strike price 13 Strip 266 Structured product 275 STT 251 Sub-broker 260 Swaps 7, 8 Swaption 6 Swing trader 157 Switching 199 Synthetic index futures 228 Synthetic put option 262 Synthetic short 179, 228 Systematic risk 263

T T+1 working day settlement 22 Tata Motors 144 Tax 262 Taxation of derivatives 251 TCS 143 ‘Teji’, ‘Mandi’ and ‘Fatak’ 3 Terminologies in options 12

285

Terry J. Watson, 1998 99 Terry J. Watson, Futures and Options in Risk Management 131 The Board of Banking Supervision 203 Theoretical value 164 Theta 164, 169 Thorp and Kssouf 89 Time condition 21 Time decay 157 Time risk 246 Time value 14, 23, 164, 262 TMs 18, 23 Tools to measure market sentiment 112 Trade confirmation 262 Trading interest 58 Trading mechanism 26 Trading member-wise position limits 26 Types of orders 21 Types of volatility 124

U U.S. Treasury 272 UK’s FTSE 100 198 Underlying asset price 156 Underlying stock 176 Units of Mutual Funds & Gilt Funds 32 Unlimited loss 216 Up-and-in option 275 Up-and-out option 275

V Value at Risk (VAR) 34, 275 Variance 162 Vega 132, 161, 169, 275 Vega-neutral 163 Vol-Vol 275 Volatility 16, 23, 142, 154, 275 Volatility arbitrage 153, 154 Volatility change 153, 154 Volatility index 139 Volatility risk 245, 247 Volatility skew 151, 154

286

Index

Volatility smile 130, 154 Volatility trading 137 Volume analysis 121 Volume PC ratio 111

W Warrant 275 Weather derivatives 5, 275 Weather variables 275

Weighted PC 121 Weighted PC ratio 110 Wholesale price index 41 Worst scenario loss 19 Writing of options 23

Y Yield 272

DR. K. SASIDHARAN Dr. K. Sasidharan is currently the Director of Derivative Research Forum and the Chairman of Centre for Resource Development and Research (CRDR), Kochi, Kerala. He has 27 years of banking experience, primarily in credit and foreign exchange (especially currency derivatives), and nine years of teaching experience in Kerala’s premier business schools. He is an approved Research Guide with the Faculty of Management Studies, University of Kerala. Besides, he is a life member of the Institute of Banking and Finance, Mumbai; the Indian Society for Training and Development, New Delhi; and the Institute of Management Development and Research, Trivandrum. He is also a corporate trainer with numerous training programmes to his credit. He has presented papers in national and international seminars, contributed to other books, published research-based and general articles and co-authored a book titled Financial Services and System, published by Tata McGraw Hill Education (2008). Dr. Sasidharan is an Associate Editor of Management Researcher published from Trivandrum.

ALEX K. MATHEWS Alex K. Mathews is currently the Research Head at Geojit BNP Paribas Financial Services Ltd, one of the leading brokerage houses in India based in Kochi, Kerala. As a renowned financial analyst with over two decades of industry experience, Mathews is an empanelled analyst for channels like ETnow, UTVi, CNBC, CNBC Awaz, ZEE News, Manorama News and Doordarshan. He has written several articles in leading newspapers and magazines like Business Line, Business Standard and The Economic Times among others. His views on financial markets and economics have been cited by many international news agencies like Reuters, Bloomberg and Dow Jones. He has co-authored a book titled Financial Services and System published by Tata McGraw Hill Education (2008) and presented many research papers in international conferences. He is a life member and an Academic Council Member of the Centre for Resource Development and Research, and Honorary member of the Derivative Research Forum, Aluva, Kerala.