The Impact of Derivative Trading on Spot Market Volatility ... · PDF fileThe Impact of...

Interdisciplinary Journal of Research in Business Vol. 1, Issue. 7, July 2011(pp.117-131)

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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.

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126

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


127

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.


128

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.


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.


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.


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.

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