a study on volatility of indian stocks and index – pre and post derivatives era

7/30/2019 A STUDY ON VOLATILITY OF INDIAN STOCKS AND INDEX PRE AND POST DERIVATIVES ERA

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International Journal of Management (IJM), ISSN 0976 6502(Print), ISSN 0976 6510(Online)

Volume 1, Number 2, July - Aug (2010), IAEME

106

A STUDY ON VOLATILITY OF INDIAN STOCKS ANDINDEX PRE AND POST DERIVATIVES ERA

Govind Chandra PatraRegional College of Management Autonomous (MBA Dept.)

Bhubaneswar, Orissa, PIN-751023

E-mail: [email protected]

Dr. Shakti Ranjan MohapatraPrincipal,Centre for IT Education (CITE)

Bhubaneswar, Orissa, PIN-751010

E-Mail: [email protected]

ABSTRACT

It has been almost a decade since the introduction derivatives instruments like

Options and Futures trading in Indian bourses and almost two decades since the

introduction and implementation of liberalization, privatization and globalization policies

in Indian economy. This has resulted in sea change in growth and development of Indian

economy and enhanced activity and trade in Indian stock markets. This paper examines

and compares return and volatility of Indian cash market before the introduction of

derivatives with that after the introduction of derivatives, mainly the futures trading in

index and stocks. The results reveal that introduction of derivatives have significantly

affected volatility of index and most of the stocks and resulted in destabilizing the

underlying cash market and thus rejects market completion hypothesis.

INTRODUCTION:

Indian capital markets has witnessed major transformations and structural changes

since past one or two decades as a result of initiation of liberalization, privatization and

globalization policies and consequential financial sector reforms. Introduction of

derivative instruments in Indian stock exchanges is one such important step in the right

direction, the aim of which was being to prevent age old badla transaction, greater

stabilization of markets and introduction of sophisticated risk management tools.

International Journal of Management (IJM),

ISSN 0976 6502(Print), ISSN 0976 6510(Online)

Volume 1, Number 2, July - Aug (2010), pp. 106-128

IAEME, http://www.iaeme.com/ijm.html

IJM

I A E M E


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Worldwide, the futures trading on stock markets has grown rapidly since their

introduction because it has contributed in achieving economic functions such as price

discovery, portfolio diversification, enhanced liquidity, speculation and hedging against

the risk of adverse price movements. Movements in cash market gets greatly influenced

by speculation, hedging and arbitraging activities in futures market. Thus, it becomes

important to understand the influence of one market over the other and its consequential

impact upon the magnitude of change in return and volatility.

Two main bodies of theories exist in financial literature about the relationship

between derivatives market and the underlying spot market. The theoretical literature

proposes both a destabilizing forces hypothesis that predicts enhanced volatility and a

market completion hypothesis that supports reduced volatility in the underlying spot

market. In favor of the former hypothesis, it is argued that the inflow and existence of

speculators in futures markets may produce destabilizing forces, which among other

things creates undesirable bubbles in the cash market. However, the contrary view is that

the introduction of futures trading leads to more complete markets, enhances information

flow and thereby improves investment choices. Moreover, futures may bring more

private information to the market and allow for a quicker dissemination of information.

Further speculative activity may be transferred from spot to futures market and thereby

dampening the spot market volatility. Thus, the uncertainty of the existing theoretical

literature implies that the issue of whether and how derivative markets affect underlying

spot markets remains mainly an empirical one. Thus, the aim of this study is to examine

the impact of derivatives trading on the cash market volatility in Indian stock exchanges

utilizing the highly popular time variant Generalized Autoregressive Conditional

Heteroskedasticity (GARCH) class of models.

LITERATURE REVIEW

Cox (1976) argues that futures trading can alter the available information and thus

spot market volatility for two reasons. First, futures attract additional investors to the

market. Second, as the transaction costs in futures market are lower than the spot market,

new information may be transmitted to the futures market more quickly thus making it

more efficient.


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Ross (1989) assumes that there exists economies that is devoid of arbitrage and

proceeds to provide a condition under which no arbitrage situation will be sustained.

Rosss condition for no arbitrage implies that the variance of price change will move

according to the rate of information flow. The implication of this condition is that the

volatility of asset price will increase as the rate of information flow increases and vice

versa. Thus, if derivatives trading enhances flow of information, then in the absence of

arbitrage opportunity, the volatility of spot price must increase.

The empirical literature dealing with the impact of derivatives trading upon the

underlying spot market include Chin (1991), Antoniou (1995), Darrat and Rehman

(1995), Kumar (1995), Choudhury (1997), Pericil (1997), Antoniou, Holmes and

Priestley (1998), Chatrath and Song (1998), Lee and Tong (1998), Bollen (1998),

Abhayankar (1998), Trillo (1999), Dennis & Sim (1999), Gulen (2000), Darrat (2000),

Chang (2000), Sahlstrom (2001), Mckenzie (2001), Rahman (2001), Claessen & Mittnik

(2002), Mazouz (2004). Apart from the above studies carried out in international markets,

the literature on impact of derivatives on Indian spot market include Gupta (2002),

Thenmozhi (2002), Shenbagaraman (2002), Nath (2003), Thomas (2003), Hetamsaria

(2003), Kumar & Mukhopadhyay (2003), Joshi & Mukhopadhyay (2003), Nagraj &

Kumar (2004), Singh & Bhatia (2006). Most of them had examined the impact of

derivatives trading, mainly futures and option trading, on the volatility of underlying spot

market. Alternatively, their main emphasis was on measuring the volatility level of

underlying spot market and derivatives market separately to reach some conclusion on

whether derivatives trading stabilize or destabilize the spot market. Except some, most of

them are of opinion that derivatives market stabilizes the spot market by reducing its

volatility.

By examining the impact of futures trading on stock market volatility, Antoniou,

Holmes and Priestley (1998) have tried to extend the traditional analysis of examiningwhether futures trading have increased stock market volatility by considering the issue of

volatility, asymmetries and market dynamics. Their results exhibit that though the futures

trading has had a limited impact on the level of stock market volatility, the asymmetric

responses of volatility to the arrival of news has been significantly lowered in the post

futures period. Rather than having a detrimental effect on the underlying market, the


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authors have found an improvement in the way that news is transmitted into prices

following the onset of futures trading.

Darrat, Rahman and Zhong (2000) had examined the role of index futures trading

in spot market volatility in the context of S&P 500 spot and futures index over a period

from November 1987 to November 1997. By applying the E-GARCH methodology, the

authors had tried to explicitly analyse the casualty and feedback relationships between

volatilities in spot and futures markets, and also have tested various hypotheses in the

context of multivariate model that incorporates other macro state variables. Their results

support that index futures trading may not be blamed for observed volatility in the spot

market, rather volatility in futures market itself is an outgrowth of a turbulent cash

market.

Gulen and Mayhew (2000) have examined stock market volatility before and after

the introduction of equity index futures trading in 25 countries using various models that

account for asynchronous data, conditional heteroskedasticity, asymmetric volatility

responses and the joint dynamics of each countrys index with the world market portfolio.

They have found that the futures trading is related to an increase in conditional volatility

in US and Japan, but in most other countries, they observed either no significant effect or

a volatility dampening effect. They also documented that the market in most countries are

significantly more integrated with the world market after the introduction of stock

futures.

Mckenzie, Brailsford and Faff (2001) have tried to examine whether and to what

extent, the introduction of trading in share futures contracts on individual stocks has

impacted upon the systematic risk, and volatility of underlying shares. The authors have

found that there was a general reduction of systematic risk in post futures period for the

stocks that are traded in stock futures market, not for any stock from a control sample.

Apart from this, they have also evidenced a decline in unconditional variance of thosestocks.

Shenbagaraman (2003) had explored the possibility whether the introduction of

futures and options trading in India have any real impact on the volatility of S&P CNX

Nifty index. Though her GARCH (1,1) model failed to reveal any result in favour of


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significant impact of derivatives introduction on the Indian spot market volatility, the

nature of volatility has found to be changed during the post futures period.

By using static and conditional variance, Nath (2003) has studied the behaviour of

volatility in equity market for pre and post derivatives period in India for a set of 20

stocks randomly selected from NIFTY and Junior NIFTY basket as well as benchmark

indices itself. Their conditional volatility results on different stocks revealed that for

most of the stocks, the volatility has come down in the post derivatives period. While for

only few stocks in the sample, the volatility in post derivatives has either remained more

or less same or has increased marginally. As far as the results on the benchmark indices

are concerned, they have found that the volatility of S&P CNX NIFTY and Junior

NIFTY have fallen in the post derivatives period.

Rahman (2001) has tried to examine the impact of trading in DJIA index futures

and future options on the conditional volatility of component stocks. By estimating the

conditional volatility of intraday returns for each of the thirty stocks comprising Dow

Jones index before and after the introduction of derivatives in a GARCH framework, he

had attempted to investigate whether the introduction of futures and future options could

increase the volatility of the underlying stocks. His results clearly revealed that no

structural changes in the conditional volatility of component stocks after the introduction

of index futures and futures options on DJIA.

Thomas & Thenmozhi (2003) have examined the impact of derivatives trading in

Indian cash market volatility. The change in volatility in underlying cash indices during

pre and post derivatives period is examined using GARCH models. Their results have

revealed that S&P CNX futures trade have reduced spot market volatility and therefore

support the stabilizing effect hypothesis. Their results also show a significant reduction in

the volatility persistence in post derivatives period.

Hetamsaria and Swain (2003) have tried to empirically test how the introductionof index futures affect the underlying market in India. They had tried to compare the

volatility of NIFTY index during pre and post futures period and also the volatility of the

spot and futures markets. Apart from this, they also made an attempt to test the impact of

futures introduction through a multiple regression model. Their empirical evidences


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suggested that the introduction of futures trading would not destabilize the underlying

market and there was also a decline in volatility in the post futures period.

Nagraj and Kumar (2004) have tried to examine whether the changes in the

structure of spot index volatility evolution process in India are due to the activity of

futures trading. They had used the GARCH process to capture the relationship between

the trading activity and spot index volatility in Indian market. Consistent with the existing

literature, their results also documented a positive relationship of spot market volatility

with trading volume, while a negative relationship with open interest.

RESEARCH OBJECTIVES

Since the introduction of derivative instruments in India is a recent phenomenon,

it becomes pertinent to look into different aspects of derivatives trading and their impacton the underlying spot market. The present research is being contemplated with the

following specific objectives:

1. To examine whether there is any real impact of derivative trading on the returnand/or volatility of the spot market i.e., to test whether the onset of the futures

stabilizes or destabilizes the underlying spot market. In other words, to examine

the volatility at the index and individual stock level, of Indian stock market before

and after the introduction of futures trading.

As far as the impact of derivatives on the underlying spot market is concerned,

there are two different schools of thought. These are stabilization and destabilization

effect of derivatives trading. Theoretical as well as empirical literature supports both the

facts that derivatives trading might have stabilizing effects on the underlying market and

vice versa. In other words, there are a number of literature supporting the fact that the

volatility of the underlying spot market have come down after the onset of derivatives

(futures and options) trading. At the same time, it has also been proved that the

derivatives trading sometimes have caused for an increase in spot market volatility and

therefore have destabilized the spot market.


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SAMPLE DATA

Returns of index and different underlying stocks are used to test the impact of

derivatives viz. futures trading on the underlying spot market in India. The index used

here is S&P CNX Nifty and stocks selected for the purpose are RELIANCE

INDUSTRIES, INFOSYS, HINDUSTAN UNILEVER, HDFC, HINDALCO, ACC,

TISCO, L&T, SBI and TELCO. These are high turnover, high profit making blue chip

stocks included for computation of popular sensitive indices like BSE Sensex and NSE

CNX S&P Nifty representing diverse sectors of broad economy and continuously traded

both in cash and futures markets of stock exchanges. To compare the return and volatility

of the underlying stocks during the pre and post futures period, the daily closing price

data are taken into account. Daily logarithmic returns are calculated from the dailyclosing price observations over a period from 1st January 1995 to 31st December 2009.

The whole sample period again divided into pre and post futures period. Pre-futures

period starts from 1st January 1995 and continues up to the initiation of futures trading i.e.

on 12th June 2000 (for index) or on 9th November 2001(for Stocks). On the other hand,

post-futures period include the period starting from these respective dates for index and

stocks till 31st December 2009. Data on the Index and underlying stocks have been

collected from the NSE of India web site (www.nseindia.com). All the time series data

are adjusted for non-synchronous trading effect, if any.

METHODOLOGY

Two approaches are taken into consideration for testing the impact of derivatives

upon the return and volatility of underlying spot market in NSE. The first approach deals

with investigating whether there is any significant effect of derivatives trading on the

return and volatility of the underlying cash market. This has been done by including a

dummy variable, representing the initiation of derivatives trading, both in the conditional

mean and variance equation of the spot return series in the underlying market. The

second approach is based on a comparison of the return volatility of the underlying spot

market both before and after the introduction of derivatives trading. In order to compare

the underlying market separately in two different periods of time, we have focused on the

descriptive statistics as well as the conditional variance of the underlying market during


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those periods. Two sample periods considered for such comparison are pre- future period,

i.e. before introducing the index futures in India; and the post- futures period, i.e. after the

initiation of index futures in Indian equity market. Conditional measures of volatility are

based on ARCH family of models that include simple ARCH and its generalized version

GARCH framework with different specifications. Now the whole series of examinations

have been done both at the index level as well as at the underlying stock level.

According to the first approach, the focus is on to check whether the dummy

variable, as a representation of derivatives trading , both in the conditional mean and

variance equation have a significant explanatory power, and if so, whether it is positive or

negative. If the dummy coefficient is significant and negative in the conditional variance

equation, the derivatives i.e. futures trading is expected to reduce the spot market

volatility and therefore to stabilize the underlying market. At the same time, if the

coefficient is found to be positively significant, then the futures trading is expected to

have a significant destabilization effect on the volatility of underlying market.

As per the second approach, first of all the focus would be on the descriptive

statistical measures such a standard deviation, skewness and kurtosis of the underlying

indices and of stocks both before and after the introduction of futures trading. Any

significant change in those measures can represent whether derivatives have any role in

the underlying asset. Apart from this, the comparison of conditional variance in

underlying spot market both before and after the introduction of derivatives can prove its

impact. Any significant decrease in any of these measures during post-derivatives period

reveals that the underlying market tends to be stabilized after the introduction of

derivatives trading and vice versa.

Now, as per the first approach, an effort has been made to test the impact of

derivatives by way of including a dummy variable both in the conditional mean and

conditional variance equation in a simple GARCH (1, 1) framework. The dummyvariable represents the introduction of derivatives viz. futures trading in India. Such

conditional mean and conditional variance equations with errors following a univariate

GARCH (1. 1) process can be represented as:


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2

Spot t = 0 + I Spot t-1 +D futures+ t

i=1

AND

h t = 0 +12

t-1+2ht-1+Dfutures

It is to be worth noted that all return series in the entire study has been calculated

as the difference of the log of daily closing index or stock price as the case may be.

Rt = ln Ct ln Ct-1

Now, after conducting regression, if the dummy coefficients and in the

conditional mean and conditional variance equations becomes statistically significant,

then the conclusion will be derivatives (futures) trading has some real impact on the

return and volatility of the underlying spot market. Alternatively, if coefficient only in

the conditional variance equation is found to be significant, then the conclusion will be

the futures trading has an influence only on the volatility of the underlying spot market

but not on the return series.

Again, as per the second approach, first some descriptive significant statistical

measures of the index and respective stocks are compared between the pre-futures and

post futures period. Non time series measure of volatility such as standard deviation

implicitly assumes that price changes in spot markets are serially uncorrelated and

homoskedastic. Since the measure of standard deviation suffers from such limitations,

hence it will not be possible to identify whether such differences in our observations are

caused due to the introduction of derivatives trading only. It might be caused simply due

to the successive return dependence of index/stocks. Therefore in order to capture the

time varying nature of volatility, it is also attempted to apply GARCH (1,1) model for

conditional variance. Currently, GARCH model to measure volatility is widely used in

financial literature published both inside as well as outside the country. For its timevarying nature, it has the special ability to capture volatility clustering, asymmetric effect

in the return series. It may be worth noted here that volatility clustering refers to a large

change in the level of volatility followed by another large change whether positive or

negative. Asymmetric effect refers to the effect of positive news of a certain magnitude

on returns is lesser than the impact of negative information of similar magnitude. Thus,


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GARCH (1,1) framework has been widely found to be the most prudent representation of

conditional variance.

Thus, our next effort will be on comparison of conditional volatility both during

the pre and post futures period to investigate the impact of derivatives trading on the

underlying spot market. The spot market volatility before and after the introduction of

derivatives trading can be examined by using the same two equations for conditional

mean and volatility but excluding the dummy variables representing the introduction of

derivatives trading. The ARCH (1) and GARCH (1,1) models taken into computation

here for deriving the above objectives are summarized below :

2Spot t = 0 + I Spot t-1 + t

i=1

h t = 0 +12

t-1 for ARCH (1)

h t = 0 +12

t-1+2ht-1 for GARCH (1,1)

EMPIRICAL RESULTS AND DISCUSSIONS

The results of different models applied are depicted in tables from 1 to 8. Table 1

and table 2 depicts the impact of futures trading within a GARCH (1,1) framework on

return and volatility of underlying spot market Though results of all the coefficients both

in a conditional mean and conditional variance equation are derived, but our focus will be

only on the significance of the futures dummy coefficients and included in both

the equations.

The results in conditional mean equation from Table-1 clearly reveal that the

introduction of futures trading in India has significant influence on Nifty index and most

of the stocks taken into consideration except HINDUSTAN UNILEVER, HINDALCO,

ACC, L&T and TELCO. This means introduction of derivatives has significant impact

upon return of S&P NIFTY and five out of ten stocks like RELIANCE INDUSTRIES,INFOSYS, HDFC, TISCO and SBI.


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TABLE -1

IMPACT OF FUTURES TRADING ON RETURN OF UNDERLYING SPOT

MARKETConditional Mean Equation

2

Spot t = 0 + I Spot t-1 +D futures+ ti=1

Index/Stocks 0 AR(1) 0 AR(2) 2 Dummy

-0.0025 -0.1345 -0.6087 0.0021CNX NIFTY

-(0.853) -(4.934) -(1.879) (2.398)

0.0018 0.0675 0.0847 0.0035RELIANCE

INDUSTRIES (2.351) (2.983) (1.543) (3.545)

0.0006 0.1155 -0.0226 0.0027INFOSYS

(1.855) (2.081) -(1.116) (2.789)

0.0003 0.1893 0.0798 0.0005HINDUSTHAN

UNILEVER -(0.119) (1.777) (1.331) (0.764)-0.0010 0.0378 -0.0447 0.0018HDFC

(0.335) (3.987) -(2.777) (2.018)

0.0001 -0.0588 0.0753 -0.0002HINDALCO

-(0.289) -(0.875) ((1.752) -(0.567)

-0.0006 0.0255 0.1988 0.0014ACC

-(0.728) (0.998) (2.053) (1.011)

-0.0011 0.1296 0.0174 0.0123TISCO

-(0.148) (2.545) (0.887) (3.112)

0.0009 0.0788 -0/0234 0.0054L&T

(0.545) (1.323) -(0.976) (1.788)

-0.0015 0.1301 -0.0887 0.0024SBI-(1.618) (1.759) -(1.111) (2.201)

0.0012 0.0885 0.0032 0.0014TELCO

(1.198) (3.398) (0.202) (0.929)

Looking at Table-2, onset of derivatives trading is observed to be having

significant impact on volatility of CNX NIFTY index and on stocks like RELIANCE

INDUSTRIES, INFOSYS, TISCO and L&T. This means futures trading does not able to

exert impact on volatility of six out of ten stocks taken into consideration like

HINDUSTAN UNILEVER, HDFC, HINDALCO, ACC, SBI and TELCO. Apart from

NIFTY index, RELIANCE INDUSTRIES, INFOSYS, and TISCO are the stocks which

have been substantially influenced by both return and volatility of conditional mean and

variation equation with the advent of futures trading in India. Therefore, as a whole the

trading of stock futures in India has been found to have a significant impact on almost

fifty percent of the stocks as well as the index.


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TABLE -2

IMPACT OF FUTURES TRADING ON VOLATILITY OF UNDERLYING SPOT

MARKETConditional Variance Equation

h t = 0 +12t-1+2ht-1+Dfutures

Index/Stocks 0 ARCH(1) 1 GARCH(1) 2 Dummy

0.0002 0.1730 0.8867 0.0000CNX NIFTY

(4.554) (5.769) (45.456) (2.769)

0.0004 0.2778 0.6854 0.0001RELIANCE

INDUSTRIES (5.778) (4.137) (20.987) (2.554)

0.0004 0.0030 0.7922 -0.0000INFOSYS

(4.900) (3.347) (29.786) -(3.267)

0.0000 2.1511 0.5567 -0.0001HINDUSTHAN

UNILEVER (0.764) (1.771) (21.674) -(0.355)

0.0001 0.4573 0.8786 0.0006HDFC

(2.435) (!.979) (45.787) (1.547)

0.0000 0.0581 0.5439 -0.0000HINDALCO

(1.183) (4.412) (37.890) -(0.989)

0.0000 0.4718 0.6785 -0.0001ACC

(0.642) (1.802) (25.670) -(0.890)

0.0001 1.3004 0.0655 0.0012TISCO

(1.055) (1.370) (5.659) (2.784)

0.0002 0.1002 0.9005 0.0015L&T

(3.458) (6.198) (64.878) (2.989)

0.0003 0.2229 0.7678 0.0000SBI

(2.983) (4.987) (16.090) (1.555)

0.0001 0.1132 0.7988 0.0003TELCO

(1.738) (6.085) (12.896) (1.003)


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The next question comes into mind is whether the impact is favorable or

unfavorable to the underlying index and stocks. In other words, whether futures

introduction has stabilized or destabilized the underlying futures spot market.

Stabilization effect can be achieved by increasing returns and reducing volatility in the

underlying spot market and vice versa. Therefore, in order that stabilization has occurred

after introduction of derivatives, the futures dummy coefficient in conditional mean

equation should be positive and that in conditional variance equation should be negative.

Positive dummy in the mean equation represents increased return and negative dummy in

the volatility equation represents reduced volatility and vice versa. Out of observations in

table-1, except HINDALCO, all other stocks and index show positive sign means positive

return. Out of observations in table-2 in conditional volatility equation, except INFOSYS,

HINDUSTAN UNILEVER, HINDALCO and ACC, all other stocks and index show

positive sign meaning increased volatility. Therefore, it can be inferred that the prime

objective of introduction of futures trading in Indian markets have not yet been fully

achieved. Though, most assets are showing positive returns after introduction of futures,

simultaneously most of them also exhibit enhanced volatility thereby causing destability

in underlying spot market to a considerable extent.

Now in Tables 3 and 4, some significant descriptive statistical measures of daily

spot index returns are calculated for both pre and post futures period. The measures

include average return, volatility measure like standard deviation, measure of asymmetry

and peakedness like skewness and kurtosis etc. Out of observations, except INFOSYS all

other stocks and NIFTY index show an increased average return during post futures

period. But as far as static measure of volatility like standard deviation or risk is

concerned, except INFOSYS, HINDUSTAN UNILEVER, ACC and TELCO stocks, it

has increased for all other six stocks and NIFTY index signaling increased volatility in

post futures period. This can prove further destabilization of spot market afterintroduction of futures trading in India. As far as skewness is concerned, it can be seen

that almost all return series are negatively skewed both during pre and post futures

period. Though being negatively skewed for both the periods, the extent of negative

asymmetry has only increased for NIFTY index, INFY and L&T stocks. This represent

that for almost all the stocks, the chance of positive deviation of return is higher than that


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of negative deviation. In other words, there is a higher possibility of increase in return

series from its average figure than a fall in the return. The kurtosis figures of all

observations are quite high, representing that all the return series are leptokurtic.

Leptokurtic means that chances of large deviation from its mean value is comparatively

higher than that of any other series following normal probability distribution. This is also

known as fat tailed distribution, because of the fatness of the tails, representing larger

probability of deviation. Abnormal figures for kurtosis for some of the stocks may

represent the presence of outliers in the return series which means there is some abnormal

and extreme return figures for those stocks at some point of time. The significance of

Jarque-Bera statistics for all the stock return series both during pre and post futures

period clearly reveals that all stock returns are non-normally or asymmetrically

distributed.

TABLE -3DESCRIPTIVE STATISTICS FOR DAILY SPOT INDEX RETURNS DURING PRE &

POST FUTURES PERIOD

Index/Stocks MEAN MEDIAN MAX MIN STD.

DEV

Pre-Futures 0.0005 0.0006 0.1508 -0.1356 0.0220CNX NIFTY

Post Futures 0.0007 0.0020 0.1897 -0.2081 0.0289

Pre-Futures 0.0003 0.0006 0.2187 -0.5432 0.0564RELIANCEINDUSTRIES Post Futures 0.0016 0.0014 0.1367 -0.1896 0.0831

Pre-Futures 0.0021 0.0005 0.2674 -0.1287 0.0823INFOSYS

Post Futures 0.0016 0.0012 0.1543 -0.9852 0.0562

Pre-Futures -0.0008 0.0001 0.1133 -2.5467 0.0456HINDUSTHANUNILEVER Post Futures 0.0004 -0.0003 0.1054 -0.2097 0.0183

Pre-Futures 0.0007 0.0003 0.1243 -1.4563 0.0674HDFC

Post Futures 0.0009 0.0005 0.2123 -0.9867 0.0747

Pre-Futures 0.0001 0.0003 0.0953 -0.4567 0.0189HINDALCO

Post Futures 0.0003 0.0002 0.0834 -0.8562 0.0256

Pre-Futures -0.0021 -0.0016 0.2156 -1.8974 0.0862ACC

Post Futures 0.0005 0.0019 0.0673 -0.1342 0.0593

Pre-Futures 0.0005 0.0011 0.1203 -0.1986 0.0356TISCOPost Futures 0.0012 0.0004 0.1576 -0.1364 0.0409

Pre-Futures 0.0008 0.0001 0.0976 -0.5478 0.0187L&T

Post Futures 0.0019 0.0021 0.2015 -0.3894 0.0258

Pre-Futures 0.0010 -0.0015 0.1124 -0.1167 0.0568SBI

Post Futures 0.0013 0.0021 0.2012 -0.1453 0.0656

Pre-Futures 0.0009 -0.0002 0.1551 -1.6743 0.0715TELCO

Post Futures 0.0013 0.0018 0.1235 -0.3278 0.0542


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TABLE -4

DESCRIPTIVE STATISTICS FOR DAILY SPOT INDEX RETURNS DURING PRE &

POST FUTURES PERIOD

Index/Stocks SKEWNESS KURTOSIS JARQUE-

BERA

Pre-Futures 0.0756 8.4536 3451.88CNX NIFTY

Post Futures -0.9934 10.6440 5067.94

Pre-Futures -8.9981 123.5747 889765RELIANCE

INDUSTRIES Post Futures -4.4987 58.6981 88936

Pre-Futures -10.9981 109.8674 554567INFOSYS

Post Futures -22.4563 867.8642 12479804

Pre-Futures -40.9561 1007.9563 10003782HINDUSTHAN

UNILEVER Post Futures -0.1982 29.5431 194200

Pre-Futures -33.8745 554.0981 11459876HDFC

Post Futures -11.5167 235.9857 984567

Pre-Futures -1.5346 88.6534 9924HINDALCOPost Futures 2.4351 102.5633 10052

Pre-Futures -32.5672 998.1514 5484965ACC

Post Futures -2.5987 29.8136 86539

Pre-Futures 0.5540 10.5967 233TISCO

Post Futures 0.0564 21.9346 874

Pre-Futures -1.9897 33.4411 7158L&T

Post Futures -20.5431 894.9165 9911227

Pre-Futures 0.2784 5.4511 274SBI

Post Futures -0.6378 18.9454 9981

Pre-Futures -22.5766 988.2841 69507124TELCO

Post Futures -2.5434 100.8680 5011It is a well documented fact that volatility of asset returns can not be fixed over a

period of time, i.e. can not be time invariant. Alternatively, it is expected to be

heteroskedastic which means conditional upon time or time variant. This compels the

utilization of ARCH series of models to be better indicators of measurement of volatility.

The results of volatility and return of NIFTY and ten stocks during pre and post futures

period within ARCH (1) framework are depicted in Tables 5 and 6. The focus here will

be on the significant change in ARCH coefficients in the conditional variance equation.

The observations point out that though being statistically significant during both the

periods, the ARCH coefficient (1) in the conditional variance equation is higher during

post futures period for the index and stocks except INFOSYS, HDFC, L&T and SBI.

This higher value of ARCH coefficient (1) reveals an increase in time varying volatility


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of NIFTY and six other stocks like RELIANCE, HINDUSTAN UNILEVER,

HINDALCO, ACC, TISCO and TELCO after the introduction of futures trading in India.

TABLE -5

VOLATILITY & RETURN FOR PRE-DERIVATIVES PERIOD UNDER ARCH(1)Index/Stocks Conditional Mean Equation

2

Spot t = 0 + I Spot t-1 + ti=1

Conditional Variance

Equationh t = 0 +1

2t-1

0 AR(1) 1 AR(2) 2 0 AR(1) 1

0.0005 0.1254 -0.0234 0.0003 2.1567CNX NIFTY

(0.7854) (2.1898) -(0.7489) (2.8768) (1.1157)

0.0009 0.1567 0.0765 0.0023 0.8997RELIANCE

INDUSTRIES (1.8980) (3.1242) (1.8761) (9.4664) (1.8767)

0.0016 0.2341 -0.0125 0.0014 0.2875INFOSYS

((2.4805) (3.9852) -(0.1451) (5.1534) (3.2456)

0.0002 -0.1240 0.0265 0.0007 1.3456HINDUSTHAN

UNILEVER (0.7875) -(1.9854) (1.0008) (3.5558) (2.3089)

0.0012 0.0786 -0.1456 0.0019 0.2288HDFC

(1.7546) (1.1119) -(1.0192) (1.7876) (5.4367)

-0.0001 0.0394 0.0448 0.0003 0.7653HINDALCO

-(0.3578) ((1.3896) (0.9882) (4.6752) (2.1896)

-0.0010 -0.0581 0.1177 0.0005 0.0009ACC

-(0.2765) -(1.7896) (1.9890) (9.8584) (0.6557)

0.0007 0.7744 -0.0276 0.0011 0.2032TISCO

(0.9018) (2.8872) -(1.2716) (1.7947) (3.8899)

0.0012 0.2189 0.1617 0.0027 0.1647L&T

(1.7612) (3.5570) (1.9987) (21.8762) (5.6789)

0.0008 0.1287 -0.0176 0.0021 0.9218SBI

(1.2545) (1.9872) -(0.8967) (12.0002) (1.4569)

-0.0002 0.0973 0.0025 0.0008 1.3377TELCO

-(0.5025) (0.8769) (0.5689) (7.5439) (3.2397)


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TABLE -6

VOLATILITY & RETURN FOR POST DERIVATIVES PERIOD UNDER ARCH(1)

Index/Stocks Conditional Mean Equation2


Conditional Variance

Equationh t = 0 +1

2t-1

0 AR(1) 1 AR(2) 2 0 AR(1) 1

0.0009 0.1564 -0.0189 0.0007 0.1623CNX NIFTY

(1.5676) (3.9871) -(0.4563) (2.0098) (1.8988)

0.0015 -0.0254 -0.0342 0.0008 0.2345RELIANCEINDUSTRIES (2.3141) -(1.0234) -(0.9876) (1.0554) (3.3455)

0.0021 0.9871 0.0562 0.0005 8.3379INFOSYS

(3.7434) (1.9750) (1.1238) (10.9897) (1.5783)

0.0004 0.0345 0.0673 0.0001 -0.0006HINDUSTHAN

UNILEVER (0.7652) (0.7689) (1.5467) (6.7843) -(0.8967)0.0018 0.0556 -0.0254 0.0004 -0.0019HDFC

(2.1373) (1.7781) -(0.6675) (3.4443) -(2.4453)

-0.0002 0.0017 0.1988 0.0002 0.2261HINDALCO

-(0.1547) (0.1787) (2.9967) (4.4986) (3.4410)

0.0004 -0.0456 -0.0187 0.0007 8.2210ACC

(0.9854) -(0.7450) -(0.5639) (9.8575) (2.4328)

0.0017 0.2144 -0.0179 0.0001 0.2908TISCO

(2.3874) (4.5345) -(0.9875) (1.6784) (4.8761)

0.0030 0.3142 0.0764 0.0005 4.9883L&T

(5.9876) (5.9082) (1.0677) (6.9951) (1.7672)

0.0026 0.0865 -0.0788 0.0002 -0.0004SBI(3.1768) (2.0891) -(2.2233) (1.9091) -(0.2155)

0.0013 0.0234 -0.0156 0.0001 0.5167TELCO

(2.3546) (0.8921) -(0.3457) (2.8676) (4.0010)

The results of volatility and return of NIFTY and ten stocks during pre and post

futures period within a GARCH (1,1) framework are exhibited in Tables 7 and 8. The

main focus here will be on testing the significance GARCH coefficients 2. The

observations point out here also that the GARCH coefficient (2) in the conditional

variance equation is higher during post futures period for the index and stocks except SBI

and TELCO. This higher value of GARCH coefficient reveals an increase in time varying

volatility of NIFTY and almost all the stocks except only SBI and TELCO after the

initiation of futures trading in India. The slight discrepancy in ARCH and GARCH

results arise due to the fact that ARCH coefficient in the conditional variance equation

being a lagged residual captures any new or recent information and its impact. Wherein,


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GARCH coefficient or lagged volatility only can capture the effect of some old news.

Therefore, these are also known as recent and old news coefficient.

TABLE -7

VOLATILITY & RETURN FOR PRE-DERIVATIVES PERIOD UNDER GARCH(1,1)



Conditional Variance Equation

h t = 0 +12

t-1+2ht-1

0 1 2 0 1 2

0.0007 0.0567 -0.0209 0.0008 0.1954 0.8677CNX NIFTY

(0.5437) (1.2478) -(0.9581) (3.2548) (2.5434) (12.1437)

0.0015 0.2236 -0.1227 0.0010 0.3562 0.5434RELIANCE

INDUSTRIES (0.8799) (6.8887) -(2.1848) (3.5537) (1.9822) (8.9828)

0.0019 0.2151 0.1452 0.0011 0.2367 0.7122INFOSYS

(1.3187) (3.5642) (2.9675) (4.1536) (1.4546) (18.4222)

0.0001 0.0579 0.0154 0.0001 -0.0018 0.4823HINDUSTHAN

UNILEVER (0.2287) (1.8934) (0.6745) (1.8590) -(2.1188) (11.4567)

0.0016 0.0207 -0.0456 0.0005 0.0983 0.3145HDFC

(1.1123) (0.7539) -(2.0121) (2.6541) (1.9729) (7.6781)

-0.0001 -(0.0038 0.0456 0.0000 -0.0157 0.3587HINDALCO

-(0.0016) -(0.2047) (1.2387) (0.7543) -(0.8965) (6.9540)

-0.0006 0.0376 0.2187 0.0001 0.1494 0.1439ACC

(0.3452) (2.1394) (3.4587) (1.7439) (3.2537) (2.3548)

0.0004 0.1443 0.0393 0.0003 0.0945 0.4296TISCO

(0.1895) (2.2652) (1.1201) (1.6754) (1.9346) (10.6667)

0.0012 0.2219 -0.0154 0.0008 0.2267 0.7767L&T

().7854) (3.2548) -(0.5893) (2.4543) (5.8482) (20.9567)

0.0015 0.1867 0.1157 0.0006 0.1476 0.4892SBI

(1.5432) (1.9845) (2.5428) (2.1141) (4.2898) (27.4514)

0.0003 -0.0242 0.1189 0.0004 0.0135 0.1727TELCO

(0.2589) -(0.8745) (1.8974) (0.9867) (1.3536) (9.6727)


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TABLE -8

VOLATILITY & RETURN FOR POST DERIVATIVES PERIOD UNDER ARCH (1,1)



Conditional Variance Equation

h t = 0 +1

2

t-1+2ht-1

0 1 2 0 1 2

0.0015 0.1153 -0.0545 0.0001 -0.0015 0.6652CNX NIFTY

(3.3345) (3.4562) -(1.8564) (3.4563) -(2.9893) (12.4986)

0.0027 0.2098 -0.0015 0.0005 -0.0021 0.9986RELIANCE

INDUSTRIES (5.1257) (6.8343) -(0.6457) (2.1765) -(1.5567) (28.5762)

0.0033 0.2245 0.0916 0.0010 0.1042 1.0054INFOSYS

(6.9872) (6.3567) (0.9856) (3.9853) (3.4589) (50.3218)

-0.0012 0.0757 -0.3322 0.0000 -0.0001 0.3232HINDUSTHAN

UNILEVER -(1.4587) (1.4592) -(1.8582) (3.6647) -(0.2268) (15.6430)

0.0019 0.1845 0.0463 0.0000 0.0152 0.8675HDFC

(3.8865) (4.9542) (1.2635) (2.4582) (1.5491) (33.4934)

-0.0008 0.0365 0.0134 0.0000 0.0004 0.4435HINDALCO

-(0.8564) (0.9435) (1.1543) (4.1956) (0.9846) (8.8722)-0.0016 0.0876 -0.0756 0.0000 0.0009 0.5547ACC

(1.9832) (1.1176) -(1.4552) (2.8756) (1.3854) (22.9865)

0.0019 0.0542 0.0365 0.0002 -0.0943 0.7870TISCO

(3.2783) (0.9545) (0.8897) (3.2189) -(2.4563) (40.3564)

0.0035 0.3456 -0.0678 0.0005 0.1872 1.2346L&T

(8.5431) (7.8734) -(1.5347) (4.5645) (3.1786) (88.9597)

0.0024 0.2676 -0.0531 0.0003 0.3726 0.8739SBI

(4.6744) (5.9858) -(2.3421) (2.8945) (5.9737) (20.3537)

0.0013 0.0892 0.1372 0.0001 0.1777 0.2245TELCO

(2.3654) (1.2272) 1.4256 (1.6789) (2.8111) (4.9571)


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CONCLUSION

The observations envisage that trading of futures in Indian stock exchanges have

significantly affected the return and volatility of financial assets. The overall conclusion

from our observations is that introduction of derivatives has favorable impact upon return

of S&P NIFTY index and most of the stocks. Also, it has significant impact on volatility

of NIFTY index and four out of ten stocks which means that six other stocks under test

didnt exhibit much influence of futures trading. Also, except HINDALCO, all other

stocks and index show positive sign means positive return after the onset of derivatives

trading. But, in conditional volatility equation, except INFOSYS, HINDUSTAN

UNILEVER, HINDALCO and ACC, all other stocks and index show positive sign

meaning increased volatility. Therefore, it can be inferred that the prime objective tostabilize the cash market have not yet been fully achieved after the initiation of futures

trading. Though, most assets are showing positive returns after introduction of futures,

simultaneously they also exhibit enhanced volatility thereby causing destability in

underlying spot market to a considerable extent The results achieved may also indicate

presence of some other factors destabilizing the Indian stock markets. Most prominent

example might be the impact of US led recession, sluggishness and destability in

international financial markets causing great deal of volatility and worries in Indian stock

markets and returns of financial assets.

The same destabilization of underlying spot market has also been emphasized

through the comparative analysis done on spot market volatility before and after the

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a study on volatility of indian stocks and index – pre and post derivatives era

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