stock futures on spot market volatility

“Impact of Stock Futures on Spot Market Volatility: A Study on Nifty” .

M.P.Birla Institute of Management, Bangalore

1

A Dissertation Report On

“Impact of Stock Futures on Spot Market Volatility: A study on Nifty”

Submitted in partial fulfillment of requirement for the award of the degree of Master of Business Administration of Bangalore

University

By

ABHISHEK. C SARAFF

Reg. No: 05XQCM6004

Under the Guidance and Supervision Of

Dr. NAGESH. S MALAVALLI

M.P.BIRLA INSTITUTE OF MANAGEMENT

Associate Bharathiya Vidya Bhavan #43, Race Course Road, BANGALORE‐560001

2005-2007



2

DECLARATION I hereby declare that this dissertation entitled “IMPACT OF

STOCK FUTURE VOLATILITY ON SPOT MARKET: A STUDY

ON NIFTY” is the result of my own research work carried out under

the guidance and supervision of Dr. Nagesh S. Malavalli, M P Birla

Institute of Management Bangalore.

I also declare that this dissertation has not been submitted

earlier to any Institute/organization for the award of any degree or

diploma.

Place: Bangalore

Date: Abhishek Saraff



3

CERTIFICATE

I hereby certify that this dissertation entitled “IMPACT OF


ON NIFTY” is the result of research work carried out by Mr.

ABHISHEK SARAFF bearing Reg No: 05XQCM6004 under the

guidance of DR. NAGESH S. MALAVALLI, M P Birla Institute of

Management, Bangalore.

Place: Bangalore Dr N.S.

Malavalli

Date: PRINCIPAL



4

GUIDE CERTIFICATE

I hereby certify that this dissertation entitled “IMPACT OF


ON NIFTY” is an offshoot of the research carried out by Mr.

ABHISHEK SARAFF Reg No: 05XQCM6004 UNDER my guidance

and supervision.

Place: Bangalore

Date: Dr N.S. Malavalli



5

ACKNOWLEDGEMENT

I would like to express my sincere gratitude to my research guide

DR. NAGESH S. MALAVALLI, Principal, M.P.Birla Institute of

Management, Bangalore for his constant encouragement and guidance

in the course of the research investigation.

I would like to thank DR. T.V. NARSHIMA RAO, Adjunct

Faculty, M.P.Birla Institute of Management, Bangalore for his

constant guidance and timely support in the course of the research.

I would also like to thank PROF. S. SANTHANAM, Adjunct

Faculty, M.P.Birla Institute of Management, Bangalore who helped

me to analysis data with his expertise knowledge in statistics.

And further I would like to thank all the faculty members of

MPBIM who have helped me in completing my project. I have gained

a lot of knowledge throughout the course of carrying out this project.

I would like to sincerely thank my Parents and all my Friends

who have helped me in completing this project by providing me with

the psychological and academic support.



6

ABHISHEK SARAFF

CHAPTERS PARTICULARS PAGE NO Abstract 1

1 Introduction 1.1 Background of the study 2 1.2 Dervivatives at NSE 2 1.3 Trading Mechanism 3 1.4 Importance of the study 3

2 Literature Review 2.1 survey of empirical literature 5-18

3 Research Methodology 3.1 Research problem statement 19 3.2 Scope of the study 19 3.3 Objective of the study 19 3.4 Data 20 3.5 Hypothesis of study 20 3.6 Statistical Procedure 21-23 3.7 Actual collection of data 23 3.8 Tools used for testing hypothesis 23 3.9 Sources of secondary data 24 3.10 Softwares used for data analysis 24

4 Data Analysis & Interpretation 4.1 Empirical Results 25 4.2 Presentations of data 26-62 4.3 Interpretation 63

5 Conclusion 64 6 Bibliography 65



7



8

ABSTRACT: The paper examines the impact of stock futures on spot market volatility, i.e.,

Nifty. There are two contradicting schools of thoughts with regards to this: one,

which advocates that the derivatives product has impact on spot market volatility,

while the other, which apposes this. Ever since the introduction of stock futures in

various market all over the world, numerous studies have been carried out upon

the effects of futures listing on underlying cash market volatility.

In India, stock futures trading were started by the National Stock Exchange

with a view to bring stability in the market for commodities and to keep a check

on the spiraling prices of shares. The underlying research work aims at finding out

impact of stock futures on the spot market volatility or whether after introduction

of stock futures have fluctuated drastically compared to before the introduction of

stock futures. For the purpose of determining the above, the spot prices two years prior

the introduction of futures and two year prices after introduction of futures were

obtained from the National Stock Exchange website. The log natural was

calculated on the collected data and subsequently Augmented Dickey Fuller Test

of Stationarity was used to conduct the test of stationarity on the calculated log

natural. Subsequently standard deviations prior and after the introduction of

futures was calculated and the values were subjected to an F-test. Based on the

results of the above we were able to conclude that there is no impact of stock

futures on the volatility of spot market (CNX Nifty).

Also during the course of the literature review various information about

the stock futures were identified i.e. advantages of futures trading, trading, history

of futures trading, technical terminologies and about National Stock exchanges

was learnt during the course of review of the literature.



9



10

Chapter-1

1.1 Background of the Study:

The Indian capital market has witnessed radical changes, especially during the last

decade. Having discarded the age old practices like open outcry trading system,

physical form of shares and new settlement procedure, and others, the markets are

now operating with world class practices and products. The reforms in the capital

markets have helped to improve efficiency in many aspects namely, the

dissemination of information, transparency in operations, prohibiting unfair trade

practices.

The most certain thing about the markets is uncertainty, which leads to

risk. One such risk is financial risk, due to the changes in stock market prices. To

manage such risks, financial instruments, known as financial derivatives, have

been developed and introduced into the Indian capital markets as well as across

the globe, over a period of time. Based on the recommendations of L.C. Gupta

Committee, SEBI permitted the stock exchanges viz., BSE and NSE in India to

introduce financial derivatives in June 2000. The first product was futures on

indices followed by options on indices, individual stock options and futures.

The term derivative implies that it has no independent value. Its value is

derived from the value of the other asset. According to the L.C. Gupta

Committee’s Report, derivatives means a forward future or option contract for a

predetermined fixed duration, linked for the purpose of contract fulfillment to

value of specified real or financial asset or to an index security.

1.2 Derivatives market at NSE: The derivatives trading on the exchange commenced with S&P CNX Nifty Index

futures on June 12, 2000. The trading in index options commenced on June 4,

2001 and trading in options on individual securities commenced on July 2, 2001.

Single stock futures were launched on November 9, 2001. The index futures and

options contract on NSE are based on S&P CNX Nifty Index. Currently, the

futures contracts have a maximum of 3-month expiration cycles. Three contracts

are available for trading, with 1 month, 2 months and 3 months expiry. A new



11

contract is introduced on the next trading day following the expiry of the near

month contract.

1.3 Trading mechanism: The futures and options trading system of NSE, called NEAT-F&O trading

system, provides a fully automated screen–based trading for Nifty futures &

options and stock futures & options on a nationwide basis and an online

monitoring and surveillance mechanism. It supports an anonymous order driven

market which provides complete transparency of trading operations and operates

on strict price–time priority. It is similar to that of trading of equities in the Cash

Market(CM) segment. The NEAT-F&O trading system is accessed by two types

of users. The Trading Members(TM) have access to functions such as order entry,

order matching, order and trade management. It provides tremendous flexibility to

users in terms of kinds of orders that can be placed on the system. Various

conditions like Good-till-Day, Good-till-Cancelled, Goodtill- Date, Immediate or

Cancel, Limit/Market price, Stop loss, etc. can be built into an order. The Clearing

Members(CM) use the trader workstation for the purpose of monitoring the

trading member(s) for whom they clear the trades. Additionally, they can enter

and set limits to positions, which a trading member can take.

1.4 Importance of the study:

The impact of derivatives on the cash market volatility is a much debated and

widely studied research topic. Ever since the Chicago Board of Trade introduced

the commodity futures in 1865, various markets have been studied at different

time periods. The concern over how trading in index futures and options affect the

spot market has been an interesting subject for investors, academicians, regulators

and exchanges. Financial derivatives were introduced in India, mainly as a risk

management tool for both institutional and retail investors. The two main

functions of derivative market are: Price discovery and hedging. The available

literature offers two different kinds of arguments. Some authors like Gupta and

Kumar, Golaka C Nath found that the overall volatility of the underlying stock

market was reduced after introduction of derivative contracts on indices in India.

The other side of the argument is that the volatility of the spot market could



12

increase with the derivatives products because of speculation and arbitrage

strategies.

The results of this study are crucial to investors, stock exchange officials

and regulators. Derivatives play a very important role in the price discovery

process and in completing the market. Their role in risk management for

institutional investors and mutual fund managers need hardly be overemphasized.

This role as a tool for risk management clearly assumes that derivatives trading do

not increase market volatility and risk. The results of this study will throw some

light on the effects of derivative introduction on the efficiency and volatility of the

underlying cash markets.

The present paper attempts to study the impact of introduction of stock

futures on the underlying spot market volatility i.e., Nifty.



13



14

Chapter-2

SURVEY OF THE EMPIRICAL LITERATURE: The introduction of equity index futures markets enables traders to transact large

volumes at much lower transaction costs relative to the cash market. The

consequence of this increase in order flow to futures markets is unresolved on

both a theoretical and an empirical front. Stein (1987) develops a model in which

prices are determined by the interaction between hedgers and informed

speculators. In this model, opening a futures market has two effects; (1). The

futures market improves risk sharing and therefore reduces price volatility, and

(2). If the speculators observe a noisy but informative signal, the hedgers react to

the noise in the speculative trades, producing an increase in volatility.

In contrast, models developed by Danthine (1978) argue that the futures

markets improve market depth and reduce volatility because the cost to informed

traders of responding to mis-pricing is reduced. Froot and Perold (1991) extend

Kyle’s(1985) model to show that market depth is increased by more rapid

dissemination of market-wide information and the presence of market makers in

the futures market in addition to the cash market. Ross (1989) assumes that there

exists an economy that is devoid of arbitrage and proceeds to provide a condition

under which the no-arbitrage situation will be sustained. It implies that the

variance of the price change will be equal to the rate of information flow. The

implication of this is that the volatility of the asset price will increase as the rate of

information flow increases. Thus, if futures increase the flow of information, than

in the absence of arbitrage opportunity, the volatility of the spot price must

change. Overall, the theoretical work on futures listing effects offer no consensus

on the size and the direction of the change in volatility. We therefore need to turn

to the empirical literature on evidence relating to the volatility effects of listing

index futures, index options, stock futures and stock option.



15

BEHAVIOUR OF STOCK MARKET VOLATILITY AFTER DERIVATIVES Golaka C Nath INTRODUCTION The introduction of equity and equity index derivative contracts in Indian market

has not been very old but today the total notional trading values in derivatives

contracts are much ahead of cash market. On many occasions, the derivatives

notional trading values are double the cash market trading values. Given such

dramatic changes, we would like to study the behavior of volatility in cash market

after the introduction of derivatives. Impact of derivatives trading on the volatility

of the cash market in India has been studied by Thenmozhi (2002),

Shenbagaraman (2003), Gupta and Kumar (2002) . Gupta and Kumar (2002) did

find that the overall volatility of underlying market declined after introduction of

derivatives contracts on indices. Thenmozhi (2002) reported lower level volatility

in cash market after introduction of derivative contracts. Shenbagaraman (2003)

reported that there was no significant fall in cash market volatility due to

introduction of derivatives contracts in Indian market. Raju and Karande (2003)

reported a decline in volatility of the cash market after derivatives introduction in

Indian market. All these studies have been done using the market index and not

individual stocks. These studies were conducted using data for a smaller period

and when the notional trading volume in the market was not significant and before

tremendous success of futures on individual stocks. Today derivatives market in

India is more successful and we have more than 3 years of derivatives market.

Hence the present study would use the longer period of data to study the behavior

of volatility in the market after derivatives was introduced. The study would use

indices as well as individual stocks for analysis.

METHODOLOGY This study uses the daily stock market data from January 1999 (for IGARCH data

used is from January 1998) to October 2003. Thus the daily returns are calculated

using the following equation:



16

Where Rt is the daily return, Pt is the value of the security on day t and Pt-1 is the

value of the security on day t-1.

Standard deviation of returns is calculated using the following methods:

Where R is the average return over the period. This study calculates the rolling

standard deviation for 1 year window as well as for a 6 month window to capture

the conditional dynamics. Next volatility is calculated using Risk Metrics method

with l = 0.94 (IGARCH) and the initial volatility was computed using one year

data from January 1998 to December 1998. Then we have used a GARCH model

to estimate the daily volatility. In the linear ARCH (q) model originally

introduced by Engle (1982), the conditional variance ht is postulated to be a linear

function of the past q squared innovations.

In empirical applications of ARCH (q) models a long lag length and a large

number of parameters are often called for. An alternative and more flexible lag

structure is often provided by the generalized ARCH, or GARCH (p, q) model

proposed independently by Bollerslev (1986) and Taylor (1986). In many

applications especially with daily frequency financial data the estimate for a1 +a2

+ ... +aq + b1 + b2 + ... + b p turns out to be very close to unity. Engle and

Bollerslev (1986) were the first to consider GARCH processes with a1 + a2 + ...

+a q + b1 + b2 + ... + b p = 1 as a distinct class of models, which they termed

integrated GARCH (IGARCH). In the IGARCH class of models a shock to the

conditional variance is persistent in the sense that it remains important for future

forecasts of all horizons.



17

DATA and DATA CHARACTERISTICS The study uses two benchmark indices: S&P CNX NIFTY and S&P CNX NIFTY

JUNIOR along with selected few stocks for studying the volatility behavior during

the period January 1999 to October 2003. 20 stocks have been considered a from

the NIFTY and Junior NIFTY category. Out of the 20 stocks, 13 have single stock

futures and options while 7 do not have the same. Futures and options are

available on S&P CNX NIFTY but not on S&P CNX NIFTY Junior.

CONCLUSION The paper studies the behavior of volatility in equity market in pre and post

derivatives period in India using static and conditional variance. Conditional

volatility has been modeled using four different method: GARCH(1,1), IGARCH

with l = 0.94, one year rolling window of standard deviation and a 6 month rolling

standard deviation. We have considered 20 stocks randomly from the NIFTY and

Junior NIFTY basket as well as benchmark indices itself. Also static point

volatility analysis has been used dividing the period under study among various

time buckets and justified the creation of such time buckets. While studying

conditional volatility it is observed that for most of the stocks, the volatility has

come down in the post derivative period while for only few stocks in the sample

(details are in Annexure II and III) the volatility in the post derivatives has either

remained more or less same or has increased marginally. All these methods

suggested that the volatility of the market as measured by benchmark indices like

S&P CNX NIFTY and S&P CNX NIFTY JUNIOR have fallen after in the post

derivatives period.

The finding is in line with the earlier findings of Thenmozhi (2002),

Shenbagaraman (2003), Gupta and Kumar (2002) and Raju and Karande (2003).

The earlier studies used shorter period of data and pre single stock futures and

options period data while we have used data for a longer period that has taken into

account various cyclical trends into consideration.



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EFFECT OF INTRODUCTION OF INDEX FUTURES ON STOCK MARKET VOLATILITY: THE INDIAN EVIDENCE O.P. Gupta INTRODUCTION

The Indian capital market has witnessed a major transformation and structural

change during the past one decade or so as a result of on going financial sector

reforms initiated by the Government of India since 1991 in the wake of policies of

liberalization and globalization. The major objectives of these reforms have been

to improve market efficiency, enhancing transparency, checking unfair trade

practices, and bringing the Indian capital market up to international standards. As

a result of the reforms several changes have also taken place in the operations of

the secondary markets such as automated on-line trading in exchanges enabling

trading terminals of the National Stock Exchange (NSE) and Bombay Stock

Exchange (BSE) to be available across the country and making geographical

location of an exchange irrelevant; reduction in the settlement period, opening of

the stock markets to foreign portfolio investors etc. In addition to these

developments, India is perhaps one of the real emerging markets in South Asian

region that has introduced derivative products on two of its principal existing

exchanges viz., BSE and NSE in June 2000 to provide tools for risk management

to investors. There had, however, been a considerable debate on the question of

whether derivatives should be introduced in India or not. The L.C. Gupta

Committee on Derivatives, which examined the whole issue in details, had

recommended in December 1997 the introduction of stock index futures in the

first place (1). The preparation of regulatory framework for the operations of the

index futures contracts took another two and a half-year more as it required not

only an amendment in the Securities Contracts (Regulation) Act, 1956 but also the

specification of the regulations for such contracts. Finally, the Indian capital

market saw the launching of index futures on June 9, 2000 on BSE and on June

12, 2000 on the NSE. A year later options on index were also introduced for

trading on these exchanges. Later, stock options on individual stocks were

launched in July 2001. The latest product to enter in to the derivative segment on

these exchanges is contracts on stock futures in November 2001. Thus, with the

launch of stock futures, the basic range of equity derivative products in India



19

seems to be complete day volatility. It has been estimated by using Parkinson’s

[1980] extreme value estimator, which is considered to be more efficient.

The Data The data employed in the study consists of daily prices of two major stock market

indices viz., the S&P CNX Nifty Index (henceforth Nifty Index) and the BSE

Sensex (BSE Index) for a four year period from June 8, 1998 to June 30, 2002.

For each of these indices four sets of prices were used. These were daily high,

low, open, and close prices. Likewise, daily high, low, open, and close prices were

used from June 9, 2000 to March 31, 2002 for the BSE Index Futures (7) and from

June 12, 2000 to June 30, 2001 for the Nifty Index Futures. The necessary data

have from collected from the Derivative Segments of both of these exchanges.

CONCLUSIONS This paper has been aimed at examining the impact of index futures introduction

on stock market volatility. Further, it has also examined the relative volatility of

spot market and futures market. The study utilized daily price data (high, low,

open and close) for BSE Sensex and S&P CNX Nifty Index from June 1998 to

June 2002. Similar data from June 9, 2000 to March 31, 2002 have also been used

for BSE Index Futures and from June 12, 2000 to June 30, 2002 for the Nifty

Index Futures. The study has used four measures of volatility: (a) the first is based

upon close-to-close prices, (b) the second is based upon open-to-open prices, (c)

the third is Parkinson’s Extreme Value Estimator, and (d) the fourth is Garman-

Klass measure volatility (GKV).

The empirical results reported here indicate that the over-all volatility of the

underlying stock market has declined after the introduction of index futures on

both the indices in terms of all the three measures i.e. ln (Ct/Ct-1) ln (Ot/Ot-1) and

ln (Ht/Lt). It must, however, be noted that since the introduction of index futures

the Indian stock market has witnessed several changes in its market micro-

structure such as the abolition of the traditional `badla system, reduction in the

trading cycle etc. Therefore, these results should be interpreted in the light of

these changes. However, there is no conclusive evidence, which suggests that, the



20

futures volatility is higher (lower) in comparison to the underlying stock market

for both the indices in terms of all the four measures of volatility. In fact, there is

some evidence that the futures volatility is lower in some months in comparison to

the underlying stock market for both of these indices. These results are in contrast

to those reported for the other emerging markets. The study, being first in the

Indian context, has several policy implications for regulators, policy makers, and

investors.



21

DO FUTURES AND OPTIONS TRADING INCREASE STOCK MARKET VOLATILITY? Dr. Premalata Shenbagaraman

INTRODUCTION

In the last decade, many emerging and transition economies have started

introducing derivative contracts. As was the case when commodity futures were

first introduced on the Chicago Board of Trade in 1865, policymakers and

regulators in these markets are concerned about the impact of futures on the

underlying cash market. One of the reasons for this concern is the belief that

futures trading attract speculators who then destabilize spot prices. This concern is

evident in the following excerpt from an article by John Stuart Mill (1871):

“The safety and cheapness of communications, which enable a deficiency

in one place to be, supplied from the surplus of another render the fluctuations of

prices much less extreme than formerly. This effect is much promoted by the

existence of speculative merchant. Speculators, therefore, have a highly useful

office in the economy of society”.

Since futures encourage speculation, the debate on the impact of speculators

intensified when futures contracts were first introduced for trading; beginning

with commodity futures and moving on to financial futures and recently futures on

weather and electricity. However, this traditional favorable view towards the

economic benefits of speculative activity has not always been acceptable to

regulators. For example, futures trading were blamed by some for the stock

market crash of 1987 in the USA, thereby warranting more regulation. However

before further regulation in introduced, it is essential to determine whether in fact

there is a causal link between the introduction of futures and spot market

volatility. It therefore becomes imperative that we seek answers to questions like:

What is the impact of derivatives upon market efficiency and liquidity of the

underlying cash market? To what extent do derivatives destabilize the financial

system, and how should these risks be addressed? Can the results from studies of

developed markets be extended to emerging markets?



22

This paper seeks to contribute to the existing literature in many ways. This is the

first study to examine the impact of financial derivatives introduction on cash

market volatility in an emerging market, India. Further, this study improves upon

the methodology used in prior studies by using a framework that allows for

generalized auto-regressive conditional heteroskedasticity (GARCH) i.e., it

explicitly models the volatility process over time, rather than using estimated

standard deviations to measure volatility. This estimation technique enables us to

explore the link between information/news arrival in the market and its effect on

cash market volatility. The study also looks at the linkages in ongoing trading

activity in the futures market with the underlying spot market volatility by

decomposing trading volume and open interest into an expected component and

an unexpected (surprise) component. Finally this is the first study to our

knowledge that looks at the effects of both stock index futures introduction as well

as stock index options introduction on the underlying cash market volatility. The

results of this study are crucial to investors, stock exchange officials and

regulators. Derivatives play a very important role in the price discovery process

and in completing the market. Their role in risk management for institutional

investors and mutual fund managers need hardly be overemphasized. This role as

a tool for risk management clearly assumes that derivatives trading do not increase

market volatility and risk. The results of this study will throw some light on the

effects of derivative introduction on the efficiency and volatility of the underlying

cash markets.

METHODOLOGY One of the key assumptions of the ordinary regression model is that the errors

have the same variance throughout the sample. This is also called the

homoskedasticity model. If the error variance is not constant, the data are said to

be heteroskedastic. Since ordinary least-squares regression assumes constant error

variance, heteroskedasticity causes the OLS estimates to be inefficient. Models

that take into account the changing variance can make more efficient use of the

data. There are several approaches to dealing with heteroskedasticity. If the error

variance at different times is known, weighted regression is a good method. If, as

is usually the case, the error variance is unknown and must be estimated from the



23

data, one can model the changing error variance. In the past, studies of volatility

have used constructed volatility measures like estimated standard deviations,

rolling standard deviations, etc, to discern the effect of futures introduction. These

studies implicitly assume that price changes in spot markets are serially

uncorrelated and homoskedastic. However, findings of heteroskedasticity in stock

returns are well documented (Mandelbrot 1963), Fama (1965), Bollerslev (1986).

Thus the observed differences in variances from models assuming

homoskedasticity may simply be due to the effect of return dependence and not

necessarily due to futures introduction. The GARCH model assumes conditional

heteroscedasticity, with homoskedastic unconditional error variance. That is, the

model assumes that the changes in variance are a function of the realizations of

preceding errors and that these changes represent temporary and random

departures from a constant unconditional variance, as might be the case when

using daily data. The advantage of a GARCH model is that it captures the

tendency in financial data for volatility clustering. It therefore enables us to make

the connection between information and volatility explicit, since any change in the

rate of information arrival to the market will change the volatility in the market.

Thus, unless information remains constant, which is hardly the case, volatility

must be time varying, even on a daily basis.

The impact of stock index futures and option contract introduction in the Indian

market is examined using a unvaried GARCH (1, 1) model. The time series of

daily returns on the S&P CNX Nifty Index is modeled as a univariate GARCH

process. Following Pagan and Schwert (1990) and Engle and Ng (1993), we need

to remove from the time series any predictability associated with lagged world

returns and/or day of the week effects. Further, it is required to control for the

effect of market wide factors, since one is interested in isolating the unique impact

of the introduction of the futures/options contracts. Fortunately for the Indian

stock market there is an index, the Nifty Junior, which comprises stocks for which

no futures contracts are traded. As such, it serves as a perfect control variable for

us to isolate market wide factors and thereby concentrate on the residual volatility

in the Nifty as a direct result of the introduction of the index derivative contracts.

Therefore the study introduces the return on the Nifty Junior index as an

additional independent variable



24

CONCLUSION In this study one has examined the effects of the introduction of the Nifty futures

and options contracts on the underlying spot market volatility using a model that

captures the heteroskedasticity in returns that characterize stock market returns.

The results indicate that derivatives introduction has had no significant impact on

spot market volatility. This result is robust to different model specifications.

However, futures introduction seems to have changed the sensitivity of nifty

returns to the S&P500 returns. Also, the day-of-the week effects seem to have

dissipated after futures introduction.

Later the model is estimated separately for the pre and post futures period and

finds that the nature of the GARCH process has changed after the introduction of

the futures trading. Pre-futures, the effect of information was persistent over time,

i.e. a shock to today’s volatility due to some information that arrived in the market

today, has an effect on tomorrow’s volatility and the volatility for days to come.

After futures contracts started trading the persistence has disappeared. Thus any

shock to volatility today has no effect on tomorrow’s volatility or on volatility in

the future. This might suggest increased market efficiency, since all information is

incorporated into prices immediately.

Next, using a procedure inspired by Bessembinder and Sequin (1992), it is found

that after the introduction of futures trading, one is unable to pick up any link

between the volume of futures contracts traded and the volatility in the spot

market.



25

Impact of Financial Derivative Products on Spot Market Volatility: A Study on Nifty S V Ramana Rao

Introduction

The Indian capital market has witnessed radical changes, especially during the last

decade. Having discarded the age old practices like open outcry trading system,

physical form of shares and new settlement procedure, and others, the markets are

now operating with world class practices and products. The reforms in the capital

markets have helped to improve efficiency in many aspects namely, the

dissemination of information, transparency in operations, prohibiting unfair trade

practices.

The most certain thing about the markets is uncertainty, which leads to

risk. One such risk is financial risk, due to the changes in stock market prices. To

manage such risks, financial instruments, known as financial derivatives, have

been developed and introduced into the Indian capital markets as well as across

the globe, over a period of time. Based on the recommendations of L.C. Gupta

Committee, SEBI permitted the stock exchanges viz., BSF and NSE in India to

introduce financial derivatives in June 2000. The first product was futures on

indices followed by options on indices, individual stock options and futures.

The term derivative implies that it has no independent value. Its value is

derived from the value of the other asset. According to the L.C. Gupta

Committee’s Report, derivatives means a forward future or option contract for a

predetermined fixed duration, linked for the purpose of contract fulfillment to

value of specified real or financial asset or to an index security.

The impact of derivatives on the cash market volatility is a much debated

and widely studied research topic. Ever since the Chicago Board of Trade

introduced the commodity futures in 1865, various markets have been studied at

different time periods. The concern over how trading in index futures and options

affect the spot market has been an interesting subject for investors, academicians,

regulators and exchanges. Financial derivatives were introduced in India, mainly

as a risk management tool for both institutional and retail investors. The two main

functions of derivative market are: Price discovery and hedging. The available

literature offers two different kinds of arguments. Some authors like Gupta and



26

Kumar, Golaka C Nath found that the overall volatility of the underlying stock

market was reduced after introduction of derivative contracts on indices in India.

The other side of the argument is that the volatility of the spot market could

increase with the derivatives products because of speculation and arbitrage

strategies.

The results of this study are crucial to investors, stock exchange officials

and regulators. Derivatives play a very important role in the price discovery

process and in completing the market. Their role in risk management for

institutional investors and mutual fund managers need hardly be overemphasized.

This role as a tool for risk management clearly assumes that derivatives trading do

not increase market volatility and risk. The results of this study will throw some

light on the effects of derivative introduction on the efficiency and volatility of the

underlying cash markets.

The present paper attempts to study the impact of introduction of index

futures, index options, stock options and stock futures on the underlying spot

market volatility i.e., Nifty.

Methodology

The study is carried out using ordinary linear regression. The impact of the

introduction of derivative products on the volatility of Nifty-—the spot market

under study—is determined by comparing its volatility before and after the

introduction of derivative products. This can be done by calculating the

descriptive statistics, after eliminating the effect of various factors. For instance,

to nullify the effect of various market-wide factors, Nifty 500 taken into account,

which is a broad-based index for the Indian capital market; similarly to eliminate

the yesterday’s impact on the today’s market, Lag Nifty has also been included in

the regression equation. In this study, the Nifty volatility is regressed with Nifty

500, Lag Nifty and a dummy variable. The dummy variable assumes the value of

1’ for the post-derivative products period and 0’ for the pre-derivative products

period. The sign of the dummy coefficient signifies a fall or rise in the volatility

with the inception of derivative products. The data has been analyzed using



27

ordinary least square technique. To examine the impact of derivative products on

the volatility of the spot market, the following regression model has been used.

Nt =β0 + β1 Nifty 500 + β2 Dt

Nt is the standard deviation of Nifty return series

Nifty 500 represents the standard deviation of Nifty 500 return series is the

dummy variable.

Hypothesis

Using the above methodology, the following hypothesis was tested.

H0: Derivative products like index futures, index options, stock options and stock futures

influence the underlying spot market volatility, i.e., Nifty.

H1: Derivative products like index futures, index options, stock options and stock

futures do not influence the underlying spot market volatility, i.e., Nifty.

Conclusion

This study has examined the impact of index futures, index options, stock option

and stock futures on the volatility of Nifty. A regression analysis has been done to

examine the changes in volatility with the help of dummy variables. The

coefficients of the dummy variables are positive, indicating that Nifty volatility

has increased with the introduction of the above- mentioned derivative products

such as index futures, index options, stock options and stock futures. This may be

due to the speculative operations and the FII’s active participation in the market.

The market players might have been attracted to the futures and options segment,

because of low transaction costs and the leverage advantage available in the

market. Index trading may not be blamed for the volatility in the spot market

because, in an excessively volatile cash market, the fear among investors

motivates them to engage in more hedging activities in the future market, which in

turn leads to volatility.



28



29

CHATER-III

3.1 RESEARCH PROBLEM STATEMENT In the last decade, many emerging and transition economies have started

introducing the derivatives contracts. As was the case when commodity trading

were first introduced on Chicago Board of Trading in 1865, policy makers and

regulators were worried about impact of future on the underlying cash market.

One of the reason for this concern was futures trading attracts speculators who

then destabilize the spot market. In India too, Stock Future were introduced

during November 2001 with the purpose of offering the investors a hedging tool

to minimize their risk aroused mixed feeling amongst the inventors. The general

belief was, after the introduction Stock Future the market has become more

volatile. Implying there is more return at the cost of more risk. The purpose of this

study is to test the impact of index futures on spot market volatility.

3.2 SCOPE OF THE STUDY The scope of the study is to find out: Whether the introduction of stock futures

reduces stock market volatility. The Study does not intend to find out whether

changes any other international markets affected the market during the same

period.

3.3 Objective of the study: The introduction of derivatives products in Indian capital market has

not been very old, but today the notional trading values in the future

trading valves in the futures is ahead of cash market. Sometimes, the

notional trading value of the derivatives is higher than the cash market

trading values. The purpose of the study is to examine the impact of

introduction of stock futures on the underlying spot market volatility,

i.e., Nifty.



30

3.4 DATA 3.4.1 Nature of data: The nature of the data for the above study will be a time series secondary data

showing heteroskedastic nature.

3.4.2 Sources of data: The data employed in the study consists of daily closing prices of 40 stocks traded

on the CNX Nifty. These data will be collected from National Stock Exchange

website.

3.4.3 Data Period: Data is collected for 4 years.

Before introduction: 9th Nov 1999 to 9th Nov 2001

After introduction: 10th Nov 2001 to 7th Nov 2003

3.5 HYPOTHESIS OF STUDY: H0: Introduction of Stock Futures has no impact on the volatility of

Spot

Market.

H1: Introduction of Stock Futures has an impact on the volatility of

Spot

Market.



31

3.6 STATISTICAL PROCEDURE

3.6.1 CALCULATION OF LOG NATURALS: As this volatility is calculated using historical prices this is called Historical

volatility. We have used the daily stock market data from June 1995 to May 2005.

We have calculated daily returns using the following equation:

Rt = LN (Pt/Pt-1)*100 Where Rt is the daily return, Pt is the value of the security on day t and Pt-1 is the

value of the security on day t-1.

3.6.2 UNIT ROOT TEST: A unit root test tests whether a unit root is present in an autoregressive model. The

most famous test is the Dickey-Fuller test. Another test is the Phillips-Perron test.

Theory of Stationarity: Following are different ways of thinking about whether a time series variable Xt is

stationary or has a unit root:

In the AR (1) model, if F=1, then X has a unit root. If |F| <1 then X is

stationary.

If X has a unit root, then its autocorrelations will be near one and will not

drop much as a lag length increases.

If X has a unit root, then it will have a long memory. Stationary time series

do not have long memory.

If X has a unit root then the series will exhibit trend behavior.

If X has a unit root, then DX will be stationary. For this reason, series with

unit root are often referred to as difference stationary series.

The stationarity condition of the data series used in the study has been

tested using Augmented Dickey Fuller Test.



32

Augmented Dickey-Fuller Test (Unit root testing) In Statistics and econometrics, an Augmented Dickey-Fuller test (ADF) is a

test for a unit root in a time series sample. It is an augmented version of the

Dickey-Fuller test to accommodate some forms of serial correlation.

Testing Procedure

The testing procedure for the ADF test is the same as for the Dickey-Fuller test

but it is applied to the model.

Where μ is a constant, β the coefficient on a time trend and p the lag order of the

autoregressive process. Imposing the constraints μ = 0 and β = 0 corresponds to

modeling a random walk and using the constraint β = 0 corresponds to modeling a

random walk with a drift. By including lags of the order p the ADF formulation

allows for higher-order autoregressive processes. This means that the lag length p

has to be determined when applying the test. One possible approach is to test

down from high orders and examine the t-values on coefficients.

The unit root test is then carried out under the null hypothesis γ = 0 against the

alternative hypothesis of γ < 0. Once a value for the test statistic computed it can

be compared to the relevant critical value for the Dickey-Fuller Test. If the test

statistic is less than the critical value then the null hypothesis of γ = 0 is rejected

and no unit root is present.



33

3.6.3 STANDARD DEVIATION: The standard deviation of Log Natural returns is calculated using the following

methods:

Where R is the average return over the period.

3.6.4 F- TEST:

F- Test = (Standard Deviation 1)2

(Standard Deviation 2)2

3.7 Actual collection of data: The data includes the following:

1) Two years prior to introduction of Stock Futures

2) Two years after the introduction of Stock Futures

3.8 Tools used for testing of hypothesis:

The following statistical tools were used to analyze the data:

1) Log Natural

2) Augmented Dickey Fuller Test (for stationarity)

3) Standard Deviation

4) F- Test



34

3.9 Source of Secondary data:

• Books and Journals

• Capital Line

• Internet

• J Stor

3.10 Other software used for data analysis:

The following software’s were used for data analysis:

1) SPSS

2) E-VIEWS

3) EXCEL SPREADSHEET



35



36

EMPIRICAL RESULTS

Daily closing prices for 40 stocks of CNX Nifty were obtained from

www.nseindia.com over the period 9th Nov 1999 to 9th Nov 2003. The data

comprises 502 observations for each stock related to the period prior to the

introduction of Stock Future and the remaining 499 observations for each stock to

the period after the introduction of Stock Futures. Continuously compounded

percentage returns are estimated as the log price relative. That is for an index with

daily closing price Pt, its return Rt is defined as log (Pt/Pt-1). All the return series

(before and after introduction period) are subjected to Augmented Dickey Fuller

test. The data under consideration is stationary and unit root test doesn’t exist.

The tables below show the calculations of monthly standard deviation of all 40

companies for prior and after introduction of stock futures. Then the average of

standard deviation is calculated for prior and after introduction is calculated. The

F -test is computed on the average standard deviation. If the F-test value is less

than tabulated value, the null hypothesis is accepted which implies that the

introduction of stock futures has no impact on spot market volatility. It can be

observed from the graph that the Post- futures volatility is more compared to Pre-

future period. This broadly suggests that the introduction of stock futures has not

stabilized the spot market. The inference cannot be drawn from these figures

alone, as they are not supported by all statistical data.



37

ABB (Monthly Standard Deviation):

ABB DATE Before DATE After

Nov-99 0.06152 Nov-01 0.02451Dec-99 0.03006 Dec-01 0.01283Jan-00 0.02967 Jan-02 0.012Feb-00 0.04568 Feb-02 0.02673Mar-00 0.03624 Mar-02 0.01979Apr-00 0.03326 Apr-02 0.0189

May-00 0.02341 May-02 0.01889Jun-00 0.02819 Jun-02 0.01764Jul-00 0.02953 Jul-02 0.01368

Aug-00 0.02139 Aug-02 0.00737Sep-00 0.01344 Sep-02 0.0117Oct-00 0.02005 Oct-02 0.03845




Avg 0.0265 Avg 0.0276 F test 1.0795

Interpretation:

If F-Test value is above 2.08 tabulated value = Significant

If F-Test value is below 2.08 tabulated value = Non-significant

Since F-Test value is less than tabulated value, null hypothesis is accepted

and there is no impact of stock futures on volatility of spot market.

ABB Share Price (Before)

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

1 27 53 79 105 131 157 183 209 235 261 287 313 339 365 391 417 443 469 495

No. of Obse v a t i ons

ABB Share Price (After)

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

1 22 43 64 85 106 127 148 169 190 211 232 253 274 295 316 337 358 379 400

No. of Obse r v a t i ons



38

ACC (Monthly Standard Deviation):

ACC DATE Before DATE After







Avg 0.034 Avg 0.028 F test 0.67

Interpretation:





Acc Share Price (Before)

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

1 21 41 61 81 101 121 141 161 181 201 221 241 261 281 301 321 341 361 381

No. of Obse r v a t i on

ACC Share Price (After)

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

1 22 43 64 85 106 127 148 169 190 211 232 253 274 295 316 337 358 379 400

N o. of Obse r v a t i on



39

Arvind Mills (Monthly Standard Deviation):

Arvind Mills DATE Before DATE After







Avg 0.04 Avg 0.04

F test 1.03

Interpretation:





Arvind Mills Share Price (Before)

-0.1500

-0.1000

-0.0500

0.0000

0.0500

0.1000

0.1500

0.2000

0.2500

1 29 57 85 113 141 169 197 225 253 281 309 337 365 393

N o . o f Ob servat io ns

Arvind Mills Share Price (After)

-0.1000

-0.0500

0.0000

0.0500

0.1000

0.1500

0.2000

1 23 45 67 89 111 133 155 177 199 221 243 265 287 309 331 353 375 397




40

BAJAJ AUTO (Monthly Standard Deviation) :

BAJAJ AUTO DATE Before DATE After







Avg 0.02 Avg 0.03

F test 1.303

Interpretation:





Bajaj Auto Share Price (Before)

-0.15

-0.1

-0.05

0

0.05

0.1

1 23 45 67 89 111 133 155 177 199 221 243 265 287 309 331 353 375

No. of Observ ations

Log

Nat

ural

s

Bajaj Auto Share Price (After)

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

1 24 47 70 93 116 139 162 185 208 231 254 277 300 323 346 369 392


Log

Nat

ural



41

BHEL (Monthly Standard Deviation):

BHEL DATE Before DATE After







Avg 0.04 Avg 0.03

F test 0.603

Interpretation:





BHEL Share Price (Before)

-0.1500

-0.1000

-0.0500

0.0000

0.0500

0.1000

0.1500

0.2000

1 24 47 70 93 116 139 162 185 208 231 254 277 300 323 346 369 392


Log

Nat

ural

BHEL Share Price

-0.1500

-0.1000

-0.0500

0.0000

0.0500

0.1000

0.1500

1 24 47 70 93 116 139 162 185 208 231 254 277 300 323 346 369 392


Log

Nat

ural



42

BPCL(Monthly Standard Deviation) :

BPCL DATE Before DATE After







Avg 0.04 Avg 0.03

F test 0.636

Interpretation:





BPCL Share Price (Before)

-0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

1 21 41 61 81 101 121 141 161 181 201 221 241 261 281 301 321 341 361 381

N o. of Obse r v a t i ons

BPCL Share Price (After)

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

1 23 45 67 89 111 133 155 177 199 221 243 265 287 309 331 353 375 397




43

CIPLA (Monthly Standard Deviation):

CIPLA DATE Before DATE After







Avg 0.03 Avg 0.02

F test 0.574

Interpretation:





Cipla Share Price (Before)

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

1 22 43 64 85 106 127 148 169 190 211 232 253 274 295 316 337 358 379


Log

Nat

ural

s

Cipla Share Price (After)

-0.12

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

1 23 45 67 89 111 133 155 177 199 221 243 265 287 309 331 353 375 397




44

COLGATE (Monthly Standard Deviation):

COLGATE DATE Before DATE After







Avg 0.018 Avg 0.02

F test 1.2527

Interpretation:





Colgate Share Price (Before)

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

1 22 43 64 85 106 127 148 169 190 211 232 253 274 295 316 337 358 379


Log

Nat

ural

Colgate Share Prices (After)

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

1 23 45 67 89 111 133 155 177 199 221 243 265 287 309 331 353 375 397


Log

Nat

ural



45

DABUR(Monthly Standard Deviation) :

DABUR DATE Before DATE After







Avg 0.046 Avg 0.03

F test 0.3399

Interpretation:





Dabur Share Price (Before)

-2.5

-2

-1.5

-1

-0.5

0

0.5

1 22 43 64 85 106 127 148 169 190 211 232 253 274 295 316 337 358 379


Log

Nat

ural

Dabur Share Price (After)

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

1 23 45 67 89 111 133 155 177 199 221 243 265 287 309 331 353 375 397


Log

Nat

ural



46

DR REDDY (Monthly Standard Deviation):

DR REDDY DATE Before DATE After







Avg 0.038 Avg 0.03

F test 0.5592

Interpretation:





Dr Reddy Share Price (Before)

-0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

1 22 43 64 85 106 127 148 169 190 211 232 253 274 295 316 337 358 379

No. of Obse r v a t ons

Dr Reddy Share Price (After)

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

1 22 43 64 85 106 127 148 169 190 211 232 253 274 295 316 337 358 379 400




47

ESSAR OIL (Monthly Standard Deviation):

Essar Oil DATE Before DATE After







Avg 0.039 Avg 0.09

F test 5.2808

Interpretation:



Since F-Test value is more than tabulated value, null hypothesis is rejected

and there is impact of stock futures on volatility of spot market.

Essaar Oil Share Price (Before)

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

1 21 41 61 81 101 121 141 161 181 201 221 241 261 281 301 321 341 361 381


Essar Oil Share Price (After)

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

1 22 43 64 85 106 127 148 169 190 211 232 253 274 295 316 337 358 379 400




48

GLAXO (Monthly Standard Deviation):

GLAXO DATE Before DATE After







Avg 0.025 Avg 0.03

F test 1.0726

Interpretation:





Glaxo Share Price (Before)

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

1 22 43 64 85 106 127 148 169 190 211 232 253 274 295 316 337 358 379


Glaxo Share Price (After)

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

1 23 45 67 89 111 133 155 177 199 221 243 265 287 309 331 353 375 397

N o. of Obse r v a t ons



49

GRASIM (Monthly Standard Deviation):

GRASIM DATE Before DATE After







Avg 0.034 Avg 0.02

F test 0.529

Interpretation:





Grasim Share Price (Before)

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

1 22 43 64 85 106 127 148 169 190 211 232 253 274 295 316 337 358 379


Grasim Share Price (After)

-0.1

-0.05

0

0.05

0.1

0.15

1 22 43 64 85 106 127 148 169 190 211 232 253 274 295 316 337 358 379 400




50

GUJARATH AMBUJA CEMENT (Monthly Standard Deviation):

GUJAMBCEM DATE Before DATE After







Avg 0.034 Avg 0.03

F test 0.5438

Interpretation:





Gujrath Ambuja Cement Share Price (Before)

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

1 22 43 64 85 106 127 148 169 190 211 232 253 274 295 316 337 358 379


Log

Nat

ural

Gujrath Ambuja Cement Share Price (After)

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

1 22 43 64 85 106 127 148 169 190 211 232 253 274 295 316 337 358 379 400




51

HDFC BANK (Monthly Standard Deviation):

HDFCBANK DATE Before DATE After







Avg 0.028 Avg 0.02

F test 0.722

Interpretation:





HDFC Bank Share Prices (Before)

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

1 21 41 61 81 101 121 141 161 181 201 221 241 261 281 301 321 341 361 381

N o. of Obse r v a t ons

HDFC Bank Share Price (After)

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

1 22 43 64 85 106 127 148 169 190 211 232 253 274 295 316 337 358 379 400




52

HDFC (Monthly Standard Deviation):

HDFC DATE Before DATE After







Avg 0.028 Avg 0.03

F test 1.1862

Interpretation:





HDFC Share Price (Before)

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

1 21 41 61 81 101 121 141 161 181 201 221 241 261 281 301 321 341 361 381


HDFC Share Price (After)

-0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

1 29 57 85 113 141 169 197 225 253 281 309 337 365 393

N o. o f Ob servat io ns



53

HERO HONDA (Monthly Standard Deviation):

HEROHONDA

DATE Before DATE After Nov-99 0.02135 Nov-01 0.12888 Dec-99 0.037566 Dec-01 0.01928 Jan-00 0.029683 Jan-02 0.0294 Feb-00 0.022306 Feb-02 0.03164 Mar-00 0.025893 Mar-02 0.03099 Apr-00 0.027902 Apr-02 0.0177

May-00 0.037436 May-02 0.02412 Jun-00 0.032869 Jun-02 0.01949 Jul-00 0.028086 Jul-02 0.02065

Aug-00 0.044448 Aug-02 0.02257 Sep-00 0.041322 Sep-02 0.02016 Oct-00 0.016282 Oct-02 0.03898

Nov-00 0.014536 Nov-02 0.02551 Dec-00 0.042296 Dec-02 0.02182

Jan-01 0.00846 Jan-03 0.0139 Feb-01 0.02841 Feb-03 0.02616 Mar-01 0.024006 Mar-03 0.03105 Apr-01 0.013224 Apr-03 0.03141



Avg 0.0422 Avg 0.038

F test 0.79392

Interpretation:







54

HINDUSTAN LEVER (Monthly Standard Deviation):

HINDLEVER

DATE Before DATE After Nov-99 0.015578 Nov-01 0.01238

Dec-99 0.019735 Dec-01 0.00991 Jan-00 0.030375 Jan-02 0.0122 Feb-00 0.041748 Feb-02 0.01747 Mar-00 0.041877 Mar-02 0.02114 Apr-00 0.040231 Apr-02 0.01931



Nov-00 0.024763 Nov-02 0.01199 Dec-00 0.02246 Dec-02 0.01127 Jan-01 0.023276 Jan-03 0.00784Feb-01 0.033639 Feb-03 0.01423Mar-01 0.023494 Mar-03 0.01553Apr-01 0.015677 Apr-03 0.02518



Avg 0.0455 Avg 0.026

F test 0.31693

Interpretation:







55

ICICI BANK (Monthly Standard Deviation):

ICICIBANK



May-00 0.0145 May-02 0.0267 Jun-00 0.04734 Jun-02 0.03375

Jul-00 0.06348 Jul-02 0.02649

Aug-00 0.04376 Aug-02 0.01495

Sep-00 0.03254 Sep-02 0.01507 Oct-00 0.03614 Oct-02 0.02553

Nov-00 0.04303 Nov-02 0.03614 Dec-00 0.08417 Dec-02 0.02097 Jan-01 0.03055 Jan-03 0.01931

Feb-01 0.02343 Feb-03 0.01218 Mar-01 0.02004 Mar-03 0.01422 Apr-01 0.04404 Apr-03 0.01518



Avg 0.038 Avg 0.031

F test 0.678

Interpretation:







56

IDBI (Monthly Standard Deviation):

IDBI







Avg 0.032 Avg 0.037

F test 1.301

Interpretation:







57

INFOSYS TECHNOLOGY (Monthly Standard Deviation):

INFOSYSTECH





Nov-00 0.0204 Nov-02 0.02075 Dec-00 0.028357 Dec-02 0.01921 Jan-01 0.036026 Jan-03 0.02361 Feb-01 0.031988 Feb-03 0.01836 Mar-01 0.065239 Mar-03 0.01939 Apr-01 0.082789 Apr-03 0.08254



Avg 0.046 Avg 0.036

F test 0.62514

Interpretation:







58

IPCL (Monthly Standard Deviation):

IPCL









Avg 0.0322 Avg 0.034

F test 1.11941

Interpretation:







59

ITC (Monthly Standard Deviation):

ITC








Avg 0.0284 Avg 0.025

F test 0.77037

Interpretation:







60

MAHINDRA & MAHINDRA (Monthly Standard Deviation):

M&M








Avg 0.0356 Avg 0.033

F test 0.83712

Interpretation:







61

MTNL (Monthly Standard Deviation):

MTNL




Nov-00 0.042972 Nov-02 0.01779 Dec-00 0.027316 Dec-02 0.01984

Jan-01 0.032635 Jan-03 0.04744 Feb-01 0.033097 Feb-03 0.02554 Mar-01 0.05271 Mar-03 0.01403 Apr-01 0.025448 Apr-03 0.02364



Avg 0.0337 Avg 0.034

F test 0.98945

Interpretation:







62

NATIONAL ALUMINIUM (Monthly Standard Deviation):

NATIONALUM







Avg 0.04 Avg 0.038

F test 0.92072

Interpretation:







63

ONGC (Monthly Standard Deviation):

ONGC









Avg 0.0264 Avg 0.03

F test 1.27258

Interpretation:







64

ORIENTAL BANK (Monthly Standard Deviation):

ORIENTBANK








Avg 0.0196 Avg 0.033

F test 2.91141

Interpretation:



Since F-Test value is more than tabulated value, null hypothesis is rejected

and there is impact of stock futures on volatility of spot market.



65

RANBAXY (Monthly Standard Deviation):

RANBAXY

DATE Before DATE After








Avg 0.0301 Avg 0.029

F test 0.91024

Interpretation:







66

RELIANCE (Monthly Standard Deviation):

RELIANCE







Avg 0.026 Avg 0.027

F test 1.10778

Interpretation:







67

SAIL (Monthly Standard Deviation):

SAIL









Avg 0.0333 Avg 0.042

F test 1.55911

Interpretation:







68

SBIN (Monthly Standard Deviation):

SBIN









Avg 0.0252 Avg 0.027

F test 1.16576

Interpretation:







69

SIEMENS (Monthly Standard Deviation):

SIEMENS








Avg 0.0273 Avg 0.027

F test 1.00389

Interpretation:







70

SUN PHARMA (Monthly Standard Deviation):

SUNPHARMA









Avg 0.0433 Avg 0.031

F test 0.50608

Interpretation:







71

TATA POWER (Monthly Standard Deviation):

TATAPOWER









Avg 0.0315 Avg 0.028

F test 0.76657

Interpretation:







72

VSNL (Monthly Standard Deviation):

VSNL DATE Before DATE After Nov-99 0.046269 Nov-01 0.12746 Dec-99 0.063825 Dec-01 0.03183 Jan-00 0.031741 Jan-02 0.10231 Feb-00 0.037092 Feb-02 0.02521 Mar-00 0.238654 Mar-02 0.01189 Apr-00 0.057014 Apr-02 0.00766



Nov-00 0.030404 Nov-02 0.02686 Dec-00 0.024505 Dec-02 0.01743

Jan-01 0.069399 Jan-03 0.02706




Avg 0.0452 Avg 0.037

F test 0.68573

Interpretation:







73

WIPRO TECHNOLOGIES (Monthly Standard Deviation):

WIPRO








Avg 0.0523 Avg 0.038

F test 0.5323

Interpretation:







74

4.3 Interpretation:

The tables above show the calculations of monthly standard deviation of all 40

companies for prior and after introduction of stock futures. Then the average of

standard deviation is calculated for prior and after introduction. The F -test is

computed on the average standard deviation. If the F-test value is less than

tabulated value, the null hypothesis is accepted which implies that the introduction

of stock futures has no impact on spot market volatility.



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CHAPTER-V

Conclusion:

The study was conducted to examine the impact of stock futures

on the spot market volatility of Nifty. F-test has been done to examine

the changes in volatility with the help of monthly standard deviations.

The F-test value is less than the tabulated value so the null hypothesis

has been accepted which emphasis on the fact that the introduction of

stock futures didn’t affected the spot market volatility of Nifty. Fourty

(40) companies are selected from CNX Nifty Index, in which 38

companies F-test value is less than tabulated value and 2 companies F-

test value is greater than the tabulated value, which is statistically

significant. From this we can infer that the there is no impact of

introduction of stock futures on spot market volatility.

The volatility of the market is influence by Index Future, Index

Option, Future option and other uncontrollable factors which lead to

the further research work.



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78

BIBLIOGRAPHY: REFERNCE BOOKS:

Basic Econometrics - Damodar N.Gujarati, (fourth edition)

WEBSITES:

www.nseindia.com

www.finance.yahoo.com

www.google.com

www.investorpedia.com

REFERNCE ARTICLES:

Impact of futures introduction on underlying index volatality: evidence

from India - kotha kiran kumar. & chiranjit mukhopadhyay

Behaviour of stock market volatility after derivatives - golaka c nath

Do futures and options trading increase stock market volatility? Dr.

premalata shenbagaraman

Effect of introduction of index futures on stock market volatility: the

indian

evidence - o.p. gupta

Impact of financial derivatives products on spot market volatility: A study

on Nifty. – S V Ramana rao

stock futures on spot market volatility

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