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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. 2 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. 4 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 9 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. 11 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 15 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. 17 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 18 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 Stock Markets", Reserve Bank of India, Occasional Papers. Board Jhon, Sandamann Gleb and Sutcliffe Charles (2001), " The Effect of Futures Market Volume on Spot Market Volatility", Journal of business Finance and Accounting, Vol. 28, No. 7&8, October, 306-686. 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 Use", Economic and Political Weekly, Oct 11. 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 Spot Market Fluctuations: Perpetrator of Volatility or Victim of Regret?" The Journal of Financial Research, Vol. XXV, No. 3, 431-444. 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 the Spot Market: An Empirical Study" the ICFAI Journal of Applied Finance, Vol. 9, No. 8, November. Jeanneau Serge Marian Micu (2003), "Volatility and derivatives turnover: a tenuous relationship" BIS Quarterly Review, March 2003. Karmakar Madhusudan, Roy Malay K (1996), "Stock Price Volatility and Its Development Implications—Indian Experiences" Finance India, Vol. X No. 3, September, Pages 585– 603. Mayhew Stewart (2000), ―The Impact of Derivatives on Cash Markets: What Have We Learned?‖ Terry College of Business, University of Georgia, February. Nath Golaka C (2003), "Behaviour of Stock Market Volatility after Derivatives" NSE NEWS, National Stock Exchange of India, November. Rahman Shafiqur (2001), "The introduction of derivatives on the Dow Jones Industrial Average and their impact on the volatility of component stocks" The Journal of Futures Markets, Vol. 21, No. 7, July, pg. 633. Raju M. T. and Kiran Karande (2003), "Price Discovery and Volatility on NSE Futures Market" Securities and Exchange Board of India, Working Paper Series, No. 7, March. Schwert G. William (1990), "Stock Market Volatility: Ten years after the Crash‖ NBER Working Papers, December 1997. Shenbagaraman Premalata (2003), "Do Futures and Options trading increase stock market volatility" NSE NEWS, National Stock Exchange of India, Jan. 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 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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 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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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