45

CHAPTER -2 LITERATURE REVIEW

2.1 Introduction

The trading of financial derivatives has received extensive attention, while at

the same time it has led to a debate over its impact on the underlying stock market

from various facets by the academicians. The researchers all over the world have done

research on derivative trading and were able to find out various facts about derivative

and its trading. In this literature review efforts have been made to bring into the

picture the research done about various issues throughout the world by the

researchers.

The literature survey and review is presented in four sections: first, the review

of studies fundamental to capital market of India; second, the review of studies

relating to the testing of capital market efficiency; third, the review of studies

concerning the volatility study; last, the review of studies analyzing the causal relation

between spot and index futures market.

2.2 Capital Market of India

There has been a wide range of studies concerning financial sector reforms in

general, and capital market reforms in particular, since mid 1980s in India. This

section highlights certain important studies that are context relevant. Several studies

such as Sahni (1985), Kothari (1986), Mookerjee (1988), Lal (1990), Chandra (1990),

Franscis (1991), Ramesh Gupta (1991,1992), Raghunathan (1991), Varma (1991),

Gupta (1992), and Sinha (1993) comment upon the Indian capital market in general

and trading systems in the stock exchanges in particular and suggest that the systems

therein are rather antiquated and inefficient, and suffer from major weakness and

malpractices. According to most of these studies, significant reforms are required if

the stock exchanges are to be geared up to the envisaged growth in the Indian capital

market.

Barua et al (1994) undertakes a comprehensive assessment of the private

corporate debt market, the public sector bond market, the govt. securities market, the

housing finance and other debt markets in India. This provides a diagnostic study of

46

the state of the Indian debt market, recommending necessary measures for the

development of the secondary market for debt. It highlights the need to integrate the

regulated debt market with the free debt market, the necessity for market making for

financing and hedging options and interest rate derivatives, and tax reforms.

Cho (1998) points out the reasons for which reforms were made in Indian

capital market stating the after reform developments. Shah (1999) describes the

financial sector reforms in India as an attempt at developing financial markets as an

alternative vehicle determining the allocation of capital in the economy.

Shah and Thomas (2003) review the changes which took place on Indias

equity and debt markets in the decade of the 1990s. This has focused on the

importance of crises as a mechanism for obtaining reforms.

Mohan (2004) provides the rationale of financial sector reforms in India, policy

reforms in the financial sector, and the outcomes of the financial sector reform process

in some detail.

Shirai (2004) examines the impact of financial and capital market reforms on

corporate finance in India. Indias financial and capital market reforms since the early

1990s have had a positive impact on both the banking sector and capital markets.

Nevertheless, the capital markets remain shallow, particularly when it comes to

differentiating high-quality firms from low-quality ones (and thus lowering capital

costs for the former compared with the latter). While some high-quality firms (e.g.,

large firms) have substituted bond finance for bank loans, this has not occurred to any

significant degree for many other types of firms (e.g., old, export-oriented and

commercial paper-issuing ones). This reflects the fact that most bonds are privately

placed, exempting issuers from the stringent accounting and disclosure requirements

necessary for public issues. As a result, banks remain major financiers for both high-

and low-quality firms. The paper argues that India should build an infrastructure that

will foster sound capital markets and strengthen banks incentives for better risk

management.

47

Chakrabarti and Mohanty (2005) discuss how capital market in India is evolved

in the reform period. Thomas (2005) explains the financial sector reforms in India

with stories of success as well as failure.

Bajpai (2006) concludes that the capital market in India has gone through

various stages of liberalization, bringing about fundamental and structural changes in

the market design and operation, resulting in broader investment choices, drastic

reduction in transaction costs, and efficiency, transparency and safety as also

increased integration with the global markets. The opening up of the economy for

investment and trade, the dismantling of administered interest and exchange rates

regimes and setting up of sound regulatory institutions have enabled time.

Gurumurthy (2006) arrives at the conclusion that the achievements in the

financial sector indicate that the financial sector could become competitive without

involving unhealthy competition, within the constraints imposed by the macro-

economic policy stance.

Mohan (2007) reviews Indias approach to financial sector reforms that set in

process since early 1990s. Allen, Chakrabarti, and De (2007) concludes that with

recent growth rates among large countries second only to Chinas, India has

experienced nothing short of an economic transformation since the liberalisation

process began in the early 1990s.

Chhaochharia (2008) arrives at the conclusion that India has a more modern

financial and banking system than China that allocates capital in a more efficient

manner. However, the study is skeptical about who would emerge with the stronger

capital market, as both the country is facing challenges regarding their capital markets.

Prasad and Rajan (2008) argues that the time has come to make a more

concerted push toward the next generation of financial reforms. The study advocates

that a growing and increasingly complex market-oriented economy and its greater

integration with global trade and finance will require deeper, more efficient, and well-

regulated financial markets.

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The survey and review of literature about the financial sector reforms in India

reveals that the reforms have been pursued vigorously and the results of the reforms

have brought about improved efficiency and transparency in the financial sector. The

reforms also brought into inter-linkage of financial markets across the globe leading to

new product development and sophisticated risk management tools. Derivatives in

general perform as an instrument to hedge the risk arising from movement in prices

not only in commodity markets but also in securities market.

Bose, Suchismita conducted research on (2006) found that Derivatives products

provide certain important economic benefits such as risk management or redistribution

of risk away from risk-averse investors towards those more willing and able to bear

risk. Derivatives also help price discovery, i.e. the process of determining the price

level for any asset based on supply and demand. These functions of derivatives help in

efficient capital allocation in the economy. At the same time their misuse also poses

threat to the stability of the financial sector and the overall economy.

Routledge, Bryan and Zin, Stanley E of Carnegie Mellon University conducted

research on Model Uncertainty and Liquidity in year 2001. Extreme market

outcomes are often followed by a lack of liquidity and a lack of trade. This market

collapse seems particularly acute for markets where traders rely heavily on a specific

empirical model such as in derivative markets.

Sen Shankar Som and Ghosh Santanu Kumar (2006) studied the relationship

between stock market liquidity and volatility and risk. The paper also deals with time

series data by applying Cochrane Orchutt two step procedures. An effort has been

made to establish a relation between liquidity and volatility in their paper. It has been

found that there is a statistically significant negative relationship between risk and

stock market liquidity. Finally it is concluded that there is no significant relationship

between liquidity and trading activity in terms of turnover.

Shenbagraman (2004) reviewed the role of some non-price variables such as

open interests, trading volume and other factors, in the stock option market for

determining the price of underlying shares in cash market. The study covered stock

option contracts for four months from Nov. 2002 to Feb. 2003 consisting 77 trading

49

days. The study concluded that net open interest of stock option is one of the

significant variables in determining future spot price of underlying share. The results

clearly indicated that open interest based predictors are statistically more significant

than volume based predictors in Indian context.

All the existing studies found that the Equity return has a significant and

positive impact on the FII (Agarwal, 1997; Chakrabarti, 2001; and Trivedi & Nair,

2003). But given the huge volume of investments, foreign investors could play a role

of market makers and book their profits i.e., they can buy financial assets when the

prices are declining thereby jacking-up the asset prices and sell when the asset prices

are increasing (Gordon & Gupta, 2003). Hence, there is a possibility of bi-directional

relationship between FII and the equity returns.

Masih AM, Masih R, (2007), had studied Global Stock Futures: A Diagstinoc

Analysis of a Selected Emerging and Developed Markets with Special Reference to

India, by using tools correlation coefficients , grangers causality test, augmented

Dicky Fuller test (ADF), Elliott, Rothenberg and Stock point optimal test. The

Authors, through this paper, have tried to find out what kind of relationship exists

between emerging and developed futures markets of selected countries.

Kumar, R. and Chandra, A. (2000), had studied that Individuals often invest in

securities based on approximate rule of thumb, not strictly in tune with market

conditions. Their emotions drive their trading behavior, which in turn drives asset

(stock) prices. Investors fall prey to their own mistakes and sometimes others

mistakes, referred to as herd behavior. Markets are efficient, increasingly proving a

theoretical concept as in practice they hardly move efficiently. The purely rational

approach is being subsumed by a broader approach based upon the trading sentiments

of investors. The present paper documents the role of emotional biases towards

investment (or disinvestment) decisions of individuals, which in turn force stock

prices to move.

Srivastava, S., Yadav, S. S., Jain, P. K. (2008), had conducted a survey of

brokers in the recently introduced derivatives markets in India to examine the brokers

assessment of market activity and their perception of benefits and costs of derivative

50

trading. The need for such a study was felt as previous studies relating to the impact of

derivatives securities on Indian Stock market do not cover the perception of market

participants who form an integral part of the functioning of derivatives markets. The

issues covered in the survey included: perception of brokers about the attractiveness of

different derivative securities for clients; profile of clients dealing in derivative

securities; popularity of a particular derivative security out of the total set; different

purposes for which the clients are using these securities in order of preference; issues

concerning derivatives trading; reasons for non usage of derivatives by some

investors.

The investors are using derivative securities for different purposes after its

penetration into the Indian Capital market. They use these securities not only for risk

management and profit enhancement but also for speculation and arbitrage. High net

worth individuals and proprietary traders account for a large proportion of broker

turnover. Interestingly, some retail participation was also witnessed despite the fact

that these securities are beyond the reach of retail investors (because of complexity

and high initial cost).

Naresh, G., (2006), studied the dynamic growth of the Derivatives market,

particularly Futures & Options and the perceived risks to the financial sector continue

to stimulate debate on the proper regulation of these instruments. Even though this

market was initially fuelled by various expert teams survey, regulatory framework,

recommendations byelaws and rules there is still a debate on the existing regulations

such as why is regulation needed? When and where regulation needed? What are

reasonable and attainable goals of these regulations? Therefore this article critically

examines the views of market participants on the existing regulatory issues in trading

Derivative securities in Indian capital market conditions.

The emergence and growth of the market for derivative instruments is because

of the willingness of risk-averse economic agents to guard themselves against

uncertainties arising out of fluctuations in asset prices. By providing investors and

issuers with a wider array of tools for managing risks and raising capital, derivatives

improve the allocation of credit and the sharing of risk in the global economy,

51

lowering the cost of capital formation and stimulating economic growth. Now that

world markets for trade and finance have become more integrated, derivatives have

strengthened these important linkages between global markets, increasing market

liquidity and efficiency, and have facilitated the flow of trade and finance.

Following the growing instability in the financial markets, the financial

derivatives gained prominence after 1970. In recent years, the market for financial

derivatives has grown in terms of the variety of instruments available, as well as their

complexity and turnover. Financial derivatives have changed the world of finance

through the creation of innovative ways to comprehend, measure, and manage risks.

India started as a controlled economy and gradually moved towards a world where

prices fluctuate every day. Derivatives in this context will provide wide range of

benefits to the investors, as a risk management instruments. Thus allowing derivative

trading is required to attract more investments not only from domestic sources but also

from off shores. In this respect the liberalization process has resulted into

development of derivatives market in India.

2.3 Efficiency of Index Futures Market

The process of financial liberalization in the developing countries has brought

into the increased flow of funds from the developed countries. The rapid changes in

the field of Information Technology, has lead to a progressive integration of the

emerging markets with the developed markets. The Indian markets are no exception to

this phenomenon. Due to liberalization India is attracting not only foreign direct

investments but also foreign portfolio investments. Financial futures contracts are key

instruments in portfolio management, as they allow for risk transference. Moreover,

derivative markets play an important role within the price discovery process of

underlying assets. Stock index futures have relatively lower transaction costs and

capital requirements, so the arrival of external information is quickly incorporated into

prices as investors expectations are updated. Thus stock index futures markets have

experienced a substantial increase in trading activity, since the introduction of futures

on indices.

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In the context of above information plays a vital role not only in efficient price

discovery but also in the creation of bubbles (lack of information flow). The role of

bubbles in financial markets is intricately connected to the question of informational

efficiency. The reason is both that bubbles above and below fundamental values are a

violation of market efficiency, and that the fundamental value itself and deviations

from it can only be defined with reference to a framework of informational efficiency

in a market (cp. Rolls critique in Roll, 1977). Because of this observation, this section

starts with a short introduction to the topic of market efficiency and briefly reviews

efficiency of Stock market after the introduction of index futures.

The subject of market efficiency has been intensely studied over last 40 years.

The main principle of market efficiency was consolidated in 1970 by Eugene Fama in

his Efficient Capital markets: A review of theory and empirical work. Fama defined

an efficient market as A market in which prices always fully reflect available

information, and proposed the classifications of weak-form, semi-strong form, and

strong-form market efficiency to concretize the available information. These three

categories have by now become the standard in descriptions of market efficiency.

Nonetheless, the history of the efficient market hypothesis had begun earlier.

Bachelier (1900) laid the theoretical groundwork for the efficient market hypothesis,

which was postulated half a century later by Maurice Kendall. Kendall (1953) found

that stock prices evolved randomly and that his data offered no way to predict future

price movements. The explanation for this phenomenon, the efficient market

hypothesis, initially seemed counterintuitive to the academic community. However,

after the first shock had passed, scholars quickly embraced the theory and began to

document its validity in real-world markets by studying empirical data.

To do so, they developed different frameworks to model the characteristics of

market prices. The first type of framework based on expected return efficient

markets includes such well-known models as the fair game model, the random walk

and the sub-martingale models, as well as the market model and the famous capital

asset pricing model (CAPM) of Sharpe (1964); Lintner (1965);Mossin (1996).

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In the years from the 1950s to the 1970s, most studies based on the CAPM and

fair game models found evidence consistent with the efficient market hypothesis.

Despite some evidence to the contrary from the variance-based literature, by the early

1970s markets had therefore largely come to be considered to be efficient in the

semistrong form, as defined by Fama (1970).

A second class of models used to test market efficiency focuses on variance as

the key characteristic. Among them are the model of Shiller (1981), who reported that

stock prices were too volatile to be efficient when compared to subsequent dividend

payouts, and the model of Marsh and Merton (1986), which showed that Shillers

results could be reversed by a change in assumptions regarding the dividend model.

The reply of Schwartz (1970) to the seminal paper of Fama (1970) could also be

considered to fall into the category of variance efficient market models, as it

propagated the use of models that tested for variance-based strategies to generate

excess returns in capital markets.

In the finance literature, there exist a number of studies investigating the

efficiency of Futures Market. Stensis (1983), Garbade and Sibler (1983),

Protopapadakis and Stoll (1983), French (1986), Kawaller (1987), Fatimah (1994),

Cheung and Fung (1997), Hall (2001), Yang (2001), Singh (2001), Thomas and

Karande (2002), Sahadevan (2002), Campbell and Diebold (2002), Zhong (2004), and

Isabel and Gilbert (2004) have investigated the price discovery efficiency of

commodity futures market in different countries viz., USA, United Kingdom,

Malaysia, India, Mexico etc. respectively and found that futures market is efficient.

Granger et al., (1998), Covrig and Melvin (2001), Anderson et al., (2002) and

Yan and Zivot (2004) examined the price discovery efficiency of currency futures

market in various economies like Hong Kong, Indonesia, Japan, South Korea,

Malaysia, Philippines, Singapore, Thailand, Taiwan, USA respectively and found that

futures market is efficient for underlying currencies.

Chan (1992), Hasbrouck (1995), Jong and Donders (1998), Booth (1999),

Turkington and Walsh (1999), Menkveld (2003), Chuang (2003), Raju and Karande

(2003), Barclay and Hendershott (2004), Sharma and Gupta (2005), So and Tse

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(2005) and Gupta and Singh (2006) evaluated the prices discovery efficiency of equity

futures in different countries namely USA, Netherlands, Germany, Australia, Taiwan,

India, Hong Kong respectively and observed significant evidence of efficient price

discovery through equity futures market.

Yang (2001) applied different econometric methods in order to find the optimal

variance ratio in the Australian Futures Market during the period 1 January 1988 to 12

December 2000. Specifically, he used the OLS Regression, the Bi-variate Vector

Autoregressive model (BVAR), the Error Correction model (ECM) and the

multivariate diagonal VEC GARCH model. It was generally found that GARCH time

varying hedge ratios provide the greater portfolio risk reduction but they do not

produce the greater profit return. So, it is obvious that is a matter of investor to decide

in which product to invest, the less risky or the more profitable.

Chuang (2003) examined the price discovery efficiency of TAISEX (Taiwan

Stock Exchange Capitalisation Weighted Index Futures) and MSCI (Morgan Stanley

Capital International Taiwan Index Futures) during 1998-99 and found strong

statistical evidence of market efficiency in its weak form.

Hoque, Kim and Pyun, (2006) tested the market efficiency of eight different

Asian emerging markets (Hong Kong, Indonesia, Malaysia, Korea, Singapore,

Philippines, Taiwan and Thailand). They took weekly closing prices from April 1990

to February 2004. They used variance ratio test to find out whether these eight

markets prove to be mean reverting or not. The basic findings were that five markets

(Indonesia, Malaysia, Philippines, Singapore and Thailand), show specific mean-

reverting and predictive behavior of stock prices while two markets (Taiwan and

Korea) show some mean- reverting and unpredictable patterns in the time series.

Gupta and Singh (2006) also made an attempt to investigate the price discovery

efficiency of the Nifty futures by considering lengthy time frame and their results

showed the evidences that futures market has been an efficient price discovery

vehicle.

55

Floros and Vougas (2008) examine efficiency of the Greek stock index futures

market from 1999 to 2001. The results show that the Greek Futures markets are

informationally more efficient than underlying stock markets.

Zhang et al (2010) tests the random walk hypothesis and weak form market

efficiency in the VIX futures market using a variety of tests. A unit root in the

aggregated market price series suggests that the VIX futures market is efficient. For

the individual VIX futures price series, 51 of 54 futures contracts meet the sufficient

condition for an efficient market: the prices are found to follow a random walk either

because there is a unit root or because the increments are not correlated. Overall, the

market for VIX futures has been efficient since the first day of trading.

Thus, it is observed that the study of efficiency of the futures market is very

important from the point of view of an emerging market like India. But the literature is

relatively thin in this direction.

2.4 Volatility of Index Futures Market

The volatility of stock futures market has been studied by a number of

researchers from different angles. Despite a disagreement of the researchers regarding

the kind of influence that the market of derivatives has on the underlying market, most

of them agree that it is beneficial even if the volatility is increased or decreased

because the derivatives market acts as a catalyst for the dissemination of information.

Particularly, Danthine (1978) concluded that the derivatives market increases the

depth of a market and consequently reduces its volatility.

The destabilization theory argues that the introduction of futures trading

increases spot volatility. For example, Harris (1989) documents marginal increases in

the variances of S&P 500 stocks after trading in S&P 500 index futures began.

Lockwood and Linn (1990) report similar variance increases when index futures

began trading in 1982. Brorsen (1991) finds that futures trading tend to reduce

autocorrelations and increase the volatility of index stock returns. Lee and Ohk (1992)

document that the volatility of stock returns in Australia, Hong Kong, Japan, the U.K.,

and the U.S. rose significantly, following the introduction of index futures. On the

other hand, Antoniou and Holmes (1995), and Antoniou, Holmes, and Priestly (1997)

56

also document increases in spot volatilities after the introduction of index futures,

however this increase is attributed to an increase in the rate of flow of information to

spot markets.

On the other side, Edwards (1988a, b), Grossman (1988), and Bechetti and

Roberts (1990) find that S&P 500 index futures have an insignificant impact on cash

market volatility. Schwert (1990) maintains that the growth in stock index futures and

options trading has not caused increases in volatility. Similar conclusions are reached

by Becketti and Roberts (1990), Kamara, Miller and Siegel (1992), Pericli and

Koutmos (1997), Galloway and Miller (1997), and Darat, Rahman and Zhong (2002),

who document that introduction of stock index futures has either decreased or not

significantly increased the volatility in spot markets, confirming the stabilization

theory.

Hodgson et al (1991) study the impact of All Ordinaries Share Index (AOI)

futures on the Associated Australian Stock Exchanges over the All Ordinaries Share

Index. The study spans for a period of six years from 1981 to 1987. Standard

deviation of daily and weekly returns is estimated to measure the change in volatilities

of the underlying index. The results indicate that the introduction of futures and

options trading has not affected the long-term volatility, which reinforces the findings

of the previous U.S. studies. However, there was a problem of confounding variables

such as floating of Australian dollar in late 1983, deregulation of stock exchanges,

foreign bank ownership and mutual fund investment rules during 1984.

Chan et al (1991) estimate the intraday relationship between returns and returns

volatility in the stock index and stock index futures. The study covers both S&P500

and Major Market Index futures. Results indicate a strong inter market dependence in

the volatility of the cash and futures returns. It is also shown that the intraday

volatility patterns that originate either in stock or futures market demonstrate

predictability in the other market.

Bessembinder and Seguin (1992) examine whether greater futures trading

activity (volume and open interest) is associated with greater equity volatility. Their

findings are consistent with the theories predicting that active futures markets enhance

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the liquidity and depth of the equity markets. They provide additional evidence

suggesting that active futures markets are associated with decreased rather than

increased volatility.

Herbst et al (1992) examine the informational role of the end-of-day returns in

the stock index futures for the period 1982 to 1988. Volatility is estimated from the

standard deviation of the returns. It is shown that the end of day return volatility is

positively correlated to the next day's spot returns.

Kamara et al (1992) observe the stability of S&P 500 index returns with the

introduction of S&P 500 index futures. They also assess the change in the volatility of

S&P 500 index due to the introduction of futures trading for the period 1976 to 1987.

The changes in the volatilities are examined using parametric and non-parametric

tests. The variance ratio F-tests used by Edwards (1988a, b) are sensitive to the

underlying assumption of normally distributed stock returns. Apart from F-tests,

Kolmogorov-Smirnov two-sample test and Wilcoxon Rank sum test are used to find

out if the dispersion is significantly high in the post-futures period. The results show

that the daily returns volatility is higher in the post futures period while the monthly

returns remain unchanged. He concludes that increase in volatility of daily return in

the post-futures period is necessarily not related to the inception of futures trading.

James (1993) studied the impact of price discovery by futures market on the

cash market volatility. The study is conducted using Garbade and Silber model to

estimate the price discovery function of the futures market. The results affirm that

futures market is beneficial with respect to cash market as it offers better efficiency,

liquidity and also lowers the long-term volatility of the spot market.

Jegadeesh and Subrahmanyam (1993) compare the spread in NYSE before and

after the introduction of futures on S&P 500 index as volatility can also be measured

in terms of individual stock bid-ask spread. They find that average spread has

increased subsequent to the introduction of futures trading. When they repeat their test

by controlling for factors like price, return variance, and volume of trade, they still

find higher spreads during the post-futures period. Overall results of Jegadeesh and

Subrahmanyam (1993) suggest that introduction of index futures did not reduce

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spreads in the spot market, and there is weak evidence that spreads might have

increased in the post futures period.

Darrat et al (1995) examines if futures trading activity has caused stock price

volatility. The study is conducted on S & P 500 index futures for a period of 1982 -

1991. The study also examines the influence of macro-economic variables such as

inflation, term structure rates on the volatility of the S&P 500 stock returns. Granger

causality tests are applied to assess the impact on stock price volatility due to futures

trading and other relevant macro-economic variables. The results indicate that the

futures trading have not caused any jump volatility (occasional and sudden extreme

changes in stock prices). Term structure rates and OTC index have caused the stock

price volatility while, inflation and risk premium have not influenced the volatility of

stock prices.

Gregory et al (1996) examined how volatility of S&P 500 index futures affects

the S&P 500 index volatility. The study also examines the effect of good and bad

news on the spot market volatility. The change in the correlation between the index

and futures before and after October 1987 crash is also examined. Volatility is

estimated by E-GARCH model. It is shown that the bad news increases the volatility

than the good news and the degree of asymmetry is much higher for the futures

market. The correlation between the S&P 500 index future and S&P500 index

declines during the October 1987 crash.

Min and Najand (1999) investigated lead and lag relationship in returns and

volatilities between cash market and KOSPI 200 futures interactions. This study

depended on some ten-minute's price data belonging to the periods of 3 May 1996 and

16 October 1996 when futures transactions were introduced over KOSPI 200. Granger

causality analysis was used in the study. As for the analysis results; futures market

leads the cash market by as long as 30 minutes. The trading volume has significant

explanatory power for volatility changes in both spot and futures markets. Futures

transactions have stronger influence than cash transactions and the futures transactions

have stronger influence over cash market volatility.

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Gulen and Mayhew (2000) find that spot volatility is independent of changes in

futures trading in eighteen countries and that information-less futures volume has a

negative impact on spot volatility in Austria and the UK.

Thenmozhi (2002) showed that the inception of futures trading has reduced the

volatility of spot index returns due to increased information flow. According to

Shenbagaraman (2003) the introduction of derivative products did not have any

significant impact on market volatility in India. Raju and Karande (2003) also

reported a decline in volatility of S&P CNX Nifty after the introduction of index

futures.

Nath (2003) studied the behavior of stock market volatility after derivatives

and arrived at the conclusion that the volatility of the market as measured by

benchmark indices like S&P CNX Nifty and S&P CNX Nifty Junior has fallen during

the post-derivatives period. The finding is in-line with the earlier findings of

Thenmozhi (2002), and Raju and Karande (2003).

Bandivadekar and Ghosh (2003), and Sah and Omkarnath (2005) also

investigated the behaviour of volatility in cash market in futures trading era. They also

found that futures trading have led to reduction in volatility in the underlying asset

market but they attributed the degree of decline in volatility in the underlying market

to the trading volume in futures market. They inferred that as the trade volume in the

Futures and Options segment of BSE is very low, the volatility in BSE has not

significantly declined; whereas in the case of NSE (where the trade volume is at the

peak), the volatility in NIFTY has reduced significantly

Mallikarjunappa and Afsal (2007) studied the volatility implications of the

introduction of derivatives on the stock market in India using S&P CNX IT index and

found that clustering and persistence of volatility in different degrees before and after

derivatives and the listing in futures has increased the market volatility.

Kanas (2009), using a time-varying regime-switching vector error correction

approach, finds that the NIKKEI stock index cash and futures prices are jointly

characterized by regime switching, which is time-varying and dependent upon the

basis, the interest rate, the volatility of the cash index, and the US futures market.

60

Gannon (2010) develops simultaneous volatility models that allow for

simultaneous and unidirectional volatility and volume of trade effects. Intraday data

from the Australian cash index and index futures markets are used to test these effects.

Overnight volatility spillover effects are tested with the data from the S and P 500

index using alternative estimates of the United States volatility. It is found that the

simultaneous volatility model is robust to alternative specifications of returns

equations and to misspecification of the direction of volatility causality.

2.5 Causality between Stock index and Index Futures Market

There exist a number of studies in India and abroad concerning the

investigation of the lead-lag relation between spot and index futures markets.

Kawaller, Koch and Koch (1987) estimated the lead-lag relation between S&P 500

index futures and S&P 500 index using simultaneous equation model and found a

stronger leading role of the futures market to the infrequent trading of component

stocks. Finnerty and Park (1987) discovered a significant lead-lag relationship

between futures and spot prices. Herbst et al. (1987) examined the relation between

S&P 500 futures, value line futures and spot indexes and concluded that the futures

prices lead their spot indices together with indications of higher lead for longer time

spans.

Stoll and Whaley (1990) use ARIMA model and ordinary least squares to

estimate the lead-lag between S&P 500 index futures, Major Market Index futures and

the underlying spot market. The results indicate that S&P 500 and Major Market

Index futures lead the cash market by 10 minutes and they attribute this to faster

dissemination of information into futures market. The findings are consistent with the

evidence gathered by Koch and Koch (1987), MacKinlay and Ramasamy (1988).

Schwarz et al (1991) examine the price leadership of index futures over the

spot market and test the dynamic efficiency of index futures as a price discovery

vehicle. However, they use Garbade & Silber model to quantify the price discovery

function of the futures market. The study is done on the Major Market Index for the

sample period 1985 to 1988. The results show that the spot and futures are integrated

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such that average mispricing leading to arbitrage is eliminated within one to seven

days.

Tang, Mak and Choi (1992) studied the causal relationship between stock index

futures and cash index prices in Hong Kong, which revealed that futures prices cause

cash index prices to change in the pre-crash period but not vice versa. In the post-

crash period, they found that bi-directional causality existed between the two

variables.

Chan (1992) estimated the lead-lag relation between major market index and

major market index futures under conditions of good and bad news, different trading

intensities and under varying market wide movements. ARMA models were used and

it was observed that the futures market led the spot market, and this was primarily due

to faster information processing by the futures market. However, under bad news it

was the cash index that led over the futures market while, there was no effect on the

lead-lag relation during different trading intensities.

Ghosh (1993) tested the applicability of the Cointegration and error correction

models between S&P 500 index and its index future prices and concluded that there

exist Cointegration relationship between the index and its future prices.

Abhyankar (1995) observed that there are several reasons that why the lead-lag

relationship may exist. First, the lead-lag relationship between the spot index and

future markets may be caused by infrequent trading of the composite stocks. That is,

when the index is reweighed every time, some composite stocks prices taken by the

index may well not be the price on which the market just traded. It is particularly the

case when the stocks are not traded frequently. Therefore, the price the index takes is

only the stale price. If index future price is supposed to reflect the instantaneous

feedback to the changes in market-wide information flow, the spot index containing

stale prices should lag behind the future price. Second, liquidity difference between

these two markets may be the cause for the lead-lag relationship. If composite firms in

the index take longer time to trade than the index future, market-wide information will

be infused more quickly into the pricing than into spot market. Therefore, it is obvious

that the lead-lag relationship is a function of relative liquidity rather than the absolute

62

liquidity. Third, market frictions (e.g. transaction costs, capital requirements, and

short-selling restriction) can make the future markets more attractive to traders with

private information to exploit the information advantage. In addition, Chan (1992)

pointed out other possible sources for the lead-lag effect. For example, Gottlieb and

Kalay (1985) believed that discreteness of spot stock prices as a friction is the source

of the lead-lag relationship. Amihud and Mendelson (1980) claimed that stabilization

of specialists is a potential source. Fishman and Longstaff (1989) argued that dual

trading practice in the future market may have caused the lead-lag relationship

between these two markets. Fourth, exactly by the same token, when firm specific

information is available to some traders, they may prefer to trade on spot market for

that particularly stock rather than trading on the future market (e.g. Subrahmanyam,

1991). That is, there might be the case that the spot market can lead the future market

to some extent.

Teppo et al (1995) study the two-way causality between the Finnish stock

index futures and the stock index for a period of one year from 1989 - 1990. Granger

Causality tests are applied on the daily returns due to non-availability of intra-day

data. The results indicate that the futures market provides predictive information for

both frequent and infrequently traded stocks while the reverse causality is found to be

weak.

West (1997) examined the lead-lag relationship in returns on Share Price Index

futures and the All Ordinaries Index between 1992 and 1996 in Australia. It has been

found that the market for index futures leads the equities market by up to 15 minutes.

This relationship does vary slightly across the individual years in the sample. These

findings are consistent with overseas research. However, the extent of the lead is

smaller. There is also very strong evidence of the futures market lagging the equities

market by 5 minutes at certain times. The extent of this feedback between the index

futures market and the index appears to be greater than in overseas markets.

Abhyankar (1998) revisited the relationship using 5-minute returns by

regressing spot returns on lagged spot and futures returns, and futures returns on

63

lagged spot and futures returns using EGARCH. It was found that the futures returns

led the spot returns by 15 - 20 minutes.

Mukharjee, Kedarnath and Mishra (2000) in their working paper series made

an effort to investigate the possible lead lag relationship both in terms of return and

volatility, among the NIFTY spot index and index futures market in India and also to

explore the possible changes (if any) in such relationship around the release of

different types of information. The results suggests that though there is a strong

contemporaneous and bi-directional relationship among the returns in the spot and

futures market, the spot market has been found to play comparatively stronger leading

role in disseminating information available to the market, and therefore said to be

more efficient. The lead-lag relation is asymmetric with weaker evidence that the spot

index leads futures and stronger evidence that the stock index futures market leads the

spot market.

Abdullah (2001) used a multiple regression model is used as the methodology

to test for the lead and lag relationship between the stock index futures returns and

KLSE CI returns. The study finds that there is a strong contemporaneous relationship

and there exists a lead effect from the futures market to the spot market by one day in

sub-periods 1 and 3. Sub-period 2 shows a mix lead-lag relationship between the two

markets. For the whole period under review, the relationship has been found to be

ambiguous and inconclusive.

Antoniou, Pescetto and Violaris (2001) addressed the important relationship

between stock index and stock index futures markets in an international context. The

results from applying the VAR-EGARCH methodology to the French, German and

UK stock index and their corresponding stock index futures markets provide evidence

of feedback effects in both mean and variance within and between countries.

However, when the interdependence of market is allowed to influence the spot-futures

price relationship, a feedback effect between the two UK markets is found. In

particular, there appear to be significant bi-directional price spillover effects between

French and UK markets.

64

Ap Gwilym, Owain and Buckle, Mike (2001), had studied the lead-lag

relationship between the FTSE100 stock index and its derivative contracts. Their

paper examines the lead/lag relationships between the FTSE100 stock market index

and its related futures and options contracts, and also the interrelation between the

derivatives markets. Both the index futures and index options contracts are found to

lead the cash index as predicted. However, the call option market appears to

marginally lead both the index futures and the put option market. In the only previous

paper to examine the inter-market relationships between a stock index and related

futures and options contracts, Fleming et al (Journal of Futures Markets, 16, 353-87,

1996) maintain that relative trading costs determine which market leads. As the

trading costs of calls and puts are similar, other factors must be driving the

relationships observed in this paper. They hypothesize that informed traders with

bullish expectations wishing to gain leverage from the options market will buy calls

or, with greater risk, sell puts. As market sentiment was bullish for most of the sample

period examined, this could explain the call market leads reported.

Chris et al (2001) estimate the lead-lag relation between the FTSE 100 stock

index futures and the FTSE 100 index. Based on the results obtained, they develop a

trading strategy based on the predictive abilities of the futures market. The study is

conducted using Co-integration and Error Correction model, ARMA model and vector

auto-regressive model. The results indicate that futures lead the spot market

attributable to faster flow of information into futures market mainly due to lower

transaction costs. It is shown that the error co-integration model predicts the correct

direction of the spot returns 68.75% of the time.

Brooks, Rew and Ritson (2001) investigated the lead-lag relation for FTSE 100

index for 1996-1997 by incorporating error correction model, error correction model

with cost of carry, ARIMA and VAR. They compared the forecasting ability of

models, and different trading strategies under error correction model with cost of carry

models. They found the leading power of futures market and underline the higher

predictive ability of error correction model - cost of carry model.

65

Abdullah, Mahdhir, Nasir, Annuar Mohd., Mohamad, Shamsher, Aliahmed,

Huson Joher and Hassan, Taufiq (2002), studied the launching of the futures contract

on the Kuala Lumpur Stock Exchange Composite Index. Due to its recentness in the

country, many issues pertaining to this equity derivatives instrument have not been

explored. Thus, the development of stock index futures opens many opportunities for

research in this area. Their study examines the temporal relationship between the price

of the Kuala Lumpur Stock Exchange Composite Index futures contract (FKLI) and

its underlying stock index, the Kuala Lumpur Stock Exchange Composite Index

(KLSE CI). The five-year period under study is split into three sub-periods to observe

the price co-movement pattern under different volatility levels. The study finds that

futures market tends to lead the spot market by one day during the periods of stable

market, and there is a mixed lead-lag relationship between the two markets during the

period of highly volatile market.

Asche and Guttormsen (2002) examined the relationship between spot and

future prices. Findings indicate that futures prices leads spot prices, and that futures

contracts with longer time to expiration leads contracts with shorter time to expiration.

Frino and West (2002) examined the lead-lag relationship in returns on stock

index futures and the underlying stock index for the Australian market between 1992

and 1997. They found that futures returns lead index returns by twenty to twenty-five

minutes and there was some evidence of feedback from the equities market to the

futures market.

Hyun-Jung and Smith (2004) investigates impact of KOSPI 200 futures on spot

market trading. Results show that futures trading increase the speed at which

information is impounded into spot market prices, reduces the persistence of

information and increases spot market volatility. The spot and futures prices are co-

integrated and there is bidirectional causality between the two markets.

Gee and Karim (2005) analyzed the lead-lag relationship between spot and

futures markets of the Malaysian Kuala Lumpur Composite Index (KLCI) by

employing the cointegration and error-correction approach. The results of the Error-

correction model (ECM) suggest that futures price lead spot price and the change in

66

futures price is relatively more efficient as compared to spot price. The results also

indicate that spot price do lead futures price but the lead-lag relationship is relatively

weak as compared to the impact of futures price on spot price.

Sah and Omkarnath (2005) examined the nature and extent of relation between

NSE-50 Futures and volatility of S&P CNX Nifty. They used Granger causality test to

study relationship between volatility and futures market activity. The sample data

consisted of daily closing prices of S&P Nifty and turnover from June 12, 2000

through March 25, 2004 for near month and from June 12 through January 29, 2004

for middle month and far month contracts. Their empirical study suggested that

futures market activity destabilized the underlying market. The direction of causation

was bi-directional in case of near month; however, causality ran from Nifty Futures to

volatility of S&P Nifty in case of far month contract.

Bose (2007) analysed whether the Indian Stock Index Futures market plays an

important role in the assimilation of information and price discovery in the stock

market. Using Futures prices for the S&P CNX Nifty Index traded on the National

Stock Exchange of India, we find that there is significant information flow from the

futures to the spot market and futures prices/returns have predictive power for the spot

prices.

Hadgal (2007) focused on the possible lead-lag relationship among the spot and

futures market in India. There is no conclusive proof of any lead or lag relationship, as

the futures market in India as not mature and it is imperfect.

A new study of Kasman and Kasman (2008) examined the impact of futures on

volatility of the underlying asset (via GARCH model) including the question of

whether a cointegrating relation exists between spot prices and futures prices (via

ECM model). They used the Istanbul Stock Price Index 30 (ISE 30) futures and spot

prices and concluded that there is a long run relation (nearly one-to-one) between spot

and futures prices and causality runs from spot prices to future prices, but not vice-

versa.

Reddy and Sebastin (2008) studied the temporal relationship between the

equities market and the derivatives market segments of the stock market using various

67

methods and by identifying lead-lag relationship between the value of a representative

index of the equities market and the price of a corresponding index futures contract in

the derivatives market. The study observed that price innovations appeared first in the

derivatives market and were then transmitted to the equities market. The dynamics of

such information transport between stock market and derivatives market were studied

using the information theoretic concept of entropy, which captures non-linear dynamic

relationship also.

Debasish and Mishra (2008) examined the lead-lag relationships between the

NSE Nifty stock market index and its related futures and options contracts, and also

the interrelation between the derivatives markets. The study finds that both the index

futures and index options contracts lead the cash index.

Debasish (2009) investigated the effect of futures trading on the volatility and

operating efficiency of the underlying Indian stock market by taking a sample of

selected individual stocks. The results of this study suggest that there is a trade-off

between gains and costs associated with the introduction of derivatives trading at least

on a short-term perspective. The study offers a unique contribution in examining the

impact of introduction of index futures trading in NSE Nifty index and the index

futures covering a period since introduction of index futures in Indian Capital Market.

The results suggest that the market would have to pay a certain price, such as a loss of

market efficiency for the sake of market stabilization.

Pradhan and Bhat (2009) investigated price discovery, information and

forecasting in Nifty futures markets. Johansens (1988) Vector Error Correction

Model (VECM) is employed to investigate the causal relationship between spot and

futures prices. The Johansens VECM results found that the spot market leads the

futures market and spot prices tend to discover new information more rapidly than

futures prices.

Floros (2009) examined the price discovery between futures and spot markets

in South Africa over the period 2002 to 2006. Granger causality, VECM and ECM-

TGARCH(1,1) results suggest a bidirectional causality (feedback) between futures

68

and spot prices. We show that futures and spot play a strong price discovery role

(FTSE/JSE Top 40 futures prices lead spot prices and vice-versa).

Bada (2009) examined the lead-lag relationship between the Istanbul Stock

Exchange 30 (ISE 30) Index and index futures prices at the Turkish Derivatives

Exchange using daily observations from February 2005 to May 2008. It is found out

that spot prices lead the futures prices for ISE 30 Index contrary to the results for

different countries.

Athanasios (2010) examined the dynamic relationship between the FTSE/ASE-

20 spot price index, the FTSE/ASE-20 futures price index and their respective

volatilities. The empirical results provide strong evidence that both feedback and

simultaneously characterize the dynamic relationship between futures trading activity

and volatility of the underlying market.

In a recent work Debasish (2011) examines the long-term relationship between

spot prices and futures prices. The study finds a single long-term relationship for each

of the selected companies across the six sectors.

Theissen (2011) reconsidered the issue of price discovery in spot and futures

markets. He used a threshold error correction model to allow for arbitrage

opportunities to have an impact on the return dynamics. It has been found that the

futures market leads in the process of price discovery.

The study of Antoniou and Garrett (1993) for October 1987 with FTSE 100

index, Abhyankar (1998)s study on the FTSE 100, and Vector Autoregressive-

Exponential Generalized Autoregressive Conditional Heteroskedastic

(VAREGARCH) model of Antoniou et al. (2001) by using multivariate analysis for

France, Germany and the UK confirm that futures markets lead spot markets. For

other countries with smaller size derivatives exchange the studies all support that the

lagged values of futures affect the adjustment in spot prices. For Greece Floros and

Vougas (2007), for India Sinha and Sharma (2005) on Nifty Futures confirm the

results of the literature on the direction of lead-lag relation.

69

The lead-lag effect between spot index market and index future market has

lured many investigations into its profitability implication in real market transactions.

This type of investigations is interesting in the sense that if traders can take advantage

of these effects and make profits, these opportunities should disappear quickly since

many other will quickly do it too, which implies that the efficient market hypothesis is

invalid in the context. For example, Leitch and Tanner (1991) found that the accuracy

of statistical forecast may not necessarily positively link with trading profitability.

2.6 TO SUM UP

The review of literature provides sufficient evidences that equity futures market

has been an efficient price discovery vehicle. Even in India, the studies found

significant causal relationship between these two markets. The current study examines

specifically the price discovery efficiency of Indian equity futures market. Thus, the

current study will be of great benefit for the traders and will help to fill the gap in the

literature.

The current discussions also delineate the controversial role of speculators, and

recognize the impact of price manipulation and herding behavior on equity markets.

Research on the volume-volatility relationship shows that there is a positive

correlation between the two, but it is not clear as yet whether volume can represent the

information signal per se. Regarding the role of derivatives, evidence both supports

and refutes the argument of market stabilization, while new research areas, such as

individual share futures, feedback trading, and volatility futures, are explored.

Looking ahead, one should be very open-minded when analyzing stock market

data, since it is difficult to identify and model all the factors responsible for price

swings. Most of the time, the interaction among drivers of volatility makes it difficult

to isolate their precise impact, while economic theory could remain silent as to why

markets have moved toward a certain direction. From a methodological perspective,

modeling and forecasting equity volatility warrants even further investigation. What is

complicated but at the same time appealing is to design an empirical framework that

attenuates the intricate characteristics of a stochastic global environment.

70

The previous literatures clearly explains that though the pricing formula for

futures derives the fair value depending on the spot market prices, the empirical work

shows us that futures prices mostly lead the spot prices. However, the literature is very

thin in the sense that there exist almost no studies examining the relation between spot

and index futures markets in the aftermath of global financial crisis. Furthermore,

there exist only a few such studies in the context of Indias capital market. Therefore,

the main purpose of this study is to investigate whether futures prices lead the spot

prices for NSE 50 (Nifty) in India or other way around.

Therefore, it is inferred that an empirical study concerning the investigation of

the efficient market hypothesis, capital market volatility, and lead-lag relationship

between stock index and index futures markets is very significant for an emerging

market economy like India. In India, the derivatives market is an innovation of the last

decade and thus, empirical studies of this kind would certainly unveil important policy

implications for the robust growth and development of capital market of the country.

Furthermore, the extant literature is very thin concerning studies of this kind in the

context of India. So, it is imperative to conduct a research on the topic of investigating

the relationship between spot and index futures markets in India with the benchmarks

of stock market efficiency and volatility.

71

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