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Interdisciplinary Journal of Research in Business Vol. 1, Issue. 7, July 2011(pp.117-131)
117
The Impact of Derivative Trading on Spot Market Volatility: Evidence for
Indian Derivative Market
KoustubhKanti Ray
Assistant Professor, Financial Management
Indian Institute of Forest Management (IIFM)
Post Box No-357, Nehru Nagar, Bhopal, M.P (India). E-mail: [email protected]
Ajaya Kumar Panda Assistant Professor, Finance
IBS Hyderabad, Donthanapally, Shankarapalli Road
Hyderabad- 501504, A.P (India) E-mail: [email protected]
ABSTRACT
The impact of derivatives trading on the underlying stock volatility, and its characteristics, is still debated both
in the economic literature and among practitioners. The aim of this study is to analyse the effect of the
introduction of derivatives on the volatility of the Indian stock exchange. This study mainly addresses two
issues: first, the study analyses the stock market volatility in the pre and post derivative period and Secondly,
whether the `derivatives effect’, if confirmed, is immediate or delayed. The results show that some of the stocks
experienced changes in the structure volatility after implementation of derivatives and experiencing a stronger
persistence of volatility in comparison to pre derivative period. Most of the stocks became disintegrated with
market benchmark index after introduction of derivatives.
Keywords: Derivatives in India, GARCH and Stock market volatility, Stock market volatility, Spot and
Derivative Markets
1. INTRODUCTION
The purpose of this paper is to examine the stabilization issue on the beginning of derivative trading on the spot
market of the Indian stock exchange. The stabilization issue involves the study of the spot price volatility
behaviour. If derivative trading does improve the information transmission efficiency, the volatility clustering
behaviour in spot price volatility will be narrowed. The speculative forces attracted by the lower transaction cost
feature in derivatives may intense spot price volatility and increase information transmission from derivatives to
spot markets. Therefore it may be reported that the introduction of derivatives trading significantly affects the
volatility of the underlying spot market. This has been a major source of concern for both fund managers and
regulators. As a corollary, the impact of derivatives trading on the volatility of the underlying spot market is
intensely debated. One viewpoint suggests that speculative trades in derivative markets tend to stabilize or even
reduce volatility of the underlying spot market (Baldauf and Santoni, 1991; Antoniou and Foster, 1992; Pericli
and Koutmos, 1997; Galloway and Miller, 1997; Dennis and Sim, 1999; Rahman, 2001). On the other hand,
some researchers have found that excessive speculation in derivative markets destabilizes and increases
volatility of the spot market (Lee and Ohk, 1992; Antoniou and Holmes, 1995). Under these conditions the aim
of this study is to analyse the effect of the introduction of derivatives on the volatility of the Indian stock
exchange. This study mainly addresses two issues: first, the study analyses the stock market volatility in the pre
and post derivative period and Secondly, whether the `derivatives effect‟, if confirmed, is immediate or delayed.
The paper is divided into five sections, including introduction and conclusions. Section-2 presents a brief review
of the theoretical literature and of the main results of previous empirical studies. Section-3 presents the data set
and methodology used. Section-4enumerates the data analysis and empirical results of the study. The final
section provides conclusions.
2. REVIEW OF LITERATURE
There are two different perspectives exist in the literature about the relationship between derivative markets and
underlying spot markets. The first group of researches supports the argument that derivative trading destabilizes
the underlying spot market by increasing its volatility. The presence of uninformed traders in the derivatives
market is, according with Cox (1976), the main cause of destabilization of the underlying cash market.
Essentially the identical argument has been proposed by Finglewski (1981), who affirmed that a lower level of
information of futures traders, compared with that of cash market participants results in increased cash market
volatility. To the same conclusion arrived, Stein (1987) stating that futures markets attract uninformed traders
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because of their high degree of leverage; the activity of those traders reduces the information content of prices
and increases spot market volatility.
The other literature presents arguments in favour of the idea that derivatives markets have a favourable effect on
the underlying cash markets. According to Schwarz and Laatsch (1991), futures markets are an important means
of price discovery in spot markets. Powers (1970) argued that futures markets increase the overall market depth
and informativeness. Stroll and Whaley (1988) stated that futures markets enhance market efficiency. The
model proposed by Danthine (1978) implies that futures trading increases market depth and reduces spot market
volatility.
Although many studies have been carried out trying to understand whether futures markets destabilize or not
cash markets, the empirical research findings are still not in agreement.Simpson and Ireland (1982); Froewiss
(1978) as well as Corgel and Gay (1984) proposed that weekly spot price volatility was not affected by the
introduction of futures. They concluded that futures did not affect spot market volatility. Following these early
researches other studiesevidence on the impact of the introduction of futures trading on the spot market
volatility is mixed. Edwards (1988a&, b) found a decreased stock market volatility for the S&P500 after the
introduction of the stock index futures contract. Santoni (1987) suggested that an increase in the S&P500 futures
contract trading volume does not increase the volatility of the underlying index. Hodgson and Des Nicholls
(1991) concluded that stock index futures trading did not affect the long-term volatility of the Australian Stock
Exchange but left unanswered the question for the short-term volatility. Bessembinder and Seguin (1992) found
evidence that unexpected S&P500 futures trading was positively related to spot market volatility but the
relationship between spot market volatility and expected futures volume was negative.Anotoniou and Holmes
(1995) suggested, for the London Stock Exchange, an increased volatility following the introduction of the
FTSE100 index futures contract. Board et al. (1997) found that contemporaneous futures market trading had no
effect on spot market volatility but lagged futures volume has been found to have a small positive effect.
Bologna (1999) showed that the introduction of stock index futures trading in the Italian Stock Exchange has led
to diminished volatility and that lagged futures volume is inversely related to stock market conditional volatility.
Some other studies (e. g., Kamaraet al., 1992; Jagadeesh and Subramanyam, 1993; Narasimhan and
Subrahmanyam, 1993; Peat and McCrrory, 1997) show that the volatility of the prices of underlying assets
increases after the introduction of derivative trading. Edwards (1988); Herbst and Maberly (1992); Antoniou and
Holmes (1995) find that the introduction of the index futures resulted in increased level of volatility in the short
run, but no significant impact is found in the long run. On the other hand, many other studies across the
countries and asset markets show that the volatility comes down after introduction of derivative trading (for
example Basal et al., 1989 and Conrad, 1989 in US; Robinson, 1993; Aitken et al., 1994 in Australia; Kumar et
al., 1995 in Japan).Gulen and Mayhew (2000) examine the impact of introduction of futures trading in twenty
five countries and obtain mixed results. They found that the volatility in majority of the markets has decreased
but it has also increased in some countries including US and Japan. Antoniou and Foster (1992) investigate the
effects of introduction of futures contract on Brent Crude Oil on its spot market. They find no substantial change
in volatility between the pre and post-futures periods. Pericli and Koutmos (1997) investigate the behaviour of
conditional variance after the introduction of index futures and options. They use a non-linear exponential
GARCH model to account explicitly for the asymmetry in stock return volatility. They report a reduction in the
volatility of the S&P 500 index after the introduction of futures trading. Galloway and Miller (1997) investigate
the effects of futures on the volatility of the Mid-cap 400 index and reject the view that index futures increase
volatility of securities included in the index. Lamoureux and Pannikath (1994); Freund et al. (1994) and Bollen
(1998) find that the direction of the volatility is not consistent over time. Spyrou (2005) and Alexakis (2007)
find that futures trading at Athens Stock Exchange have assisted on incorporation of information into spot prices
more quickly but it has not a deterministic impact on the volatility of underlying spot market. In another study,
Antoniou, Holmes and Priestley (1998) suggest that although introduction of futures contracts does not have a
detrimental effect on the underlying market, it has some influence on the dynamics of the stock market. They
report an improvement in the way the news is transmitted into prices following the introduction of futures
trading. In contrast, Lee and Ohk (1992), who examine the spot market volatility in Australia, Hong Kong,
Japan, UK and USA using data for 500 business days before and 500 business days after the start of futures
trading, conclude that stock market volatility increases significantly (with the exception of the Australian and
the Hong Kong stock markets) after the introduction of stock index futures.
There are few studies have been conducted to examine the impact of derivative trading on Indian stock markets.
Thenmozhi (2002), in her study on the relationship between CNX Nifty futures and the CNX Nifty index finds
that derivative trading has reduced the volatility in the cash segment. Gupta (2002) concludes in his study that
the overall volatility of the stock market has declined after the introduction of the index futures.
Bandivadekarand Ghosh (2003) conclude that while the „futures effect‟ plays a definite role in the reduction of
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volatility in the case of S&P CNX Nifty, in the case of BSE Sensex, where derivative turnover is considerably
low, the effect is rather ambiguous. In a study examining the impact of derivative trading at individual stock
level, Nath (2003) observes that the volatility has come down in the post-derivative trading period for most of
the stocks. Raju and Karande (2003) also find that the introduction of futures has reduced volatility in the cash
market. Other studies (including,Thenmozhi and Sony, 2004 and Vipul, 2006) also reach at similar conclusions.
However on the other hand, Shenbagaraman (2003) finds no evidence of any link between trading activity
variables on the futures market and spot market volatility. However, he observes that the structure of volatility
has changed in post-derivative period. In the similar line Samanta and Samanta (2007) found mixed results at
the level of individual stocks. Afsal and Mallikarjunappa (2007) find that the derivative trading has no impact
on the spot market in the Indian context.
3. DATA SET AND METHODOLOGY
3.1. Data set
The Security and Exchange Board of India (SEBI) allowed the trading on index futures on May 25, 2000. The
trading of BSE Sensex futures commenced at Bombay Stock Exchange (BSE) on June 9, 2000 and on June 12,
2000 trading of Nifty-futures commenced at National Stock Exchange (NSE). In the June 2001 index options
and in July 2001 stock options were introduced. Futures on individual stocks were introduced in November
2001. Under this background, thepresent paper studies the impact of derivatives introduction and its impact on
the volatility of the underlying securities in India. The study is based on a sample of daily returns of fifteen
stocks on which the derivative products are available for trading. These stocks are ACC Ltd., Grasim Industries
Ltd., Housing Development Finance Corporation. Ltd., HDFC Bank Ltd., Hero Honda Motors Ltd., Hindalco
Industries Ltd., Hindustan Unilever Ltd., ITC Ltd., Larsen & Toubro Ltd., Ranbaxy Laboratories Ltd., Reliance
Industries Ltd., State Bank Of India, Tata Motors Ltd., Tata Power Co. Ltd., Tata Steel Ltd..These are the
companies those have implementedderivative trading as soon as it was allowed by SEBI. Hence the present
study analyses the effect of derivatives on the volatility of the Indian stock market by dividing the time periods
into pre derivative period (i.e. 2nd
Jan, 1998 to 29th June, 2001) and post derivative period (i.e. 2
nd July 2001 to
31st Dec 2009).
3.2. Methodology of the study
To capture the persistence of volatility before and after the introduction of derivatives in Indian stock
market the present study used ARCH and GARCH models and the long run equilibrium relationships of the
markets before and after introduction of derivative trading are measured my Engle-Granger cointegration
techniques. Before estimating the models, the unit root properties of the country bench mark indices are tested
by using DF, ADF and PP techniques.In selection of optimum lag length for the variables of the model, present
study used Final Prediction Error (FPE), Akaike information criterion (AIC), Schwarz criterion (SC),
Likelihood Ratio (LR) criterion and Hannan-Quinn (H-Q) information criterion to estimate optimum lag length
of the estimated variables. These models have identified 4 lags for each of the variables in the VAR model.
Conditional Heteroscedastic Models of Volatility
The prime motivation behind the development of conditional volatility models is twofold. First, the linear time
series models were inappropriate in the sense that they provide poor forecast intervals, and it was contended that
like conditional mean, variance (volatility) could as well evolve over time, and hence it was important to model
them both simultaneously. Secondly, an assumption of Classical Linear Regression Model (CLRM) is that the
variance of the error term is constant. If the errors are heteroscedastic, but assumed to be homoscedastic, an
important implication would be that standard error estimates could be wrong. It is unlikely in the context of
financial time series that the variance are constant over time and it makes sense to consider a model that does
not assume that variance is constant. An attempt in this regard was made by Engle (1982) who proposed the
Auto Regressive Conditional Heteroscedastic (ARCH) model. Another important feature of many series of
financial asset returns which provides a motivation for the ARCH class of models is known as “volatility
clustering” or “volatility pooling”. This volatility clustering describes the tendency of large changes in asset
prices (of either sign) to follow large changes, and small changes (of either sign) to follow small changes. Hence
the current level of volatility tends to be positively correlated with its level during the immediate preceding
periods.
The ARCH Model
The first model that provides a systematic framework for volatility modeling is the ARCH model of Engle
(1982). The model shows that it is possible to simultaneously model the mean and variance of a series. As a
preliminary step to understand Engle‟s methodology, let‟s estimate a stationary ARMA model
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ttt yy 110 where 2var,0 ttE for all t and forecast 1ty . A forecast of 1ty is
conditional expectation of 1ty in period t, given the value of ty as ttt yyE 101 . If we use this
conditional mean to forecast 1ty , the forecast error variance is 22
1
2
101 ttttt EyyE .
Instead, if unconditional forecasts are used, the unconditional forecast is always the long run mean of the ty
sequence that is equal to 10 1 . The unconditional forecast error variance is
2
1
22
2
3
11
2
111
2
101 1..........1 ttttt EyE
Since 111 2
1 , the unconditional forecast has a greater variance than the conditional forecast. Thus,
conditional forecasts are preferable. Similarly, if the variance of t is not constant, we can estimate any
tendency for sustained movements in the variance using an ARMA model.
A way to build an ARCH model consists of three steps. Step (1) builds an econometric model for example an
ARMA model for the return series to remove any linear dependence in the data and use the residual series of the
model to test for ARCH effects. Step (2) specifies the ARCH order and performs estimation. Step (3) involves
checking the fitted ARCH model carefully and refining it if necessary.
The GARCH Model
GARCH models explain variance by two distributed lags, one on past squared residuals to capture high
frequency effects or news about volatility from the previous period measured as the lag of the squared residual
from mean equation, and second on lagged values of variance itself to capture long term influences. In the
GARCH (1, 1) model, the variance expected at any given data is a combination of long run variance and the
variance expected for the last period, adjusted to take into account the size of the last periods observed shock. In
the GARCH model estimates for financial asset returns data, the sum of coefficients on the lagged squared error
and lagged conditional variance is very close to unity. This implies that shocks to the conditional variance will
be highly persistence and the presence of quite long memory but being less than unit, it is still mean reverting.
Representing the GARCH model, Let the error process be such thatttt hv where 12 v and
2
110 tth ,
then
it
p
i
iit
q
i
it hh
1
2
1
0 (1)
Since tv is a white noise process that is independent of past realization of it , the conditional and
unconditional means of t are equal to zero. By taking the expected values of t , it is easy to verify that
0 ttt hEvE . The important point is that the conditional variance of t is given by ttt hE
2
1 .
Thus, the conditional variance of t is given by th in equation (1).
The generalized ARCH (p, q) model of equation (1) is called as GARCH (p, q) that allows for both
autoregressive and moving average components in the heteroskedastic variance.
The Cointegration Test
The long run equilibrium relationship between the variables can be detected through cointegration technique.
When the variables contain a unit root, cointegration technique of time series is used to establish a long run
relationship among them. In general, if two or more variables are integrated of the same order and their linear
combination is found to be stationary then the two variables are said to be co-integrated. A principal feature of
co integrated variables is that their time paths are influenced by any deviations from long run equilibrium
relationship. If the system returns to equilibrium then movement of some variables must respond to the
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magnitude of disequilibrium. This dynamics is implied by the error correction model, which shows the degree of
adjustment of short run deviation from equilibrium. If two variables are cointegrated then the result of their
causality will not give valid conclusion. Thus, error correction technique is used. The study of cointegration uses
two methods namely Engel-Granger (1987) and Johansen (1988) Maximum Likelihood Procedure. Though the
present study undertakes multivariate analysis, it has used Johansen Maximum Likelihood Procedure to identify
the long run equilibrium relationships between the variables.
4. DATA ANALYSIS AND EMPIRICAL RESULTS
In order to study the persistence of volatility, the present study has used log return series of the stock prices of
the studied companies. As per the sample of pre derivative and post derivative periods, the descriptive statistics
are presented in table-1.1 and 1.2 respectively. The main purpose of studying the descriptive statistics of the
return series is to analyze the nature of the real valued random return distribution with respective to features of
normality. Although this part of the analysis never contributes much to the basic objectives of the study, but
carries lots of insights to carry out the time series modeling that the study is intended to do. Looking at the
descriptive statistics of the log return series, the common impression says the return series are not normally
distributed. The peakedness of a probability distribution on the basis of kurtosis identifies most of the return
series containing excess kurtosis, i.e. having values more than 3. The similar insight is also carried by the
statistics of skewness that measures the asymmetry of the probability distribution. It is quite obvious that the
each of the respective series is having unique kurtosis and skewness statistics. Analyzing the descriptive
statistics at pre derivative period (Table-1.1), it has been found that the price return series of Tata motor is little
closer to normality. Similarly the return series of GIND, HDFC Bank and SBIis relatively less excess kurtosis.
Hence these four return series is expected to have a rounded peak and a shorter thinner tail. In contrast to it,
L&T is having the highest kurtosis among the group implying a sharper peak and longer, flatter tail. This also
implies more of variance due to infrequent extreme deviations. But altogether, all the fifteen log return series
have excess kurtosis implying the deviation from normality. Looking at the asymmetry property through
skewness, it has been observed that only ACCis negatively skewed in pre-derivative period. Overall, the
property of normality is studied by Jarque-Bera statistics that finds, none of the series are normally distributed.
The descriptive statistics of post derivative periods (Table-1.2)also finds that all the return series are having
excess kurtosis and relatively higher kurtosis than pre derivative period except HHM and L&T. This implies that
the peakedness of the probability distribution of the return series has increased after the implementation of
derivative except the price return of above two companies. Hence the return distributions are supposed to have
relatively sharper peak and longer, flatter tail after implementing derivatives. A lower kurtosis of HHM and
L&T in post derivative period shows a decrease in the peakedness of their probability return distribution. At the
same time RelianceIND is experiencing a higher kurtosis in comparison with its pre derivative period. Looking
at the level of asymmetry, through the statistics of skewness, it has been found that the return distribution of
Hindal, HUL,Ranbaxy, RelianceIND, SBI, TATAM, TATAP, and TATAS has become negatively skewed in
post derivative period. Studying normality, the Jarque-Bera statistics finds a non normal distribution of the
return series of all the companies. Broadly the statistical nature of the return distributions of the companies has
changed after the implementation of derivative. The return distributions have moved more towards a sharper
peak, longer and flatter tail. This may contribute to the structure of vitality and its level of persistence.
Time Series Properties of the Return Distributions:
The time series properties of the return series are tested through DF, ADF and PP techniques to trace out
stationarity of the variables. The estimated -statistic and the respective probability values are presented in
Table- 2.1 and 2.11.The reasonbehind using the three methods subsequently lies on their methodological
loopholes. DF tests unit root by using one period lag value where as ADF considers an optimum lags. As a
result, there is a scope that the unit root properties tested by ADF may be biased by serial autocorrelations.
Hence PP moves one step ahead and tests unit root by considering optimum lag values in one hand and rectifies
the problem of serial correlation on the other. Hence the combination of the three methods is expected to trace
out the unit root properties properly. It has been observed that all the log return distributions are stationary at
level for both pre and post derivative periods. This implies the return distributions are mean reverting and can be
readily used for further time series modeling like ARCH, GARCH and impulse response functions of Vector
Auto regression model. But in order to trace out the cointegrating relationships between individual company
return with the country bench mark index i.e. CNX Nifty in our case, is studied by using time series of closing
prices taken in natural log transformation.
Introduction of Derivatives and its Impact on Persistence of Volatility of the Return Distribution:
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The volatility of the concern stocks are studied by using ARCH and GARCH models. Table-3.1, 3.2, 4.1 and 4.2
present the test statistics of ARCH-LM and GARCH (1, 1) model in pre and post derivative period respectively.
Table 3.1 and 3.2 represents to pre derivative period where as Table 4.1 and 4.2contains estimated results of
post derivative period. It has been observed that all the stocks both in pre and post derivatives period have
ARCH effects suggested by ARCH-LM test statistics. This implies that the presence of a significant influence of
previous period error terms on current return distribution. But the structure of volatility will be clearer by
GARCH estimates when the entire volatility of the return distribution will be segregated with ARCH
coefficients and GARCH coefficients. Table 3.2 and 4.2 contains the GARCH estimates with ARCH term
represented by α (alpha) and GARCH term represented by β (beta). ARCH component reflects the influence of
random deviations in previous period error terms on σ which is a function of random error terms and realized
variance of previous periods. Similarly, GARCH coefficient measures the part of the realized variances in the
previous period that is carried over in to the current period. The sum of ARCH coefficient and GARCH
coefficient (α + β) determines the short run dynamics of the resulting volatility time series. More specifically, a
large ARCH error coefficient (α) means that volatility reacts intensely to market movements and a large
GARCH error coefficient (β) indicates that shocks to conditional variance take a long time to die out. So
volatility is persistence. Hence current volatility can be explained by past volatility that tends to persist
overtime. If α is relatively high and β is relatively low, then volatility tends to be spikier. It has been observed
that out of 15 stocks of our study, 8 stocks are experiencing changes in their pattern of volatility after the
implementation of derivative. The stricture of volatility in the returns of companies like ACC, HHM, LT,
Ranbaxy, Reliance Industry, Tata Power and Tata Services remain almost unchanged after they implement
derivatives. The negligible change in the ARCH and GARCH components before and after implementation of
derivatives hardly matters to a significant change. But the return of the companies likes G Ind, HDFC Bank,
Hindal,HUL, HDFC, ITC, SBI and Tata motorsexperienced changes in the structure volatility after
implementation of derivatives but Tata Motors realized relatively a smaller change. Analyzing the changes in
the structure of volatility deeply, it has been noticed that the ARCH coefficient of GIndhas declined and
GARCH coefficients has increased significantly after the implementation of derivatives. This implies that in the
post derivative period, the price return of GInd has became less sensitive to the recent past error distributions but
contains a stronger persistence of volatility that takes a longer time to die out. From the modeling of point of
view, GInd is the only return series that is solved with GARCH (1, 2) model (Table-4.3). Similarly, the stocks of
HDFC Bank, Hindal, HDFC, SBI and Tata Motors experienced a lower ARCH coefficients (α) and higher
GARCH coefficients (β). It clearly implies that after implementation of derivatives, these companies become
less sensitive to the immediate market movements, but at the same time experiencing a stronger persistence of
volatility in comparison to pre derivative period. Hence current volatility to these stock returns can be well
analyzed by the help of past return volatility. Ranbaxy was showing an integrated GARCH model in pre
derivative period as the sum of (α + β) was greater than one. This means the GARCH error terms of Ranbaxy is
following random walk in prior to implementation of derivative. But this specific structure of the error
distribution of Ranbaxy return has not found in post derivative period. Lastly the GED parameters of the return
series are presented in last column of the ARCH and GARCH tables. The probability values are presented in the
parenthesis next to the estimates of GED parameter. The novelty of taking the generalized error distribution of
the return series for the ARCH and GARCH modeling is the rejection of null hypothesis of normality which is
clearly explained in descriptive statistics tables. Finally the highly significant probability of GED parameter puts
a strong and valid argument for considering the GED distribution of the return series under volatility modeling.
Hence broadly we can conclude that implementation of derivative has really mattered a lot in making a
significant change in the structure of volatility for some of the companies.
Impact of Derivatives in Long Run Equilibrium Relationships with Market Bench Mark Index
Table 5.1 presents the estimated test statistics of Engle- Granger co integration models. It has been noticed that
before implementation of derivatives, the price returns of GInd, Hindal, L & T, Ranbaxy, SBI and Tata Services
are found to be integrated with NIFTY, showing their long run equilibrium relationships with market bench
mark index. The estimated Z statistics of Engle- Granger co integration models are stationary at 10%, 5% and
1%level respectively. But after the implementation of derivatives the estimated Z statistics of Engle- Granger co
integration models are found to be non-stationary. That implies, the price return series of these stocks does not
contain any long run equilibrium relationships with the market bench mark index (NIFTY) after implementing
derivatives trading. But the case of HDCF is just opposite with respect to all the studied companies in our case.
Before introducing derivatives trading, the price returns of HDFC were not integrated with NIFTY, but
immediate after derivative trading, HDFC become highly integrated with NIFTY. Hence, its long run
equilibrium relationships with NIFTY have increased after doing derivative trading. Another common
observation regarding the long run equilibrium relationships between the stock market depicts that all the
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companies except HDFC are not integrated with NIFTY after introduction of derivative trading in Indian stock
market. All the stocks are making their independent move irrespective of market movements.
5. CONCLUSION
In this paper the GARCH technique was used to analyse the relationship between introduction derivatives
trading and corresponding stock market volatility for the Indian stock exchange. It has been observed that both
in pre and post derivatives periods, the stock returns carry a significant influence of previous period error terms
on current return distribution. Out of 15 stocks in our study, 8 stocks are experiencing changes in their pattern of
volatility after the implementation of derivative. The returns of companies like ACC, HHM, LT, Ranbaxy,
Reliance Industry, Tata Power and Tata Services remain almost unchanged after they implement derivatives.
But the returns of G Ind, HDFC Bank, Hindal, HUL, HDFC, ITC, SBI and Tata motorsexperienced changes in
the structure volatility after implementation of derivatives. The price return of GInd has become less sensitive to
the recent past error distributions but contains a stronger persistence of volatility that takes a longer time to die
out. But after implementation of derivatives, the returns of HDFC Bank, Hindal, HDFC, SBI and Tata Motors
become less sensitive to the immediate market movements, and experiencing a stronger persistence of volatility
in comparison to pre derivative period. Hence current volatility to these stock returns can be well analyzed by
the help of past return volatility. The return distribution of Ranbaxy was following random walk in pre
derivative period, but this specific structure is not found in post derivative period proving some short of stability
in error distributions after introducing derivative tradings. Looking at the long run equilibrium relationships it
has been noticed that before implementation of derivatives, the price returns of GInd, Hindal, L & T, Ranbaxy,
SBI and Tata Services had some long run equilibrium relationships with market bench mark index. But after the
implementation of derivatives these stocks do not contain any long run equilibrium relationships with the same.
But the long run equilibrium relationships of HDFC with market index have increased after implementation of
derivative trading.
REFERENCES
1. Afsal E. M. and Mallikarjunappa, T. (2007), “Impact of Stock Futures on the Stock Market Volatility”,
The ICFAI Journal of Applied Finance, Vol. 13 No.9, pp.54-75.
2. Aitken, M; Frino, A. and Jarnecic, E. (1994), “Option Listings and the Behaviour ofUnderlying
Securities: Australian Evidence”, Securities Industry Research Centre of Asia-Pacific (SIRCA)
Working Paper, Vol. 3, pp. 72-76.
3. Alexakis, P. (2007), “On the Effect of Index Futures Trading on Stock Market Volatility”,International
Research Journal of Finance and Economics, Vol. 11, 7-20.
4. Antoniou, A. and Foster, A. J. (1992) The effect of futures trading on spot price volatility: evidence for
Brent crude oil using GARCH, Journal of Business Finance & Accounting, 19, 473–84.
5. Antoniou, A. and Holmes, P. (1995) Futures trading and spot price volatility: evidence forthe FTSE-
100 stock index futures contract using GARCH, Journal of Banking and Finance, 19, 117–29.
6. Antoniou, A., Holmes, P. and Priestley, R. (1998) The effects of stock index futures trading on stock
index: an analysis of the asymmetric response of volatility to news, Journal of Futures Markets, 18,
151–66.
7. Baldauf, B. and Santoni, G. J. (1991) Stock price volatility: some evidence from an ARCH model,
Journal of Futures Markets, 11, 191–200.
8. Bandivadekar, S. and Ghosh, S. (2003), “Derivatives and Volatility in Indian Stock Markets”, Reserve
Bank of India Occasional Papers, Vol. 24(3), pp. 187-201.
9. Basal, V. K.; Pruitt, S. W. and Wei, K. C. J. (1989), “An Empirical Re-examination of the Impact of
COBE Option Initiation on the Volatility and Trading Volume of Underlying Equities: 1973-1986”,
Financial Review, Vol. 24, pp. 19-29.
10. Bessembinder, H. and Seguin, P. J. (1992) Futures-trading activity and stock price volatility, Journal of
Finance, 47, 2015-34.
11. Board, J., Sandmann, G. and SutcliVe, C. (1997) The effect of contemporaneous futures market
volume on spot market volatility, LSE Financial Markets Group -Discussion Paper Series, 277,
September.
12. Bollen, N. P.B. (1998), “A Note of the Impact of Options on Stock Return Volatility”, Journal of
Banking and Finance, Vol. 22, pp. 1181-1191.
13. Bologna, P. (1999) TheeVect of stock index futures trading on the volatility of the Italian
stockexchange: a GARCH examination, Ente per gliStudiMonetariBancari e Finanziari Luigi Einaudi
-Temi di Ricerca, 12.
14. Conrad, J. (1989), “The Price Effect of Option Introduction”, Journal of Finance, Vo. 44, pp. 487-498.
Interdisciplinary Journal of Research in Business Vol. 1, Issue. 7, July 2011(pp.117-131)
124
15. Corgel, J. B. and Gay, G. D. (1984) The impact of GNMA futures trading on cash market volatility.
AREUEA Journal, 12, 176-90.
16. Cox, C. C. (1976) Futures trading and market information, Journal of Political Economy, 84, 1215–37.
17. Danthine, J. (1978) Information, futures prices and stabilizing speculation, Journal of Economic
Theory, 17, 79-98.
18. Dennis, S. A. and Sim, A. B. (1999) Share price volatility with the introduction ofindividual shares
futures on the Sydney futures exchange, International Review of Financial Analysis, 8, 153–64.
19. Edwards, F. R. (1988a) Does futures trading increase stock market volatility? Financial Analysts
Journal, 44, 63-9.
20. Edwards, F. R. (1988b) Futures trading and cash market volatiity: stock index and interest rate futures,
Journal of Futures Markets, 8, 421-39.
21. Engle Robert F. (1982), Autoregressive Conditional Heteroscedasticity with Estimates of the Variance
of United Kingdom Inflation, Econometrica, vol. 50, issue 4, pp. 987-1007
22. Engle Robert F. and Clive W. J. Granger (1987), Co-integration and Error Correction: Representation,
Estimation, and Testing, Econometrica,vol. 55, issue 2, pp.251-76
23. Finglewski, S. (1981) Futures trading and volatility in the GNMA market, Journal of Finance, 36,
445–57.
24. Freund, S. P.; McCann, D. and Webb, G. P. (1994), “A Regression Analysis of the Effect of Option
Introduction on Stock Variance”, Journal of Derivatives, Vol. 6, pp. 25-38.
25. Froewiss, K. (1978) GNMA futures: stabilizing or destabilizing? Federal Reserve Bank of San
Francisco Economic Review, 20-9.
26. Galloway, T. M. and Miller J. M. (1997) Index futures trading and stock return volatility: evidence
from the introduction of MidCap 400 Index Futures, Financial Review, 32, 845–66.
27. Gulen, H. and Mayhew, S. (2000), “Stock Index Futures Trading and Volatility in International Equity
Market”, Journal of Futures Markets, Vol. 20, pp. 661-685.
28. Gupta O.P., (2002), “Effects of Introduction of Index Futures on Stock Market Volatility:The Indian
Evidence“, paper presented at Sixth Capital Market Conference, UTI Capital Market, Mumbai.
29. Herbst, A. F. and Maberly, E. D. (1992), “The Information Role of End of the Day Returns in Stock
Index Futures”, Journal of Futures Markets, Vol. 12, pp. 595-601
30. Hodgson, A. and Des Nicholls, D. (1991) The impact of index futures markets on Australian
sharemarket volatility, Journal of Business, Finance and Accounting, 18, 267-80.
31. Jagadeesh, N. and Subrahmanyam, A. (1993), “Liquidity effect of the Introduction of S&P500 Index
Futures Contracts on the Underlying Stocks”, Journal of Business, Vol. 66, pp. 171-187.
32. Johansen, Soren, (1988), Statistical analysis of cointegration vectors, Journal of Economic Dynamics
and Control, Elsevier, vol. 12(2-3), pp. 231-254
33. Kamara, A., Millar, T. and Siegel, A. (1992), “The Effect of Futures Trading on the Stability of the
S&P 500 Returns”, Journal of Futures Markets, Vol. 12, pp.645-658.
34. Kumar, A.; Sarin, A. and Shastri, K. (1995), “The Impact of Listing of Index Options on
theUnderlying Stocks”, Pacific-Basin Finance Journal, Vol.3, pp. 303-317.
35. Lamoureux, C.G. and Pannikath, S.K. (1994), “Variations in Stock Returns: Asymmetries and Other
Patterns”, Working Paper, John M Olin School of Business, St. Louis MO.
36. Lee, S. B. and Ohk, K. Y. (1992) Stock index futures listing and structural change in time-varying
volatility, Journal of Futures Markets, 12, 493–509.
37. Narasimhan, J. and Subrahmanyam, A. (1993), “Liquidity Effects of the Introduction of the S&P 500
Index Futures and Contracts on the Underlying Stocks”, Journal of Business, Vol. 66, pp. 171-187.
38. Nath, Golaka C. (2003), “Behaviour of Stock Market Volatility After Derivatives”, NSENEWS,
National Stock Exchange of India, November Issue.
39. Peat, P. and McCorry, M. (1997), “Individual Share Futures Contract: The EconomicImpact of Their
Introduction on Underlying Equity Market”, University of Technology Sydney, School of Finance and
Economics, Working Paper No. 74,
40. Pericli, A. and Koutmos, G. (1997) Index futures and options and stock market volatility, Journal of
Futures Markets, 17, 957–74.
41. Powers, M. J. (1970) Does futures trading reduce price fluctuations in the cash markets? American
Economic Review, 60, 460-4.
42. Raju, M. T. and Karande, K. (2003), “Price Discovery and Volatility on NSE Futures Market”,
SEBI Bulletin, Vol. 1(3), pp. 5-15.
43. Rahman, S. (2001) The introduction of derivatives on the Dow Jones industrial averageand their impact
on the volatility of component stocks, Journal of Futures Markets, 21, 633–53.
44. Robinson, G. (1993), “The Efeect of Futures Trading on Cash Market Volatility”, Bank ofEngland
Working Paper No. 19, www.ssrn.com./Abstract id=114759.
Interdisciplinary Journal of Research in Business Vol. 1, Issue. 7, July 2011(pp.117-131)
125
45. Samanta, P. and Samanta, P. K. (2006), “Impact of Index Futures on the Underlying Spot Market
Volatility”, ICFAI Journal of Applied Finance, Vol. 13, No. 10, pp. 52-65.
46. Santoni, G. J. (1987) Has programmed trading made stock prices more volatile? Federal ReserveBank
of St. Louis Review, May, 18-29.
47. Schwarz, T. V. and Laatsch, F. (1991) Price discovery and risk transfer in stock index cashand futures
markets, Journal of Futures Markets, 11, 669-83.
48. Shenbagaraman, P. (2003), “Do Futures and Options Trading Increase Stock Market Volatility”, NSE
Research Initiative paper No. 20, http://www.nseindia.com/content/reserch/paper60.pdf
49. Simpson, W. G. and Ireland, T. C. (1982) TheeVect of trading on the price volatility of GNMA
securities, Journal of Futures Markets, 2, 357-66.
50. Spyrou, S. I. (2005), “Index Futures Trading and Spot Price Volatility”, Journal of Emerging market
Finance, Vol. 4, 151-167.
51. Stein, J. C. (1987) Informational externalities and welfare reducing speculation, Journal ofPolitical
Economy, 96, 1123–45.
52. Stoll, H. R. and Whaley, R. E. (1988) Volatility and futures: message versus messenger, Journal of
Portfolio Management, 14, 20-2.
53. Thenmozhi, M. (2002), “Futures Trading Information and Spot Price Volatility of NSE -50Index
Futures Contract”, NSE Research Initiative Paper No. 18,
http://www.nseindia.com/content/reserch/paper59.pdf
54. Thenmozhi, M. and Sony, T. M. (2002), “Impact of Index Derivatives on S&P CNX Nifty Volatility:
Information Efficiency and Expirations Effects”, ICFAI Journal of Applied Finance, Vol. 10, pp. 36-
55.
55. Vipul (2006), the Impact of the Introduction of the Derivatives on Underline Volatility: Evidence from
India, Applied Financial Economics, Vol. 16, pp.687-694
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Table: 1.1: Descriptive Statistics (pre)
ACC GIND HDFC
B HHM
HINDA
L HUL HDFC ITC LT
RAN
BAX
Y
RELIA
NCEIN
D
SBI TATA
M
TATA
P
TATA
S
Mean -2.20E-05 -0.0001 0.00111 0.0006 7.17E-05 0.0004 0.0008 0.000 0.0012 0.0004 0.000909 -0.0001 -0.0017 0.0001 -0.000
Median -0.002 -0.001 0.00000 0.0000 -0.001 -0.001 -0.002 0.000 0.0010 -0.001 0.0000 -0.001 -0.004 -0.002 -0.002
Maximum 0.1200 0.1870 0.09800 0.2060 0.16900 0.1130 0.1130 0.0980 0.3570 0.2330 0.1920 0.1140 0.15200 0.1480 0.1460
Minimum -0.203 -0.135 -0.115 -0.138 -0.089 -0.121 -0.136 -0.107 -0.146 -0.128 -0.104 -0.096 -0.127 -0.144 -0.164
Std. Dev. 0.0373 0.0380 0.03282 0.0298 0.02918 0.0244 0.0316 0.0297 0.0310 0.0327 0.0317 0.0303 0.03772 0.0328 0.0322
Skewness -0.16496 0.1394 0.26394 0.5291 0.37270 0.3270 0.0263 0.1278 0.6655 0.5223 0.4370 0.1935 0.15732 0.2594 0.0574
Kurtosis 4.661801 4.0088 3.68694 6.6621 5.21467 6.0183 5.2687 4.4536 12.735 6.6934 5.534 3.9973 3.38392 5.3347 4.4844
J-B Stat 103.4551 39.483 27.0509 763.49 196.802 343.77 185.61 78.512 8734.0 530.99 259.066 41.249 8.88058 206.15 79.892
Prob. 0.00000 0.0000 0.00000 0.0000 0.00000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.00000 0.01179 0.0000 0.0000
Obs. 865 865 865 1261 865 865 865 865 2171 865 865 865 865 865 865
Table: 1.2: Descriptive Statistics (post)
ACC GIND HDFC
B HHM
HINDA
L HUL HDFC ITC LT
RANB
AXY
RELIA
NCEIN
D
SBI TATA
M
TATA
P
TATA
S
Mean 0.00087 0.0009 0.0009 0.001 0.0004 0.000 0.0009 0.000 0.00 0.000 0.00083 0.001 0.0011 0.001 0.001
Median 0.001 00.000 0.000 00.00 0.001 00.00 00.000 00.00 0.00 0.001 0.002 0.001 0.001 0.001 0.002
Maximum 0.103 0.129 0.152 0.128 0.168 0.091 0.19 0.105 0.22 0.188 0.191 0.184 0.175 0.211 0.153
Minimum -0.173 -0.115 -0.119 -0.092 -0.196 -0.163 -0.117 -0.111 -0.11 -0.198 -0.291 -0.16 -0.181 -0.212 -0.162
Std. Dev. 0.0244 0.0230 0.025 0.022 0.0280 0.020 0.025 0.020 0.03 0.025 0.0253 0.025 0.0293 0.027 0.031
Skewness -0.352 0.1080 0.3028 0.132 -0.27 -0.089 0.401 0.107 0.491 -0.306 -0.9199 -0.127 -0.120 -0.251 -0.336
Kurtosis 6.552 6.4591 5.6529 4.805 7.685 6.433 7.369 5.650 7.26 10.69 16.703 7.174 6.5897 9.764 6.027
J-B stat 1159.32 1061.5 654.42 239.2 196.5 104.4 174.3 625.1 650.8 526.7 1685.5 154.6 114.8 406.5 850.1
Prob 0.0000 0.0000 0.0000 0.000 0.0000 0.000 0.0000 0.000 0.000 0.000 0.0000 0.000 0.0000 0.000 0.000
Obser 2121 2121 2121 1725 2121 2121 2121 2121 815 2121 2121 2121 2121 2121 2121
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Table 2.1
The estimated -statistic values from Unit root Test of log Returns of the stocks (Pre)
Variable Intercept Alone Intercept + Trend
DF ADF PP DF ADF PP
ACC -28.37 -28.41(3) -28.40(2) -28.40 -28.4(2) -28.38(2)
G Ind -25.85 -25.84(2) -25.82(2) -24.62 -25.8(2) -25.9(3)
HDFCB -29.25 -29.28(2) -29.28(2) -28.08 -29.26(2) -29.26(2)
HHM -32.85 -26.34 (2) -32.97(2) -32.84 -26.33(3) -32.96(3)
Hindal -28.31 -28.32(3) -28.32(3) -27.37 -28.31(3) -28.31(3)
HUL -21.45 -28.09(3) -28.08(2) -25.29 -28.09(2) -28.08(2)
HDFC -24.09 -30.36(3) -30.4(3) -28.62 -30.39(2) -30.43(2)
ITC -29.50 -29.50(2) -29.50(2) -29.42 -29.49(3) -29.48(3)
LT -34.88 -48.35(3) -48.40(3) -44.11 -48.40(2) -48.45(3)
Ranbaxy -26.36 -27.39(3) -27.42(2) -26.88 -27.39(3) -27.43(2)
Reliance Ind -24.72 -30.71(3) -30.69(2) -28.46 -30.69(3) -30.67(2)
SBI -25.15 -28.19(2) -28.19(2) -27.42 -28.18(3) -28.18(2)
TataM -19.25 -26.57(3)
-26.60(2) -23.09 -26.56(2) -26.58(2)
TataP -11.36 -27.66(3) -27.65(2) -17.52 -27.69(3) -27.68(2)
TataS -28.58 -28.97(3) -28.97(2) -28.81 -28.95(2) -28.96(3)
NIFTY -21.34 -22.43 (2) -27.67(2) -20.21 -22.23(2) -19.34(3)
Note: The critical values for unit root test are: -3.49 and -2.88 (without trend) and -4.04, -3.45 (with trend)
respectively for 1% and 5% level.The above figures implystationarity at 1% and 5% level.
Table 2.2
The estimated -statistic values from Unit root Test of log Returns of the stocks (Post)
Variable
Intercept Alone Intercept and Trend
DF ADF PP DF ADF PP
ACC -20.95 -45.01(2) -45.00(2) -32.64 -45.00(2) 44.99(2)
G Ind -31.72 -44.87(3) -44.86(3) -39.75 -44.87(2) -44.87(3)
HDFCB -38.97 -43.02(2) -42.98(2) -41.93 -43.01(3) -42.97(2)
HHM -40.72 -26.99(2) -40.83(3) -40.78 -26.98(3) -40.82(3)
Hindal -11.20 -41.23(2) -41.21(3) -20.07 -41.22(3) -41.21(3)
HUL -19.63 -45.47(2) -45.47(2) -31.51 -45.47(2) -45.47(3)
HDFC -37.05 -34.91(3) -44.82(2) -44.37 -34.91(2) -44.81(2)
ITC -25.21 -48.20(2) -45.29(3) -37.38 -48.19(3) -48.29(3)
LT -13.32 -25.16(2) -25.12(3) -19.39 -25.15(3) -25.11(3)
Ranbaxy -19.28 -43.82(2) -43.82(3) -30.22 -43.82(3) -43.82(3)
Reliance Ind -21.55 -44.53(3) -44.51(3) -33.15 -44.52(3) -44.51(3)
SBI -26.79 -43.24(2) -43.19(3) -36.46 -43.23(2) -43.18(3)
TataM -12.72 -42.29(2) -42.28(2) -22.18 -42.31(3) -42.29(2)
TataP -20.61 -34.25(2) -43.19(3) -31.38 -34.24(3) -43.18(3)
TataS -24.77 -42.87(3) -42.87(2) -35.04 -42.86(2) -42.86(2)
NIFTY -21.33 -33.23(2) -21.65(2) (2) -34.12 -37.34 (2) -44.67(3)
Note:The critical values for unit root test are: -3.49 and -2.88 (without trend) and -4.04, -3.45 (with trend)
respectively for 1% and 5% level. The above figures implystationarity at 1% and 5% level.
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Table 3.1
ARCH-LM Test for the Stock before implementing derivative
Mean Equation Variance Equation
F-stat Prob. Obs. R-
squared Prob. F-stat Prob. Obs. R-
squared Prob.
ACC 10.09 0.000 38.77 0.00 0.418 0.658 0.83 0.657
G Ind 11.87 0.000 34.37 0.00 1.321 0.266 3.96 0.265
HDFCB 31.79 0.000 59.41 0.00 0.873 0.417 1.75 0.416
HHM 09.81 0.000 56.55 0.00 1.534 0.176 7.66 0.174
Hindal 19.24 0.000 54.34 0.00 0.794 0.529 3.18 0.527
HUL 12.99 0.000 25.31 0.00 1.422 0.241 2.84 0.241
HDFC 20.33 0.000 107.5 0.00 0.838 0.540 5.04 0.538
ITC 10.57 0.000 50.14 0.00 1.08 0.369 5.40 0.368
LT 22.72 0.000 87.42 0.00 1.35 0.239 6.75 0.239
Ranbaxy 5.20 0.000 25.44 0.00 1.11 0.351 7.80 0.350
Reliance Ind 24.32 0.000 87.84 0.00 0.399 0.808 1.60 0.807
SBI 19.72 0.000 89.00 0.00 0.702 0.590 2.81 0.591
TataM 5.77 0.000 33.56 0.00 1.45 0.215 5.79 0.214
TataP 76.20 0.000 181.26 0.00 0.99 0.395 2.98 0.394
TataS 11.04 0.000 52.10 0.00 0.721 0.928 1.36 0.928
Table 3.2
GARCH (1, 1) Model for the Stock before implementing derivative
α β α + β GED Parameter
ACC 0.133 (0.000) 0.783 (0.00) 0.917 1.40 (0.00)
G Ind 0.235 (0.001) 0.592 (0.00) 0.827 1.49 (0.00)
HDFCB 0.215 (0.001) 0.550 (0.00) 0.766 1.19 (0.00)
HHM 0.167 (0.000) 0.750 (0.00) 0.917 1.03 (0.00)
Hindal 0.319 (0.000) 0.485 (0.00) 0.804 1.02 (0.00)
HUL 0.085 (0.001) 0.875 (0.00) 0.960 1.04 (0.00)
HDFC 0.525 (0.000) 0.339 (0.00) 0.865 1.11 (0.00)
ITC 0.085 (0.001) 0.884 (0.00) 0.969 1.14 (0.00)
LT 0.151 (0.000 0.831 (0.00) 0.983 1.30 (0.00)
Ranbaxy 0.125 (0.000) 0.878 (0.00) 1.003 1.17 (0.00)
Reliance Ind 0.242 (0.000) 0.616 0.00) 0.859 1.24 (0.00)
SBI 0.148 (0.000) 0.769 (0.00) 0.918 1.54 (0.00)
TataM 0.115 (0.000) 0.821 (0.00) 0.936 1.57 (0.00)
TataP 0.146 0.000) 0.800 0.00) 0.950 1.24 (0.00)
TataS 0.122 (0.000) 0.811 (0.00) 0.933 1.38 (0.00)
Note: ( ) presents probability values.
Interdisciplinary Journal of Research in Business Vol. 1, Issue. 7, July 2011(pp.117-131)
129
Table 4.1
ARCH-LM Test for the Stock in Post implementing derivative
Mean Equation Variance Equation
F-stat Prob. Obs. R-
squared
Prob. F-stat Prob. Obs. R-
squared
Prob.
ACC 16.29 0.00 78.65 0.00 0.636 0.671 3.18 0.670
G Ind 40.13 0.00 149.52 0.00 1.49 0.200 5.98 0.200
HDFCB 33.45 0.00 126.14 0.00 1.21 0.303 4.85 0.302
HHM 15.69 0.00 60.74 0.00 0.72 0.578 2.88 0.577
Hindal 240.30 0.00 216.07 0.00 0.104 0.746 0.103 0.745
HUL 38.42 0.00 143.58 0.00 0.369 0.830 1.479 0.831
HDFC 47.62 0.00 175.13 0.00 0.126 0.974 0.495 0.973
ITC 20.74 0.00 99.11 0.00 0.647 0.663 3.23 0.663
LT 14.41 0.00 54.13 0.00 0.043 0.996 0.173 0.996
Ranbaxy 54.14 0.00 240.55 0.00 1.85 0.099 9.24 0.099
Reliance Ind 8.244 0.00 48.49 0.00 0.866 0.503 4.33 0.502
SBI 39.03 0.00 074.93 0.00 1.06 0.379 5.31 0.377
TataM 67.78 0.00 341.93 0.00 0.922 0.477 5.53 0.477
TataP 78.01 0.00 330.06 0.00 0.73 0.597 3.676 0.596
TataS 94.68 0.00 387.63 0.00 0.349 0.882 1.75 0.882
Table 4.2
GARCH (1, 1) Model for the Stock after implementing derivative
α β α + β GED Parameter
ACC 0.129 (0.00) 0.848 (0.00) 0.977 1.20 (0.00)
HDFCB 0.065 (0.00) 0.922 (0.00) 0.987 1.37 (0.00)
HHM 0.134 (0.00) 0.773 (0.00) 0.908 1.26 (0.00)
Hindal 0.123 (0.00) 0.871 (0.00) 0.994 0.30 (0.00)
HUL 0.165 (0.00) 0.691 (0.00) 0.857 1.32 (0.00)
HDFC 0.132 (0.00) 0.844 (0.00) 0.977 1.33 (0.00)
ITC 0.148 (0.00) 0.780 (0.00) 0.928 1.23 (0.00)
LT 0.166 (0.00) 0.826 (0.00) 0.992 1.45 (0.00)
Ranbaxy 0.120 (0.00) 0.843 (0.00) 0.964 1.137 (0.00)
Reliance Ind 0.228 (0.00) 0.693 (0.00) 0.922 1.26 (0.00)
SBI 0.09 (0.00) 0.894 (0.00) 0.985 1.33 (0.00)
TataM 0.093 (0.00) 0.879 (0.00) 0.973 1.5 (0.00)
TataP 0.126 (0.00) 0.850 (0.00) 0.977 1.25 (0.00)
TataS 0.114 (0.00) 0.866 (0.00) 0.980 1.49 (0.00)
Note: ( ) presents probability values.
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130
Table 4.3
GARCH (1, 2) Model for the Stock G Ind after implementing derivative
α β1 β2 α + β1+ β2 GED
Parameter
G Ind 0.144 (0.00) 0.468 (0.04) 0.362 (0.08) 0.974 1.28 (0.00)
Table 5.1
Test Statistics of Engle- Granger Co-integration Model
Pre Derivatives Period Post Derivatives Period
Pair of Return Z Stat Prob. Pair of Return Z Stat Prob.
ACC → NIFTY -2.47 0.12 ACC → NIFTY -2.55 0.10
GInd → NIFTY -3.04 *** 0.02 GInd → NIFTY -1.98 0.29
HDFCB → NIFTY -1.76 0.39 HDFCB → NIFTY -3.42 0.01
HHM → NIFTY -1.78 0.39 HHM → NIFTY -2.05 0.26
Hindal → NIFTY -3.40** 0.01 Hindal → NIFTY -2.08 0.26
HUL → NIFTY -3.85 0.00 HUL → NIFTY -2.32 0.16
HDFC → NIFTY -1.34 0.61 HDFC → NIFTY -4.06* 0.00
ITC → NIFTY -2.92 0.04 ITC → NIFTY -2.93 0.04
LT → NIFTY -4.16* 0.00 LT → NIFTY -1.55 0.50
Ranbaxy → NIFTY -2.98*** 0.03 Ranbaxy → NIFTY -2.55 0.10
RelianceInd → NIFTY -1.95 0.30 RelianceInd → NIFTY -2.61 0.08
SBI → NIFTY -3.11*** 0.02 SBI → NIFTY -2.32 0.16
TataM → NIFTY -1.26 0.64 TataM → NIFTY -2.20 0.20
TataP → NIFTY -2.00 0.28 TataP → NIFTY -1.95 0.30
TataS → NIFTY -3.08*** 0.02 TataS → NIFTY -1.07 0.42
Note: The critical values for Enger- Granger unit root test (with lag) are: -3.78, -3.25 and -2.98 respectively for
1%, 5% and 10% level. The symbols *, ** and *** implies stationarity at 1%, 5% and 10% level respectively.
Interdisciplinary Journal of Research in Business Vol. 1, Issue. 7, July 2011(pp.117-131)
131
Abbreviations Used For the Stocks/Companies
ACC A C C Ltd.
G Ind Grasim Industries Ltd.
HDFCB H D F C Bank Ltd.
HHM Hero Honda Motors Ltd.
Hindal Hindalco Industries Ltd.
HUL Hindustan Unilever Ltd.
HDFC Housing Development Finance Corpn. Ltd.
ITC I T C Ltd.
LT Larsen & Toubro Ltd.
Ranbaxy Ranbaxy Laboratories Ltd.
Reliance Ind Reliance Industries Ltd.
SBI State Bank Of India
TataM Tata Motors Ltd.
TataP Tata Power Co. Ltd.
TataS Tata Steel Ltd.