Chapter-6 Price Discovery in NSE Nifty Index and Nifty Futures Markets

6.1 Introduction

The mechanics of price discovery arc important to both investors and regulators.

Since the trading process may introduce noise that results in inaccurate prices, it has

implications for traders interested in avoiding pricing errors and policy makers concemed

with market stability. Poor price discovcry may lead to heightened price volatility,

including bubbles or sudden market crashes, when priccs arc predominantly influenced

by short-Hill disturbanccs. A recent concern for market participants is whether the

proliferation of alternative trading venues may have adversely affectcd the price

formation process through market fragmentation.

Price discovery is the proecss of uncovering an asset's full-information or

permanent value. The unobservable permanent price reflects the fundamental value of the

stock. It is distinct from the observable price, which can bc decomposed into its

fundamental value and its transitory effects. Thc latter consists of price movements due to

the hid-ask bounce, temporary order imbalances, inventory adjustments, and rounding

effects.

Price discovery is the process of buyers and sellers arriving at a transaction price

for a given quality and quantity of a product at a given time and place. Price discovery

involves several intelTelated concepts, among them,

• Market structure (number, size, location, and competitiveness of buyers and

sellers);

• Market behavior (buyer procurement and pricing methods);

• Market information and price reporting (amount, timeliness, and reliability of

information); and

• Futures markets and risk management alternatives.

Futures trade assumes significance in a volatile ready market and pnce risk

management because of the price discovery. The price discovery is the process of

determining the price of a commodity/stock, based on supply and demand factors. The

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expectations theory hypothesizes that the current futures price is a consensus forecast of

the value of the ready (spot) price in the future.

Price discovery is the process hy which markets attempt to find equilibriulll prices

(Schreiber and Schwarz. 1986). The concept of price discovery traces back to the mid

80's when both academics and governmental authorities tried to describe the mechanisms

of different markets and how the process ends in a concct price. Fair market prices retlect

the demand of all traders and should not be affected by incomplete information. sudden

change in the depth and width of the market or the trading system as a whole.

Security markets arc more apt to consist of diversely informed traders who

collectively possess incomplete information about the assets being traded. rather than

being characterized by asymmetric information. With the individual trade. the underlying

information is made public through the trade price itself. Trading activity based on less

than full infomlation and past prices. markets may collcctively error at times or convcrge

to a new price.

Numerous factors. in addition to the underlying information change and liquidity

trading, cause the price changes to occur (Schreiber and Schwarz, 1986). Even small

orders may have huge impacts on the share price for low volumes traded of stocks. Sticky

limit order books with outstanding orders can re!lect past prices and information

situation. And the process of finding the correct price will in itself cause the actual price

to !luctuate. The advent of new information will generate a succession of trades and price

changes while traders digcst the news, including the price movement. and the markct

searches for a new equilibrium priee (Schreiber and Schwarz. 1986).

Main theme of this research is "price discovery function that measures the

information content of quotes in forecasting the next transaction". Price discovery is one

of the central functions of financial markets. In the market microstructure literature, it has

been variously interpreted as, "The search for an equilibrium price" (Schreiber and

Schwartz (1986)), "gathering and interpreting news" (Baillie et. al. (2002)), "The

incorporation of the information implicit in investor trading into market prices"

(Lehmann (2002). These interpretations suggest that price discovery is dynamic in

nature, and an efficient price discovery process is characterized by the fast adjustment of

market prices from the old equilihrium to the new equilibrium with the alTival of new

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information. In particular, Madhavan (2002) distinguishes dynamic price discovery issues

from static issues such as trading cost determination.

One notable institutional trend of financial markets is the trading of identical or

closely related assets in multiple market places. This trend has raised a number of

important questions. Does the proliferation of alternative trading venues and the resulting

market fragmentation adversely affect the price discovery process? How do the dynamics

of price discovery of an asset depend on market characteristics, such as transaction eosts

and liquidity? What institutional structures and trading protocols facilitate the

information aggregation and price discovery process? In contrast to the wide literature on

transaction costs, however, the studies on price discovery arc relatively limited. In a

recent survey of market microstructure studies, Madhavan (2002) remarks, "The studies

surveyed above can be viewed as analyzing the influence of structure on the magnitude of

the friction variable. What is presently lacking is a deep understanding of how structure

aspects return dynamics, in particular, the speed (italics as cited) of price discovery." In

this research, we propose an approach to directly characterize the speed of price

discovery in the context of NSE Nifty Index and Nifty Futures markets.

Future markets contribute in two important ways to the organization of economic

acti vity:

(i) They facilitate price discovery;

(ii) They offer means of transferring risk or hedging.

In this Research it has been focused on the first contribution. Price discovery

refers to the use of future prices for pricing cash market transactions (Working, 1948;

Wiese, 1978; and Lake 1978). In general, price discovery is the process of uncovering an

asset's full information or permanent value. The unobservable permanent priee reflects

the fundamental value of the stock or commodity. It is distinct from the observable price,

which can be decomposed into its fundamental value and its transitory effects. The latter

consists of price movements due to factors such as bid-ask bounce, temporary order

imbalances or inventory adjustments.

Price discovery and risk transfer (i.e. Hedging) have been considered as the pivot

functions of the futures market in all the economics (Telser (1981)). As we know, futures

are the standardized forward contracts, which arc traded on stock exchanges. Cost-of-

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Carry model is followed to determine the price of the futures contract. which implies that

futures represent the prospective price of the underlying asset in the cash market

(Garbade and Sibler (1983». For example; if the Nifty futures is traded at 2500 level and

the Nifty cash market Index at 2450. (if cost-of-carry model holds good) it implies that

the futures will direct the next price move in the cash market. thus the next price of the

underlying asset will be approximately 2500. Price discovery is a function of the cost-of­

carry model. which implies that price discovery. will be true only if cost-of-carry model

holds good (Turkington and Walsh (1999».

In other words. if at any timc the futurcs arc mispriced thcn lead-lag relationship

between futures and cash market may be disturbed. which will result into wrong decision

for the traders to take position in the cash market on the basis of the price movement in

the futures market. In addition, if the futures are mispriced then hedging through

arbitrage positions in the cash and the futures market will not work in the interest of the

traders.

In addition. an efficient cost-of-carry relationship between the futures and cash

market results in the comovement of price series in two markets. Comovement of price

series of both markets is an evidence that price movement in both markets is

cointegrated, but evidence of cointegration docs not tell anything regarding the speed of

price discovery in the market; rather it conveys very significant information regarding the

strength of the basis (i.e. Futures Price -Cash Price) (Booth et a1.. (1999». If on the date

of the maturity of the contract. price series in two markets converges (sec Fig-6.1). it

implies that cost-of-carry model holds good and both the series have long run

relationship. If reverse holds, then it implies that the futures arc mispriced and may not be

an efficient price discovery vehicle (Garbade and Sibler (1983)). For an efficient

convergence on the maturity date the basis is required to be predictable. but predictable

basis does not necessarily imply that speedier price discovery takes place in the futures

market (Fortenbery and Zapata (1997».

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Fig -6.1 Movement of Futures and Equity price

Price

\ Equity Pric>"

I, I, Time'

Price discovery mechanism refers to absorbing the new infonnation, and

reflecting it into the market prices. P,ice discovcry in the cash market has been a serious

issue for debate for the traders, professionals, regulatory bodies and the academicians.

Thrce different schools (i.e. Fundamental Analysis. Technical Analysis and Efficient

Market Hypothesis) have emerged to analyze the reaction of the prices to new

information. Fama made significant efforts in this regard and in 1970. he came out with

formal definitions of the market efficiency. He classified the market efficiency into three

categories i.e. Weak Form Efficiency. Semi-Strong Form Efficiency and Strong Form

Efficiency.

A market is said to be weak form efficient if the cunent market price and past

price are uncorrelated (i.e. the asset price movements are random). A market is known as

semi-strong efficient, if it absorbs and reflects the market information as well as the

public information (viz; corporate actions. political announcement etc.). Strong form

efficient market is one. which neglects the chances of even insiders to make abnormal

profits on the basis of first hand information.

In the developed economies viz; U.S.A. and U.K.. markets are found to be

efficient but reverse holds in case of the emerging markets like India. Taiwan,

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Bangladesh etc. (Mobarek and Keasey (2000)). Thus in the emerging markets. relative

pricing efficiency of the futures market may work like a lantern in the dark coal mine. If

in emerging markets, futures market is able to react to the market information

immediately, when these becomes available then, it will certainly help the regulators to

control the volatil ity in the cash market and the confidence of the traders can be restored

in a market like India. where people burnt their hands in early and mid 90's due to the

overwhelming participation in the market hy one trader (i.e. Big Bull Ilarshad Mehta). If

in India, futures market acts as an efficient price discovery vehicle, traders will be getting

more confidence to trade in the cash market because they will know that futures market is

their to guide their prospcctive actions in the market and they can protect themselves

from the possible loss by tak.ing (Beta weighted) reverse positions in the futures market

(Branllen and Ulvcling (1984)).

Efficient price discovery m the futures market has many advantages for the

traders as well as for the regulators. Traders can manage their risk exposure in the cash

market by taking reverse positions in the futures market. In many stock markets it has

been observed that the volatility in the cash mark.et has reduced in the post futures trading

era as compared to the volatility in the pre futures trading era (Gulen and Mayhew

(2000)). Reductioll in the magnitude of volatility will certainly work for the benefit of all

traders (both retail as well as big traders). Reduction in volatility ensures relatively stable

price movements in the market, which will help the traders to take their decision in the

market (subject to the experience and exposure of the trader in the market) (long and

Donders (1998)). The regulatory bodies can also be benefited through efficient price

discovery in the futures market (Raju and Karande (2003)). They can simulate the

reforms through futures market and then directly implement the same in the cash market.

The reaction of the futures market to such reforms will certainly help the regulatory

bodies to evaluate the probability of success/failure of the reform in the cash market, thus

they can make appropriate modifications, if necessary.

In India, equity futures are of relatively recent origin and were introduced in the

phased manner. In the first phase index futures trading was introduced on l2'h June 2000

and in the second phase, stock futures trading was permitted on 9th Nov 2001. The trade

volume in both the markets has been increasing by leaps and bounds. These days'

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significant efforts are being taken to investigate the efficiency of Indian equity futures

market. Raju and Karande (2003) investigated the price discovery cfTiciency of the Indian

equity futures market but they could not conclude anything on the basis of short time

dimension. 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 lead-lag relationship between the two markets.

6.2 Importance of Price Discovery

When a market experiences stress and its negative impacts persist over a period of

time, the market's liquidity declines and market conditions become unstable. Under such

stress, the price discovery function an important mechanism in markets is debilitated,

which leads to higher price volatility and lower market liquidity. At the most general

modeling perspective, each of the observable prices of an asset in multiple markets can

be decomposed into two components: one retlecting the common efficient (full­

information) price shared by all these markets (Garbade and Silber (1979)); and one

retlecting the transitory frictions that arise from the trading mechanism, such as the bid­

ask bounce, liquidity effects, and rounding errors. Evolving as a random walk, the

common efficient price captures the fundamental value of the financial asset and its

innovation impounds the expectation revisions of investors (thus new information) about

the asset payoff of observed prices respond to the common efficient price innovation

characterizes the dynamics of price discovery. Unfortunately, as emphasized by

Hasbrouck (2002), the common efficient price (and its innovation) is generally

unobservable. Therefore. identifying the common efficient price innovations is a

necessary step before any meaningfuimcasure of price discovery can be constructed.

How do markets arrive at prices? There is perhaps no question more central to

economics. This research focuses on price formation in financial markets, where the

question looms especially large: How, if at all. is news about macroeconomic

fundamentals incorporated into stock prices, bond prices and foreign exchange rates?

Unfortunately the process of price discovery in financial markets remains poorly

understood. Traditional "efficient markets" thinking suggests that asset prices should

completely and instantaneously retleet movements in underlying fundamentals.

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Conversely, several prominent authors have recently gone so far as to assert that asset

prices and fundamentals may be largely and routinely disconnected. Experiences such as

the late 1990s U.S. technology-driven market bubble would seem to support that view,

yet simultaneously it seems clear that financial market participants pay a great deal of

attention to data on underlying economic fundamentals. The notable difficulty of

empirically mapping the links between economic fundamentals and asset prices is indeed

striking.

The central price-discovery question has many dimensions and nuances, including

but not limited to the following. How quickly, and with what patterns, do adjustments to

news occur? Does announcement timing matter? Are the magnitudes of effects similar for

"good news" and "bad news," or, for example, do markets react more vigorously to bad

news than to good news'? Quite apart from the direct effect of news on assets prices, what

is its effect on financial market volatility? Do the effects of news on prices and volatility

vary across assets and countries. and what are the links? Are there readily identifiable

herd behavior and/or contagion effects? Do news effects vary over the business cycle?

Just as the central question of price discovery has many dimensions and nuances,

so too docs a full answer. In this research we progress by characterizing the simultaneous

response of foreign exchange markets as well as the domestic and foreign stock and bond

markets to real-time India macroeconomic news. More precisely, we seek to better

understand the links between asset prices and fundamentals by simultaneously

combining:

• High-quality and ultra-high frequency asset prIce data across markets and

countries, which allows us to study plicc movements in (near) continuous time;

• Synchronized survey data on market participants' expectations, which allow us to

infer "surprises" or "innovations" when news is announced; and

• Advances in statistical modeling of volatility. which facilitates efficient

inference.

By so doing, we can probe the workings of the marketplace in new and powerful

ways, focusing on episodes where the source of price movements is well identified,

leading to a high signal-to-noise ratio.

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6.3 Price Discovery in Different Markets

a. Price Discovery in Tick Time

A central question in market microstructure is how information about the

value of an asset is incorporated in its market price. In fragmented markets,

information about the value of an asset arrives to the market from multiple

sources. The process of how information from different sources is incorporated

into the value of an asset has become known as price discovery. An example of

such a fragmented market is the NSE India, a multiple dealer market where each

dealer contributes to the price discovery process. Interesting questions for such a

market are which dealer contributes most, how quick the discovery process

works, and how it depends on markct circumstances like liquidity, volatility and

trading intensity.

An important measure for the contribution to pnce discovery is the

information share introduced by Hasbrouck (1995). Infornlation shares are

defined as the part of the variance of the random walk component of returns that

can be attributed to a particular market or dealer. Unfortunately this variance

decomposition rs not unique when price changes are contemporaneously

correlated. As a solution, Hasbrouck (1995) suggests alternative Choleski

decompositions to establish upper and lower bounds. The Choleski

decompositions work well in many cases where the number of different markets

or market participants is small and the differences among them are large enough.

b. Price Discovery in Multiple-Dealer Markets

"Price discovery" is a dynamic process in which a diverse group of traders

and market makers gather, evaluate, and interpret disparate pieces of information;

coordinate trading demands; and generate market-clearing prices. Though it is a

principal function of securities markets, price discovery has received littIe, but

groWlllg attention from financial economists. The literature includes few

theoretical models or empirical studies, especially for multiple-dealer markets.

The classical notion of price discovery involves a Walrasian auctioneer who

observes quantities supplied and demanded at different prices and determines the

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price that clears the market. With this framework economists ask. "How do prices

work?" but not "How are prices set?" And. while tbis model yields obvious

practical advantages, it trivializes complex and economically important pricing

decisions. In reality, many individuals generate prices concurrently, and the

interactions among institutional features, preferences. and asset characteristics

imply that the quotes we observe in markets vary in their informative quality.

c. Price Discovery in Thinly Traded Markets

Price discovery is an important function performed by futures markets.

Effective futures markets should generate prices that express consciously formed

opinions on cash prices in the future, and should transmit that information

throughout the marketing system in a timely manner (Working, 1942; Tomek,

1980). Because of its importance, the effectiveness of futures markets in

performing this function has been investigated extensively in the literature. The

more recent studies have shown that futures prices playa dominant role in the

discovery and transmission of price information. In the absence of effective price

discovery. researchers have conjectured that limited trading volume associated

with thin markets has adversely affected price discovery.

Traditionally, a thin market has been understood to be a market in which

the number of transactions over a given period of time is insufficient to ensure

efficient price discovery. Peterson identifies three major concerns related to thin

markets: first, those prices may not accurately renect supply and demand

conditions in the market; second, that thinness will contribute to higher price

volatility; and third, that thinness (duc to the magnified impact of individual

transactions) increases the incentive for market manipulation.

d. Price Discovery in Informationally Linked Markets

When a security is traded in more than one market, investors have several

avenues to trade and exploit information. An investor who wants to trade the

Nikkei 225 stock index, for example, can do so in the spot market in Tokyo and,

during the same opening hours, in the futures market in Osaka or Singapore.

Where frictionless and continuous information sharing across markets exist,

trading should be considered as taking place in a single market with simultaneous

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prIce changes in the stocks, stock indices, and derivative instruments. If the

markets were not frictionless, some markets would appear to be more attractive

than others because of concerns relating to transaction costs, regulation, and

liquidity, leading to differences in price discovery across the exchanges. As the

process of globalization of trading and competition among exchanges for order

flow accelerates, it is impot1ant to determine the nature and location of price

discovery.

e, Price Discovery in Commodity Markets

Instability of commodity prices has always been a major concern of the

producers as well as the consumers in an agriculture-dominated country like

India. Farmers' direct exposure to price fluctuations, for instance, makes it too

risky for them to invest in otherwise profitahle activities. There are various ways

to cope with this problem. Apart from increasing stability of the market through

direct government intervention, various actors in the farm sector can better

manage their activities in an environment of unstable prices through derivative

markets, These markets serve a risk-shifting function, and can be used to lock-in

prices instead of relying on uncet1ain price developments. This problem can be

sorted out by making survey of the price risk management system prevailing in

agricultural commodity markcts and to empirically investigate how efficicnt is the

price discovery function of futures for ensuring bettcr hcdge against price

uncertainty in some selected commodities.

6.4 Efficient Market Hypothesis of Price Discovery

Paul Sammuelson developed Efficient Market Hypothesis (EMH) in 1965.

Eugene Fama formulated EMH later in 1970. The EMH suggests whether, at any given

time, prices fully reflect all available information on a particular stock and/or market.

Thus, according to the EMH, no investor has an advantage in predicting a return on a

stock price since no one has access to information not already available to everyone else.

In other words, the hypothesis says that capital markets are efficient and that security

prices fully rellect all available information.

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Based on EMH, there are three identified classifications of market efficiency,

which arc aimed at reflecting the degree to which it can be applied to markets.

• Strong efficiency. This is the strongest version, which states that all information

in a market, whether public or private, is accounted for in a stock price. Not even

insider information could give an investor an advantage.

• Semi·strong efficiency· This form of EMIl implies that all public information is

calculated into a stock's current share price. Neither fundamental nor technical

analysis can be used to achieve superior gains.

• Weak efficiency· This type of EMH claims that all past prices of a stock are

reflected in today's stock price. Therefore, technical analysis cannot be used to

predict and beat a market.

The EMH is an appealing description of competitive market equilibrium. An

efficient market impounds new information into prices quickly and without bias. Prices

fully reflect available information. Market participants adjust the available supply and

aggregate demand in response to publicly available information soars to generate market·

clearing prices. In major stock markets, where millions of dollars are 'voting', it seems

plausible that a rational consensus will be reached as to the share prices which best reflect

the prospects for future cash flows givcn available information.

Although the EMil may be an elegant economic concept, even a normatively

desirable condition, it may not be true. Prices in securities markets may not fully reflect

available information due to all kinds of outside factors. The early literature on market

efficiency was widely interpreted as supportive. But. by the late 1970s, the anomalous

evidence was growing and began to command attention. There is now a substantial body

of empirical research, which casts doubt upon the degree of market efficiency (Fall,

1992).

6.5 Price Discovery Processes

Prices of a financial product are discovered through trading activities among

market participants. This process by which prices adjust to incorporate new infomlation

is referred to as the price discovery (hereafter PO) process.

Analyses will be conducted according to the following framework.

IS I

i. Intraday and Intraweek patterns

Information is generated (24 X 7) 24 hours a day, 7 days a week. However, no

financial market is open for such long hours. In this sense, all market participants face

a price risk in not being able to trade at the prices, which ret1ect the generated

information when the market is closed. In addition, there may be some clustering of

important public information at certain times of the day or week, which also affects

trading activity in the market. Presumably, there are some distinct intraday and

intraweck patterns of PO, ret1ecting market participants' behaviour in coping with

these issues.

ii. PD process after arrival of public information

There must be some type of public information, which systematically affects the

PO process in government securities market. The paper analyses the effect of

statistical announcements, notification of open market operations by central banks,

and releases of policy rate changes on the PO process, as examples of such

information. Presumably, some unique patterns in trading volume, price volatility,

and bid-ask spread are observed after the arrival of this information.

iii. Inter-linkage between the cash and futures markets

If similar products are traded in more than one market, this leads to the question

of which market incorporates new information first. This question regarding PO

speed is examined, with a focus on the relationship between the cash and futures

government securities markets. This is based on the assumption that PO speed is a

proxy for market liquidity, i.e., the market is more liquid when PO speed is high,

because the degree of information content is high. Presumably, PO speed depends on

relative accessibility to the two markets.

6.6 Pricing of futures

Futures can be priced in following manner.

6.6.1 The Pricing of Futures Contracts

The price of futures is dependent on the spot price of the underlying asset

and the cost of holding (or carrying) the underlying asset until the delivery date of

the futures contract. The caITying costs refer to the costs associated with purchasing

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and carryll1g a commodity for a specified period time l9. Basically, there are four

costs associated with can'ying: storage costs, insurance costs, transportation costs,

and financing costs. For financial futures storage, transportation and insurance costs

arc almost zero; therefore, they can be ignored in calculations of financial futures

pnces.

Based on arbitrage arguments20, the theoretical futures pnce can be

determined by adding the calTying costs to the cash price of the underlying asset.

This can be expressed as follows:

F = P + P X C or F = P (1 + C) -------------------------------- (6.1)

F= Futures Price.

P= Cash Market Price.

C= Cost of can'ying, expressed as a fraction of the cash price.

According to arbitrage principles, the futures price must be equal to or less

than the cash price of the underlying commodity plus the carrying charges

necessary to hold the spot commodity forward until delivery. Mathematically, this

rule can be expressed as follows:

F:SP (1 + C) -------------------------------------------------- (6.2)

If prices do not meet this criterion, a trader can bOlTOW funds to buy the

spot commodity, sell the futures contract, and can'y the commodity to delivery

against the futures contract. This is called 'cash-and-caITy' arbitrage; it continues

until the difference between cash and futures prices n,UTOWS relevant carrying

costs.

The futures price must be equal to or greater than the cash price plus the cost of

carrying the goods to delivery. This rule can be expressed as follows:

F ~ P (1 + C) -------------------------------------------------- (6.3)

If the price of futures does not conform to this rule, there will be an

arbitrage opportunity for traders. This is called 'reverse cash-and-carry' arbitrage.

19 . EJwarJs & Ma, Op.Clt. p.7S.

2" Kolb, 1993, op.cit., p. 39 - 40; scc also: Fabozzi, & Modigliani, Op.Cil., pp.224-226.

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In order to prevent cash-and-carry arbitrage and reverse cash-and-carry

arbitrage, the requirements expressed in both equations 6.2 and 6.3 must be met.

Together these equations imply that:

F = P (I + C) ---------------------------------------------------- (6.4)

This equation holds under stable market conditions. There are no

transaction costs and no restrictions on the usc of proceeds from short sales. It is

assumed. however. that there is an unlimited ability to borrow or lend money and

that all borrowing and lending is done at the same interest rate 21. There is also an

assumption that commodities can be stored indefinitely without any change in the

characteristics of the commodity and that there arc no taxes are imposed. When

these assumptions are no longer valid, a divergence occurs between the actual

futures price and the theorctical futures price22•

Sometimes underlying assets earn a cash yield plior to the settlement date.

This cash yield must be deducted from the cost of the futures. When applying the

cash yield earned on the asset to Equation 6.1, we get the following formula23:

(Cash yield: Y)

F = P (I + C - Y) ---------------------------------------------- (6.5)

The difference between cash amI futures prices is called the 'basis'. On the

settlement date, the futures price must be equal to the cash price. As the delivery

clate approaches, therefore. the futures price must converge with the cash price.

This is illustrated in Equation 6.4 By the date of delivery. carrying costs are

approaching zero, the yield can be earned by holding investment approaches at

zero, and then the futures price will be close to the cash market price.

Basis = Cash Price - Futures Price --------------------------- (6.6)

On the settlement date, the cash price is equal to the futures price, so the

basis is zero. If futures prices are accurately retlected by the full-carry

21 The Equation 1.1 does not allow for continuous compounding of interest costs, hut captures only the simple interest financing cost.

22 Fabozzi & Modigliani, op.cit.. p.224-225.

23 Ibid .• p.225-226.

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relationship. then they arc higher than cash prices. and the basis is negative. This

circumstance is referred to as a 'contango' market. It means that the relationship

between futures prices and cash prices is determined solely by the cost of

carryIng.

When futures pnces are determined by considerations other than or in

addition to the cost of carrying factors, the market is said to be characterized by

'Backwardation'. where the futures price is less than the cash price. In this case.

the basis is positive. Backwardation is also used to refer to a market in which the

futures price is above the cash price but still below the full-eany futures priee24•

6.6.2 The cost of carry model

It uses fair value calculation of futures to decide the no-arbitrage limits on the

price of a futures contract. This is the basis for the cost-or-CatTY model where the

price of the contract is defined as:

F=S+C ----------------------------------------------- (6.7)

Where:

F Futures price

S Spot price

C Holding costs or cany costs

This can also be expressed as:

F=S (l +r)T ------------------------------------------ (6.8)

Where:

r =Cost of financing

T= Time till expiration

If F<S(l+rl or F>SO+r)T arbitrage opportunities would exist I.e.

whenever the futures price moves away from the fair value, there would be

chances for arbitrage. We know what the spot and futures prices are. but what are

the components of holding cost? The components of holding cost vary with

24 Edwards & Ma. op.cit" p.~7.

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contracts on different assets. At times the holding cost may even be ncgative. In

the case of commodity futures, the holding cost is the cost of financing plus cost

of storage and insurance purchased etc. In the case of equity futures, the holding

cost is the cost of financing minus the dividends returns.

6.6.3 Pricing equity index futures

A futures contract on the stock market index gives its owner the right and

ohligation to buy or sell the portfolio of stocks characterized by the index. Stock

index futures are cash settled; there is no delivery of the underlying stocks. In

their short history of trading, index futures have had a great impact on the world's

securities markets. Indeed, index futures trading have been accused of making the

world's stock markets more volatile than ever before. The critics claim that

individual investors have been driven out to the equity markets because the

actions of institutional traders in both the spot and futures markets cause stock

values to gyrate with no links to their fundamental values. Whether stock index

futures trading is a blessing or a curse is debatable. It is certainly true, however,

that its existence has revolutionized the art and science of institutional equity

portfolio management.

The main di fferences between commodity and equity index futures are that:

• There are no costs of storage involved in holding equity.

• Equity comes with a dividend stream, which is a negative cost if you are

long the stock and a positive cost if you are short the stock.

Therefore, Cost of carry = Financing cost - Dividends. Thus, a crucial

aspect of dealing with equity futures as opposed to commodity futures is an

accurate forecasting of dividends. The better the forecast of dividend offered by a

security, the better is the estimate of the futures price.

•

•

Pricing index futures given expected dividend amount

The pricing of index futures is also based on the cost-or-carry model, where

the carrying cost is the cost of financing the purchase of the portfolio

underlying the index, minus the present value of dividends obtained from the

stocks in the index portfolio.

Pricing index futures given expected dividend yield

156

If the dividend now throughout the year is generally uniform. i.e. if there are

few historical cases of clustering of dividends in any particular month, it is

useful to calculate the annual dividend yield.

T F=S (1 +r-q) ------------------------------------- (6.9)

Where:

F =futures price

s= spot index value

r =cost of financing

q =expected dividend yield

T =holding period

6.6.4 A Simple Model of Price Discovery

It is well known that prices of related securities like pnccs In spot and futures

markets cannot diverge without bound because they are linked by an arbitrage

relationship. The link between this arbitrage relationship (and the associated cost­

of-carry pricing model) and the existence of a cointegralion relationship between

the spot and futures prices has been extensively presented in the literature (e.g.

Wahab and Lashgari [1993 J). This research suggests that inference concerning the

spot and futures price dynamics should be based on Error Correction Models

(ECM) where the elTor-correction components indicate,

• The proportion of disequilibrium from one period that IS cOITected III a

later period and

• The relative magnitude of adjustments In each market towards

·l·b· 25 equI I num .

Another important insight from this research states that the spot and

futures prices should share a common stochastic trcnd because spot and futures

markets are essentially two different places to trade the same underlying

25 See Banerjee, Dolado. Galhraith and Hendry [19931 Or Llitkepohl [19931. chap. 13 for a general analysis

of cointcgration and see Brenner and Kroner [1995] for an analysis of the link between arhitrage and

cuintcgration.

157

commodity. Based on this feature. Hasbrouck [1995] proposes a measure of price

discovery that is used in this study. In the following. the ECM and the common

trend model is presented in the context of a very simple model of price dynamics

inspired from Hasbrouck [1995J.

Suppose. first, that a claim on some future cash Ilows is traded in two

different markets, a cash market and a futures market. Obviously. it exists only

one true value for this claim and we call this value the efficient price. As standard

in the finance literature, we assume that the efficient price follows a random walk:

p, = p,-I + 11, ------------------------------------- (6.10)

Where the increments 11, are assumed to retlect new information about the

future cash tlows. The actual cash and futures price dynamics is driven by this

common random walk and it may be specified by an unobservable component

model:

P,=K+p, 1+£, -------------------------------------------- (6.11)

Where PI=(SI FI)' is a (2 x I) price vector. SI and FI are the spot and the

futures prices observed at time t, K is a (2 x I) vector of means, I is a unit vedor

and G/ is a vector of disturbances which is assumed to be a zero-mean covariance

stationary stochastic process. The process "/ allows spot and futures prices to

deviate from the efficient price due to market specific phenomenon such as a

liquidity pressure. Price discovery refers to the impounding of (new) information

into spot and futures prices and is related to the contributions to the efficient price

increments from the spot and futures markets.

Suppose, for example, that the informational content of orders and trades

(other than arbitrage-based orders and trades) in one market is larger than the

informational content of orders and trades (other than arbitrage-based orders and

trades) in the other market. These asymmetric contributions to the common

efficient price discovery create a price discrepancy between the two markets that

causes some investors to submit arbitrage orders. Therefore, arbitrage appears as

an e'Tor-correction mechanism that prevents the spot and futures prices from

diverging without bound. Furthermore, in our informational framework, the

deviation frol11 the cash and catTy relationship disappears only when all the

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relevant information is shared by the two markets and impeded in the spot and the

futures prices. As a result, cash and futures prices ultimately renect the same

information, as suggested by the common (unobservable) efficient price model.

Inference concerning the error correction mechanism may be obtained

from an ECM for price changes and inference concerning the common

(unobservable) efficient price may be obtained from the vector moving average

(VMA) representation for the actual data. Additional motivation for these models

is that ECM and VMA arc alternative forms of a cointegrated system. In our

bivariate framework, and due to the arbitrage constraint. S, and F, are expected to

be cointegrated of order one, yielding a unique cointegrating vector. Following

Johansen [1988], the ECM for price changes is given by:

P-I

l',.Pt = L ITi l',.P'i+ U (W P'P- WK) + e, ------------------- (6.12)

i=1

Where the n, arc (2 x 2) coefficient matrices, (f. is a (2 x I) vector of

coefficients for the cointegrating vector, ~ is the (2 x I) cointegrating vector and c,

is a (2 x I) vector of serially-uncorrelated disturbances with Cov(e,) = n. Note

that in our spot-futures framework ~ should be equal to (I - I). Furthermore, the

coefficients of the vector u should indicate the proportion of the mispricing

observed in a given period that is corrected in a later period as well as the relative

magnitude of the adjustments in the spot and futures markets. The VMA

specification is directly obtaincd from the ECM and is givcn by:

l',.Pt = L \)Ii e'_i ------------------------------------------------- (6.13)

i=O Assuming that e, = 0 for I < 0, the cumulative price impact to an

innovation arriving at time I = 0 may bc obtained as:

In the present case, the matrix sum (IVO + II'I + IV2 + ... ) forms a (2 x 2) matrix

whose rows must be identical because the long term response of each price to an

159

innovation must be identical if the prices are r;ot to diverge. Denoting If! onc of

these (identical) rows, the variance of the efficient price may be obtained as:

(}2'l = 'f'Q'f" ---------------------------------------------------- (6.15)

In ordcr to measure the contribution of each market to the price discovery,

lIasbrouck [1995 J proposes to compute the quantities Si given by:

'f'2Q, I II

Sj = ------------------------------------------------- (6.16)

'f'Q'I"

Where i refers to the relevant market (i = s, f for the spot and the futures

markets, respectively), Due to the possibility of cross-colTclation in price

innovations, the matrix [2 lIlay not be diagonal and may be replaced by its

Cholesky factor. In this case, the price contribution of the market i is obtained by

the quantity:

('f' F)2 Sj = ------------------------------------------ (6,17)

('f' F) I ('f' F),

Where F is the Cholcsky factor (F is the lowcr triangular matrix such that

n = FFI) and where (J 2 q is now given by ('VF)I ('VF)'. Note that the Cholesky

decomposition forces a potential important asymmetry on the system since a

shock to the tirst variable affects the first variable initially, while a shock to the

second variable has a contemporaneous effect on both variables, Then, the

ordering of the variables matters and the price contributions will be computed for

the two different orderings of the variables. The index contribution is maximized

when index price is placed first and the futures contribution is maximized when

the futures priee is placed tirst.

6.7 Review of Literature

Futures and cash markets contribute to the discovery of a unique and common

unobservable price that is the efficient price. Besides the traditional role of risk sharing

assigned to futures markets, these markets play an important role in the aggregation of

160

information (sce, for cxample, Grossman [1977 J, Bray [19S I [ and Brannen and UI veling

[1984]), Thc case of stock index futures is analyzed in Subrahmanyam [1991] and in

Kumar and Seppi [1994]. Futures and cash markets contribute to the discovery of a

unique and common unobservable price that is the efficient pricc. The contribution of

each market to the price discovery depends, at least in part, on thc microstructure of these

markets, including the level of transparency, the liquidity supply mechanism, the rules

governing the priority of ordcrs, the constraints on short salcs and the sellielllent

mechanism.

Furthcrmore, stocks and futures are traded in different market structures, a pure

limit ordcr market for the stocks and a 1100r market for the futures. As suggcstcd by

Biais, Foucault and Salanie [1998], this difference affects the liquidity suppliers'

behavior and has an impact on the level of liquidity of the market. In paI1icular, it appcars

that the cost of liquidity is hll'ger in the 1100r market than in the limit ordcr market due to

the absence of incentive to undercut on large ask prices (overbid on low bid priccs) in the

1100r JJ];Jrket. On the contrary, limit order markets appear to entail emcient risk sharing

and competitive pricing. This may affect the order submission behavior of informed

traders in a way that is favorable to the stock market. As a conscquence, traditional

results indicating that information is first aggregated in the futures market (e.g. Stoll and

Whaley [1990]) may in fact rel1ect, at least paI1ially, differences in market

microstructure.

On the empirical ground, most of the studies emphasize some kind of causality

between futures and cash markets returns (e.g. Stoll and Whaley [1990]) and/or between

futures and cash markets return volatility (e.g. Chan, Chan and Karolyi [1991]). While

these analyses provide some evidence about the relationship between the cash and futures

markets, they do explicitly take into account neither the equilibrium relationship between

cash and futures prices nor the price discovery process. A more satisfactory specification

may be built on the observation that these two characteristics are linked to two particular

forms of the cointegration property of futures and cash prices, the en'or correction form

(hereafter, ECM) and the common trend form. Wahab and Lashgari [1993J provide an in­

dcpth analysis of the relationship betwecn stock index cash and futures markets using an

ECM framework. Nevertheless, the evidence presented in their paper should be

161

interpreted with caution due to the usc of non-synchronous daily closing pnces. The

analysis of Shyy, Vijayraghavan and Scott-Quinn [1996j is more closely related to our

work because it deals with the French market and uses intraday data. Furthermore, the

empirical analysis presented in their paper suggests that traditional results concerning the

lead-lag relationship betwecn cash and futures markets may be caused by a stale price

effect due to non-synchronous trading among stocks. While this result is appealing, it

should also be considered with caution because Shyy. Vijayraghavan and Scott-Quinn

[199()j use data on the second nearest contract, which is characterized by a very low level

of activity. As far as the choice of the data is concerned, part of our work may be viewed

as an extension and analysis of the robustness of the results of Shyy, Vijayraghavan and

Scott-Quinn [1996] to the case of the first nearest contract which is characterized by a

high level of activity. Finally, Hasbrouck [1995] uses the common trends representation

of a set of cointegrated variables to measure the contribution to the efficient price

innovation from a particular market.

Investigation of causal relationship between futures and cash prices is not a new

phenomenon. At the international as well as at national level, significant efforts have

been made to evaluate the price discovery efficiency of different futures markets (viz;

commodity futures. cUITency futures. equity futures, etc.). Stensis (1983), Garbade and

Sibler (1983), Protopapadakis and Stoll (1983), French (1986), Kawaller (1987), Mohd.

Fatimah (1994), Cheung and Fung (1997), Hall (2001), Yang Jian (2001), Singh (2001),

Thomas and Karande (2001), Sahadevan (2002), Campbell and Diebold (2002), Zhong

(2004), and Isabel and Gilbert (2004) investigated the price discovcry efficiency of

commodity futures market in differcnt countries viz; America, United Kingdom,

Malaysia, India, Mexico etc. respectively. All rescarchers (except for Sahadevan (2002))

found strong lead-lag relationship between the futures and spot prices.

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

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

in various economies like; Hong Kong, Indonesia, Japan, South Korea, Malaysia,

Philippines. Singapore, Thailand. Taiwan, America respectively and they observed strong

bilateral causality between both markcts. Moreover. they found that futures market is

efficient for underlying currencies. in the sense that it leads the cash market.

162

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

Turkington and Walsh (1999), l\lenkvcld (2003), Chuang (2003), Raju and Karande

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

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

different countries namely; America, Netherlands, Germany, Australia, Taiwan, India,

Hong Kong respectively. Except for Barclay and Hendershott (2004), all researchers

observed significant evidence of efficient price discovery through equity futures market.

They all found that equity and futures prices were cointegrated and the causality from the

futures to cash market was significant as compared to the causality from reverse side.

Citing the above studies makes one thing very clear that investigating the causal

relationship between futures and cash market is not a new phenomenon, For many

markets in different economies at different time frames, price discovery efficiency of the

futures market has been investigated and the review of literature provides strong evidence

favoring the argument that futures market is an efficient price discovery vehicle.

In America, price discovery efficiency of futures market has been far investigated

for all types of futures viz; commodity futures, equity futures and cUlTency futures etc.

Stensis (1983), Garbade and Sibler (1983), French (1986), Chan (1992), Cheung and

Fung (1997), Hall et aI., (200 I), Yang J ian et aI., (200 I), Campbell and Diebold (2002)

and Isabel and Gilbert (2004) examined the causal relationship between the spot and

futures price on Chicago Board of Trade (CBOT) and they observed that spot market

significantly followed the futures market and the futures market price movements

provides a basis for predicting the prospective spot market price changes,

In addition to commodity futures, Hasbrouck (1995), Menkveld (2003) and

Barclay and Hendershott (2004) investigated the price discovery efficiency of the equity

futures market on NYSE and NASDAQ during 1993. 1997-98 and 1993-99 and except

for Barclay and Hendershott (2004), all found significant causal relationship between the

cash and futures prices, which is an essential condition for price discovery efficiency of

futurcs market. Although Barclay and Hendershott (2004) found weak lead-lag

relationship between cash and futures prices but their study does not completely reject the

hypothesis rather their results arc statistically significant and have little economic use.

Moreover, first order co integration is one of the essential features of the cash and Futures

163

prices. which reflects that both futures as well as cash prices are non-stationary as found

by different scholars in various speculative markets.

In addition to America. significant efforts have been made to investigate the price

discovery efficiency of the eljuity futures market in different economies viz. India,

Taiwan, Mexico and Hong Kong. Raju and Karande (2003) investigated the causality

relationship between eljuity futures and cash market on NSE (National Stock Exchange

of India), but found mixed results regarding the causality relationship between two

markets. The reason for the confusing results may be the short time period (i.e. Three

Years) considered for the study but when the same market was examined by considering

lengthy time frame (i.e. Five Years) by Gupta and Singh (2006), they found strong

bilateral causality between cash and futures market. Moreover by applying Impulse

Response Analysis, they found that the causality from the futures to cash market was

stronger as compared to the causality from cash market to futures market.

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

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

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

evidence of bilateral causality and inferred that basis movement was an efficient

predictor of the prospective cash market price movements. So & Tse (2005) made an

attempt to examine the causality relationship between cash and futures market on Hang

Seng Index Market, and by considering the time frame of three years (i.e. 1999-2002),

they found significant bilateral causality between these two markets.

Booth et aI., (1999) and Upper & Werner (2002) conducted studies on German

stock markets and found strong evidence of infonnation traveling from the futures market

to the spot market and they evidently highlighted and supported the price discovery role

of the futures market. Gulen and Mayhew (2000) conducted a wonderful study

considering the behaviour of cash market prices during the post futures trading era for 25

countries. They observed that except for America and Japan, in all countries, the

magnitude of volatility during post futures trading era has gone down. Gupta (2001),

Shenbagaraman (2003) and Raju and Karnade (2003) observed the same in Indian capital

market. Kiran and Nagaraj (2003) observed that futures market could do better during the

market crash due to terrorist attack on America on 11 th Sept., 20ll! and concluded that

164

futures market provides better information during the high volatility period and the basis

looks very strong during the high volatility period, which means that both markets moves

into same direction.

Thus, the review of literature provides sufficient evidences that equity futures

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

by Raju and Karande (2003) and Gupta and Singh (2006) found significant causal

relationship between these two markets. The current study examines specifically the

price discovery efficiency of Indian equity futures market during high volatility periods

i.e. period around I Ilh Sept., 200 I (Terrorist attack on Amcrica), 171h May, 2004 (Ever

highest Indian stock market crash due to political uncertainty) and 14th June 2006 (Global

Sell off and Jittery effects in Indian markets). Thus, the current study will be of great

benefit for the traders and will help to fill the gap in the literature.

6.7.1 Research Contribution

Price Discovery in Derivatives market is very interesting and wide topic. Related

to this area of research as mentioned in Chapter-I, following research papers are

published to facilitate the present study.

• "Arbitrage opportunities in Intraday trading between futures, options and

cash markets - Case study on NSE India"

This research paper is mainly focused on Arbitrage opportunities being

created between Futures, Options and Cash markets over a period of time due to

number of reasons such as inside information, dividendlbonus, shorting in

markets etc. The research has mainly contributed in terms of Arbitrage

opportunities based on three approaches, (a) Transaction prices (b) Bid-ask quotes

and (c) Transaction prices that are checked for trade direction using bid-ask

prices. Overall the percentage of observations violating no-arbitrage bounds is

dramatically reduced under (b) and (c) relative to (a). Overall this research has

highlighted significant opportunities for Arbitragers between all three markets,

hence it can also help in understanding price discovery process related to this

chapter.

16S

• "Stock Index Futures & Options and Predictability of Intraday Index Price

Changc-NSE (National Stock Exchange of India) Case study."

As we know that NSE is currently forefront in the world for single stock

index futures and stock futures are having a mammoth 70'70 of share in overall

derivative segment, it becomes paramount importance to analyze price discovery

between NSE's Index price and Index futures in Intraday setting. This research

examines the impact of index futures options on the predictability of intraday

index price changes by examining the NSE futures contract. The predictor

variables examined were,

(I) Lagged cash index vailies

With regard to the lagged cash index as a predictor variable. the ratio of

significant positive to significant negative coefficients in the regression equation

remained unchanged following futures option initiation on the NSE. Although the

changes in the ratios were not consistent across all lag times, the value of lagged

index price changes as a predictor variable appears to be unaffected by NSE

futures option initiation.

(2) Lagged flltllres price changes

The impact of futures option initiation on the price discovery role of the futures

contract was evaluated through the lagged futures price change variable. The ratio

of significant positive to significant negative coefficients on the lagged futures

price change variable dropped slightly following futures option initiation. These

results indicate that the value of the lagged futures price change variable or the

price discovery role of the futures contract appears to havc diminished somewhat

following initiation of the futures option contract.

(3) Theoretical fuilires mispricings.

With respect to the theoretical futures mispricing variable there was a reduction in

the ratio of significant positive to significant negative coefficients in the post­

futures option period. This relative decrease in the number of significant positive

coefficients indicates that the value of the theoretical futures mispricing variable

as a predictor of subsequent intraday index plice changes is diminished following

futures option initiation.

166

• "Short selling and Price Discovery -A Thematic Case Study on Indian Stock

Markets"

This research paper discuss the impact of the decision of the Indian

Financial authority permitting short selling to FlI (Foreign Institutional Investors)

on Derivatives market. This is a useful contribution as the SEBI parent authority

of Indian Financial Markets is really under the process to allow short selling in

Indian markets. This paper mainly highlights possible impact and behavior of

Indian capital markets after introduction of short selling and its effect in other

markets. It contributes on two issues.

o What is the effect of short-sale restrictions on skewness, volatility, the

probability of market crashes, Price discovery and liquidity?

o What is the effect on the market expected return or cost of capital?

6.8 Price Discovery between Index and Futures Markets in India- NSE Case

6.8.1 Introduction

It is very well known that the Indian capital market has witnessed a major

transformation and structural change from the past one decade as a result of ongoing

financial sector reforms. Gupta (2002) has rightly pointed out that improving market

efficiency, enhancing transparency, checking unfair trade practices and bringing the

Indian capital market up to a certain international standard are some of the major

objectives of these reforms. Due to such reforming process, one of the important step

taken in the secondary market is the introduction of derivative products in two major

Indian stock exchanges (viz. NSE and BSE) with a view to provide tools for risk

management to investors and also to improve the injol7l1alionai ejjicienc/6 of the cash

market.

26 According to Fama (1970), a market is said to be infomKltionally efficient if the prices ::llways reflect all

the information availahle to the market. Depending on availability of information, Fama suggested three

types of market efficiency- Weak form, Semi-strong form and Strong form efficiency. This proposition to

market efficiency is Icmlcd as Efficient Market Hypothesis (EMH).

167

Many emergmg and transition economies had started introducing derivative

contracts since 1865 when the commodity futures were first introduced on the Chicago

Board of Trade. The Indian capital markets have experienced the launching of derivative

products on June 9, 2000 in BSE and on June 12, 2000 in NSE by the introduction of

index futures. Just after one year, index options were also introduced to facilitate the

investors in managing their risks. Later stock options and stock futures on underlying

stocks were also launched in July 2001 and Nov. 2001 respectively.

In India, derivatives were mainly introduced with view to curb the increasing

volatility of the asset prices in financial markets and to introduce sophisticated risk

management tools leading to higher returns by reducing risk and transaction costs as

compared to individual financial assets. Though the onset of derivative trading has

significantly altered the movement of stock prices in Indian spot market, it is yet to be

proved whether the derivative products have served the purpose as claimed by the Indian

regulators. In an efficient capital market where all available information is fully and

instantaneously utilized to determine the market price of securities, prices in the futures

and spot market should move simultaneously without any delay. However, due to market

frictions such as transaction cost, capital market microstructure effects etc., significant

lead-lag relationship between the two markets has been observed.

Therefore the present study is being contemplated with the following specific objectives:

• Investigating the Price Discovery efficiency betwccn the Nifty Index and Nifty

Index futures market in India, both in terms of return and volatility; and

• Analyzing the possible explanations behind the variation in the prices of Index

and Futures over time. In this regard, the important propositions / hypothesis

attempted to be tested are:

o Futures market leads (if at all) the spot market not because of infreguent

trading of component stocks;

o The leading role of futures market will be greater around macroeconomic

information release; and

o The leading role of futures market weakens around the firm-specific

annOUnCCITICnts.

168

6.8.2 Methodology

Methodology deals with selection of econometric techniques, data, calculation of

returns, volatility. identification of benchmark Index and other related matters. Prices in

the cash market and futures market are expected to be inter-related. The products traded

arc similar in nature. Stock index futures value is derived from the value of the cash

market price plus interest rate. Any information; economic. political, social and other

inlluences changes in prices either in spot market or in futures market. Since futures

market has lesser trading costs, higher liquidity than spot market the information is first

expected to be retlected in the plices of futures and then it is expected to 110w to cash

market (Kawaller et.al., 1987). However, this may not be true in all circumstances.

Sometimes it can happen that the information is first discounted in the cash market and

then moves on to futures market. Alternatively, information is rel1ected simultaneously in

both the markcts. In this research what is attempted to measure is the speed of the

information tlow and its early impact on prices.

There arc some econometric techniques to measure the direction as well as the

intensity of the information now. Among others, Granger causality, Spectral Analysis

and cointegration are more appropriate techniques useful to find out speed of infonnation

tlow and its intensity on prices. In order to choose an appropriate technique between

these, the prices in their levels are tested for cointegration and found to be cointegrated.

Therefore the cointegration technique is preferred to Granger causality.

The use of cointegration analysis and error correction models explicitly takes into

account non-stationmity and enables one to distinguish between short run deviations from

equilibrium indicative of price discovery and long run deviations that account for

efficiency and stability. If two series (such as futures and spot prices) are non-stationary

but that a linear combination of the two vmiables (such as the basis) is stationary so that

both are cointegrated then a bivariate dynamic model that uses only first differences (with

lags) is misspecified because it Ignores interim short run adjustments to long run

equilibrium.

The link between cointegration and causality stems from the fact that if spot and

futures prices are cointcgrated, then causality must exist in at least one direction and

possibly in both directions. Cointegration implies that each series be represented by an

169

elTor cOlTection model that includes last period's equilibrium as well as lagged values of

the first differences of each variable, temporal causality can be assessed by examining the

statistical significance and relative magnitudes of the error cOlTection coefficients and the

coefficients on the lagged variables.

The possibility that one variable in a system of cointegrated series is exogenous

(independent) within the error correction process motivates the use of elTor cOlTection

models in evaluating price discovery. The cointegrating vectors define the long run

equilibrium while the error correction dynamics characterise the price discovery process,

the process whereby markets attempt to find equilibrium. The primary purpose in

estimating the Error Correction Model (ECM)27 is to implement price leadership tests

between futures and spot prices. Tests of causality between cointegrated variables should

be conducted in an error correction framework because standard tests of causality

overlook the reversion to equilibrium channel of causality represented by ~ (basis).

Causality tests in the ECM framework involve testing significance of the coefficients u

and B. If these coefllcients arc jointly insignificant, then there is no Granger causality and

hence there is no price discovery.

6,8.2.1 Econometric Techniques28

Price changes in one market influence price changes in the other market so as to

bring about a long run equilibrium relationship as given by the equation:

F, - ao - p J S, =e,----------------------------------- (6.18)

Where F, and S, are contemporaneous cash and futures prices at time t; ao and BJ

are parameters and e, is the classical error term (deviation from equilibrium). According

to Engle and Granger (1987), if and St are non-stationary* but the deviations, are

stationary, then S, and F, are cointcgratcd** and equilibrium exists between F, and St. For

27 The error correction component in the Error Correction Model (ECM) indicates: (i) the proportion of

disequilibrium from one period that is corrected in il later period, and (ii) the relative magnitude of

adjustments in each Il1Jrkel towards equilibrium.

2R Gujarati Damodar N (1995), Basic Econometrics, 3nJ edition. McGra\v Hill Inc.

170

F, and S, to be cointegrated, they must be integrated of the first order. Performing unit

root test on each univariate price series deten11ines the order of integration. If each series

is non-stationary in the levels, but the first dilTerenccs and deviations c" are stationary,

the prices are cointegrated of order (1.1), denoted CI(I.I) with 13 1 as the cointegrating

coefficient. An error correction model exists for each series, which is not subject to

spurious results. Ordinary least squares (OLS) is inappropriate if futures or spot prices are

non stationary because the standard enors are not consistent.

* Broadly speaking a data series is said to be stationary if its mean and variance

arc constant (non-changing) over time and the value of covariance between two

time periods depends only on the distance or lag between the two time periods

and not on the actual time at which the covariance is computed (Gujarati, 1995).

The correlation between a series and its lagged values arc assumed to depend only

on the length of the lag and not on when the series started. This property is known

as stationarity and any series obeying this is called a stationary time series. It is

also referred to as a series that is integrated of order zero or as 1(0) (Ramanathan,

2002).

** Suppose X, and Y, arc two non-stationary selies. In general we would expect

that a combination of X, and Y, is also non-stationary. However, a particular

combination may be stationary. If such a combination exists, we say that X, and

Y, are cointegrated. Two cointegrated series will thus not drift far apart overtime

ego futures and spot prices, consumption and income (Ramanathan, 2002). The

econometric technique regression assumes that mean values are stationary (do not

change much) over any study period. If the mean values of a parameter keep

changing from period to period then estimated coefficients will not provide

unbiased estimates. Therefore, it is necessary to test the stationarity of the

dependent and independent variables.

171

Cointegration29 implies that each series can be represented by an error correction

model that includes last period's equilibrium error as well as lagged values of the first

differences of each variable. lienee. temporal causality can be assessed by examining the

statistical significance and relative magnitudes of the error correction coefficients and the

coefficients on the lagged variables. In this study. the error correction model can be

written as:

Rf.t = aor+ alr(FI. I - SI.I) + ~Ir Rf.I-1 + ~2r Rs.II + En ------------- (6.19)

Rs.l = Uos + a l.s (FI-I - SI.I) + ~"Rs.lI + ~2s Rf.I-1 + Esl ------------ (6.20)

Where Rf.1 is Nifty futures returns and Rs.l is Nifty Index returns. air and al s are

the error correction terms and I3s represent short run effects.

Each of the above two equations is interpreted as having two parts. The first part

IS the equilibrium error. This measures how the left hand side variable adjusts to the

previous period's deviation from long run equilibrium. The remaining portions of the

equations are the lagged first differences. which represent short run effects of the

previous period's change in price. If air is statistically insignificant the current period

change in the futures price to period's deviation from long run equilibrium. If both and B2f

are statistically insignificant then the spot markct price does not Granger cause futures

market price.

Both the dependent and explanatory variables behavior varies over time. If both

dependent and independent variables are non-stationary then the estimates of simple

regressIOn arc incorrect and the results will mislead. Therefore, it is necessary to test

whether the variables are stationary or not. Some of the most widely used techniques to

test stationarity are Dickey Fuller test and Augmented Dickey Fuller test and Phillip

Perron test. In this study both Phillip Perron Dickey Fuller test Augmented Dickey Fuller

test has been used to test for the unit root in variables. The results are presented in Table-

6.2 & 6.3.The hypothesis of unit root has becn rejectcd at one per cent significant level.

A non-stationary timc series is said to be integrated in order one. often denoted by

1(1). if the series is stationary aftcr the first-order differencing. An (n x I) vector time

2'J Co-intergration and error correction; Repre:.;cntation, estimating and testing- By RobcI1 Eagle and

W.J.Granger, Econometric<l, Vol 55, No.2 (March 19~7), 251-276.

172

series Yl is said to be cointegrated if each of the series taken individually is 1(1) while

some linear combination of the series A' Y, is stationary for some nonzero vector A

(Hamilton, 1994). The theory of cointegration relates to the study of the efficiency of a

futures market in the following way. Let S, be the cash price at time t and Fl . i be futures

price taken at i periods before the contract matures at time t, where i is the number of

periods ahead, then some linear combination of S, and F,.i is expected to be stationary­

that is there exist a and b such that Z, is stationary with mean 0:

Zl = Sl - a - bFli (I) --------------------------------- (6.21)

If both S, and F,.i are I (I), a condition that usually holds for prices, the vector

process (S, and Fl.i) is cointegrated. This cointegration between Sl and Fl.i is a necessary

condition for market efficiency (Lai and Lai (1991)). Cointegration ensures that there

exists a long-run equilibrium relationship between the two series. If Sl and Fl.i are not

cointegrated, they will drift apart without bound, so that the futures price provides little

information about the movement of the cash price.

In addition to cointcgration, market efficiency also requires an unbiased forecast

of futures price on cash price i.e. a = 0 and b =1 in equation (6.21). Therefore, the market

efficiency should be tested in two steps: first to examine the cointegration relationship

between the two price series Sl and Fl. I. if cointegration exists then parameters restriction

a = 0 and b = I is tested. The second step may consist of multiple tests: a = 0 and b = I

jointly or each individually. The constraint b = I is a more important indicator of market

efficiency, because "a" is non-zero under the existence of risk premium and/or

transportation costs even when the market is efficient. That is why we also test them

separately through a = 0 and b = I are often tested jointly. The cointegration relationship

and the parameter restrictions can be tested using Johansen's approach as outlined below.

6.8.2.2 Cointegration Tests

Before testing for cointegration. each individual price series should be examined

for I (I) /irst. Phillips-Perron unit root test is the common method (Booth et. al .. (1999)).

If both the futures and cash price series are I (I), Johansen's cointegration tests can be

conducted. Consider a general k'" order V AR model:

173

K-J

;\.. Y, = D + n Y'I + ~ r j ;\.. Y" + L, -------------------------- (6.22)

i - J Where Y, is an (n x I) vector to be tested for cointegration, and 6 Y, = Y, - Y,.,: 0

is the deterministic term which may take different forms such as a vector of zeros or non­

zero constants depending on properties of the data to be tested nand f: and are matrices

of coefficients; and k is chosen so that £, is a multivariate normal white noise process

with mean () and finite covariance matrix.

The cointegration relationship can be detected by examllung the rank of the

coefficient matrix n, because the number of cointegration vectors equals the rank of n. In

particular, the 0 rank i.e. n = () implies no eointcgration. In a bivariable case, i.e. n = 2,

the two variables are cointegrated only if the rank of n equals I (Johansen and Juselius

(1990)).

Johansen (1998) suggcsted two test statistics to test the null hypothesis that there

are at most r cointegration vectors. The null hypothesis can be equivalently statcd as the

rank of n is at most r, for r = O. I, ... n-1. The two test statistics are based on trace and

maximum eignvalues, respectively,

I.Trace = -T 1: III( \- I.i) i~r-I -------------------------- (6.23)

j.'lax = -T 111(1- I.r-l) ____________________________ (6.24)

Where A, ...... A, are the r largest squared canonical correlations between the

residuals obtained by regressing 6 Y, and Y,.,on 6 Y,." 6 Y,.2 , ..... , fI. Y,.k.' and

respectively. The critical values have been taken from Johansen and Juselius (1990).

In our test for efficiency of futures market, Y, = (S" F,.,), n = 2, and the null

hypothesis should be tested for r = 0 and r = I. If r = () cannot be rejecled, we will

conclude that there is no cointegration vector, and therefore, no cointegration. On the

other hand, if r = () is rejected, and r = I cannot be rejected. we will conclude that there is

a cointegration relationship.

174

6.8.3 Data Source and Time Period

Index futures on S&P CNX Nifty and BSE Sensex started trading on National

Stock Exchange (NSE) and on The Stock Exchange. l'vlumbai (ESE) respectively in June

2000. Volumes traded on BSE arc negligible and they account for less than one per cent

of the total number of contracts traded on NSE. Therefore, for the purpose of the research

study of price discovery only Index futures on S&P CNX Nifty are considered. Daily

closing values of NSE Nifty Index futures and NSE Nifty Index arc considered from June

20()() till December 2006 (i.e. 1644 observations) refer Appendix-I, Trading activity

slowly picked up in Index futures and peaked in September 200 I. Thereafter with some

!luctuations the activity has been very high. Number of contracts traded varied between

1.00,00 to 7,00,00 per month during September 200 I to December 2006. The study

period is divided into two sub periods on the basis of low volumes (June 2000 to

December 2003) and high volumes (January 2003 to December 2(06). The distinction is

made to assess the impact of volume (liquidity) on long run price equilibrium. Returns

are calculatcd as log of ratio of present day's price to previous day's price.

In order to examine the Price Discovery between the underlying spot market and

the futures market, the basic data used in this study consist of intraday price histories for

the nearby contract of nifty index futures, nifty cash index recorded Daily closing values

of Index futures and S&P CNX Nifty have been taken from June 2000 to December 2006

(i.e. 1644 observations), Returns (Rll have been calculated as log of ratio of present day's

price to previous day's price (i.e. RI = In (PI /PI.1))30. Data relating to the price series have

been obtained from website of NSE (www.nseindia.com).

6.8.4 Results and Analysis

Prior to discussing the lead-lag relationship between the futures and cash markets,

Table- 6.1 discusses the descriptive statistics of the NSE eash as well as the futures

market and the spread between futures and the cash prices (i.e. Basis). The results of

Table- 6.1 clearly show that the futures and cash market returns are asymmetric and

3D Ibrahim e{ "I. [J 9991 and Kar cLal. [20IN)/. p.1252

175

highly volatile. Asymmctry in the cash market and futures market returns is not a new

phenomenon. Risk averse nature of the traders in the market may be the prominent cause

for the asymmetric returns (Moolman (2004)). Asymmetric behavior has been observed

in basis as well, which implies that the comovcmcnt of both series does not havc constant

variance.

Significance of the Jarque-Bcra (JB) test statistics does not only imply that the

returns are asymmetric, but it also means that the returns are not normally distributed,

which is the precondition for any market to be efficient in the weak form (Fama (1965),

Stevenson and Bear (1970), Reddy (1997) and Kamath (1998).

Coefficient of Box-Ljung (LB) statistic provides very interesting and useful

information. Basis is predictable for full period as well as for all sub periods. Basis are

serially correlated at 1 % signiticance level, which means for full period as well as for all

sub periods there is strong comovemcnt between futures markct and the cash market.

Figure-6.2 provides same information through the plot of futures and cash prices and the

spread between these two. Both futures and cash market shows upward co movement

trend and more or less basis scems to be stationary during the whole period (see table

6.2). In Figure-6.2 comovcment between cash and the futures market is so strong that it is

very diflieult to see the curve of cash and futures market separately.

Figure- 6.2 Spread betwecn the Nifty Futures and Cash priccs

NM FUTURES NIfTY bluis

.----.----... -----~."

-.------~.,~~-.----- .~~,--. -"",--~-- ----.. .-----..... -___________________________ J'5L4

176

ARIABLE N I\IEAN VARIANCE SKEW KURT J-B LB(I)

NES." OSIS FULL FUTURES 1644 4.753E-04 2. I 52E-04 -U50 14.262 7807.06 4.615**

CASH 1644 4.854E-04 1.9X7E-04 -0.976 7.774 1548.42 19.106

BASIS 1644 -1.9116 58.80S -1.219 2.735 }50.07* 842.110'

00-01 FUTURES 154 -1.l7E-OJ 3.007E-04 -0.90 I 3.H26 32.91 * 0.501

CASH 154 -1.13E-03 2.998E-04 -0.444 1.440 26.99* 1.469

BASIS 154 2.1787 47.137 -2.327 9.536 539.17* 76.606*

01-02 FUTURES 247 3.464E-05 1.971 E-04 -0.597 }.OSI 14.68* 2.640

CASH 247 -3.07E-05 1.979E-04 - 0.576 2.428 16.96* 7.175*

BASIS 247 -3.8506 57.211 -0.922 0.899 80.10* 159.699*

02-03 FUTURES 25} -6.04E-04 8.339E-05 0149 0.230 80.85* 0.392

CASH 253 -6.09E-04 9.852E-05 0.101 0.757 52.83* 0.310

BASIS 253 1.0859 13.368 - 0.263 1.282 33.6.,* 109.726*

03-04 .·UTURES 252 2.3 I SE·03 2. I 66E·04 ·0.215 ·0.244 112.88* 1.477

CASH 252 2.324E-03 2.061 E-04 -0.347 0.135 91.61 * 5.177**

BASIS 252 0.7010 31.484 0.159 0.348 75.21 * 121.703*

04-05 FUTURES 253 4.590E·04 3.427E·04 ·2.693 27.306 6507.80 0.004

CASH 253 4.451 E-04 2.679E-04 -2.290 20.022 3262.61 17.465*

BASIS 253 -4.1866 66.822 -1.627 4.781 144.49* 148.207*

05-06 FUTURES 240 1.661 E-03 1.434E-04 -0.562 0.731 50.49* 0.057

CASH 240 1.673E-03 I. I 65E-04 - 0.696 0.866 51.12* 2.337

BASIS 240 -8.1629 70.736 -3.22 ·OA63 421.04* 92.343*

06-07 FUTURES 245 3.661 E-05 2.784E-02 -1.745 8.523 89.53* 0.015

CASH 245 3.493E·05 2.425E·02 - 1.599 7.569 78.19* 5.209

BASIS 245 -5.9869 67.568 -2.146 2.578 289.09* 119.587*

One Year FUTURES 250 -1.09E·03 2.370E·04 ·0.836 4.470 51. 63* 2.637

11 'h Sept CASH 250 -1.09E-03 2.469E-04 - 0.398 1.830 20.86* 5.404**

WOI(Pre) BASIS 250 -0.1248 66.732 -1.624 3.593 113.55* 179.981*

~ne Year FUTURES 255 1.279E-03 2.526E-04 ·0.769 2.865 25.33* 0.094

II 'h Sept CASH 255 1.260E·03 2.276E-04 ·0.832 2.825 29.74* 0.763

)0 I( Post)) BASIS 255 -3.9508 39.033 - 0.373 -0.759 156.05* 63.967*

Jne Year FUTURES 253 1.910E-03 2.572E-04 -0.659 1.550 40.48* 1.\45

lth l\lay, CASH 253 1.922E-03 2.445E-04 -0.863 2.748 32.07* 3.275***

!OO4(Pre) BASIS 253 -5.83E-02 42.150 -0.353 0.858 53.62* 145.059*

)ne Year FUTURES 252 2.640E-03 2.673E-04 0.848 10.158 568.19* 0.178 l7th .May, CASH 252 2.337E-03 2.036E-04 0.837 7.198 214.47* 1.\08

004(Post) BASIS 252 252 -6.7853 81.425 - 1.651 4.185 129.23* 68.195*

)ne Year FUTURES 249 1.43E·08 1.570E-03 ·0.566 3.620 73.63* 3.925

22 nd ~lay, CASH 249 I.32E·06 1.499E-09 -0.426 3.130 69.79* 3.569**

2006(Pre) BASIS 249 -0.1548 70.526 -I. 985 3.641 121.52* 186.981 *

One Year FUTURE 251 2.310E-07 2.38E·09 0.799 9.264 489.29* 0.159 ~2n{) :\Iay, CASH 251 2.137E-05 2.011 E·02 0.726 7.032 199.36* 1.020

W06(1'ost) BASIS 251 242 ·5.3486 79.566 - 1.214 3.8189 117 .45* 63.258*

177

Table 6.1 Descril}tive Statistics

*Significant at I % level of significance, ** Significant at 5% level of significance

and ***Significant at 10% level of significance.

Comovemcnt of two series is one of the pre-condition for the relatively speedier

price discovery in one market. Comovement of futures and cash market price series

implies that long run relationship exists between both the markets. 10hansens

cointegration has been applied and Table-6.4 discusses the cointegration results.

Table 6.2 Unit Root Test Results (Philips Perron)

Philips Perron Test Results Variables At Levels First Difference

With Drift With Drift and With Drift With Drift and

Trend Trend Full Period (Close to Close)

Futures 2.40 -3.27 -1370.28* -1307.36* Niftv 2.52 -2.98 -1241.67* -1182.50* Basis -570.68* -610.13*

2000-01 Futures -6.03 -9.87 -75.cJ2* -75.14*

Nifty -6.18 -9.94 -83.97* -84.21 * Basis -98.41 * -92.06*

2001-02 Futures -6.31 -6.05 -220.03* -215.84*

Nifty -6.65 - -6.31 -192.37* -88.19* Basis -54.34* -59.97*

2002-03 Futures -6.24 -6.31 -266.80* -264.19*

Nifty -6.74 - -6.88 -251.08* -248.85* Basis -119.10* -119.63*

2003-04 Futures -0.99 -8.08 -228.91 * -224.51 *

Nifty -0.94 -8.31 200.82* -197.04* Basis -116.34* -120.11*

2004-05 Futures -1.82 -7.77 -216.59* -207.72*

Nifty -UO -7.42 -199.57* -190.08* Basis -92.80* -116.22*

2005-06 Futures -0.12 -19.53** -178.56* -176.77*

Nifty -0.25 -17.85 -160.62 * -158.85* Basis -82.15* -85.53*

178

2006-07 Futures -0.96 -11.74** -199.69* -193.11*

Nifty -0.87 -10.99 -187.23* -181.55*

Basis -102.34* -109.69* One Year Pre 11th Sept, 2001

Futures -3.70 -15.35 -268.72* -269.02*

Nifty -3.67 -15.77 -241.91* -242.24*

Basis -108.59* -119.95* One Year p()st 11th Sept, 2001

Futures -1.39 -27.24* -230.24* -212.37* Nifty -1.60 -23.75* -223.04* -206.20* Basis - 108.59* -119.95*

One Year Pre 17th May, 2004

Futures -2.69 1.46 -250.05* -213.80* Nifty -2.65 1.41 -227.05* -193.79* Basis -83.27* -83.32*

One Year Post 17th May, 2004

Futures -4.06 -35.65* -199.11* -195.71* Nifty -2.83 -23.99* -94.03* -100.65* Basis -199.11 * -195.71*

One Year Pre 22"" MaL 2006 Futures -3.91 -28.35 -215.72* -221.02*

Nifty -3.77 -26.62 -204.51* -209.24* Basis -178.59* -185.95*

One Year Post 22nd May, 2006 Futures -4.70 -39.35 -302.72* -321.02*

Nifty -4.67 -38.62 -284.51 * -302.24* Basis -201.59* -211.95*

... ... . - .. * SignIfIcant at 5'70 Slgnlhcance level. ** SlgnIhcant at 10 % SignIficance level.

Table 6.3 Unit root test results (Augmented Dickey- Fuller test statistic)

Variable Augmented Significance level Optimal number ()f Nifty futures -2.85 0.18 7 Nifty Index -2.81 D.19 7

Nifty futures -13.30** 0.01 2 Nifty Index returns - 8.84** 0.01 6

** - Significant at I per cent level

The results of the unit root tests for Nifty futures and Nifty Index are given in

Table-6.3, which indicates that Nifty futures and Nifty Index arc not stationary at their

levels but their returns are stationary. From table-6.4 it is clear that both nIarkets have

179

stable long run relationship, though in the sh0l1 run they may be in the disequilibrium.

Presence of the cointegration bctween two price series implies that both the series are

integrated of onler I, which has already been shown with the help of unit root testing in

Table-6.2.

Presence of long run relationship implies that if both the price series contribute to

same nature of information the there may exist causality relationship between these two.

For this purpose Grange Causality testll has been applied. Table-6.6 discusses the results

of Granger causality. The results of Granger causality as presented in Table-6.6 are very

interesting. Bi-directional causality has been observed, which implies that both futures

and cash market contributes to the price movement in other series. In order to study the

extent of causality between both the markets V AR methodology has been applied, the

results of which have been attached in the appendix.

Table 6.4 Johansens Cointegration Test ResuIts<

Vector (r) A max A Trace

Full Period (Close to 0 71.30· 76.10· I 4.90 4.90

2000-01 0 49.70· 52.40-I 2.70 2.70

2001-02 0 29.70 33.00 1 3.30 3.30

2002-03 0 44.00· 48.50-I 4.50 4.50

2004-05 0 22.20· 22.90, 1 0.70 0.70

2005-06 0 40.60· 46.00-I 5.40 5.40

2006-07 0 39.80· 44.50· 1 3.50 4.80

31 The Granger Callsality tesl on the spot and futures index return has heell applied through a ncar-V AR approach.

180

One Year Pre II th Sept.,

2001

1 23.50, 26.60· 0 3.10 3.10

One Year Post 11 th Sept.,

2001 I 24.30, 31.20· 0 6.90 6.90

One Year Pre 17th May,

2004 1 22.00· 27.70· 0 5.70 5.70

One Year Post 17th May,

2004 I 20.80· 24.20· 0 3.50 3.50

One Year Pre 22nd May, 2006 I 25.60· 30.80· 0 8.70 8.50

One Year Post 22nd May,

2006 1 22.90· 26.60· 0 6.40 6.70

. .. .. * SlgIllhcant at 5% level of slgIllflcance.

c Critical values for the Johansens cointegration test have been taken from

Johansen S. and K. Juselius (1990).

The results of the cointegration tests for Nifty futures and Nifty Index are tabulated

below.

Table 6.5 Cointegration test results

Cointcgration Cointegrating vector -1.02

Engle Granger 5.72** p-valne 0.01

Optimal number of lags 4 ** - SIgnIficant at I per cent level

The above results indicate that Nifty futures and Index are cointegratcd of order I.

181

Table 6.6 Granger Causality Results

Dependent I Futures Cash Full Period (Close to Close)

Futures ---------- 10.00-Cash 17.04- ---------

2000-01 Futures ---- 13.80-

Cash 13.38- ----

2001-02 Futures ---- 11.00-

Cash 10.56' ----

2002-03 Futures ---- 13.28-

Cash 13.90- ----

2003-04 Futures ---- 25.50-

Cash 24.90' ----

2004-05 Futures ---- 37.78-

Cash 34.96- ----

2005-06 Futures ---- 20.7?'

Cash IS.IS- ----2006-07

Futures ---- 27.22-Cash 31.SI- ----

One Year Pre 11 th Sept.,

2001

Futures ---- 11.23-Cash II.OS- ----

One Year Post 11 th Sept.,

2001 Futures ---- 14.05*

Cash 13.S4* ----

One Year Pre 17th May,

2004 Futures ---- 29.45*

Cash 29.73* ----

One Year Post 17th May,

2004 Futures ---- 26.42*

Cash 25.41 * ----

IS2

One Year Pre 22",1 May,

2006 Futures ---- 32.45*

Cash 32.73* ----

One Year Post 22"" May,

2006 Futures ---- 29.81 *

Cash 29.99* ----. .. ... * Slgl1lt!cant at 1'70 level of slglllficance .

The results of the price discovery regression are tabulated below.

Table 6.7: Price discovery results (June 2000 - Dec 2006)

Coefficient Value t-statistic Significance

aor -0.0007 -1.26 0.21

au -0.2109* -2.22 0.03

PH -0.0732 -0.51 0.61

P2f 0.1496 1.06 0.29

30s -0.0006 -0.99 0.32

al s -0.0062 -0.06 0.95

Pis 0.0197 0.14 0.89

PZs 0.0938 0.65 0.52 . -* - Slgl1lt!cant at 5 per cent level

Table 6.8: Price discovery results (June 2000 - Dec 2003)

Coefficient Value t-statistic Significance

aor -0.0009 -1.12 0.26

aH -0.0549 -0.45 0.65

PH -0.3789 -1.16 0.25

P2f 0.4809 1.69 0.09

30s -0.0009 -1.05 0.29

al s 0.1604 1.22 0.22

Pis 0.4101 1.28 0.20

PZs -0.2894 -0.76 0.45

183

From the Table-6.7 above it is clear that for the entire period (June 2000 to

December 2006) there is no causality from either futures to spot or vice versa. Also, only

the futures market (and not the spot market) responds to a deviation from equilibrium.

From the Table-6.8 above it is clear that for the period (June 2000 to December

2003) there is no causality from either futures to spot or vice versa. Also, neither the

futures market nor the spot market responds to a deviation from equilibrium.

Table 6.9 Price discovery results (Jan 2004 - Dec 2006)

Coefficient Value t-statistic Signifkance

aUf -0.0008 -1.05 0.29

alf -0.4788** -3.65 0.01

IlH 0.4314* 1.96 0.05

Illf -0.4140* -2.19 0.03

aos -0.0005 -0.71 0.47

als -0.2838* -2.17 0.03

Ills -0.6324** -3.42 0.01

Ills 0.7140** 3.27 0.0 I

* Significant at 5 per cent level ** Significant at I per cent level

From the Table-6.9 above it is clear that for the period (January 2004 to

December 2006) there is causality from both futures to spot and vice versa. Also, both the

futures market and the spot market respond to a deviation from equilibrium.

Tables-6.2 to 6.9 present results of co-integration and Price discovery results

(equations 6.18, 6.19 and 6.20). Table-6.5 gives information flow from one market to

another. Engle and Granger methodology has been used to find out co- integration of

futures and cash market. Null hypothesis is that both the markets are independent (not co­

integrated).

Tables-6.7, 6.8 and 6.9 present the results of pnce discovery. Excepting

coefficient af all other coefficients are found statistically insignificant even at 5 per cent

level. This indicates information gets reflected first in the futures market. From the

results it is very difficult to say how much time it takes to go to cash market. One of the

184

constraints of the data is that daily close values are used whereas the information might

get transmitted much fastcr. This particular aspect can be stated more authoritatively only

if high frequency data is used for this purpose. High frequency data is currently not

available for spot market Index in India; therefore they could not be employed in the

equation.

)<'igure- 6.3(a) Shock in futures market destabilized the cash market

Response of DlFl[LN[NIFTYll to a unit shock in DlFl[LN[N:\l FUTURES])

Figure-6.3(b) Shock in the cash market and its effect to the futures market

Response of DlFl[LN[NM FUTURES]] to a unit shock in DlFl[LN[NIFTY]) ~ . -"-~ _____ ' __ '_r~_~".~. ____ ... -- .. - '.-~~------"_51)(£-002

I

.-'-...L'-j-....l....L.L...LLL...L'-.L....LLL..'-.-JLL-'....J-'--'....J-'-"---,--'--L-l--'--.Ll-LLJ.--'-L.l....L'--1.....LLl....LLl.--'-,d,'B no'[ ·(1(12

V AR results for the aggregate period (i.e. 2000-2(06) clearly shows that both

markets affect each other up to 4 lags (see figure 6.3(a) and 6.3(b», which implies that

there is no preferable market for the traders and they cannot rely upon the price

movement of the futures market. In figure 6.3(a) shows that a unit shock in futures

market destabilized the cash market up to 4 lags and thereafter the cash market price

curve established. Similar observation can be made from a unit shock to price series in

the cash market and its effect to the futures market, as shown through figure 6.3(b). The

185

notable consideration is that cash market reacted instantaneously from very upward point

to a unit shock in the futures market whereas, reverse seems to be the case of shock to the

cash market and its reaction to the futures market.

However, when disaggregatc price series were evaluated then significant price

discovery behaviour has been found in few sub periods viz; in 2000-0 I futures market

\cad the cash market by 4 days (sec figure 6.6(a) and 6.6(b», in 2001-02 by 6 days (see

figure 6.7(a) and 6.7(b», in 2002-03 by 2 day (see figure 6.8(a) and 6.S(b», in 2003-04

hy I day (see figure 6.9(a) and 6.9(b)) and in 2004-05 hy II days (see figure 6.10(a) and

6.10(b», whereas in 2005-06 (see figure 6.II(a) and 6.II(b» there is no preferable

market available to the traders. These results are very significant from the trader's

viewpoint because 2001-02 and 2004-05 witnessed highest volatility in the capital

market. In 2001-02 stock markets crashed due to the terTorist attack on WTC (World

Trade Center) in America on 11 Ih Sept, 2001, in 2004-05 Indian economy observed Black

Monday on nIh May, 2004 when Indian markets had to be closed due to the ever highest

selling pressure because of unexpected election results and in 2005-06 Indian markets

tumbled on 22"" May, 2006 due to Global Meltdown because of huge sell-off in Chinese

market.

In order to eonfim1 the impact of these abnormalities in the market and to verify

whether during this period futures market was actually able to provide significant

information regarding the prospective price movements in the cash market, we studied

the causality relationship between the futures and cash market by taking sub periods as

one year pre abnormal event and one year post abnormal event.

Fig-6.4(a) One-Year Pre 11th September 2001

Response ofDIFl[LN[NIFTYll to a unit shock in DIFI[LN[NM FUTURESll

I .,.1...1 ..LI .-JILL I *~.-JI_..LI-'I-,-I ..LI -'-'--L-'-'---'-LL-'.I...JILLI -'.1-'--.1...1 -'.1-'--Ll.I-'L~"l I I I I I I I I I .11 SS9:(,[ 002

186

Response of DIFl[LN[NM FUTURESll to a unit shock in DIFl[LN[NIFTYll

~~. ~----------------------------------------------,

.,-'-1 -'-I ~ILIl--'-I-1I--'-I-1I--'---'------'--1 -,-I ~I--,--I --11--,--1 -,-I --,1--,--1 -,-I --,I_ILL I --,I~ILLI --,I~ILLI -,-I~ILLI -,-I ~I-,-I -,-1--,1_-,---,--,--1 -'-1-11--,---,-I--'I';~ll !aS11r 007

Fig·6.4(b) One· Year Post Illh September 2001

Response of DIFl[LN[NIFTYll to a unit shock in DIFI[LN[NM FUTURES]] --------------~--- .. ----- --.~--"-,. --;1.11£.1[-002 ,---------.---~----- ---<---,

\

1

~~:~~~: ~I ~I~~----------------------------------~! .. . I

~i -,-I --'.1-,--1 -,-I --'.I--"ILL' --'.I--!I,-'-I --'.1 ~LI ..'-1--"1-,--1 --,1-,--1 -,-I --'.'_ILLI --'.I--"ILLI --'.1-,1-,--1 ..'-1 -,1-,--1 ..LI --'.1_1LLI --,-I--"ILL1 ..LI ~.LI ..L1 -,1-,--1 ..LI -'-.,-1 "-"I 1, m" .. , U ') .~

Response of DIFI [LN[NM FUTURES II to a unit shock in DIFI [LN[NIFTY]) _ I • A-_-"'--____ ~~_~ ___ • ____________ _

l"~~

I --,-1-,-,--1-,-1-,1-,-1 +--,1--'--1 LI-1I_LI--,I--,--1 LI~LI ,-I J.I_ILLI-,-I~ILLI-,-I~ILLI ..L'~I--,--I -,-1--,1--,--1 -'-I--'I_ILL~'--~LL~LL-'---'I"l11J]o£'DII] . ~

Fig·6.S (a) One· Year Pre 17th May, 2004

Response of DIFI [LN[NIFTYII to a unit shock in DIFI [LN[NM FUTURESll -----_ •. _---

~~, ---~----------·-------~:1.'5715£_{l():1

, •• -,-1 --'.I-,---'--.\'_ILL--,-I--"ILLI --'.1_,--1 --,-1--'--"-...LLL...LLL-'-.LLI ...11--,---,-' -,-1...L-,--,-I-,I--,---,---,I-,-1 -,---,_-,---,--,--I -,-I --,1--,---,---,1 "J11 5115[ 011]

187

Responsc of D1FI [LN[NM FUTURES)] to a unit shock in DIFl[LN[NIFTY]J

i ~~I----------------------------------------------------------~I . . I

i

. .--l-' ~I~I ~l ~'~I ~I_ILLI ~I __ .L· ~'_'LL' --"'LLI -'.I..JILLI -'.I..JILL' -'.I..JI~' -,-I..JI~' -,-,-",~, l'-"'_'LL'-"..J'---"..J'LL' -"--.LL-'.I..JI~L U:?5f DO] o ~ ~

Fig-6.S (b) Onc-Year Post 17th May, 2004

Rcsponse ofDIFl[LN[NIFTYll to a unit shock in D1FI[LN[NM FUTURES)]

\' ..

··-----~~---·~1'.051Q[ 001

I " .' ~'.('!"'"=:,..' c.......,...... : "7 . , S::;. : '7'" , !' • I 1 I I

. It -"'LL I II -"'LLI --"'LLI -'.' _IL---~I_ILLI -'.'--.lol -"'~' -,-I -"'LL I -,-I -"ILL I -'.I-"ILLI -'.I-"ILL' ~I..JILLI ~I..JILL' -"t..JIL----"'~1 -,-I ..JILL~---'--.:I L;} .051 0£-002 ~ l' ~

Responsc of DIFI [LN[NM FUTURES]] to a unit shock in DIFl[LN[NIFTYll . ----- --- --------- -- .... - •.....

111I -{)87

.~

•• \-ll-"--. "LL'~ "_L.L"~ "_L'~ "_'Ll ''\', '.-"--...J.L-'...J'LL-'--"--. "Ll'~ 'LL-'-...LJ 'LL-,--" II_LI.L:.I' ..Jt--'--ll...LJ "..JLIJ. '-"--.ILc.I-'-J"",.Il2Jf.OO2

From Figure 6.4 and 6.5 it is clear that during one year prior to the II th September

attack on America, futures market could lead the cash market by I day but during one

year after the attack Indian futures market was leading the cash market by 2 days. Similar

evidence has been observed during 2004-05 when Indian capital market observed biggest

ever market crash on 17'h May 2004.

One year prior to 17'h May 2004, futures market was leading the cash market by

one day, whereas one year after the date of abnormality, futures market lead the cash

market by 2 days, which supports the evidences available in the literate, that if efficient

188

cost-of carry exists between futures and cash markets then futures market plays key role

to stahilize the cash market and traders prefer to take hedging positions through futures

market. Existence of cointegration relationship clearly indicates that cost-of-carry

relationship bctwcen both markets is efficient and further evidences from the periods of

abnormalities implies that when Indian cash market is volatile then futures market is

more preferahle for the traders to take hedging and arbitrage positions.

6.9 Summaries and Conclusion

Introduction of derivatives in the Indian capital market had been a very well

planned decision. Prior to introducing derivatives in the Indian capital market, its

prospective impacts on the market were thoroughly evaluated; L. C. Gupta Committee

Report and J. R. VemJa Committee Reports are one of the significant evidences in this

regard. After thorough review and serious debate on the issue the derivatives were

introduced in live phases viz. in the first phase index futures were introduced, followed

by index options, stock options. and Stock futures respectively and in the last interest rate

futures.

Shenbagaraman (2003), Raju and Karande (2003) and Bandivadekar and Ghosh

(2005) observed significant decline in the spot market volatility during the post futures

treading era. The current study provides significant support to the decline in volatility

hypothesis by evaluating the price discovery performance of Indian equity futures market

during the period of high volatility. The results of the study are bcnclieial for the traders

as well as the regulators. The above results assures the traders that in the event of high

fluctuations in the market they can rely upon the direction of the futures market because

it would provide them significant information regarding the prospective move in the cash

market. Thus the retail as well as Indian institutional traders can design their portfolio

and can take positions in the futures market to safeguard themselves from the t1uctuations

in the cash market. In addition, the regulators will in advance come to know regarding the

prospective price movement in the cash market and when they feel market ovclTcacting

to the information. they can take appropriate action in the interest of the common

investor. Moreover from the price movements in the futures market they can adjudge the

expected volatility in the cash market.

189

Thus on the basis of above observations, it can be concluded that Indian futures

market is an cfficient price discovery vehicle and it will certainly help the traders to take

hedging and arbitrage positions to secure maximum returns at minimum risk exposure. In

addition. the contribution of the futures market to minimize the volatility of the cash

market is an important implication of the efficient price discovery. Though futures

market has been found relatively efficient price discovery vehicle but investigation of the

behaviour of spread between the futures and cash market (i.c. Basis) will provide

significant information regarding the exact extent of price discovery of the Indian equity

futurcs market.

Figure 6.6 (a) Year 2000-01 Nifty Cash Index

Response ofDIFl[LN[NIFTYll to a unit shock in DIFl[LN[NM FUTURESll

Figure 6.6 (b) Year 2000-01 Nifty Future Index

Response of DIFl[LN[NM FUTURESll to a unit shock in DIF1[LN[NIFTY]] ------------,I'I])7'I:...{l()2

~~~.~~~~~------------------------------------------~

190

Figure 6.7 (a) Year 2001-()2 Nifty Cash Index

Response of DIF1[LN[NIFTYll to a unit shock in DIFl[LNINM FUTURES]]

Figure 6.7 (b) Year 2001-02 Nifty Future Index

Response of DIFI [LN[NM FUTURES II to a unit shock in DIFI [LN[NIFTY])

Figure 6.8 (a) Year 2002-03 Nifty Cash Index

Response ofDIFI[LN[NIFTY]) to a unit shock in DIF1[LN[NM FUTURES]) ~------------~--- 1026E-003

.. <- •

191

Figure 6.8 (b) Year 2002-03 Nifty Future Index

Response of DIFl[LN[Nl\I FUTURES]] to a unit shock in DIFl[LN[NIFTY]] ... ~~~~ .. ~.~ .. ~ _. - -~.~~~ .. ~----~ - ·--"-·~~---·--~-~-~.1492[.OO)

! ~------, ~------;I

u I I -'-".tj-"I-'-~~~I-'-I---JL.---.l---.Lli I I I I I I I LLLj,-,--,--,-,---,--,-I_,IL.L-'--'-"-' '-.;;1 19 J.4a}[ _0111 50

Figure 6.9 (a) Year 2003-04 Nifty Cash Index

Response of DIFl[LN[NIFTY]] to a unit shock in DIFI [LN[Nl\I FUTURES]]

•---,--1 LI -,-I~ILL-,--,~ILLI m'-'-'~--'-' -,-I-,---,-I-,-I-'-LI LI -,-I~I-,-I -,--,-1--,1-,-1 -'--'--'-LI LI -'-1~1--,---,--,-I-,-I-'--'--'--'-LL.LJLL,jI. 1.(12'8[-0112 I til ~

Figure 6.9(b) Year 2003-04 Nifty Future Index

Response of DIFl[LN[Nl\I FUTURES]] to a unit shock in DIFl[LN[NIFTY]]

: , .

192

Figure 6.10 (a) Year 2004-05 Nifty Cash Index

Rcsponse of DIFI[LN[NIFTYJj to a unit shock in DIF1[LN[NM FUTURESJj

-"-~-r'-

I .--'-,--'--"-, .LI """--"''---'-'--''_'L.L' ~-LI -,-I--,---,~-,-,--,--I .LI """--"'-L' -,-I ",',,-"''-'--'---'--'---'-'.-'-L' -,-I--'--LL----''--'---'-'--'--.LI --'I--"I--.;il, 4311£ 002 D SI)

Figure 6.IO(b) Year 2004-05 Nifty Future Index

Response of DIFI[LN[NM FUTURESll to a unit shock in DIFl[LN[NIFTYll -~-.~.-----,,-...... " ...... _ ... __ ._._-_._--_._ ..• -----.~-~-.------,

r-"-r-'~~~~~~~~~~~~------------------------~

r LI -'-'--'--.L..l~L..L--'--'--..L1 --"'-L' i\;' _. -L"--,--,I_IL.L..L-'-LI -,-I ..... I--,I'-'--'-'--'--.LI -,-I -,'--,--I -'-'-'-.--L-'--"--,---,--"LL..LI-.[J"l '.''':'802 8 U ~

Figurc 6.11 (a) Year 2005-06 Nifty Cash Index

Response of DIF 1 [LN[NIFTYll to a unit shock in DIFI [LN[NM FUTURESll ------,1.0941£.002

193

Figure 6.11 (b) Y car 2005-06Nifty Future Index

Response of D1Fl[LN[NM f'UTURES]l to a unit shock in D1Fl[LN[NIFTYll

1 • I 1 I ) ! I I

j 1

I I I I I L_J-,I~I'-LI -'-11-,--1 LI -"--,1-,--1 -'--J.I-"I_-,--,-,--I LI -,-I U--L l, 1mr 00' . 'ill

194