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International Journal of Management and Social Sciences Research (IJMSSR) ISSN: 2319-4421 Volume 4, No. 6, June 2015

i-Explore International Research Journal Consortium www.irjcjournals.org

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The Impact of Derivatives on Spot Market Volatility:

A Study on S&P CNX Nifty, India

Rajni Sinha, Assistant Professor, Amity Business School, Amity University, Rajasthan

ABSTRACT

The research study analyses the impact of derivatives on

Indian spot market. The data used are daily log return of

S&P CNX Nifty from Jan 1991 to Dec 2011. GARCH (1,

1) model is employed to examine the impact of information

flow following the introduction of derivatives (index

futures, stock futures and index options) on the conditional

volatility of S&P CNX Nifty. Augmented Dickey Fuller

(ADF) test is conducted to ascertain whether the return

series is stationary. Ljung-Box Q test is performed to test

whether any group of autocorrelation in the time-series

are different from zero. The research analysis provides

evidence that after the introduction of derivatives, the

conditional volatility of S&P CNX Nifty has increased.

ARCH effect (recent information) is more prominent in

index futures than in stock futures and index options, in

defining the overall conditional volatility of S&P CNX

Nifty. The impact of existing information (old news)

carries more weight with respect to all three derivatives

(index futures, stock futures and index options).

Phenomenon of volatility clustering has increased over

time after the introduction of derivatives. Conditional

volatility of S&P 500, conditional volatility of Bank Nifty

(bank index of India) and daily log returns of S&P

Banking Index (BIX, banking index of USA) and Bank

Nifty (bank index of India) are used to find the impact of

other factors on the conditional volatility of the Indian

spot market. Further analysis is done to examine the effect

of S&P Banking Index (BIX) returns and Bank Nifty on the

conditional volatility of S&P CNX Nifty in different time

periods; based on sub-prime mortgage crisis (pre-sub-

prime crisis period, sub-prime crisis period and post- sub-

prime crisis period). Vector Autoregressive analysis and

Granger Causality Wald tests are employed to verify the

interdependencies of daily log returns of banking indices

(both BIX and BANKNifty) time-series and conditional

volatility of S&P CNX Nifty. The research analysis

confirms that the conditional volatility of S&P CNX Nifty

caused by the conditional volatility of S&P 500 has

marginally increased and GARCH effect (existing

information) dominates over ARCH (recent information)

in the post derivative period. During the period of Pre-

Sub-Prime crisis, the combined effect of BANKNifty and

BIX returns influences the conditional volatility of S&P

CNX Nifty and follows the same pattern as that by the

S&P 500. However, during the period of Sub-Prime crisis,

the persistent information effect on market volatility

completely disappears and spot market volatility relies

more on the recent information. Whilst during the post

Sub-Prime crisis, the ARCH effect dominates. The

Granger Causality Wald test proves the conditional

volatility of S&P CNX Nifty affects BANKNifty rather than

BANKNifty affecting conditional volatility of S&P CNX

Nifty. The key implication of the research finding is that

the derivatives in Indian market have failed to alleviate

risk that was much required during the time of crisis. The

integration of global economy has led to the migration of

risk to Indian spot market. The collapse of banking system

in United States followed by global liquidity crunch seems

to have increased the level of volatility in the Indian

market. SEBI’s (Securities and Exchange Board of India)

measures to control volatility have failed during the recent

times and better regulations are required to reduce the

volatility in Indian spot market using better surveillance

and monitoring mechanisms and curbing out price

anomalies of derivatives.

1. INTRODUCTION

Economic liberalization and the integration of the world

economy embarked the emergence of financial

engineering and risk management. The key outcome of the

research and findings by the financial pundits, particularly

in developed economies are “Derivatives”. Derivatives are

the financial instruments whose values are derived from

the underlying assets – a currency, an interest rate, a

commodity, or a stock. Financial derivatives play a key

role to overcome the inertia of managing risk in cross-

border transactions, in trading securities and commodities,

and exposure to uncertainty arising because of interest

rates volatility within boundaries. The derivatives inject

the extra liquidity in the economy by attracting both

domestic and foreign institutional investors and increasing

the volume of trading activities. The derivative trading

also attracts speculators, who seek to profit by anticipated

increase or decrease in a particular market price. This in

turn provides the additional capital needed to facilitate the

liquidity. Also, derivatives provide additional .channel to

.invest with .lower trading .cost; by facilitating the

investors to extend their settlement through future

contracts.

Derivatives are traded either over the counter by derivative

dealers or in organized exchanges. Prior to the world-wide

market crash of 2007, OTC derivatives were not



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standardized and proper regulations were not in place.

However, after the economic meltdown and the financial

catastrophe brought in by the exotic financial derivatives,

ISDA (International Swap and Derivative Association)

and regulatory boards of individual sovereigns have set

new regulations and better stringent laws are enforced to

enable more transparency and better estimates of both

liquidity and volume of transactions. However, the key

question still remains unanswered. “Does derivative

trading induce greater volatility in the market?”

Researchers all across globe have studied the impact of

derivatives on market volatility in both developed and

emerging economies and their findings have been

inconclusive over the years. In emerging economy such as

India, the impact of derivative trading on the stock market

volatility has received considerable attention in the recent

times, particularly after the market crash of 2001 because

of dot com bubble and world-wide economic meltdown as

an outcome of sub-prime crisis in 2007. The exchange

traded derivative products such as Futures and Options

have become important instruments of the price discovery,

hedging risk and portfolio diversification in last one

decade.

One of the key reasons for the emergence and the

popularity of the derivatives in the Indian market are FIIs

(Foreign Institutional Investors). The global integration

and liberalization of Indian economy has led to the surplus

inflow of capital in Indian Bourse.

During the Sub-Prime mortgage crisis (Sep 2007- Feb

2009) and post Sub-Prime crisis, India‟s growth has been

the major attraction for the foreign investors. The majority

of this inflow has been in the derivatives market,

particularly Nifty50 index future. The relaxed regulation

by SEBI (Securities and Exchange Board of India) by

lifting the daily upper cap on the transaction amount for

FIIs in derivative trading is another reason for the influx of

capital in the Indian market. However, the question of

whether the extra injection of liquidity, chiefly in Indian

derivatives market has affected the overall volatility is still

under investigation and hence is the main motivations of

this research study.

The other motivating factor for this research analysis is the

present economic climate; that started with Sub-Prime

Mortgage crisis, followed by collapse of banking systems,

(particularly in USA and there after migrating to rest of

world) and finally credit crunch paralyzing the global

economic system.

India is an integral part of global economic system, with a

sustained growth of over 6.5% during the economic

meltdown. No doubt economic turmoil arising because of

credit crunch has affected the overall volatility in the

Indian spot market but the question is „to what extent‟.

How the collapse of banking system in United States has

led to the induction of volatility in Indian market and how

systemic economic collapse is correlated to Indian

derivative market. Has derivatives been successful in

reducing the volatility in the Indian spot market or has

volatility been more influenced by the overall economic

climate. This research study analysis all these above

questions in detail.

1.1 History of Derivatives in India

The history of derivatives may be new for the developing

and emerging economies but it has existed since long time

in the developed economies. The history is surprisingly

longer. Historically, farmers used derivative instruments to

protect themselves from decline in price of their crop

either because of over production or delay in rain. In

Osaka, Japan, the use of derivatives dates back to 1650,

when the first derivative as „future contract‟ was

introduced in the „Yodoya‟ rice market. In India, the

commodities derivative market dates back to 19th century

with organized trading in cotton through the establishment

of Cotton Trade Association in 1875. The exchange traded

instruments such as futures and options are lately

introduced since June 2000 at two major stock exchanges:

the National Stock Exchange (NSE) and Bombay Stock

Exchange (BSE). Index futures, Index options, Stock

futures and Stock options are the four major derivative

contracts traded in both these exchanges.

1.2 Role of Financial Derivatives

Risk Management/Hedging: The actual reasoning behind

the introduction of derivatives is hedging the pre-existing

risk, in order to offset the potential losses in the underlying

or spot market. In India, the governing body for derivative

trading and its regulations is Securities and Exchange

Board of India (SEBI), which along with its international

counterpart ISDA frames regulations that encourages

derivative trading for the purpose of risk management.

Speculation: Speculation is the other prime motive of

derivative trading (i.e. taking positions to profit from the

anticipated price movements). In practice, it may be

difficult for the policy makers like SEBI and ISDA to

distinguish whether a particular trade was for the purpose

of hedging or speculation. The balanced efficient market

requires the participation of both hedgers and speculators.

However, it is still arguable whether speculation in

derivative market destabilizes the spot market and

increases the market volatility.

Market efficiency: Proponents of derivative trading

believe, derivative trading provides information in market

and hence leads to more information symmetry. The

available information in the market is altered for two

reasons: first, additional traders are attracted in the market

either to hedge risk or speculate on anticipated market

price; second, new information may be transmitted to the



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derivative market more quickly than in spot market

because of the low transaction costs compared to the spot

market. Thus, derivative markets provide a secondary

route for the transmission of information to the spot

markets, thereby affecting the spot market volatility.

To a large extend, the success of derivatives trading

depends upon the choice of products. The derivative

products traded in Indian stock exchange are futures and

options. Raju and Ghosh (2004) have provided evidence

for the consideration of volatility in the Indian stock

market as tools of analysis of risk factors. There is

certainly a concern of attention for stock price volatility

due to the coupling of national markets in currency,

commodity and stock with world markets and existence of

common institutional investors across globe.

There are two components of volatility arising in any

international market: The volatility arising due to

information based price changes and volatility arising due

to speculative trading/ noise trading, i.e., destabilizing

volatility. As a concept, volatility is simple and intuitive. L

C Gupta Committee Report on Derivatives in December

1997 recommended the introduction of derivatives in the

Indian capital market. The report highlighted the phase

wise introduction of derivatives, first the stock index

futures, followed by index options, stock options and stock

futures, once after the market matures. Following the

recommendations and pursuing the integration policy,

futures on benchmark indices (Sensex 30 and Nifty 50)

were introduced in June 2000. The policy was followed by

introduction of index options on indices in June 2001,

followed by options on individual stocks in July 2001.

Stock futures on individual stocks were introduced in

November, 2001.

1.3 Classification of Derivatives traded in Indian

Market

By definition, derivatives are the financial instruments

whose value depends upon the underlying assets, which

could be stock prices/market indices, interest rates, etc.

Derivatives products are specialized contracts2 which

signify an agreement or an option to buy or sell the

underlying asset to extend up to the maturity time in the

future at a prearranged price.

Futures: A futures contract is an agreement between two

parties to buy or sell an asset at a certain time in the future

at a certain price. In NSE, the following futures are traded:

Index futures on S&P CNX NIFTY, Bank Nifty, CNX IT,

Stock futures on certain specified Securities and Interest

Rate Futures. All the futures contracts in NSE are cash-

settled.

Options: An Option is a contract which gives the right,

but not an obligation, to buy or sell the underlying at a

stated date and at a stated price. While a buyer of an

option pays the premium and buys the right to exercise his

option, the writer of an option receives the option

premium and is obliged to sell/buy the asset if the buyer

exercises it on him. In NSE, the following options are

traded: Index options on S&P CNX NIFTY, Bank Nifty,

CNX IT, and Stock options on certain specified Securities.

All the option contracts in NSE are cash-settled.

Derivatives have been the mainstream factor in defining

the market volatility in the recent years. The theme of this

thesis is calibrating the effects of derivatives and how

much their presence has affected the volatility in Indian

Market. The research study also highlights the other

factors that affect the spot market volatility such as S&P

500 returns, Bank Nifty, S&P Banking index (BIX)

returns and effects of Sub-Prime mortgage crisis of

September 2007 (which lasted till February 2009) and

finally makes a valuable comparison of the degree of

volatility induced by the effect of introduction of

derivatives and other factors.

The rest of the research study is organized as follows:

First, literature review of already conducted researches

and their respective methodologies applied to deduce the

effects of derivatives on market volatility. Thereafter the

objective of study, hypothesis, methodology, data and the

time period for the study are explained. The variables are

identified and explained in brief, followed by Model

specifications and estimations. The results are interpreted

for each model followed by conclusion.

2. LITERATURE REVIEW

Derivative trading and its impact on stock market

volatility has been the area of interest among many

researchers across globe. Numerous theories have evolved

over the past two decades highlighting its impact on

market volatility in both developed and emerging

economies. The research studies attribute to study factors

contributing volatility in stock returns. One common

factor of all these studies is derivative trading, particularly

index futures, and has increased the volume because of

lower transactional cost relative to cash market. However

the key question still remains unanswered, whether larger

participation and increased volume has reduced the market

volatility and information asymmetry.

2.1 Empirical research conducted on spot markets

across globe Siopis and Lyroudi (2007) conducted experiments to draw

the relationship between the volatility in the Greek stock

market and the introduction of the future contracts on the

FTSE/ASE-20 index. Various volatility forecasting

approaches are used such as GARCH and EGARCH

models and the GJR model using the data for a sample

period of 10 years. During the analysis, the author has

broken the sample period into two sub-periods, one period



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before the introduction of futures trading and one after the

introduction of futures trading and applied EGARCH(1,1),

GARCH(1,1) and the TGARCH(1,1) models for the pre-

futures period and the post-futures period as well, with and

without a dummy variable. The results of this study

indicate that the introduction of futures leads to a

significant change in the spot market volatility of the

FTSE/ASE-20 index.

Poshakwale and Pok (2004) examined the impact of futures

trading on spot market volatility in Kuala Lumpur Stock

exchange. The results obtained shows that the onset of future

trading increases the spot market volatility and the flow of

information to the spot market. The results also provide

evidence that the underlying stocks respond more to the

recent news and non-underlying stocks respond more to the

old news. The lead-lag and casual relationship between the

futures trading activities and the spot market volatility is also

examined. GARCH (p,q) process is used in estimating the

volatility and the Autoregressive Conditional Heteroscedastic

model (ARCH) is used in modelling the volatility of the time

series characterized by the time varying conditional variance.

VAR results show that the impact of the previous day‟s

futures trading activity on the volatility is positive but short

(only a day). This is further confirmed by the Granger‟s

causality test.

Jeanneau Serge Marian Micu (2003) explained how the

information based speculative transaction establishes a

relationship between the volatility and derivative market. This

relationship is based on whether the information is private or

public and what type of asset is traded. Author claims that

arrival of private information always surges the volatility of

return and trading volume in both equity and equity related

futures and options.

Rahman Shafiqur (2001) examined the impact of Dow Jones

Industrial Average index futures and options on the

conditional volatility of the component stocks. The research

study is much in line with the other researchers in this field of

study. The conditional volatility of the intraday stock returns

is estimated using the GARCH model for both pre and post

periods of introduction of derivatives. The results show that

the introduction of index futures and options has not

produced any structural change in the conditional volatility of

component stocks.

Jhon, Gleb and Charles (2001) examined the hypothesis

asserting the increase in future market trading increases the

spot market volatility. The author has used the GARCH

model and Schewert Model and results indicate the rejection

of hypothesis. The dataset considered is from the UK market

(FTSE 500).

2.2 Empirical research conducted on Indian market Gahlot, Datta and Kapil (2010) examined the impact of

derivative trading on stock market volatility. The sample data

used are closing prices of S&P CNX Nifty as well as closing

prices of five derivative stocks and five non derivative stocks

from April 1, 2002 to March 31, 2005. The study uses

GARCH model to capture nature of volatility over time and

phenomenon of volatility clustering. The evidences suggest

that there is no significant change in the volatility of S&P

CNX Nifty. However, results show mixed effect in case of 10

individual stocks. These results assist investors in making

investment decision. It also helps to identify the need for

regulation.

Bandivadekar and Ghosh (2005) from Reserve Bank of India

conducted research studies on the daily return volatility of

both S&P CNX Nifty and BSE Sensex for a period of Jan

1997- Mar 2003 using the GARCH framework. The results of

the research study concluded that the introduction of

derivatives have reduced the overall volatility in the stock

market.

Nath (2003) also conducted research on the behavior of

volatility in Indian equity market for the pre and post

derivatives periods by using the conditional variance for the

period of 1999-2003. The researcher has model the

conditional volatility using different methods such as

GARCH(1,1) and considered data sample of 20 randomly

picked NIFTY and Junior Nifty stocks as well as benchmark

indices. The empirical results of the study provide evidence

that for most of the stocks the overall volatility has reduced

after the post derivative trading period. However, it is quite

primitive to generalize at that stage that the overall respective

indices S&P CNX Nifty and Nifty Juniors will follow the

same trend with regards to volatility.

Premalata (2003) explored the impact of the introduction of

the derivative contracts such as Nifty futures and option

contracts on the spot market volatility. GARCH (1,1) model

is used to capture the heteroskedasticity in returns and data

set comprises of the closing price between Oct 1995 and Dec

2002 for CNX Nifty , Nifty Junior and S&P 500 returns. The

results obtained indicate that there is no significance impact

of introduction of derivatives on the spot market volatility.

However, the empirical results also indicate the shift in the

nature of the GARCH process after the introduction of

derivatives.

Raju and Karande (2003) extended the earlier research and

obtained the price discovery and the volatility in the context

of the Nifty futures at the National Stock Exchange. The

author has used the Co-integration and the Generalized Auto

Regressive Conditional Heteroskedasticity techniques

respectively on data set between Jan 1998 and Oct 2002. The

empirical results suggest the volatility is reduced in the cash

market after the introduction of the futures.

Gupta (2002) examined the effects of introduction of the

index futures on the stock market volatility by relative

valuation technique. The dataset consists of daily price data

(high, low, open, close) of both BSE Sensex and S&P CNX

Nifty between June 1998 and June 2002. Four measures of

volatility are used based on open to open price, close to close

price, Parkinson‟s Extreme Value estimator and Garman-

Klass measure volatility (GKV). The results show the overall



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volatility of the stock market has decreased after the

introduction of the index futures on both the indices.

Ali, Rahman, and Zhong (2002) established the contrary view

of earlier research and provided evidence that suggest the

volatility in spot market has induced volatility in future

market. The author has also established casual relationship

between the volume of trades in both future markets and spot

markets. The author has used EGARCH framework and

Granger Causality Test.

The majority of the research studies have employed standard

ARCH and GARCH models to draw the relationship between

impact of introduction of derivatives and the volatility in the

spot market. The main limitation of all the research conducted

on the volatility of the Indian market following the

introduction of derivatives is: none of the research included

the effect of Sub-Prime Mortgage crisis of late 2007, the

coupling effects of world economy and transmission of risk

across globe, the effects of banking indices on the Indian

bourse and the comparative degree of influence on the overall

volatility, with respect to impact of introduction of

derivatives.

3. OBJECTIVE OF STUDY

Spot market volatility has been a major area of concern

among researchers, market makers and regulatory boards

across sovereigns, particularly in today‟s era of highly

integrated international markets. Also the role of financial

engineering has reached many folds in defining the market

dynamics in the recent years. Derivatives have been

contemplated as the brain child of financial engineering

and arguably considered as the main influencer of the cash

market volatility. Researchers have always been

enthusiastic to find the connection between the

introduction of derivatives and the volatility in spot

market. However, generalized conclusive results have

never been derived.

3.1 Hypothesis

In the context of the Indian market, the research study

postulates the following hypothesis:

Null Hypothesis (H0): The introduction of derivatives has

not reduced the overall volatility of the S&P CNX Nifty.

Alternate Hypothesis (H1): The introduction of derivatives

has reduced the overall volatility of the S&P CNX Nifty.

Test statistics is formulated to reject the null hypothesis.

3.2Descriptive Statistics

Table 1: Tabular description of descriptive statistics: S&P CNX Nifty returns, S&P 500 returns, Bank Nifty returns

and BIX returns.

Descriptive

Statistics

S&P CNX Nifty

Returns (Jan

1991-Dec 2011)

S&P 500 Returns

(Jan 1991-Dec

2011)

Bank Nifty

Returns (Jan

2000-Dec 2011)

S&P Banking

Index (BIX)

Returns (June

2002-Dec 2011)

Mean (%) 0.0527 0.0171 0.0693 -0.0361

Median (%) 0.0472 0.0190 0.0774 -0.0270

Maximum (%) 16.3343 10.9572 17.2394 22.0379

Minimum (%) -13.0539 -9.4695 -15.1381 -23.6186

Std. Dev. (%) 1.7646 1.1770 2.1174 2.7245

Skewness -0.0477 -0.2136 -0.1743 0.1466

Kurtosis 9.8443 11.6491 8.0088 18.8222

Jacque-Bera 9907.67 15857.22 3146.98 25094.94

Total number of

Observations

5075 5075 2996 2405

4. METHODOLOGY

4.1 Dataset

S&P CNX Nifty return is used in the analysis as the proxy

for the Indian market. Data set comprising of daily closing

price is collected for a period of 20 years from Jan 1991 to

Dec 2011. Daily log return of S&P CNX Nifty index is

calculated from the daily closing price.

S&P 500 (proxy for US market) returns are calculated for

the same period between Jan 1991 and Dec 2011. Bank

Nifty and BIX index returns are also calculated between

2001 and 2011(after their respective introductions in

Indian and US markets). The closing price of all the above

indices are collected from data source, ’Data-stream’.

S&P 500 is used to find the degree of influence of highly

correlated global index on the overall volatility of S&P

CNX Nifty. Banking indices Bank Nifty and S&P Banking

index „BIX‟ are used to find the effect of banking stock



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indices on the conditional volatility of S&P CNX Nifty.

Further study and analysis is conducted keeping in view

Sub-Prime Mortgage crisis.

Table 2: Date of Introduction of Derivative Products

Derivative Products Data of Introduction Underlying Index

Index Futures June 2000 S&P CNX Nifty

Stock Futures Dec 2001 S&P CNX Nifty

Index Options June 2001 S&P CNX Nifty

4.2 GARCH Model

Generalized Autoregressive Conditional Heteoscedasticity

(GARCH) model is employed to estimate the conditional

volatility of the S&P CNX Nifty. GARCH model was

independently developed by Bollerslev (1986) and Taylor

(1986). The main advantage of GARCH model is that it

captures the tendency of the volatility clustering in the

financial time series data. GARCH therefore establishes

connection between information and volatility. In GARCH

model the conditional variance at time„t‟ is dependent on

the past value of the squared error terms and the lagged

conditional variance.

GARCH(1,1) model is represented as follows:

t = α0 + β0 Xt + ut …(1)

ζt2 = α1 + β1 ut-12+ β2 ζt-12 ...(2)

Where, ζt2 = Conditional variance and ut = Error term.

Equation „1‟ represents conditional mean equation and

equation „2‟ represents conditional variance equation. β1,

the coefficient of squared error term represents the „recent

information‟ coefficient, the higher value qualifies: the

recent news in the market has greater impact on the price

change and market volatility. β2, the coefficient of lagged

variance reflects the impact of old news in the spot

market‟s price change. The higher value of β2 suggests

high level of persistence of information effect on

volatility. β1+ β2~1 indicates more integration of

volatility. The greater integration of volatility qualifies

lack of information and higher price inflexibility in the

spot market thereby preventing immediate and continuing

adjustment of price in response to demand and supply

conditions. The unconditional variance is given by α1/(1-

β1- β2). The higher value of unconditional variance

indicates higher volatility.

4.3 Unit Root Test

Augmented Dickey Fuller (ADF) test is conducted to

ascertain that the return series is stationary. Non-stationary

time series exhibit upward or downward trends over a

sustained period of time. Since such trends are often

stochastic and not deterministic, regressing a non-

stationary time series can lead to the phenomenon of

spurious regression. The followings are the consequences

of the spurious regression:

If two variables are tending over time, the

regression of one on the other could have a high R2

even if the two are totally unrelated.

The standard assumption of the asymptotic analysis

will not be valid.

„t-ratios‟ will not follow „t‟ distribution and

hypothesis tests are not valid about the regression

parameters.

Augmented Dickey Fuller test:

Yt-Yt-1=μ + (λ-1)Yt-1+ βT + εt ………(3)

T= Trend term; εt=Error term

Time series exhibit stationarity if λ-1 ≠0 and β=0

4.4 Ljung-Box Q test

Ljung-Box Q test is performed to test whether any group

of autocorrelation in the time-series are different from

zero. This test ascertains the overall randomness based on

the number of lags instead of each single lag.

H0= Returns and Squared Returns Series are white noise.

H1= Returns and Squared Returns Series are not white noise.

The test statistic is:

n is the sample size, Pk is the sample autocorrelation at lag

k, and h is the number of lags being tested.

For significance level α, the critical region for rejection of

the hypothesis of randomness is

where is the α-quantile of the chi-squared distribution with

h degrees of freedom.

Rejection of the null hypothesis suggests that the squared

returns series follow the ARCH type dependencies and

hence GARCH model is appropriate for volatility

estimation



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4.5 Vector Autoregressive Analysis and Granger

Causality Wald test

Granger Causality test is performed to determine whether

BIX (USA) index and BankNifty(India) index series is

useful in forecasting S&P CNX Nifty‟s conditional

volatility. This test is conducted during the Subprime

Mortgage Crisis period (Sep 2007-Feb 2009) and post

Subprime Mortgage Crisis period (Mar 2009-Dec

2011).The objective of this test is to ascertain the degree

of influence of macro-economic factors such as economic

meltdown (because of banking sector) on the volatility in

Indian spot market. Vector Autoregressive models are

developed to capture interdependencies among S&P CNX

Nifty‟s conditional variance (as an effect of banking

indices BIX and BANKNifty), BIX returns and

BANKNifty series during sub-prime credit crisis and post

sub-prime credit crisis. Granger Causality Wald test is

further performed on these models. Variable A is said to

Granger cause variable B, if the lags of A can improve a

forecast for variable B. In a VAR model, under the null

hypothesis that variable A does not Granger cause variable

B, all the coefficients on the lags of variable A will be zero

in the equation for variable B. A Wald test is commonly

used to test for Granger causality.

Table 3: Tabular Description of Models used in the analysis

Model 1 Index Futures as Dummy; Effect of introduction of Index Futures on the conditional volatility of

S&P CNX Nifty.

Model 2 Conditional volatility of S&P CNX Nifty during pre introduction of Index Futures

Model 3 Conditional volatility of S&P CNX Nifty during post introduction of Index Futures

Model 4 Stock Futures as Dummy; Effect of introduction of Stock Futures on the conditional volatility of

S&P CNX Nifty.

Model 5 Conditional volatility of S&P CNX Nifty during pre introduction of Stock Futures

Model 6 Conditional volatility of S&P CNX Nifty during post introduction of Stock Futures

Model 7 Index Option as Dummy; Effect of introduction of Index Options on the conditional volatility of

S&P CNX Nifty.

Model 8 Conditional volatility of S&P CNX Nifty during pre introduction of Index Options

Model 9 Conditional volatility of S&P CNX Nifty during post introduction of Index Options

Model 10 Effect of conditional volatility of S&P 500 on conditional volatility of S&P CNX Nifty.

Model 11 Effect of conditional volatility of BANK NIFTY on conditional volatility of S&P CNX Nifty.

Model 12 Effect of both BANK NIFTY & BIX returns on conditional volatility of S&P CNX Nifty; post

Introduction of Derivatives

Model 13 Effect of both BANK NIFTY & BIX returns on conditional volatility of S&P CNX Nifty during

Pre Subprime Crisis Period


Subprime Crisis Period


Post Subprime Crisis Period.

*In each model, conditional volatility of S&P CNX Nifty is calculated using GARCH(1, 1) analysis.

5. INTERPRETATION OF RESULTS

5.1 Results of Unit Root Test

Augmented Dickey Fuller test is conducted for the S&P

CNX Nifty as shown in Table 4 for the entire time-series

between Jan 1991 and Dec 2011.

Table 4: Augmented Dickey Fuller Test NIFTYRt - NIFTYRt-1 = μ + (λ−1) NIFTYRt-1+ βT + εt

Or Δ NIFTYRt = μ + (λ−1) NIFTYRt-1+ βT + εt

Dickey-Fuller test for unit root Number of observations =

3846

---------- Interpolated Dickey-Fuller ---------

Table 4: Augmented Dickey Fuller test on daily log return of S&P CNX Nifty time-series

Test Statistic 1% Critical

Value

5% Critical Value 10% Critical

Value

Z(t) -65.055 -3.960 -3.410 -3.120

MacKinnon approximate p-value for Z(t) = 0.0000

D.NIFTYR Coefficient Std. Err. T P>|t| 95% Conf. Interval

L1. NIFTYR

(λ−1)

-.9674461 .0148711 -65.06 0.000 -.9966022 -.9382901

_trend (T) -2.00e-07 1.23e-07 -1.62 0.014 -4.42e-07 4.15e-08

_cons ( μ) .0013997 .0005572 2.51 0.012 .0003073 .0024922



38

The coefficient of λ−1=-0. 9674461 at 0% significance (as

p value is 0) and trend term T is almost equal to zero at

1% significance level. This proves that S&P CNX Nifty

series is stationary since λ-1 ≠0 and coefficient of trend

term is quite negligible to equate to zero.

5.2 Results of Ljung-Box Q test

Ljung-Box Q test is performed to test the white noise for

each of the models described in Table 3. The obtained

results are summarized in Table 5. The results indicate that

in the Models: 1 to 13, the return and squared return series

of S&P CNX Nifty follows the ARCH type dependencies

and hence GARCH model is appropriate for volatility

estimation.

All the results are estimated at 5% significance level.

However Model 14, which estimates the volatility in S&P

CNX Nifty resulting by the effect of BankNifty and BIX

indices during the Sub-Prime Crisis period, fails to

confirm that the return and squared return series of S&P

CNX Nifty follows ARCH type dependencies at 5%

significance level. But the same result at 10% level of

significance is significant (p-value is 0.0732).

Ljung Box Q test conducted on Model 15, which estimates

the volatility of S&P CNX Nifty as an effect of both

BankNifty & BIX during Post Subprime Crisis Period, is

not significant at even 10% level of significance. In

models 14 and 15, Vector Autoregressive model is further

employed and Granger Causality test is conducted to

obtain the independent dependencies between each of the

three time series: BankNifty, BIX returns and conditional

volatility of S&P CNX Nifty resulting by the effect of

both BankNifty and BIX indices.

The summary of results of Ljung Box Q test for all fifteen

models is tabulated as below:

Table 5: Ljung Box Q test Q= n (n+2 ) Σ (1/n-j) ρj2

Models Portmanteau test for white noise

Portmanteau (Q)

statistic

Prob >

chi2(40)

Model 1: Index Futures as Dummy 392.2098* 0.0000

Model 2: Pre Introduction of Index Futures 326.2388* 0.0000

Model 3: Post Introduction of Index Futures 165.6561* 0.0000

Model 4: Stock Futures as Dummy 392.5361* 0.0000

Model 5: Pre Introduction of Stock Futures 350.0722* 0.0000

Model 6: Post Introduction of Stock Futures 152.3920* 0.0000

Model 7: Index Option as Dummy 392.5673* 0.0000

Model 8: Pre Introduction of Index Options 343.8029* 0.0000

Model 9: Post Introduction of Index Options 156.2231* 0.0000

Model 10: Effect of conditional Volatility of S&P 500 on the conditional

volatility of S&P CNX Nifty.

394.3084* 0.0000

Model 11: Effect of conditional Volatility of BANK NIFTY on the

conditional volatility of S&P CNX Nifty.

84.4466* 0.0001

Model 12:Effect of both BANK NIFTY & BIX returns on the conditional

volatility of S&P CNX Nifty; Post Introduction of Derivatives

58.6634* 0.0286

Model 13: Effect of both BANK NIFTY & BIX returns on the conditional

volatility of S&P CNX Nifty during Pre Subprime Crisis Period

109.4130* 0.0000

Model 14: Effect of both BANK NIFTY & BIX returns on the conditional

volatility of S&P CNX Nifty during Subprime Crisis Period

53.6398 0.0732

Model15:Effect of both BANK NIFTY & BIX returns on the conditional

volatility of S&P CNX Nifty during Post Subprime Crisis Period

61.6795* 0.0233

*Significant at 5% level.

5.3 Results of GARCH(1,1) estimate for S&P CNX

Nifty

GARCH (1, 1)estimates for all the fifteen models,

tabulated in Table 2 are computed using STATA and the

following results are obtained.

Model 1: Index Futures as Dummy; Effect of introduction

of index futures on the conditional volatility of S&P CNX

Nifty

5.4 Results of Vector Autoregressive Analysis and

Granger Causality Wald Test

Vector Autoregressive models are developed to capture

interdependencies among S&P CNX Nifty‟s conditional

variance ζ2 (caused by the combined effect of banking

indices BIX and BANKNifty), BIX returns and

BANKNifty returns during the sub-prime credit crisis and



39

post sub-prime credit crisis. Granger Causality Wald test is further performed on these models.

Table 6: Vector Autoregressive Model; Effect of both BANK NIFTY and BIX returns on the conditional volatility of

S&P CNX Nifty during Subprime Crisis Period; (04/09/2007 to 27/02/2009)

Dependent

Variable

Independent

Variable

Independent

Variable

Lag Lagged Lagged Lagged

Coefficient

of ζ2

t-

statistics

Coefficien

t of BIXR

t-

statistics

Coefficient of

NIFTYRBank

t-statistics

ζ2 BIXR NIFTYR

Bank

1 -0.216 -2.59 .0000662 0.82 0.0000198 0.14

2 -0.133 -1.16 .0000929 1.20 -0.000113 -0.86

3 0.0487 0.54 0.000139* 1.96 -0.0000407 -0.38

4 0 0 0.000110 1.43 0.000211 1.91

BIXR ζ2 NIFTYR

Bank

1 169.4 1.49 0.117 1.07 0.302 1.58

2 200.3 1.28 -0.0862 -0.81 0.110 0.62

3 180.1 1.46 -0.219* -2.26 0.219 1.51

4 0 0 -0.215* -2.05 -0.343* -2.28

NIFTYR

Bank

ζ2 BIXR 1 -69.35 -0.62 0.194 1.78 0.228 1.29

2 -37.68 -0.24 0.102 0.98 0.108 0.76

3 140.4 1.15 -0.0236 -0.25 -0.0636 -0.43

4 0 0 0.0582 0.56 -0.000664 -0.02

*Significance at 5%

Table 7: Granger Causality Wald tests; Effect of both BANK NIFTY and BIX returns on the conditional volatility of

S&P CNX Nifty during Subprime Crisis Period; (04/09/2007 to 27/02/2009)

Equation Excluded chi2 df Prob>chi2 Null

Hypothesis

ζ2 BIXR 7.0364 4 0.134 Accept

ζ2 NIFTYRBank 4.5713 4 0.334 Accept

ζ2 All 14.907 8 0.061 Accept

BIXR ζ2 4.1223 3 0.249 Accept

BIXR NIFTYRBank 12.312* 4 0.015 Reject

BIXR All 15.559 7 0.029 Reject

NIFTYRBank ζ2 2.3039 3 0.512 Accept

NIFTYRBank BIXR 4.1642 4 0.384 Accept

NIFTYRBank All 8.159 7 0.319 Accept

*Significance at 5%; H0 = Variable under „Excluded‟ column doesn‟t Granger Cause the variable under „Equation‟

Column. HA = Variable under „Excluded‟ column Granger Cause the variable under „Equation‟ Column.

The results set in Table 6 and Table 7 provides the

summary of the vector autoregressive analysis and granger

causality wald test during the sub-prime crisis. The BIX

return at t =-3 has significant impact on the conditional

volatility (ζ2). The positive sign of BIXR at t =-3 suggest

conditional volatility (ζ2) rises (falls) following the rise

(fall) in BIX returns. Also at t = -4, BANKNifty

significantly impact current BIX returns but in inverse

relation, whereas at t =-3 and t = -4, the lagged BIX

returns impact the current BIX returns. Granger Causality

Wald test conducted on the same data set further supports

the above finding. Null hypothesis: NIFTYRBank

(BANKNifty) does not Granger Cause BIXR (BIX

returns) is rejected within 5% level of confidence, whereas

both BIXR (BIX returns) and NIFTYRBank (BANKNifty)

do not Granger Cause conditional volatility (ζ2) of S&P

CNX Nifty is rejected at 10% level of confidence (since p-

value is 0.061). These findings suggest BIX Returns and

BANKNifty impact conditional volatility of S&P CNX

Nifty, BANKNifty affects BIX returns but conditional

volatility of S&P CNX Nifty doesn‟t affect either

BANKNifty or BIX Returns at any lag during the sub-

prime crisis period.



40

Table 8: Vector Autoregressive Model; Effect of both BANK NIFTY and BIX returns on the conditional volatility of

S&P CNX Nifty; Post Subprime Crisis Period; (02/03/2009 to 31/12/2011)

Dependent

Variable

Independent

Variable

Independent

Variable

Lag Lagged Lagged Lagged

Coefficient

of ζ2

t-

statistics

Coefficient

of BIXR

t-

statistics

Coefficient of

NIFTYRBank

t-statistics

ζ2 BIXR NIFTYR

Bank

1 0.394 0.58 -0.000116 -0.09 0.00103 0.44

2 -0.143 -0.39 0.000925 0.63 -0.00176 -0.81

3 -0.069 -0.22 0.000216 0.16 0.00214 0.98

4 0 . -0.00163 -1.43 0.00168 0.95

BIXR ζ2 NIFTYR

Bank

1 -9.338 -0.24 -0.0770 -1.06 0.123 0.93

2 -10.84 -0.53 0.0762 0.92 -0.0704 -0.58

3 9.882 0.55 0.00459 0.06 0.191 1.54

4 0 . -0.0366 -0.57 -0.0849 -0.85

NIFTYR

Bank

ζ2 BIXR 1 83.56** 2.90 0.239*** 4.37 0.885** 0.89

2 12.79 0.83 0.0486 0.78 0.0781 0.85

3 9.986 0.75 -0.0148 -0.26 -0.105 -1.13

4 0 . 0.0131 0.27 0.00185 0.02

*Significance at 5%

Table 9: Granger Causality Wald tests; Effect of both BANK NIFTY and BIX returns on the conditional volatility of

S&P CNX Nifty; Post Subprime Crisis Period; (02/03/2009 to 31/12/2011)

Equation Excluded chi2 df Prob>chi2 Null

Hypothesis

ζ2 BIXR 2-2267 4 0.694 Accept

ζ2 NIFTYRBank 2.6344 4 0.621 Accept

ζ2 All 2.5902 8 0.892 Accept

BIXR ζ2 0.4485 3 0.930 Accept

BIXR NIFTYRBank 4.9331 4 0.294 Accept

BIXR All 5.5451 7 0.594 Accept

NIFTYRBank ζ2 12.367* 3 0.006 Reject

NIFTYRBank BIXR 19.457* 4 0.001 Reject

NIFTYRBank All 32.294* 7 0.000 Reject

*Significance at 5%; H0 = Variable under „Excluded‟ column doesn‟t Granger Cause the variable under „Equation‟

Column. HA = Variable under „Excluded‟ column Granger Cause the variable under „Equation‟ Column .

The results set in Table 8 and Table 9 provide the

summary of the vector autoregressive analysis and granger

causality Wald test post sub-prime crisis. The conditional

volatility (ζ2) of S&P CNX Nifty at t =-1 has significant

impact on the BANKNifty. Also BIX returns at t =-1

significantly affects BANKNifty. The positive sign of

conditional volatility (ζ2) of S&P CNX Nifty and BIX

returns at t =-1 suggest BANKNifty rises (falls) following

the rise (fall) of both BIX returns and conditional volatility

(ζ2) of S&P CNX Nifty. Granger Causality Wald test

conducted on the same data set further supports the above

finding. Null hypothesis: conditional volatility (ζ2) of

S&P CNX Nifty does not Granger Cause NIFTYRBank

(BANKNifty) is rejected within 5% level of confidence.

Again the null hypothesis: BIX Returns (BIXR) does not

Granger Cause NIFTYRBank (BANKNifty) is rejected

within 5% level of confidence. These findings suggest that

both conditional volatility (ζ2) of S&P CNX Nifty and

BIX Returns affect BANKNifty at lag 1 during the post

sub-prime crisis period.

6. CONCLUSION

Previous studies have used the daily returns data of

underlying and non underlying stocks in finding the

impact of derivatives on Indian spot market. However, this

research study has concentrated on examining the impact

of introduction of derivatives trading on spot market

volatility using index level data.

The research study has established the informational

effects of derivative trading on the volatility of the Indian

spot market using GARCH (1, 1) model. The study also

examined the impact of other factors such as conditional



41

volatility of S&P 500, conditional volatility of

BANKNifty, S&P Banking Index „BIX‟ returns,

BANKNifty, and sub-prime crisis on the volatility of the

Indian spot market. The major findings obtained are

summarized below:

ARCH effect (recent information) is more prominent in

index futures than in stock futures and index options, in

defining the overall conditional volatility of S&P CNX

Nifty. The impact of existing persistent information (old

news) carries more weight with respect to all three market

traded derivatives (index futures, stock futures and index

options). Although statistically insignificant at 5% level of

significance, the positive δ0 (coefficient of Dummy

variable in each derivative type) confirms the overall

conditional volatility has increased after the introduction

of derivatives. Phenomenon of volatility clustering has

increased over time after the introduction of market traded

derivatives, which shows even after the introduction of

derivatives, the Indian market remains in either bull state

or bear state for long periods of time compared to other

global markets.

In case of other factors, the conditional volatility of S&P

CNX Nifty has mildly increased by the S&P 500 after the

introduction of derivatives in Indian market and GARCH

effect (existing information) dominates over ARCH

(recent information). In case of BANKNifty and BIX

indices returns the conditional volatility of the S&P CNX

Nifty follows the same pattern as followed by the effect of

S&P 500(GARCH coefficient is high and ARCH

coefficient is low), particularly during the period of Pre-

Sub-Prime crisis. However during the period of Sub-Prime

crisis, the persistent information effect on market volatility

completely disappears and spot market volatility relies

more on the recent information. Whilst, during the post

Sub-Prime crisis; the ARCH effect continues to dominate

over GARCH and the conditional volatility of S&P CNX

Nifty affects BANKNifty rather than BANKNifty

affecting conditional volatility of S&P CNX Nifty.

The main implication of the research analysis is that the

derivatives in Indian market have failed to alleviate the

risk that was much required during the time of crisis.

Integration of global markets in 21st century has led the

Indian market more susceptible to Sub-Prime crisis of

United States and world-wide liquidity crunch following

its effect. Above findings will definitely influence the

market regulators such as SEBI to bring better reforms in

controlling the volatility in Indian market. The main aim

of introduction of derivatives in Indian market was to

reduce the market volatility and improve market efficiency

by means of greater rate of information flow. It is very

much difficult to decouple the Indian market from the rest

of world and insulate it from crisis arising because of

global integration; however there are definite regulatory

measures on derivative trading that can control the spot

market volatility such as by strengthening the surveillance

and monitoring mechanism, implementing daily circuit

filters, daily price bands and weekly price caps to curb

abnormal price behaviour and volatility of derivatives.

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