Derivatives Trading and Its Impact on the Volatility of NSE_ India

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					Derivatives Trading and Its Impact on the

         Volatility of NSE, India
           GEL : G10, G14, G20, G19




                      1
                                     ABSTRACT


        This article examines the impact of introduction of financial derivatives trading on the

volatility of Indian stock market (an emerging stock market). It examines the theme that the

introduction of derivatives in the stock market in India would reduce the volatility (risk) in the

stock market. NSE Nifty 50 index has been used as a proxy of stock market return.

ARCH/GARCH technique has been employed in the analysis. The conditional volatility of

interday market returns before and after the introduction of derivatives products are estimated

with the (GARCH) model. The Finding suggests that derivatives trading has reduced the

volatility.




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                                 Executive Summary


     Derivatives trading in the stock market have been a subject of enthusiasm of research in the

field of finance the most desired instruments that allow market participants to manage risk in the

modern securities trading are known as derivatives. The derivatives are defined as the future

contracts whose value depends upon the underlying assets. If derivatives are introduced in the

stock market, the underlying asset may be anything as component of stock market like, stock

prices or market indices, interest rates, etc. The main logic behind derivatives trading is that

derivatives reduce the risk by providing an additional channel to invest with lower trading cost

and it facilitates the investors to extend their settlement through the future contracts. It provides

extra liquidity in the stock market.

     In recent past, the volatility of stock returns has been a major topic in finance literature.

Generally, volatility is considered as a measurement of risk in the stock market return and a lot of

discussions have taken place about the nature of stock return volatility. Therefore, understanding

factors that affect stock return volatility is an imperative task in many ways. Stock prices and

their volatility add to the concern of attention in the stock market, especially in India. The

volatility on the stock exchanges may be thought of as having two components: The volatility

arising due to information based price changes and Volatility arising due to noise trading/

speculative trading, i.e., destabilizing volatility. As a concept, volatility is simple and intuitive.

Derivatives‘ trading has been started in Indian stock market with the theme that it would reduce

the Volatility. Empirical researchers have tried to find a pattern in stock return movements or

factors determining these movements. Nath (2003) Shenbagaraman (2003) Mayhew (2000) Raju

and Karande (2003) Rahman (2001) have examined empirically the impact of derivatives trading


                                                   3
on the spot market volatility. The majority of studies have employed the standard ARCH or

GARCH model to examine volatility shifting. Mostly the findings are supporting the hypothesis

that introduction of derivatives has reduced the stock market volatility. In the case of Indian

stock market, the results are the same, but the studies are based on the shorter period.

       Extending the studies, this research article examines the impact of introduction of

financial derivatives on cash/spot market volatility in Indian stock market (an emerging stock

market) using a larger period as well as It examines the impact of trading in major derivatives

products including index futures, stock futures and index options on the conditional volatility of

stock market return and makes an effort to study whether the volatility in the Indian stock

markets has undergone any significant change after the introduction of derivatives trading. NSE

Nifty 50 index has been used as a proxy for stock market return for the period of June 2000 to

June 2006. The result is supporting the theme, in general, that derivatives trading have reduced

the volatility of Indian stock market.

Kew Words: Financial derivatives, Volatility, spot market, efficiency, risk

management.




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1 - INTRODUCTION



         The global liberalisation and integration of financial markets has created new

investment opportunities, which in turn require the development of new instruments that are

more efficient to deal with the increased risks. Institutional investors who are actively engaged in

industrial and emerging markets need to hedge their risks from these internal as well as cross-

border transactions.   Agents in liberalised market economies who are exposed to volatile

commodity price and interest rate changes require appropriate hedging products to deal with

them. And the economic expansion in emerging economies demands that corporations find better

ways to manage financial and commodity risks.

         The most desired instruments that allow market participants to manage risk in the

modern securities trading are known as derivatives. The main logic behind the derivatives

trading is that derivatives reduce the risk by providing an additional channel to invest with lower

trading cost and it facilitates the investors to extend their settlement through the future contracts.

It provides extra liquidity in the stock market. They represent contracts whose payoff at

expiration is determined by the price of the underlying asset—a currency, an interest rate, a

commodity, or a stock.

       Derivatives are traded in organized stock exchanges or over the counter by derivatives

dealers. The issue of the impact of derivatives trading on stock market volatility has received

considerable attention in recent years in India, particularly after the stock market crash of 2001.

Derivative products like futures and options on Indian stock markets have become important

instruments of price discovery, portfolio diversification and risk hedging in recent times. In the




                                                  5
last decade, many emerging and transition economies have started introducing derivative

contracts.

       The history of derivatives may be new for developing countries but it is old for the

developed countries. The history of derivatives is surprisingly longer than what most people

think. The derivatives contracts were done not formally in the old times in the informal sectors.

The advent of modern day derivative contracts is attributed to the need for farmers to protect

themselves from any decline in the price of their crops due to delayed monsoon, or

overproduction.

       The first derivative as 'futures' contracts were introduced in the Yodoya rice market in

Osaka, Japan around 1650. The contracts were evidently standardised contracts, like today's

futures. The commodity derivative market has been functioning in India since the nineteenth

century with organized trading in cotton through the establishment of Cotton Trade Association

in 1875. Exchange traded financial derivatives were introduced in India since June 2000 at the

two major stock exchanges, NSE and BSE. There are various contracts (Index futures, Stock

futures, Index options, Stock options, interest rate futures, currency options) currently traded on

these exchanges. (Shenbagaraman 2003).

1.1 - Role of Financial Derivatives

       Derivatives may be traded for a variety of reasons. Derivatives enable a trader to hedge

some pre-existing risk by taking positions in derivatives markets that offset potential losses in the

underlying or spot market. In India, most derivatives users describe themselves as hedgers and

Indian laws generally require that derivatives be used for hedging purposes only. Another motive

for derivatives trading is speculation (i.e. taking positions to profit from anticipated price

movements). In practice, it may be difficult to distinguish whether a particular trade was for



                                                 6
hedging or speculation, and active markets require the participation of both hedgers and

speculators.

       It is argued that derivatives encourage speculation, which destabilizes the spot market.

The alleged destabilization takes the form of higher stock market volatility. The reason behind it

is informational effect of the futures trading. Futures trading can alter the available information

for two reasons: first, futures trading attract additional traders in the market; second, as

transaction costs in the futures market are lower than those in the spot market, new information

may be transmitted to the futures market more quickly. Thus, future markets provide an

additional route by which information can be transmitted to the spot markets and therefore,

increased spot market volatility may simply be a consequence of the more frequent arrival and

more rapid processing of information.

    Raju and Ghosh (2004) have expressed view for the consideration of volatility in the Indian

stock market as tools of analysis of risk factors. Stock prices and their volatility add to the

concern of attention. The growing linkages of national markets in currency, commodity and stock

with world markets and existence of common players, have given volatility a new property – that

of its speedy transmissibility across markets.

    Among the general public, the term volatility is simply synonymous with risk. In their view,

high volatility is to be deplored, because it means that security values are not dependable and the

capital markets are not functioning as well as they should. Merton Miller (1991) the winner of the

1990 Nobel Prize in economics - writes in his book "Financial Innovation and Market Volatility"

…. ―By volatility public seems to mean days when large market movements, particularly down

moves, occur. These precipitous market wide price drops cannot always be traced to a specific




                                                 7
news event.... The public takes a more deterministic view of stock prices; if the market crashes,

there must be a specific reason.‖ (Cited in Raju and Ghosh 2004).

     The volatility on the Indian stock exchanges may be thought of as having two components:

The volatility arising due to information based price changes and Volatility arising due to noise

trading/ speculative trading, i.e., destabilizing volatility. As a concept, volatility is simple and

intuitive.

        In a large scale, the success of derivatives trading will depend on the choice of products to

be traded in the markets. The popularly traded and usual types of derivatives are futures and

options. The products to be traded in the stock markets need to have the following characteristics

which are mentioned by Tsetsekos Varangis (2000):

        ......a sufficiently higher as well as lower level of price volatility to attract hedgers or

speculators, a significant amount of money for speculative motive at a certain level of risk; a

significant number of domestic market participants—and possibly buyers and sellers from

abroad; a large number of producers, processors, and banks interested in using derivatives

contracts (that is, enough speculators to provide additional liquidity); and a weak correlation

between the price of the underlying asset and the price of the already-traded derivatives

contract(s) in other exchanges (basis risk).

        Introduction of derivatives in the Indian capital market was initiated by the Government

following L C Gupta Committee Report on Derivatives in December 1997. The report suggested

the introduction of stock index futures in the first place to be followed by other products once the

market matures. Following the recommendations and pursuing the integration policy, futures on

benchmark indices (Sensex and Nifty 50) were introduced in June 2000. The policy was followed

by introduction of index options on indices in June 2001, followed by options on individual




                                                 8
stocks in July 2001. Stock futures were introduced on individual stocks in November, 2001 (Nath

2003)

     By definition, derivatives are the future contracts whose value depends upon the underlying

assets. When derivatives are introduced in the stock market, the underlying asset may be anything

as component of stock market like, stock prices or market indices, interest rates, etc. Derivatives

products are specialised contracts* which signify an agreement or an option to buy or sell the

underlying asset to extend up to the maturity time in the future at a prearranged price.

     Only futures and options are used in this analysis, so these are introduced in brief.

Futures: A futures contract is an agreement between two parties to buy or sell an asset at a

certain time in the future at a certain price. Presently Index futures on S&P CNX NIFTY and

CNX IT, Stock futures on certain specified Securities and Interest Rate Futures are available for

trading at NSE. All the futures contracts are settled in cash. A futures contract is a forward

contract which trades on an exchange. Futures markets feature a series of innovations in how

trading is organised. (Shah Thomas 2000)

Options: An Option is a contract which gives the right, but not an obligation, to buy or sell the

underlying at a stated date and at a stated price. While a buyer of an option pays the premium

and buys the right to exercise his option, the writer of an option is the one who receives the

option premium and therefore obliged to sell/buy the asset if the buyer exercises it on him.

        The above description about the derivatives creates a research problem that need be

reported. What is the impact of derivatives trading on the stock market risk and return in

practice? The theoretical literature on derivatives trading is of the view that derivatives trading




*
  The contract has a fixed expiry period mostly in the range of 3 to 12 months from the date of commencement of
the contract. The value of the contract depends on the expiry period and also on the price of the underlying asset


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increase the efficiency of the stock market through minimising the risk, but the opposite effect

may also be caused by derivatives trading.

       The rest of the paper is organised as follows: First, some relevant and related literatures

have been reviewed. Thereafter the methodology, data and the time period for the study have

been explained. The variables are identified and explained in brief. Then the models have been

specified and estimated. The interpretations of the results are presented along with the result

tables. The findings are presented as conclusion.

2 - Theoretical foundations and Review of literature.

       Derivatives trading in the stock market have been a subject of enthusiasm of research in

the field of finance. Derivatives trading have two attributes on the basis of its effectiveness. So

there have often been contrary views among the researchers of what may be the impact of

derivatives trading. According to the nature of this instrument it is argued that this could enhance

the market efficiency by establishing the market. There are many empirical findings for both

there roles of derivatives trading. Here some review of literature for both these results are

presented.

       Many theories have been developed about the pros and cons of the impact of derivatives

trading in the stock market. A common agreement has been found among the studies that the

introduction of derivatives products, specially the equity index futures enables traders to transact

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

       A major theoretical argument for the benefit of derivatives trading is that it reduces the

volatility of the stock market. The logic is that it reduces the asymmetric information among the

investors and information reduces the speculation in the trading system. A variety of theoretical

arguments have been advanced over the years to explain why speculative trading in general, or



                                                10
the existence of derivatives markets in particular, might affect the volatility of the underlying

asset market.

       In recent past, the volatility of stock returns has been a major topic in finance literature.

Empirical researchers have tried to find a pattern in stock return movements or factors

determining these movements. Generally, volatility is considered as a measurement of risk in the

stock market return and a lot of discussions have taken place about the nature of stock return

volatility. Therefore, understanding factors that affect stock return volatility is an imperative task

in many ways.

       A numbers of theoretical and empirical studies have been done on the impact of the

introduction of derivatives in the stock markets on the stock return volatility. The studies are

concerned with both the developed as well as developing countries. There are two sets of views

according to the theoretical as well as empirical findings. One is of the view that introduction of

derivatives has increased the volatility and market performance, through forwarding its

speculative roles and the other view is that the introduction of derivatives has reduced the

volatility in the stock market thus increasing the stability of the stock market.

       The behaviour of volatility in the equity market in India, for the pre and post derivatives

period, has been examined using conditional variance for the period of 1999-2003 in (Nath,

2003). He modeled conditional volatility using different method such as GARCH (1,1). He has

considered 20 stocks randomly from the Nifty and Junior Nifty basket as well as benchmark

indices itself. As result, he observed that for most of the stocks, the volatility came down in the

post-derivative trading period. All these methods suggest that the volatility of the market as

measured by benchmark indices like S&P CNX Nifty and Nifty Junior have fallen in the post-

derivatives period.




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       The impacts of the introduction of the derivatives contracts such as Nifty futures and

options contracts on the underlying spot market volatility have been examined using a model that

captures the heteroskedasticity in returns that is recognised as the Generalised Auto Regressive

Conditional Heteroskedasticity (GARCH) Model in Shenbagaraman (2003). She used the daily

closing prices for the period 5th Oct. 1995 to 31st Dec. 2002 for the CNX Nifty the Nifty Junior

and S&P500 returns. Results indicate that derivatives introduction has had no significant impact

on spot market volatility but the nature of the GARCH process has changed after the introduction

of the futures trading.

       Both theoretical and empirical aspect of the question of how the speculation, in general,

and derivative securities in particular, effects the underlying asset markets has been explained in

Mayhew (2000). The theoretical research has revealed that there are many different aspects of

the relationship between cash and derivative markets. Although many models predict that

derivatives should have a stabilizing effect, this result normally requires restrictive assumptions.

At the end of the day, the theoretical literature gives ambiguous predictions about the effects of

derivatives markets.

       Price discovery and volatility have been examined in the context of introduction of Nifty

futures at the National Stock Exchange (NSE) in June 2000 applying Cointegration and

Generalised Auto Regressive Conditional Heteroscedasticity (GARCH) techniques respectively

from January1998 to October 2002 in Raju and Karande (2003). Their finding suggests that the

introduction of futures has reduced volatility in the cash market.

       The impact of trading in the Dow Jones Industrial Average index futures and futures

options on the conditional volatility of component stocks has been examined in Rahman (2001).

The conditional volatility of intraday returns for each stock before and after the introduction of




                                                12
derivatives is estimated with the GARCH model. Estimated parameters of conditional volatility

in pre-futures and post-futures periods are then compared to determine if the estimated

parameters have changed significantly after the introduction of various derivatives. The data for

this study consist of transaction prices from the 30 stocks comprising the DJIA. Transaction

prices for April through June 1997 (pre-futures period) and April through June 1998 (post-

futures period) are used. The results suggest that the introduction of index futures and options on

the DJIA has produced no structural changes in the conditional volatility of component stocks.

The null hypothesis of no change in conditional volatility from pre futures to post futures periods

cannot be rejected.

       Gupta (2002) has examined the impact of index futures introduction on stock market

volatility. Further, he has also examined the relative volatility of spot market and futures market.

He has used 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. He has used four measures of volatility the first is based upon close-to-close prices, the

second is based upon open-to-open prices, the third is Parkinson‘s Extreme Value Estimator, and

the fourth is Garman-Klass measure volatility (GKV). The empirical results indicate that the

over-all volatility of the underlying stock market has declined after the introduction of index

futures on both the indices.

       The impact of the introduction of index futures on the volatility of stock market in India

was examined employing daily data of Sensex and Nifty CNX for period of Jan 1997-March

2003 in Bandivadekar and Ghosh (2005). The return volatility has been modeled using GARCH

framework. They found strong relationship between information of introduction of derivatives




                                                13
and return volatility. They have concluded that the introduction of derivatives has reduced the

volatility of the stock market. The same study was done by Hetamsaria and Swain (2003). they

have examined the impact of the introduction of index futures on the volatility of stock market in

India applying regression analysis. They have used Nifty 50 index price data for the period of

Jan 1998 - March 2003. They found that the volatility of the Nifty return has declined after the

introduction of index futures.

       Darrat, Rahman, and Zhong (2002) have examined the impact of the introduction of

index futures on the volatility of stock market in India and causal relationship between volume in

the futures market and spot market. They have used EGARCH approach and Granger Causality

(G C) test. Their finding suggests that index futures trading may not be blamed for the increasing

volatility in the spot market. They found that volatility in the spot market has produced volatility

in the futures market.

       Board, Sandamann and Sutcliffe (2001), have tested the hypothesis that increases in the

futures market trading activity increases spot market price volatility. They used the GARCH

model and Schewert Model and found that the result does not support the hypothesis. The data

samples are taken from the U K market. Jeanneau and Micu (2003) have explained that

information based or speculative transaction also creates a link between volatility and activity in

asset and derivatives market. This link depends in part on whether the new information is private

or public and on the type of asset traded. In theory, the arrival of new private information should

be reflected in a rise in the volatility of return and trading volumes in single equity and equity

related futures and options.

       The majority of studies have employed the standard ARCH or GARCH model to

examine volatility shifting. Mostly the findings are supporting the hypothesis that introduction of




                                                14
derivatives has reduced the market volatility. These studies use daily observations to estimate

volatility, whereas interday data are used here. Given that financial markets display high speeds

of adjustment, studies based on longer intervals such as daily observations may fail to capture

information contained in intraday market movements. Moreover, because of modern

communications systems and improved technology, volatility measures based on daily

observations ignore critical information concerning intraday price patterns. Andersen (1996)

pointed out that the focus of the market microstructure literature is on intraday patterns rather

than interday dynamics.

       This study is also based on the hypothesis that the introduction of the derivatives products

has reduced the risk inefficiency in the BSE stock market. Three derivatives products (index

futures, stock futures and index options) have been used that have been introduced in the

different time periods. The time period is also for about 8 years including the most recent earning

period as 2005-2006. Derivatives turnover also have been used for the same return series.

3 - Objective of the Study

       The introduction of equity index derivative contracts in Indian market has not been very

old but today the total notional trading values in derivatives contracts are ahead of cash market.

Given such dramatic changes, the objective of this study is to study the behaviour of volatility in

cash market after the introduction of derivatives contracts. This is to examine with help of

econometric model whether the introduction of derivative contracts has reduced the risk and

inefficiency in the Indian stock market or not.

4 - Hypothesis

       One view is that derivatives trading increases volatility in the spot market due to more

highly leveraged and speculative participants in the futures market. An alternative view is that


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derivatives trading reduce spot market volatility by providing low cost contingent strategies and

enabling investors to minimize portfolio risk by transferring speculators from spot markets to

futures markets. So, for all the models, the null hypothesis (H0) is that the introduction of the

derivatives products has not reduced the volatility of spot market (NSE). The alternative

hypothesis (Halt) is that H 0 has been rejected.

5 - Methodology

        The Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized ARCH

(GARCH) models have been employed to estimate the conditional volatility of stock market

returns and the impact of the derivatives trading. GARCH model is used to test the informational

effect on the conditional volatility of stock market return.

     Stock market return is calculated from the daily closing prices of the NSE stock index S&P

CNX Nifty 50. This is one of the most important and popular indicators of the Indian stock

market performance of two national indices namely Sensex, having 30 blue chip companies‘

shares in the BSE and Nifty 50. These Indices are a good predictor of the stock market volatility.

Initially, derivatives were introduced only in the two major indices, Sensex and Nifty 50. Even at

present these two indices are the primes as underlying for derivatives trading in India. CNX Nifty

50 has been used in the analysis as the proxy of stock market return and it would help to

understand the impact on the functioning of the stock market.


       Along with CNX Nifty 50 on which derivative products are available, we also consider

the impact of return in Nifty Junior, on which derivative products have not been introduced

(within the time period 1998-2006). The reason is to know whether the derivatives products are

the only factors affecting the market volatility or there are some others factors also. A




                                                   16
comparison of fluctuations in volatility between Nifty Junior and Nifty 50 may provide a clue to

segregate the fluctuations due to introduction of future products and due to other market factors.

We have measured the informational impact of the derivative trading. We have tried to see

whether the information of the introduction of derivatives as risk controller has any impact on the

risk factor of the stock market.


       Both the major derivatives products as futures and options have been considered. In

futures, we have selected index futures and stock futures. In options, we have selected index

options. The main aim of this study is only to examine the impact of derivatives for different

period of time, so these derivatives products are used only for the division of different time

periods. So, one should not be confused that why individual stocks for stock index futures have

not been used.


        Dummy variables for these derivatives products (index futures, stock futures and index

options) are used as independent variables. Both the futures and options are actively traded in the

Indian stock market (NSE). The series have been created using ‗0‘ for pre-derivatives

introduction period and ‗1‘ for post-derivatives introduction period to get a time series as usual

used by the experts. If the coefficient on the dummy variable is statistically significant, the

introduction of has a significant impact on the spot market volatility. To address the second issue,

we divided the sample into the pre-derivatives and post- derivatives sub-sample and a GARCH

model is estimated separately for each sub-sample. Estimated parameters of conditional volatility

in pre-derivatives and post-derivatives periods are then compared to determine if the estimated

parameters have changed significantly after the introduction of derivatives. Prices in the cash

market and futures/options market are expected to be inter-related.




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           The analysis is based on daily time series data. Data for the stock indices (Nifty 50,

Nifty Junior and S&P 500) has been used for the period of January 1998 to June 2006. The

closing prices in the end of the day have been used. The whole time period is divided into two

sub-time periods. First is pre-derivatives introduction period that is from Jan 1998 to June 2000

for index futures, Jan 1998 Dec 2000 for stock futures and from Jan 1998 to June 2001 for index

options. Second sub-time period is post-derivatives period from the above categorizations to June

2006. The data sources are the official websites of the NSE, SEBI and Yahoo finance.com.


       The index futures are popular among the investors and this is the most actively trading

instrument in the derivatives products. Second important product is stock futures that were

introduced after the introduction of index futures. There was a big artificial fluctuation in the

stock market in 2001 because of stock market scam. The stock futures are introduced after the

scam. This is why we have used the period of introduction of stock futures also. Options are also

introduced in June 2001 following a forward step towards derivatives trading, so we have tested

the options also.


Table 1:- Date of Introduction of Derivatives Products


          Der. Products          Date of Intro.         Underlying Ind.


          Index Futures          June 2000              Sensex, S& P Nifty


          Stock Futures          Dec. 2001              Sensex, S& P Nifty


          Index Options          June 2001              Sensex, S& P Nifty




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       The derivatives‘ trading is not the only one factor affecting the stock market volatility

(risk), there may be other factors also. It is important to remove market-wide influences on

market return, if we are to isolate the impact of futures introduction. In order to do this we need a

proxy that is not associated with any futures/options contract, and yet captures market-wide

influences in India. For example, information news releases relating to economic conditions like,

inflation rates, growth forecasts, exchange rates, etc are likely to affect the whole market.

       It is necessary to remove the effects for all these factors on price volatility. Since the

Nifty Junior has no futures/options contracts traded on it (till June 2006), we use it as a proxy to

capture market-wide information effects and to study the market wide factors contributing to the

changes in spot market volatility index as independent variable affecting the stock market return.

Return series of S&P500 index (an USA stock market index) has been used as a proxy of the

worldwide factors affecting stock market volatility.

5.1 - The GARCH Model

     The GARCH model was developed by Bollerslev (1986) as a generalised version of Engle‘s

(1982) Autoregressive Conditional Heteroscedasticity (ARCH). In the GARCH model the

conditional variance at time ‗t‘ depends on the past values of the squared error terms and the past

conditional variances. It uses the past disturbances to model the variance of the series and allows

the variance of error term to vary over time.

       Bollerslev (1986) generalized the ARCH process by allowing the conditional variance to

be a function of prior period's squared errors as well as its past conditional variance. The

advantage of a GARCH model is that it captures the tendency in financial time series 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




                                                 19
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.

       A model with errors that follow a GARCH (1,1) process is represented as follows:



                       Yt      =       a + b1Xt + Ut                           ...1



                       ht      =       a + b1(Ut-1)2 + b2ht-1                  ...2

      Where,

        ht     =       conditional variance (sigma square)

       Ut      =       Error term



Equation '1' is called the conditional mean equation and equation '2' is called the conditional

variance equation. The coefficient of the error square term can be viewed as a ―news‖

coefficient, with a higher value implying that recent news has a greater impact on price changes.

It can be predicted as the impact of yesterday‘s (the previous time period) news on today‘s

(present time period) price changes. The coefficient of the variance (ht-1) reflects the impact of

―old news', in other words it is picking up the impact of prior news on yesterdays variance and as

such indicated the level of persistence in the information effect on volatility.

       This estimation technique enables us to explore the link between information/news

arrival in the market and its effect on cash market volatility. Estimated parameters of conditional

volatility in pre-futures and post-futures periods are then compared to determine if the estimated

parameters have changed significantly after the introduction of the futures.




                                                  20
    The GARCH (1,1) framework has been extensively found to be most parsimonious

representation of conditional variance that best fits many financial time series (Bollerslev, 1986;

Bologna and Cavallo, 2002) and thus, the same has been adopted to model stock return

volatility. ARCH and GARCH models have become widespread tools for dealing with time

series heteroskedastic models. The goal of such models is to provide a volatility measure--like a

standard deviation--that can be used in financial decision concerning risk analysis, portfolio

selection and derivative pricing.



5.2 - Description of Variables used in the Analysis

       The variables used are as follows: NIFDR, NIFDRinf0, NIFDRinf1, NIFDRstf0,

NIFDRstf1, NIFDRopt0, NIFDRopt1, NIFDRder, INDFN, STFN, NIFJDR, S&P500DR,

INDFTO and INOPTN.

        NIFDR (NSE Market Return):- This is an index of daily NSE stock market return

       calculated from the NSE Nifty CNX 50, the share price index having fifty blue chip

       shares companies.

        NIFDRinf0:- NSE return for the period of pre index futures introduction          calculated

       from the daily closing price of Nifty 50.

        NIFDRinf1:- NSE return for the period of post index futures introduction calculated

       from the daily closing price of Nifty 50.

        NIFDRstf0:- NSE return for the period of pre stock futures introduction calculated from

       the daily closing price of Nifty 50.

        NIFDRstf1:- NSE return for the period of post stock futures introduction calculated

       from the daily closing price of Nifty 50.




                                                   21
NIFDRopt0:- NSE return for the period of pre index options introduction calculated

from the daily closing price of Nifty 50.

 NIFDRopt1:- NSE return for the period of post index options introduction

calculated from the daily closing price of Nifty 50.

NIFDRder: - NSE return for the period of 2002-2006 to analyse the impact of

derivatives turnover calculated from the daily closing price of Nifty 50.

NIFJDR:- (NSE Market Return):- This is an index of daily NSE stock market return

calculated from the NSE Nifty Junior 50, the share price index having fifty blue chip

shares companies.

S&P500DR:- This is an index of daily USA stock market return calculated from the

S&P500 index, the share price index having 500 blue chip shares companies.

INDFN: - This is the indicator of a dummy variable for stock index futures in NSE Nifty

50 Index.

STFN: - This is the indicator of a dummy variable for stock futures in NSE Nifty 50

Index.

INOPTN: - This is a dummy variable for stock index options introduced in NSE Nifty

50 Index.

INDFTO: - This is turn over of the index futures for the period of 2002-2006. This

variable is proxy for derivatives turn over in the stock market.



          All the stock market returns are calculated from the daily closing prices of indices

applying the log of the ratios of the related indices. The formula may be written as Raju

(2003);




                                            22
                                     Rt             =           ln(Ct / Ct-1)

                       Where,

                       Rt       =      Stock Market Return

                       ln       =      Natural Log,

                       Ct       =      Closing Price of index at time t




5.3 - Unit Root Test

         As we have used return variables calculated from log values, we do not need to test the

problem of stationarity. But because of it being a compulsory condition for GARCH implication,

we have tested the stationarity for all the basic variables. To solve the problem of stationarity, the

Augmented Dickey-Fuller Test has been applied that is the most frequently used test for unit root

test.

         Unit root, random walk and non-stationary are near about similar things. A formal test

model to solve the problem of stationarity was firstly proposed by Dickey and Fuller that is

known as Dickey - Fuller Test (DF Test). The model or procedure tests for the presence of a 'unit

root' in the time series. The DF test starts with the assumption that a series yt is following an

Auto Regressive (1) process of this form:

                                            yt = a1 yt-1 + et

And then testing for the case that if the coefficient a1 is equal to one (unity), hence ―unit root‖ or

Yt series is non stationary.

        In case of a1 =1 then the above equation can be expressed as:

                                               yt = et




                                                   23
        And the yt series is said to be integrated of order one (I(1)) or non-stationary; while the

yt is integrated of order zero (I(0)) or stationary.

        In fact instead of testing for a1 =1 we can test an alternative version of the same thing

using this equation:

                                            yt =  yt-1 + et

And now testing whether =0, which is clearly equivalent to the above mentioned case.

        Dickey and Fuller (1979) actually consider three different regression equations that can

be used to test for the presence of a unit root:

                                            yt =  yt-1 + et

                                          yt =a +  yt-1 + et

                                      yt = a +  yt-1 + a2 t + et

        The difference between the three regressions concerns the presence of the deterministic

elements a and a2.

        The parameter of interest in all the regression equations is ; if =0, the series contains a

unit root. The test involves estimating one (or more) of the equations above using OLS in order

to obtain the estimated value of  and associated standard error. Comparing the resulting t-

statistic with the appropriate value reported in the Dickey-Fuller tables allows the researcher to

determine whether to accept or reject the null hypothesis =0.

        The most frequently used test for unit roots is the augmented Dickey-Fuller test, an

advanced form of DF Test. The ADF test simple includes AR(p) terms of the yt term in the

three alternative models. Therefore we have:




                                                   24
                                                   n

                                yt =  yt-1 +     y
                                                 i 1
                                                               i         t  i + et


                                                        n

                               yt =a +  yt-1 +     i 1
                                                                   i   y t i + et

                                                               n

                           yt = a +  yt-1 + a2 t +            y
                                                              i 1
                                                                         i    t i    + et


The difference between the three regressions again concerns the presence of the deterministic

elements a and a2. The lag length n should be determined according the AIC and SBC criteria.

Also, note that in the ADF tests note that we use different statistical tables with critical values in

each case.

        The t-test for  2 is called the (TAU)  t - statistic for which Dickey and Fuller have

computed the relevant critical values.

6 - Interpretation of Results

        In this section the result tables and figures are given with its result interpretation. First,

simple statistics have been tested then GARCH models are estimated.

[Insert table 2 here]

        In the table-2, we have analyzed the volatility (S. D) of NSE Nifty 50 for pre and post

period of introduction of derivatives with some other statistical results, and found that the

volatility in the NSE (calculated from NIFDR and) has a decreasing trend after introduction of

the derivatives as the S D values have gone down. This trend can be seen also by the graph-1 and

2 given in appendix.

[insert graphs here]




                                                       25
6.1 - Result of Unit root test

        The initial stage of analysing a prepared time series data is for GARCH estimation is test

the problem of unit root. If the time series data is not stationary, the regression result will be

spurious. The level of significance and r2 value may be high but of no use and the result will not

be significant. So at first the stationarity of the time series is tested employing Augmented D F

test.


Null hypothesis H0: z(t) is a unit root process: a = 0.

Alternative hypothesis (H1): z(t) is stationary process: a < 0.

The test statistic is the t-value of ‗a‘.

[insert table 4]

         Table-4 shows the result of ADF test. It is clear from the table that all the return

variables that are calculated from the log value are stationary as there p-values are 0.00. So H0 is

rejected in favor of H1 at the 1% significance level. The derivatives turnover variable (INDFTO)

is stationary for their first difference.



6.2 - Result of GARCH (1, 1) Estimate for NSE return

        Models of GARCH error have been estimated for several set of variables. The return

series (NIFDR) is selected to test the models. NSE return is a dependent variable. The whole data

set is divided into two period of time, Pre-derivatives introduction period and post-derivatives

introduction period. First, the GARCH model has been tested with the dummy variables for

whole length of data for each set, and then the GARCH error has been examined for the pre and

post introduction period. After this it has been tested the derivatives impact adjusted with the




                                                  26
Nifty Junior (an index of NSE stock market). At last the impact or derivatives volume on the

stock market volatility is tested with the help of GARCH model.

Model - 1:

         NIFDRt = CONS + a1NIFDRt-1 + a2INDFN + Ut

GARCH specification:

         Ht = r1U2t-1 + r2H t-1 + b1INDFN + d

Where,

         'U(t)' is the error of the OLS conditional expectation model,

         'H(t)' is its conditional variance or sigma square (  2 ),

         'd' is a constant of the GARCH model.

         The two-sided p-values are based on the normal approximation.

Note: 1- the software package used for analysis estimates the ARCH and GARCH errors in the

same process. When ARCH effect was estimated, it gave the same result as it was found in the

GARCH specifications for coefficient of U2t-1.

2- There are several models to be tested and the analysis was being long, so it has been

considered that ARCH effect is present on the basis of GARCH Application.

         Model -1 examines the impact of introduction of index futures on NSE stock market

return for the whole period under consideration. The result is tabulated in the table 5.

Note - The autocorrelation is tested by Ljung-Box Q statistics test. The LB (Q=7) test accept the

         null hypothesis at the 5% significant level. The Breusch-Pagan test is used to see the

         errors of homoskedastic with the null hypothesis that the errors are homoskedastic. The

         test rejected the null hypothesis.




                                                    27
                   These tests are done for only this model. The other models having NSEDR

         series are considered to be correct models.

[insert table-5]

         The table-5 shows that the dummy variable for stock index futures (INDFN) is

significant. The coefficient of INDF (b1 = 0.001161) is significant at one per cent significant

level, which is indication of the fact that the introduction of index futures might have made a

difference in the volatility of NSE stock returns (NIFDR). But the expected negative sign is

absent for the coefficient of the dummy variable (INDFN). So it is concluded that index futures

have increased the volatility in the stock market. The coefficient of the GARCH ‗r1‘ and ‗r2‘ are

significant indicating that there persists the informational effect in the stock market volatility.

Both the recent and old news are present in the market.

Model - 2

         NIFDRt = CONS + a1NIFDRt-1 + a2STFN + Ut

GARCH specification:

         Ht = r1U2t-1 + r2H t-1 + b1STFN + d

[insert table-6]

         Table-6 is related with the result of the impact of stock futures on the NSE return

volatility. It becomes clear from table-6 that the trading in stock futures are not significant

determinant of the volatility of the NSE stock market return as the coefficient (b1) of the dummy

variable STFN is not significant. But the negative sign indicates that the impact, whatever it may

be, is reducing the stock market volatility. In this equation model also the r1 and r2 are significant

indicating the presence of news effect on the return volatility.

         Both the above tables are examining the impact of futures trading and the result supports

different hypothesis that the introduction of index future trading has not reduced the volatility but



                                                    28
the stock futures has reduced but insignificant in the NSE spot market return. For both the

futures products the GARCH coefficient (r 1 and r2) are significant indicating the presence of

informational impact on the stock market volatility. Further analysis in extended to test whether

the introduction of the futures has reduced the volatility in the period of post introduction or not.

This analysis is done in the next two models.

Model - 3:

                   NIFDRinf0t = CONS + a1NIFDRinf0t-1 + Ut

         GARCH specification:

                   Ht = r1U2t-1 + r2H t-1 + d

[insert table-7]

Model - 4:

         NIFDRinf1t = CONS + a1NIFDRinf1t-1 + Ut

GARCH specification:

         Ht = r1U2t-1 + r2 H t-1 + d

[insert table-8]

         Table 7 shows the result of GARCH estimate for pre-index futures introduction period

and Table 8 shows the result of GARCH estimate for post index futures introduction period. For

both the period GARCH effect is significant as it is clear from the p-value. The coefficients

reported in Table 7 and 8 show that in the GARCH variance equation, (r2) ‗old news‘

components have gone up and r1 ‗recent news‘ components have also gone up in the post Index-

future period and these estimates are significant at one per cent level. It can be concluded that

introduction of the Index futures has increased the impact of recent news and reduced the

asymmetric information but at the same time, it extends the effect of uncertainty originating from

the old news.




                                                     29
         The ‗r1‘ component is the coefficient of square of the error term (ARCH effect) and the

‗r2‘ represents the coefficient of the lagged variance term (GARCH effect) in the variance

equation. ARCH effect (the coefficient r1) is an indication of ‗recent news‘ and GARCH effect

capturing the effect of ‗old news‘ (Antoniou and Holmes‘ (1995), Bologna and Cavallo‘s (2002),

Shenbagaraman (2003)). This estimation technique (GARCH) enables us to explore the link

between information/news arrival in the market and its effect on cash market volatility. The

advantage of a GARCH model is that it captures the tendency in financial data for volatility

clustering.

         The period of the introduction of the stock futures is different and result of model 2 has

shown that stock future trading is reducing the volatility, though not significantly. It is useful to

test its impact in the pre and post introduction period. The models 5 and 6 contain the analysis.

Model - 5:

         NIFDRstf0t = CONS + a1NIFDRstf0t-1 + Ut

GARCH specification:

         Ht = r1U2t-1 + r2H t-1 + d

[insert table-9]

Model - 6:

         NIFDRstf1t = CONS + a1NIFDRstf1t-1 + Ut

GARCH specification:

         Ht = r1U2t-1 + r2H t-1 + d

[insert table-10]

         The tables 9 and 10 have the result of the impact of pre and post introduction of stock

future. The result is not supporting the result that stock futures has reduced volatility in the stock

market as it suggests that the introduction of the stock futures in NSE has enhanced the volatility




                                                   30
in the stock market by increasing the uncertainty (as the old news effect has increased in the post

introduction of stock future period) in the Market. This problem may be because of stock futures

are significant factors affecting the volatility.

         Now it would be preferable if the impact of the other important derivatives products, that

is called options, is also examined as the results from the futures contracts are not so clear. The

next exercise is concerned with the options. The next three models are developed for this

purpose.

Model -7:

          NIFDRt = CONS + a1NIFDRt-1 + a2OPTN + Ut

GARCH specification:

         Ht = r1U2t-1 + r2H t-1 + b1OPTN + d

[insert table-11]

         Table-11 shows the result of impact of index options on NSE volatility. The coefficient of

the dummy variable of index options (OPTN) is significant at 1% significance level suggesting

that the introduction of index options in NSE has impacted significantly the stock market

volatility and the negative sign indicates that options have reduced the volatility (risk) in the

stock market.

         Finding favourable result now we will see what happen before and after introduction of

index options.

Model - 8:

            NIFDRopt0t = CONS + a1NIFDRopt0t-1 + Ut

GARCH specification:

         Ht = r1U2t-1 + r2H t-1 + d

[insert table 12]




                                                      31
Model - 9

          NIFDRopt1t = CONS + a1NIFDRopt1t--1 + Ut

GARCH specification:

        Ht = r1U2t-1 + r2H t-1 + d

[insert table 13]

        It becomes clear from the tables 12 and 13 that for both the period the information impact

is significant and supporting the result of the table-11. After the introduction of index options the

coefficient of recent new (r1) has gone up and old news (r2) has gone down. This indicates the

uncertainties in the stock market have gone down and the volatility has been reduced. This result

rejects the null hypothesis that derivatives have not reduced the volatility of NSE spot market.

        The above tests have resulted different things. As we have mentioned that we have taken

three time periods that are decided by three important derivatives products. Index futures were

introduced from June 2000 and 2000-2001 was the period of a big scam in the stock market.

Since 2002 the stock market was able to be taken its stable position and further even at present

the stock market has recorded an efficient position. This may be the reason that result for period

of options introduction is more trustable.

        But the question is whether the derivatives products are the only reasons to

reduce/increase the volatility, or are there some others factors affecting the volatility of stock

market return? To solve this problem the other variables as NIFJDRt, S&P500DRt and

Derivatives turnover are included as explanatory variables to control the effect of the index

futures. The following models are related to this practice.

Model 10-

   NIFDRt = CONS + a1 NIFDRt-1+ a2 INDFN + a3 NIFJDRt + a4S&P500DRt+ Ut

GARCH specification:



                                                     32
         Ht = r1 U2t-1 + r2 H t-1+ b1 INDFN + b2 NIFJDRt + b3S&P500DRt + d




         This model is constructed with the other variables which may be determinant of the Nifty

50 volatility besides derivatives products. The variables will control the impact of derivatives.

The result is tabulated in table-14.

[insert table 14]

         It is clear from the result tabulated in table-14 that the coefficient b2 for Nifty Junior

return volatility is significant at 1% significant level with negative sign indicating that the other

factors (market factors) have significantly reduced the volatility in the stock market. The

significance of the index futures effect is cleared out while including some others variables. But

the world factors are not significant.

         Overall findings from the econometric analysis is somewhat mixed, though indicating

that the stock market volatility has been effected significantly by derivatives trading. The stock

market volatility has followed a declining trend after the introduction of derivatives products

(stock index and index options) but not for the introduction of Index futures. The informational

efficiency has been increased after the introduction of derivatives trading. It has been argued that

the introduction of derivatives would cause some of the informed and speculative trading to shift

from the underlying cash market to derivative market given that these investors view derivatives

as superior investment instruments. This superiority stems from their inherent leverage and lower

transaction costs. The migration of informed traders would reduce the information asymmetry

problem faced by market makers resulting in an improvement in liquidity in the underlying cash

market.

         It is interesting to explore further whether the nature of the GARCH process was altered

as a result of the derivatives introduction. We therefore estimated the GARCH model separately


                                                      33
for the pre-futures and the post-futures period separately. The first point to note in comparing the

results before and after futures introduction is that the onset of futures trading has altered the

nature of the volatility. Before futures, the ARCH and the GARCH effects are significant,

suggesting that both recent news and old news had a lingering impact on spot volatility.

               The migration of informed traders would reduce the information asymmetry

problem faced by market makers resulting in an improvement in liquidity in the underlying cash

market. In addition, it could also be argued that the migration of speculators would cause a

decrease in the volatility of the underlying cash market by reducing the amount of noise trading.

This hypothesis is correctly certified by the study that derivatives trading (for the introduction of

index option 2001-2006) have reduced the stock market volatility.

       For the period of introduction of stock futures, it has been found that the result for this

products time period is not very clear but in general, has increased the volatility in the Indian

stock market. This was the period when stock market was facing too much fluctuation period.

The result supports some empirical finding that derivatives market is a market for speculators.

       Traders with very little or no cash or shares can participate in the derivatives market,

which is characterised by high risk. Thus, it is argued that the participation of speculative traders

in systems, which allow high degrees of leverage, lowers the quality of information in the

market. These uninformed traders could play a destabilising role in cash markets (Chatrath,

Ramchander and Song, 1995). However, according to another viewpoint, speculation could also

be viewed as a process, which evens out price fluctuations.

       The debate about speculators and the impact of futures on spot price volatility suggests

that increased volatility is undesirable. This is, however, misleading as it fails to recognize the

link between the information and the volatility. Prices depend on the information currently




                                                 34
available in the market. Futures trading can alter the available information for two reasons: first,

futures trading attract additional traders in the market; second, as transaction costs in the futures

market are lower than those in the spot market, new information may be transmitted to the

futures market more quickly. Thus, future markets provide an additional route by which

information can be transmitted to the spot markets and therefore, increased spot market volatility

may simply be a consequence of the more frequent arrival and more rapid processing of

information. (Bandivadekar and Ghosh 2005)

7 - Conclusion

       This study has examined the informational effects of the derivatives trading on the

volatility of stock market with the help of a well known test for the volatility of the financial time

series ARCH/GARCH model. NSE Nifty 50 is used as the proxy for stock market return. The

general finding is that introduction of derivatives trading has significant impact on the volatility

of the stock market return. For the assurance of the result three derivatives products have been

used for different time periods.

       To control the impact of the derivatives trading, we used some other return series and

found that derivatives are not a single factor affecting the stock market risk (volatility). There are

some other market factors also. A lot of efforts have been done by SEBI to control the volatility

of the NSE market.

       High volatility is considered as high risk in the stock market. To reduce this risk factor in

the Indian stock market, a number of steps are being adopted by the market regulators.

Introduction of derivatives trading is one of them. The analysis concluded that the derivatives

trading have done its work. It has enhanced the efficiency of the stock market by reducing the

spot market volatility.



                                                 35
       The result is different for the different periods. As, for the period of June 2000- June

2006, the analysis concludes that the volatility is increasing due to derivatives trading and for the

period of July 2001-June 2006, spot market volatility is following decreasing trend due to

Derivatives trading. This variation may be because of an artificial fluctuation in the stock market

due to big scam 2001 as it happened in the mid of 2001. This was also the reason why we used

three time periods. Anyway, we have considered more real result that is concluded for the period

of index options trading.

       But the present increasing nature of the volatility is a subject of caution. Though the

trading through derivatives in increasing day by day and more new derivatives products have

been introduced, the volatility is not much controlled. The functioning area or the size of the

market has been speeded enough. In this situation strong regulatory system is desired for well-

functioning of the stock market.




                                                 36
                                        REFERENCES

..(2002), Indian Securities Market Review, National Stock Exchange of India, Vol. IV.

....(2004-05), SEBI Annual Report, Stock Exchange Board of India.

..... Business Line (2004), "Trading in derivatives, Why should I trade in derivatives?

.....(2005), Indian Securities Market Review, National Stock Exchange do India, Vol. VII.

Agarwal Aman (2001), ―Derivatives: Wave of the Future‖, Finance India, June, Vol XV, No 2.

Bandivadekar Snehal and Ghosh Saurabh (2005), "Derivatives and Volatility on Indian

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Board Jhon, Sandamann Gleb and Sutcliffe Charles (2001), " The Effect of Futures Market

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Bologna, P and L. Cavallo (2002): ―Does the Introduction of Stock Index Futures Effectively

      Reduce Stock Market Volatility? Is the ‗Futures Effect‘ Immediate? Evidence from the

      Italian tock exchange using GARCH‖, Applied Financial Economics, Vol, 12, 183-192.

Bhaumik Suman Kumar (1997), "Stock Index Futures in India- Does the Market Justify its

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Bose Suchismita (2006), "The Indian Derivatives Market Revisited" Money & Finance, Jan –

      June, 0 6.

Darat Ali, Shafiqur Rahman, and Maosen Zhong (2002), "On The Role of Futures Trading in

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Gupta O.P. (2002), "Effect of Introduction of Index Futures on Stock Market Volatility: The

      Indian Evidence" UTI Capital Market Conference Paper, Mumbai.



                                                   37
Hetamsaria Nupur and Niranjan Swain (2003), "Impact of Introduction of Futures Market on

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Jeanneau Serge Marian Micu (2003), "Volatility and derivatives turnover: a tenuous

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Karmakar Madhusudan, Roy Malay K (1996), "Stock Price Volatility and Its Development

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Mayhew Stewart (2000), ―The Impact of Derivatives on Cash Markets: What Have We

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www.bseindia.com




                                               38
                                                             Tables and Figures

Table 2:- Descriptive Statistics for NSE Return (Jan 1998-June 2006)
                         Time periods                      Mean                        S. D.                      Max.

                            Pre INDF                    0.000468                   0.019386
                                                                                                              0.07539
                           Post INDF                    0.000486                   0.014652                   0.079691

                             Pre STF                  -5.885E-05                  0.0183268                   -0.057202

                            Post STF                   0.0009329                  0.0140794                   0.0796909

                             Pre OPT                2.622948E-05                  0.0186558                    0.075393

                            Post OPT                  0.00079976                  0.0141453                  0.07969091


Graph 1:- Trend of Volatility in Nifty 50 for the Period of Jan 1998-June 2006


                                                                  NIFDR Daily Volatility

               0.16



               0.14



               0.12



                0.1
  Volatility




               0.08



               0.06



               0.04



               0.02



                 0
                      1044781211742272803333864394925455986517047578108639169691032110411761248132013921464153616081680175218241896196820402112
                      125591021552082613143674204735265796326857387918448979501013108511571229130113731445151715891661173318051877194920212093
                      1145791231762292823353884414945476006537067598128659189711033110511771249132113931465153716091681175318251897196920412113
                      226601031562092623153684214745275806336867397928458989511014108611581230130213741446151815901662173418061878195020222094
                      1246801241772302833363894424955486016547077608138669199721035110711791251132313951467153916111683175518271899197120432115
                      32761951381912442973504034565095626156687217748278809339861049112111931265133714091481155316251697176918411913198520572129
                      1347811251782312843373904434965496026557087618148679209731036110811801252132413961468154016121684175618281900197220442116
                      428621051582112643173704234765295826356887417948479009531015108711591231130313751447151915911663173518071879195120232095
                      1448821261792322853383914444975506036567097628158689219741037110911811253132513971469154116131685175718291901197320452117
                      529631061592122653183714244775305836366897427958489019541017108911611233130513771449152115931665173718091881195320252097
                      1549831271802332863393924454985516046577107638168699229751038111011821254132613981470154216141686175818301902197420462118
                      630641071602132663193724254785315846376907437968499029551018109011621234130613781450152215941666173818101882195420262098
                      1650841281812342873403934464995526056587117648178709239761039111111831255132713991471154316151687175918311903197520472119
                      731651081612142673203734264795325856386917447978509039561019109111631235130713791451152315951667173918111883195520272099
                      1751851291822352883413944475005536066597127658188719249771040111211841256132814001472154416161688176018321904197620482120
                      832661101632162693223754284815345876406937467998529059581020109211641236130813801452152415961668174018121884195620282100
                       1852861301832362893423954485015546076607137668198729259781041111311851257132914011473154516171689176118331905197720492121
                       933671111642172703233764294825355886416947478008539069591022109411661238131013821454152615981670174218141886195820302102
                       1953871311842372903433964495025556086617147678208739269791042111411861258133014021474154616181690176218341906197820502122
                       2054881321852382913443974505035566096627157688218749279801043111511871259133114031475154716191691176318351907197920512123
                       2155891331862392923453984515045576106637167698228759289811044111611881260133214041476154816201692176418361908198020522124
                       2256901341872402933463994525055586116647177708238769299821045111711891261133314051477154916211693176518371909198120532125
                       23571001532062593123654184715245776306837367898428959481010108211541226129813701442151415861658173018021874194620182090
                       24581011542072603133664194725255786316847377908438969491012108411561228130013721444151615881660173218041876194820202092
                        34681121652182713243774304835365896426957488018549079601023109511671239131113831455152715991671174318151887195920312103
                        35691131662192723253784314845375906436967498028559089611024109611681240131213841456152816001672174418161888196020322104
                        36701141672202733263794324855385916446977508038569099621025109711691241131313851457152916011673174518171889196120332105
                        37711151682212743273804334865395926456987518048579109631026109811701242131413861458153016021674174618181890196220342106
                        38721161692222753283814344875405936466997528058589119641027109911711243131513871459153116031675174718191891196320352107
                        39731171702232763293824354885415946477007538068599129651028110011721244131613881460153216041676174818201892196420362108
                        40741181712242773303834364895425956487017548078609139661029110111731245131713891461153316051677174918211893196520372109
                        41751191722252783313844374905435966497027558088619149671030110211741246131813901462153416061678175018221894196620382110
                        42761201732262793323854384915445976507037568098629159681031110311751247131913911463153516071679175118231895196720392111
                         43771221752282813343874404935465996527057588118649179701034110611781250132213941466153816101682175418261898197020422114
                            911351882412943474004535065596126657187718248779309831046111811901262133414061478155016221694176618381910198220542126
                            921361892422953484014545075606136667197728258789319841047111911911263133514071479155116231695176718391911198320552127
                            931371902432963494024555085616146677207738268799329851048112011921264133614081480155216241696176818401912198420562128
                            941391922452983514044575105636166697227758288819349871050112211941266133814101482155416261698177018421914198620582130
                            1041572102633163694224755285816346877407938468999521016108811601232130413761448152015921664173618081880195220242096
                            961401932462993524054585115646176707237768298829359881051112311951267133914111483155516271699177118431915198720592131
                            97141194247300353406459512565618671724777830883936989105211241196126813401412148415561628170017721844191619882060
                            98142195248301354407460513566619672725778831884937990105311251197126913411413148515571629170117731845191719892061
                            99143196249302355408461514567620673726779832885938991105411261198127013421414148615581630170217741846191819902062
                            1091622152683213744274805335866396927457988519049571021109311651237130913811453152515971669174118131885195720292101
                              144197250303356409462515568621674727780833886939992105511271199127113431415148715591631170317751847191919912063
                               1451982513043574104635165696226757287818348879401002107411461218129013621434150615781650172217941866193820102082
                               146199252305358411464517570623676729782835888941994105611281200127213441416148815601632170417761848192019922064
                               147200253306359412465518571624677730783836889942995105811301202127413461418149015621634170617781850192219942066
                               148201254307360413466519572625678731784837890943996105911311203127513471419149115631635170717791851192319952067
                               149202255308361414467520573626679732785838891944997106011321204127613481420149215641636170817801852192419962068
                               150203256309362415468521574627680733786839892945998106111331205127713491421149315651637170917811853192519972069
                               151204257310363416469522575628681734787840893946999106211341206127813501422149415661638171017821854192619982070
                               1522052583113644174705235766296827357888418949471011108311551227129913711443151515871659173118031875194720192091
                                                                               1000107211441216128813601432150415761648172017921864193620082080
                                                                               1001107311451217128913611433150515771649172117931865193720092081
                                                                               993105711291201127313451417148915611633170517771849192119932065
                                                                               1003107511471219129113631435150715791651172317951867193920112083
                                                                               1004107611481220129213641436150815801652172417961868194020122084
                                                                               1005107711491221129313651437150915811653172517971869194120132085
                                                                               1006107811501222129413661438151015821654172617981870194220142086
                                                                               1007107911511223129513671439151115831655172717991871194320152087
                                                                               1008108011521224129613681440151215841656172818001872194420162088
                                                                               1009108111531225129713691441151315851657172918011873194520172089
                                                                                  106311351207127913511423149515671639171117831855192719992071
                                                                                  106411361208128013521424149615681640171217841856192820002072
                                                                                   106511371209128113531425149715691641171317851857192920012073
                                                                                   106611381210128213541426149815701642171417861858193020022074
                                                                                   106711391211128313551427149915711643171517871859193120032075
                                                                                   106811401212128413561428150015721644171617881860193220042076
                                                                                   106911411213128513571429150115731645171717891861193320052077
                                                                                   107011421214128613581430150215741646171817901862193420062078
                                                                                   107111431215128713591431150315751647171917911863193520072079
                                                                             Time Period

                                                                               NIFDRDV




Graph 2:- Trend of Volatility in Nifty Junior for the Period of Jan 1998-June 2006




                                                                                  39
                                                                  NIFJR Daily Volatility
              0.16



              0.14



              0.12



               0.1
 Volatility




              0.08



              0.06



              0.04



              0.02



                0
                     1044781211742272803333864394925455986517047578108639169691031110311751247131913911463153516081680175218241896196820402112
                     12559931361892422953484014545075606136667197728258789319841047111911911263133514071479155116231695176718391911198320552127
                     1145791231762292823353884414945476006537067598128659189711034110611781250132213941466153816101682175418261898197020422114
                     1246801241772302833363894424955486016547077608138669199721035110711791251132313951467153916111683175518271899197120432115
                     226601031562092623153684214745275806336867397928458989511013108511571229130113731445151715891662173418061878195020222094
                     32761951391922452983514044575105636166697227758288819349871049112111931265133714091481155316261698177018421914198620582130
                     1347811251782312843373904434965496026557087618148679209731036110811801253132513971469154116131685175718291901197320452117
                     428621051582112643173704234765295826356887417948479009531016108811601232130413761448152015921664173618081880195220242096
                     1448821261792322853383914444975506036567097628158689219741037110911811254132613981470154216141686175818301902197420462118
                     529631061592122653183714244775305836366897427958489019541017108911611233130513771449152115931665173718091881195320252097
                     1549831271802332863393924454985516046577107638168699229751038111011821255132713991471154316151687175918311903197520472119
                     6306498141194247300353406459512565618671724777830883936989105211241196126813401412148415561628170017721844191619882060
                     1650841281812342873403934464995526056587117648178709239761039111111831256132814001472154416161688176018321904197620482120
                     731651081612142673203734264795325856386917447978509039561018109011621234130713791451152315951667173918111883195520272099
                     1751851291822352883413944475005536066597127658188719249771040111211841257132914011473154516171689176118331905197720492121
                     832661091622152683213744274805335866396927457988519049571020109211641236130813801452152415961668174018121884195620282100
                      1852861301832362893423954485015546076607137668198729259781041111311851258133014021474154616181690176218341906197820502122
                      933671101632162693223754284815345876406937467998529059581021109311651237130913811453152515971669174118131885195720292101
                      1953871311842372903433964495025556086617147678208739269791042111411861259133114031475154716191691176318351907197920512123
                      2054881321852382913443974505035566096627157688218749279801043111511871260133214041476154816201692176418361908198120532125
                      2155891331862392923453984515045576106637167698228759289811044111611881261133314051477154916211693176518371909198220542126
                      2256901341872402933463994525055586116647177708238769299821045111711891262133414061478155016221694176618381910198420562128
                      23571001532062593123654184715245776306837367898428959481011108311551227129913711443151515871659173118031875194720192091
                      24581011542072603133664194725255786316847377908438969491012108411561228130013721444151615881660173218041876194820202092
                       34681111642172703233764294825355886416947478008539069591022109411661238131013821454152615981670174218141886195820302102
                       35691131662192723253784314845375906436967498028559089611024109611681240131213841456152816001672174418161888196020322104
                       36701141672202733263794324855385916446977508038569099621025109711691241131313851457152916011673174518171889196120332105
                       37711151682212743273804334865395926456987518048579109631026109811701242131413861458153016021674174618181890196320352107
                       38721161692222753283814344875405936466997528058589119641027109911711243131513871459153116031675174718191891196420362108
                       39731171702232763293824354885415946477007538068599129651028110011721244131613881460153216041676174818201892196520372109
                       40741181712242773303834364895425956487017548078609139661029110111731245131713891461153316051677174918211893196620382110
                       41751191722252783313844374905435966497027558088619149671030110211741246131813901462153416061678175018221894196720392111
                       42761201732262793323854384915445976507037568098629159681032110411761248132013921464153616091681175318251897196920412113
                        43771221752282813343874404935465996527057588118649179701033110511771249132113931465153716121684175618281900197220442116
                           911351882412943474004535065596126657187718248779309831046111811901264133614081480155216241696176818401912198520572129
                           1021552082613143674204735265796326857387918448979501014108611581230130213741446151815901663173518071879195120232095
                           92137190243296349402455508561614667720773826879932985104811201192126613381410148215541627169917711843191519872059
                           94138191244297350403456509562615668721774827880933986105011221194126713391411148315551629170117731845191719892061
                           1041572102633163694224755285816346877407938468999521015108711591231130313751447151915911666173818101882195420262098
                           96140193246299352405458511564617670723776829882935988105111231195126913411413148515571630170217741846191819902062
                           1071602132663193724254785315846376907437968499029551019109111631235131113831455152715991671174318151887195920312103
                           97142195248301354407460513566619672725778831884937990105311251197127013421414148615581631170317751847191919912063
                           991431962493023554084615145676206737267798328859381000107211441216128913611433150515771649172117931865193720092081
                            1121652182713243774304835365896426957488018549079601023109511671239 1324139614681540 16251697176918411913 19922064
                              144197250303356409462515568621674727780833886939992105411261198127113431415148715591632170417761848192019932065
                              145198251304357410463516569622675728781834887940993105611281200127213441416148815601633170517771849192119942066
                              146199252305358411464517570623676729782835888941994105711291201127313451417148915611634170617781850192219952067
                              147200253306359412465518571624677730783836889942995105811301202127413461418149015621635170717791851192319962068
                              148201254307360413466519572625678731784837890943996105911311203127513471419149115631636170817801852192419972069
                              149202255308361414467520573626679732785838891944997106011321204127613481420149215641637170917811853192519982070
                              150203256309362415468521574627680733786839892945998106111331205127713491421149315651638171017821854192619992071
                              151204257310363416469522575628681734787840893946999106211341206127813501422149415661639171117831855192720002072
                              1522052583113644174705235766296827357888418949471010108211541226129813701442151415861658173018021874194620182090
                                                                              991105511271199 127913511423149515671640171217841856192820012073
                                                                              1001107311451217129013621434150615781650172217941866193820102082
                                                                              1002107411461218129113631435150715791651172317951867193920112083
                                                                              1003107511471219129213641436150815801652172417961868194020122084
                                                                              1004107611481220129313651437150915811653172517971869194120132085
                                                                              1005107711491221129413661438151015821654172617981870194220142086
                                                                              1006107811501222129513671439151115831655172717991871194320152087
                                                                              1007107911511223129613681440151215841656172818001872194420172089
                                                                              1008108011521224129713691441151315851657172918011873194520212093
                                                                              1009108111531225 13061378145015221594 1679175118231895 198020522124
                                                                                 106311351207128013521424149615681641171317851857192920022074
                                                                                  106411361208128113531425149715691642171417861858193020032075
                                                                                  106511371209128213541426149815701643171517871859193120042076
                                                                                  106611381210128313551427149915711644171617881860193220052077
                                                                                  106711391211128413561428150015721645171717891861193320062078
                                                                                  106811401212128513571429150115731646171817901862193420072079
                                                                                  106911411213128613581430150215741647171917911863193520082080
                                                                                  1070114212141287135914311503157516481720179218641936 20162088
                                                                                  10711143121512881360143215041576 16611733180518771949 20342106
                                                                                            1252                1607                1962
                                                                            Time Period


                                                                                NIFJRV




Table 4:- Result of Unit Root Test
                           Variables                       Para. estimate                        t-value                        p-value
                         S&P500DR                               -1.2241                          -8.3983                       0.00000
                            NIFDR                               -1.0072                          -8.4091                       0.00000
                             NIFJR                              -0.7603                          -7.8033                       0.00000



    Table - 5: Result of GARCH (1, 1) Estimate for NSE Return for whole time Period with
                                       Index Futures
                        Parameters             Par. Estimate                           t-value                 [p-value]
                             r1                  0.211263                              12.268                  [0.00000]
                             r2                  0.751943                              46.828                  [0.00000]
                            b1                   0.001161                               1.811                  [0.05016]
                             d                   0.000022                               5.996                  [0.00000]
                         L B Q(7)                   5.89                                                        0.81732
                     Breusch-Pagan test          97.044513                                                      0.00000
                     R-square                                                              = 0.1801
                     Number of Observations                                               = 2130
                     Akaike Information criteria                                          -8.075541

    Table - 6: Result of GARCH (1, 1) Estimate for NSE Return for whole time Period with
                                       Stock Futures
                Parameters        Par. estimate                                 t-value                 [p-value]
                    r1               0.311714                                   10.035                  [0.00000]
                    r2               0.558765                                   17.047                  [0.00000]
                    b1              -0.000690                                    -1.163                 [0.24484]
                     d               0.000060                                     6.826                 [0.00000]
                  R-square                                                                  0.1803
                  Number of Observations                                                   2130



                                                                                  40
     Akaike Information Criteria                           -8.062343


 Table - 7: Result of GARCH (1, 1) Estimate for NSE Return Pre-introduction of Index
                                       Futures
              Parameters              Par. estimate            t-value      [p-value]
                  a1                   -0.390403               -11.828      [0.00000]
                  r1                    0.181832                3.587       [0.00034]
                  r2                    0.014549                0.208       [0.83555]
                  d                     0.000427                8.585       [0.00000]
     R-square                                              = 0.2199
     N. O.                                                 = 615
     Akaike Information criteria:                          -7.499612

Table - 8: Result of GARCH (1, 1) Estimate for NSE Return Post-introduction of Index
                                       Futures
               Parameters            Par. estimate            t-value      [p-value]
                   a1                 -0.344265               -12.470      [0.00000]
                   r1                  0.265994                10.690      [0.00000]
                   r2                  0.688073                29.300      [0.00000]
                   d                   0.000026                 5.496      [0.00000]
     R-square                                              = 0.1478
     Number of observations                                = 1513
     Akaike Information Criteria                           -8.292467

 Table - 9: Result of GARCH (1, 1) Estimate for NSE Return Pre-introduction of Stock
                                       Futures
           Parameters            Par. estimate                  t-value        [p-value]
               a1                 -0.410058                     -11.956        [0.00000]
               r1                  0.375793                      6.249         [0.00000]
               r2                  0.545186                      9.445         [0.00000]
                d                  0.000082                      4.034         [0.00005]
     R-square                                              = 0.1987
     N. O.                                                 = 970
     Akaike Information Criteria                           -7.698924

Table 10: Result of GARCH (1, 1) Estimate for NSE Return Post-introduction of Stock
                                      Futures
      Parameters            Par. estimate                    t-value       [p-value]
          a1                  -0.384204                     -11.567        [0.00000]
          r1                  0.306282                       14.488        [0.00000]
          r2                  0.619023                       49.334        [0.00000]
          d                   0.000033                        6.481        [0.00000]
     R-square                                               0.1558
     NO                                                    1158
     Akaike Information Criteria                           -8.369675

Table - 11: Result of GARCH (1, 1) Estimate for NSE Return for whole time Period with
                                    Index Options
          Parameters                Par. estimate                t-value        [p-value]
              r1                      0.220373                   12.874         [0.00000]
              r2                      0.719303                   42.228         [0.00000]



                                                      41
                b1                   -0.002180                 -3.817           [0.00013]
                d                    0.000025                   6.416           [0.00000]
      R-square                                           0.1722
      Number of Observations                             2130
      Akaike Information Criteria:                      -8.071036

 Table - 12: Result of GARCH (1, 1) Estimate for NSE Return Pre-introduction of Index
                                       Options:
           Parameters          Par. estimate                 t-value           [p-value]
               a1                 -0.391975                  -10.917           [0.00000]
               r1                 0.363038                     5.083           [0.00000]
               r2                 0.496792                     8.228           [0.00000]
               d                  0.000118                    11.380           [0.00000]
                  R-square                              = 0.2033
                  NO                                    = 877
                  Akaike Information Criteria:           -7.634702

 Table - 13: Result of GARCH (1, 1) Estimate for NSE Return Post-introduction of Index
                                        Options
             Parameters            Par. estimate            t-value         [p-value]
                  a1                 0.001567                3.591          [0.00033]
                  r1                 0.452367                7.388          [0.00000]
                  r2                 0.328460                6.746          [0.00000]
                  d                  0.000110               40.112          [0.00000]
      R-square                                          0.0293
      N. O.                                             1251
      Akaike Information Criteria:                      -8.496515

Table-14: Result of GARCH (1, 1) Estimate for NSE Return for the whole Period Adjusted
                        by Nifty Junior and S&P500 Return
              Parameters        Par. estimate             t-value           [p-value]
                  r1               0.285322                10.329           [0.00000
                  r2               0.637717                23.346           [0.00000]
                  b1               0.000214                0.342            [0.73267]
                  b2              -0.136321               -10.912           [0.00000]
                  b3               0.010703                0.537            [0.59148]
                  d                0.000040                5.926            [0.00000]
              R-square                                          0.1866
              NO                                                2096
              Akaike Information Criteria:                      -8.113204




                                                   42

				
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