Final Exam IIM by mnmgroup

VIEWS: 6 PAGES: 100

									1. INTRODUCTION TO CAPITAL MARKET

Meaning:

Capital markets are markets where people, companies, and governments with more funds than
they need (because they save some of their income) transfer those funds to people, companies, or
governments who have a shortage of funds (because they spend more than their income). Stock
and bond markets are two major capital markets. Capital markets promote economic efficiency
by channelling money from those who do not have an immediate productive use for it to those
who do.

Capital markets carry out the desirable economic function of directing capital to productive uses.
The savers (governments, businesses, and people who save some portion of their income) invest
their money in capital markets like stocks and bonds. The borrowers (governments, businesses,
and people who spend more than their income) borrow the savers' investments that have been
entrusted to the capital markets.

For example, suppose A and B make Rs. 50,000 in one year, but they only spend Rs.40,000 that
year. They can invest the 10,000 - their savings - in a mutual fund investing in stocks and bonds
all over the world. They know that making such an investment is riskier than keeping the 10,000
at home or in a savings account. But they hope that over the long-term the investment will yield
greater returns than cash holdings or interest on a savings account. The borrowers in this
example are the companies that issued the stocks or bonds that are part of the mutual fund
portfolio. Because the companies have spending needs that exceeds their income, they finance
their spending needs by issuing securities in the capital markets.




                                                                                                 1
The Structure of Capital Markets

Primary markets:

The primary market is where new securities (stocks and bonds are the most common) are issued.
The corporation or government agency that needs funds (the borrower) issues securities to
purchasers in the primary market. Big investment banks assist in this issuing process. The banks
underwrite the securities. That is, they guarantee a minimum price for a business's securities and
sell them to the public. Since the primary market is limited to issuing new securities only, it is of
lesser importance than the secondary market.

Secondary market:

The vast majority of capital transactions, take place in the secondary market. The secondary
market includes stock exchanges (like the New York Stock Exchange and the Tokyo Nikkei),
bond markets, and futures and options markets, among others. All of these secondary markets
deal in the trade of securities.

Securities:

The term "securities" encompasses a broad range of investment instruments. Investors have
essentially two broad categories of securities available to them:

1. Equity securities (which represent ownership of a part of a company)

2. Debt securities (which represent a loan from the investor to a company or government entity).




                                                                                                   2
Equity securities:

Stock is the type of equity security with which most people are familiar. When investors (savers)
buy stock, they become owners of a "share" of a company's assets and earnings. If a company is
successful, the price that investors are willing to pay for its stock will often rise and shareholders
who bought stock at a lower price then stand to make a profit. If a company does not do well,
however, its stock may decrease in value and shareholders can lose money. Stock prices are also
subject to both general economic and industry-specific market factors. In our example, if Carlos
and Anna put their money in stocks, they are buying equity in the company that issued the stock.
Conversely, the company can issue stock to obtain extra funds. It must then share its cash flows
with the stock purchasers, known as stockholders.

Debt securities:

Savers who purchase debt instruments are creditors. Creditors, or debt holders, receive future
income or assets in return for their investment. The most common example of a debt instrument
is a bond. When investors buy bonds, they are lending the issuers of the bonds their money. In
return, they will receive interest payments (usually at a fixed rate) for the life of the bond and
receive the principal when the bond expires. National governments, local governments, water
districts, global, national, and local companies, and many other types of institutions sell bonds.

Internationalization of Capital Markets in the Late 1990s

One of the most important developments since the 1970s has been the internationalization, and
now globalization, of capital markets. Let's look at some of the basic elements of the
international capital markets.




                                                                                                     3
CAPITAL MARKET IN INDIA: -

Coming to Indian context, the term capital market refers to only stock markets as per the
common man's ideology, but the capital markets have a much broader sense. Where as in global
scenario, it consists of various markets such as:

1. Government securities market
2. Municipal bond market
3. Corporate debt market
4. Stock market
5. Depository receipts market
6. Mortgage and asset-backed securities market
7. Financial derivates market
8. Foreign exchange market

India’s presence in International Markets:

India has made its presence felt in the IFMs only after 1991-92. At present there are over 50
companies in India, which have accessed the GDR route for raising finance. The change in
situation has been due to the following factors:

1. Improved perception of India‘s economic reforms.
2. Improved export performance.
3. Healthy economic indicator.
4. Inflation at single digit.
5. Improved forex reserves.
6. Improved performance of Indian companies.
7. Improved confidence of FIIs.

Reliance was the first Indian company to issue GDR in 1992. Since 1993, number of Indian
companies successfully tapped the global capital markets & raised capital through GDR or
foreign currency bond issues. Though there was a temporary setback due to Asian crisis in 1997.
Since 1999 even IT majors have stepped the bandwagon of international markets & raised


                                                                                              4
capital. The average size of the issue was around 75USD. And the total amount raised was
around USD 6.5billion. India has the distinction of having the largest number of GDR/ADR
issues by any country.

NSE’s Capital Market Segment

The Trading on the NSE‘s capital market commenced on November 4, 1995 and has been
witnessing a substantial growth over the years. The growth of NSE turnover figures shows a
substantial rise from Rs. 1,805 crore (US $ 574.29 million) in the year 1994-95 to Rs. 2,752,023
crore (US $ 540,141.59 million) in 2008-09. With the increase in volumes, efficient and
transparent trading platform, a wide range of securities like equity, preference shares, debt
warrants, exchange traded funds as well as retail government securities, NSE upholds its position
as the largest stock exchange in the country. The CM segment of NSE provides an efficient and
transparent platform for trading of equity, preference shares, debentures, warrants, exchange
traded funds as well as retail Government securities.




                                                                                               5
2. INTRODUCTION TO DERIVATIVE MARKET

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. Dr.
L.C.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 informational efficiency
of the cash market.

Many emerging 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 has 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.




                                                                                                  6
As far as developed markets, such as USA, UK, Japan etc., are concerned, a number of important
and in-depth studies have been carried out to examine the lead-lag relationship between the spot
and derivative, viz. futures market and also to provide the possible explanations behind such
relation and its changes over time. Therefore, the present study seeks to contribute to the
existing knowledge base and literature for examining the actual lead-lag relationship among the
Indian spot and futures market in terms of returns for the time period Jan-2007 to Dec-2008

2.1 HISTORY OF DERIVATIVE MARKET

Derivatives can be found throughout the history of mankind. In the Middle Ages, engaging in
contracts at predetermined prices for future delivery of farming products, for example, was quite
frequent. Hundreds of years ago, Japan had a semblance of an actual futures exchange. But it
was not until 1848 that the first modern, organized futures market in North America was
created—the Chicago Board of Trade.

Agricultural Futures Dominate the First 100 Years of Derivatives Trading

After the Chicago Board of Trade first opened its doors, the grain market in Chicago almost
exploded. Farmers needed to secure prices for their grain, needed to know those prices in
advance of the crops, needed a place to store the grain, and needed someone to facilitate delivery
and settlement of futures contracts.

Around that time, the first customized option contracts were offered, too. To illustrate, a well
known financier of the era, Russell Sage, offered customized options that effectively imitated
loans at interest rates that were much higher than rates allowed under the then-existing usury
laws.

After the Chicago Board of Trade, other organized derivatives markets were established in the
U.S., including the Chicago Mercantile Exchange, and later The New York Mercantile Exchange
and the Chicago Board Options Exchange. The latter two subsequently became the main driving
forces of the derivatives industry worldwide.




                                                                                                7
During the 1970s, Financial Derivatives Enter the Scene

The new era for the derivative markets was ushered with the introduction of financial
derivatives, and it continues to last to this day. Although commodity derivatives are still quite
active, particularly oil and precious metals, financial derivatives dominate trading in the current
derivative markets. In addition, although customized options existed since the 19th century at
least, the introduction of standardized options in 1973 completely overshadowed their
customized counterparts.

Another important factor impacted the derivatives markets in the 1970s—deregulation of foreign
exchange rates. When foreign exchange rates became freely floating, not only have new currency
markets developed, but also the markets for trading customized forward contracts in foreign
currencies. This market was later referred to as the interbank market because most of the
participants were, and still are, domestic and international banks. Aside from facilitating trading
in currency derivatives, the currency interbank market also set the stage for the banking industry
to become more involved in trading of other types of financial derivatives.

The Age of Deregulation in the 1980s

More deregulation of the 1980s further blurred the regulatory lines among financial services
providers, such as banks, insurance industries, securities dealers, etc. Banks in particular
discovered they could create various types of derivatives that were to be sold to corporations, as
well as to other financial institutions. The idea was to create tailored products that were designed
to alleviate risk exposure specific to certain situations and certain players.

Of course, banks were not the only ones profiting from financial derivatives designed to transfer
or lay off risks elsewhere. Investment banking firms, also called derivatives dealers, soon joined
in the burgeoning derivative markets.




                                                                                                  8
The Age of Maturity in the 1990s

Although the derivatives markets slowed down considerably by the end of the 20th century, that
did not mean that there were not a steady offering of existing, as well as new derivative products.
Derivatives exchanges also went through a period of change; some consolidated, some merged,
some became for-profit institutions. Regardless, they all had something in common—the need
for less regulation.

Aside from structural changes, some derivative exchanges also changed the way they conducted
trading. Old systems of face-to-face trading on trading floors have been replaced with electronic
trading, and telephone and computer networks. With the advent of Internet, electronic trading
evolved into e-trading. And although trading floors still dominate derivative markets in the U.S.,
it is obvious that to stay competitive, the U.S. will have to eventually embrace electronic trading.

Derivatives Markets in the 21st century

There is a general consensus that London and New York are the world‘s primary markets for
over-the-counter derivatives. Notably, a significant derivative trading is also in Tokyo, Paris,
Frankfurt, Chicago, Amsterdam, etc.

In terms of size, today the U.S. accounts for almost 35% of futures and options trading
worldwide. However, the Korea Stock Exchange is the largest derivative exchange in the world.
The second largest by volume is the Eurex (German-Swiss), followed by the Chicago Board of
Trade, the London International Financial Futures and Options Exchange, the Paris bourse, the
New York Mercantile Exchange, the Bolsa de Mercadorias & Futuros of Brazil, and the Chicago
Board Options Exchange. Note that in 2001, these exchanges traded in aggregate 70 million
derivative contracts (Source: Futures Industry, January/February 2002).




                                                                                                   9
2.2 TYPES OF DERIVATIVES

          OTC
          Exchange-Traded

Broadly speaking there are two distinct groups of derivative contracts, which are distinguished
by the way they are traded in market:

   Over-the-counter (OTC) derivatives are contracts that are traded (and privately negotiated)
    directly between two parties, without going through an exchange or other intermediary.
    Products such as swaps, forward rate agreements, and exotic options are almost always
    traded in this way. The OTC derivatives market is huge.

   Exchange-traded derivatives (ETD) are those derivatives products that are traded via
    specialized derivatives exchanges or other exchanges. A derivatives exchange acts as an
    intermediary to all related transactions, and takes Initial margin from both sides of the trade
    to act as a guarantee. The world's largest derivatives exchanges (by number of transactions)
    are the Korea Exchange (which lists KOSPI Index Futures & Options), Eurex and CME
    Group (made up of the 2007 merger of the Chicago Mercantile Exchange and the Chicago
    Board of Trade and the 2008 acquisition of the New York Mercantile Exchange). According
    to BIS, the combined turnover in the world's derivatives exchanges totaled USD 344 trillion
    during Q4 2005. Some types of derivative instruments also may trade on traditional
    exchanges. For instance, hybrid instruments such as convertible bonds and/or convertible
    preferred may be listed on stock or bond exchanges. Also, warrants (or "rights") may be
    listed on equity exchanges. Performance Rights, Cash experts and various other instruments
    that essentially consist of a complex set of options bundled into a simple package are
    routinely listed on equity exchanges. Like other derivatives, these publicly traded derivatives
    provide investors access to risk/reward and volatility characteristics that, while related to an
    underlying commodity, nonetheless are distinctive.




                                                                                                 10
KINDS OF FINANCIAL DERIVATIVES

As already discussed, the important financial derivatives are the following:

           Forwards
           Futures
           Options, and
           Swaps


FORWARDS

Forwards are the oldest of all the derivatives. A forward contract refers to an agreement between
two parties to exchange an agreed quantity of an asset for cash at certain date in future at a
predetermined price specified in that agreement. The promised asset may be currency,
commodity, instrument etc.

FUTURES

A futures contract is very similar to a forward contract in all respect excepting the fact that it is
completely a standardized one. Hence, it is rightly said that a futures contract is nothing but a
standardized forward contract. It is legally enforceable and it is always traded on an organized
exchange.

The term ‗future trading‘ includes both speculative transactions where futures are bought and
sold with the objective of making profits from the price changes and also the hedging or
protective transactions where futures are bought and sold with view to avoiding unforeseen
losses resulting from price fluctuations.

A future contract is one where there is an agreement between two parties to exchange any assets
or currency or commodity for cash at a certain future date, at an agreed price. Both the parties to
the contract must have mutual trust in each other. It takes place only in organized futures market
and according to well-established standards.




                                                                                                    11
As in a forward contract, the trader who promises to buy is said to be in ‗long position‘ and the
one who promises to sell is said to be in ‗short position‘ in futures also.

SWAPS
Swap is yet another exciting trading instrument. In fact, it is a combination of forwards by two
counter parties. It is arranged to reap the benefits arising from the fluctuations in the market-
either currency market or interest rate market or any other market for that matter.


OPTIONS

A derivative transaction that gives the option holder the right but not the obligation to buy or sell
the underlying asset at a price, called the strike price, during, a period or on a specific date in
exchange for payment of a premium is known as ‗option‘. Underlying asset refers to any asset
that is traded. The price at which the underlying asset is traded is called the ‗strike price‘.




                                                                                                  12
2.3 IMORTANCE OF DERIVATIVES


   Derivatives are becoming increasingly important in world markets as a tool for risk
    management. Derivative instruments can be used to minimize risk. Derivatives are used to
    separate the risks and transfer them to parties willing to bear these risks. The kind of hedging
    that can be obtained by using derivatives is cheaper and more convenient than what could be
    obtained by using cash instruments. It is so because, when we use derivatives for hedging,
    actual delivery of the underlying asset is not at all essential for settlement purposes. The
    profit or loss on derivative deal alone is adjusted in the derivative market.
   Moreover, derivatives do not create any new risk. They simply manipulate risks and transfer
    them to those who are willing to bear these risks. Hedging risk through derivatives is not
    similar to speculation. The gain or loss on a derivative deal is likely to be offset by an
    equivalent loss or gain in the values of underlying assets. 'Offsetting of risks' is an important
    property of hedging transactions. But, in speculation one deliberately takes up a risk openly.
    When companies know well that they have to face risk in possessing assets, it is better to
    transfer these risks to those who are ready to bear them. So, they have to necessarily go for
    derivative instruments. All derivative instruments are very simple to operate. Treasury
    managers and portfolio managers can hedge all risks without going through the tedious
    process of hedging each day and amount/share separately.
   But with the rapid development of the derivative markets, now, it is possible to cover such
    risks through derivative instruments like swap. Thus, the availability of advanced derivatives
    market enables companies to concentrate on those management decisions other than funding
    decisions.
   Derivatives also offer high liquidity. Just as derivatives can be contracted easily, it is also
    possible for companies to get out of positions in case that market reacts otherwise. This also
    does not involve much cost.
   Thus, derivatives are not only desirable but also necessary to hedge the complex exposures
    and volatilities that the companies generally face in the financial markets today.




                                                                                                  13
2.4 EMERGENCE OF DERIVATIVE MARKET IN INDIA

With globalization of the financial sector, it's time to recast the architecture of the financial
market. The liberalized policy being followed by the Government of India and the gradual
withdrawal of the procurement and distribution channel necessitated setting in place a market
mechanism to perform the economic functions of price discovery and risk management. Till the
mid – 1980's, the Indian financial system did not see much innovation. In the last 18 years,
financial innovation in India has picked up and it is expected to grow in the years to come, as a
more liberalized environment affords greater scope for financial innovation at the same time
financial markets are, by nature, extremely volatile and hence the risk factor is an important
concern for financial agents. To reduce this risk, the concept of derivatives comes into the
picture. Derivatives are products whose values are derived from one or more basic variables
called bases. India is traditionally an agriculture country with strong government intervention.
Government arbitrates to maintain buffer stocks, fix prices, impose import-export restrictions,
etc. This paper focuses on the basic understanding about derivatives market and its development
in India.

The emergence of the market for derivatives products, most notable forwards, futures, options
and swaps can be traced back to the willingness of risk-averse economic agents to guard
themselves against uncertainties arising out of fluctuations in asset prices. By their very nature,
the financial markets can be subject to a very high degree of volatility. Through the use of
derivative products, it is possible to partially or fully transfer price risks by locking-in asset
prices. As instruments of risk management, derivatives products generally do not influence the
fluctuations in the underlying asset prices. However, by locking-in asset prices, derivatives
products minimize the impact of fluctuations in asset prices on the profitability and cash flow
situation of risk-averse investors.




                                                                                                14
2.4.1. DEVELOPMENT OF DERIVATIVE MARKET IN INDIA

The first step towards introduction of derivatives trading in India was the promulgation of the
Securities Laws (Amendment) Ordinance, 1995, which withdrew the prohibition on options in
securities. The market for derivatives, however, did not take off, as there was no regulatory
framework to govern trading of derivatives. SEBI set up a 24–member committee under the
Chairmanship of Dr.L.C.Gupta on November 18, 1996 to develop appropriate regulatory
framework for derivatives trading in India. The committee submitted its report on March 17,
1998 prescribing necessary pre–conditions for introduction of derivatives trading in India. The
committee recommended that derivatives should be declared as ‗securities‘ so that regulatory
framework applicable to trading of ‗securities‘ could also govern trading of securities. SEBI also
set up a group in June 1998 under the Chairmanship of Prof.J.R.Varma, to recommend measures
for risk containment in derivatives market in India. The report, which was submitted in October
1998, worked out the operational details of margining system, methodology for charging initial
margins, broker net worth, deposit requirement and real–time monitoring requirements. The
Securities Contract Regulation Act (SCRA) was amended in December 1999 to include
derivatives within the ambit of ‗securities‘ and the regulatory framework was developed for
governing derivatives trading. The act also made it clear that derivatives shall be legal and valid
only if such contracts are traded on a recognized stock exchange, thus precluding OTC
derivatives. The government also rescinded in March 2000, the three– decade old notification,
which prohibited forward trading in securities. Derivatives trading commenced in India in June
2000 after SEBI granted the final approval to this effect in May 2001. SEBI permitted the
derivative segments of two stock exchanges, NSE and BSE, and their clearing house/corporation
to commence trading and settlement in approved derivatives contracts. To begin with, SEBI
approved trading in index futures contracts based on S&P CNX Nifty and BSE–30(Sensex)
index. This was followed by approval for trading in options based on these two indexes and
options on individual securities.




                                                                                                15
The trading in BSE Sensex options commenced on June 4, 2001 and the trading in options on
individual securities commenced in July 2001. Futures contracts on individual stocks were
launched in November 2001. The derivatives trading on NSE commenced with S&P CNX Nifty
Index futures on June 12, 2000. The trading in index options commenced on June 4, 2001 and
trading in options on individual securities commenced on July 2, 2001.

Single stock futures were launched on November 9, 2001. The index futures and options contract
on NSE are based on S&P CNX

Trading and settlement in derivative contracts is done in accordance with the rules, by laws, and
regulations of the respective exchanges and their clearing house/corporation duly approved by
SEBI and notified in the official gazette. Foreign Institutional Investors (FIIs) are permitted to
trade in all Exchange traded derivative products.

The following are some observations based on the trading statistics provided in the NSE report
on the futures and options (F&O):

   Single-stock futures continue to account for a sizable proportion of the F&O segment. It
    constituted 70 per cent of the total turnover during June 2002. A primary reason attributed to
    this phenomenon is that traders are comfortable with single-stock futures than equity options,
    as the former closely resembles the erstwhile badla system.

   Typically, options are considered more valuable when the volatility of the underlying (in this
    case, the index) is high. A related issue is that brokers do not earn high commissions by
    recommending index options to their clients, because low volatility leads to higher waiting
    time for round-trips.

   Put volumes in the index options and equity options segment have increased since January
    2002. The call-put volumes in index options have decreased from 2.86 in January 2002 to
    1.32 in June. The fall in call-put volumes ratio suggests that the traders are increasingly
    becoming pessimistic on the market.




                                                                                               16
   Daily option price variations suggest that traders use the F&O segment as a less risky
    alternative (read substitute) to generate profits from the stock price movements. The fact that
    the option premiums tail intra-day stock prices is evidence to this. If calls and puts are not
    looked as just substitutes for spot trading, the intra-day stock price variations should not have
    a one-to-one impact on the option premiums. There are no derivatives based on interest rates
    in India today. However, Indian users of hedging services are allowed to buy derivatives
    involving other currencies on foreign markets. India has a strong dollar- rupee forward
    market with contracts being traded for one to six month expiration. Daily trading volume on
    this forward market is around $500 million a day. Hence, derivatives available in India in
    foreign exchange area are also highly beneficial to the users.




                                                                                                  17
2.4.2. GROWTH OF DERIVATIVE MARKET IN INDIA :

Factors Generally Attributed As The Major Driving Force Behind Growth Of Financial
They are:
(a) Increased Volatility in asset prices in financial markets,
(b) Increased integration of national financial markets with the international markets,
(c) Marked improvement in communication facilities and sharp decline in their costs,
(d) Development of more sophisticated risk management tools, providing economic
agents a wider choice of risk management strategies, and
(e) Innovations in the derivatives markets, which optimally combine the risks and returns over a
large number of financial assets, leading to higher returns, reduced risk as well as transaction
costs as compared to individual financial assets.




                                                                                             18
Growth Of Financial Derivative Market With Respect To Cash Market

Table – 2.4.1 Market Turnover of BSE & NSE in derivative and cash market (2006-2009)

Market turnover (Rs. Crore)

 (Source : www.nseindia.com and www.bseindia.com)

Market                Calendar year

                      2005-06           2006-07        2007-08       2008-09

NSE Spot              1569558           1945287        3551038       2752023

NSE Derivatives       4824250           7356271        13090478      11010482

Total of NSE          6393808           9601558        16641516      13762505

% of NSE spot         31.15%            32.47%         21.38%        20.58%

% OF NSE Derivative 68.85%              67.53%         78.62%        79.42%




                                                                                       19
Chart 2.4.1




                                       Turnover of NSE in Derivative and Cash
                                                      Market
                                 14000000
                                 12000000
       Turnover(Rs. in crores)




                                 10000000
                                  8000000
                                  6000000
                                                                                                NSE Derivatives
                                  4000000
                                  2000000                                                       NSE Spot
                                        0
                                            2005-06       2006-07          2007-08   2008-09
                                                                    Year




Chart-2.4.2




                                    Contribution of financial derivatives against cash market on NSE


       100%

          80%

          60%
                                                                                                NSE Derivatives
          40%
                                                                                                NSE Spot
          20%

                     0%
                                      2005-06         2006-07         2007-08         2008-09




                                                                                                                  20
3. RESEARCH METHODOLOGY

Problem Statement

The players in the derivatives market often crave to find out the direction and magnitude of
interdependence of the spot and futures market. The individual have very vague idea about such
relationship between two markets. There are mathematical calculations available to express such
dependency between the markets. But the direction of such relationship can be precisely
ascertained by studying the lead-lag relationship between the two markets. A number of studies
have empirically examined the temporal relationship between the futures and spot markets.
These studies seek to find the lead-lag relations between the futures and the spot market for an
asset class and the differential speed of adjustments to flow of new information. The major gap
in those is that the studies are mainly based on the view that the futures market is determinant of
the spot market. The two way analysis showing the inter-dependence (spot dependent on futures
and futures dependent on spot) fills the above gap.

Literature Review

There is an extensive amount of literature examining the impact of derivative trading on the
return as well as on the volatility of underlying spot market, giving special emphasis on the lead-
lag relationship between the spot and the derivatives, viz., futures and options market all over the
world.

In a world of complete market and no transaction costs, any new security can be synthesized
from existing securities. Consequently, the introduction of derivatives, such as options should
have no effect on underlying assets. According to Grossman (1988), the existence of transaction
costs and incomplete markets suggests the possibility that futures or options can have an impact
on spot market volatility. Nathan Associates (1969) makes clear that diversion of speculative
interest to the option market may reduce stock trading and therefore may cause reduction in
liquidity which might increase the stock‘s return variance. However, studies by Bansal et al.
(1989), Skinner (1989), Damodoran et al. (1991) find significant increase in stock trading
volume after the onset of derivative trading. Cox (1976) argues that futures trading can alter the
available information and thus spot market volatility for two reasons. First, futures attract


                                                                                                 21
additional traders to a market. Second, as transaction costs in the futures market is lower than
those in the spot market, new information may be transmitted to the futures market more quickly.

Now, as far as the temporal relationship among the spot and futures (options) market is
concerned, several studies, attempted to examine the lead-lag relationship between the spot and
the futures market both in terms of return and / or volatility includes Ng. (1987); Kawaller,
Koch, and Koch (1987); Harris (1989); Stoll & Whaley (1990); Chin, Chan and Karolyi (1991);
Chan (1992); Abhyankar (1995); Shyy (1996); Iihara (1996); Pizzi (1998); De Jong (1998);
Chatrath (1998); Min (1999); Tse (1999); Frino (2000); Thenmozhi (2002); Anand babu (2003);
Simpson (2004) etc. Almost all of these studies have concluded that there is a significant lead-lag
relationship among the spot and the futures market, and also have tried to provide the possible
explanation behind this. Most of the studies have suggested that the leading role of the futures
market varies from five to forty minutes, while the spot market rarely leads the futures market
beyond one minute.

While explaining the causes behind such relation, Kawaller et al. (1987) attribute the stronger
leading role of the futures market to the infrequent trading of component stocks. Though, at the
same time, Stoll & Whaley (1990), Chan (1992) etc. proved the existence of such relation even
in case of highly traded stocks or after adjusting for infrequent trading of component stocks.

Chin (1991) has examined the intraday relationship among price changes and volatility of price
changes in the stock index and stock index futures markets. Unlike the fact that the index futures
markets served as the primary market for price discovery, as found in the previous studies, they
have found the stronger interdependence in both the directions in the volatility of price changes
between the cash and the futures markets than that observed in case of price changes only. Their
evidence supported that the price innovations originate in one market, e.g. cash (futures) market,
can predict the future volatility in the other, such as futures (cash), market. In other words, both
cash and futures markets serve important role in discovering the price.

Chan (1992) have investigated the intraday lead-lag relationship between MM cash index and
MM and S&P futures index returns under different situations. Their results confirmed the leading
role of the futures market even against all the component stocks. They have also empirically
proved the leading role (to a greater degree) of the futures market for the release of any market-

                                                                                                 22
wide information. Abhyankar (1995) have found the possibilities of the cash and the futures
market playing the leading role, even in different intensities, under different situations, such as
for change in transaction cost, in periods of good, moderate and bad news, for high and low
trading volume in the underlying equity market etc. But as far as the conditional volatility is
concerned, they could not found any clear pattern of one market leading the other neither during
the periods of good or bad news nor for varying levels of market activity.

By using a specially designed correlation measure that takes into account the fact that high
frequency data are often observed at irregular intervals, De Jong (1998) have confirmed that
even in the presence of significant contemporaneous correlation among the spot, futures and the
options market, the futures price changes lead both the changes in the cash index and index
option by five to ten minutes. But, among the cash and the options market, the relations are
largely symmetrical and neither market consistently leads the other. Chatrath (1998) have
examined the intraday behavior of the spot and futures market following the release of
information and also investigate the role of such information in the volatility spillover among the
two markets. Their results have supported that one market leading to greater volatility in the
other is partly driven by information and therefore the leading role played by the futures market
may be the result of new information efficiently reflected in the futures market.

Min (1999) has investigated the possible lead-lag relationships in returns and volatilities between
cash and futures markets. Their results have suggested that unlike the lead-lag relationship in the
returns of spot and futures markets, there is significant but time dependent bidirectional causality
between the markets, as far as the volatility interaction among the markets is concerned. Frino
(2000) have examined the temporal relationship among the spot and the futures market around
the release of different types of information. They have found that the lead of the futures market
strengthens significantly around the release of macroeconomic information, while, the leading
role of the futures market weakens around stock-specific information release. Therefore,
according to them the disintegration in the relationship between the two markets is mainly driven
by noise associated with trading activity around the release of different types of information.

Simpson (2004) suggest that informed traders should trade in the futures market around the
release of macroeconomic announcements; while, the leading role of futures market weakens


                                                                                                  23
through the discovery of stock specific information [Grunbicher, Longstaff and Schwartz
(1994)].

By looking at the Indian market, Thenmozhi (2002); Anand babu (2003) etc. have found that the
futures market in India has more power in disseminating information and therefore has been
found to play the leading role (for one or two days) in the matter of price discovery.

The present study attempts to investigate that as the time have changed, is there any deviation in
the result from the earlier work? Moreover as the market is becoming more and more efficient
over a period of time, the time gap between cash and futures market in disseminating the
information have decreased or not which was earlier in days.

Research Question

To investigate the lead-lag relationship between the NIFTY Spot and Futures market in India, in
terms of return. The lead-lag relationship illustrates how well the two markets are linked, and
how fast one market reflects new information from the other. If feedback between spot and
futures exist, then it is possible that investor may use this information to predict the price
movement or return movement.

Objectives

1. To compare nifty future & spot market using intraday data on minute basis

2. To compare nifty individual stocks on daily closing price.

3. To check the efficiency of the market in processing the information. An Efficient market
theory states that all market participants receive and act on all of the relevant information as
soon as it is available.

Research Design

Descriptive Study




                                                                                               24
The Sample

The sample population of the study comprises

1. Daily intra-day minute to minute price, for NIFTY spot index and settlement price of NIFTY
   futures.
2. In order to carry out the study at the stock / script level, five underlying NIFTY stocks, viz.
   RELIANCE (Reliance Industries Ltd.), INFOSYSTCH (Infosys Technologies Ltd.),
   ICICIBANK (ICICI Bank Ltd.), TATASTEEL (Tata Steel Ltd.) and DLF (DLF Ltd.).

Sources of data

Primary: None

Secondary: The study is based on the secondary data collected from the official website of
National Stock Exchange.

Period of the Study

1. Nifty

By using intraday data on minute basis from December-2009 to February-2010, an effort has
been made to investigate the possible lead-lag relationship, in terms of return, among the NIFTY
spot index and nearby contract of NIFTY futures index in India and also to explore the possible
changes (if any) in such relationship around the release of different types of information.

2. Stock

Daily closing price data from March-2009 to February-2010 of stock having very high trading
turnover in the market have been taken into consideration. At script level 242 observation for the
mentioned time period of spot market and futures market have been collected, i.e. daily closing
price in spot market and daily settlement price in futures market for each script




                                                                                               25
Statistical Tools used in the study

   Augmented Dickey-Fuller test(Unit Root Test)
   Correlogram
   Cross Correlation
   Linear Regression (Ordinaly Least Square Method)
   Granger Causality Test

Scope of the study

This study covers only the analysis of S&P CNX NIFTY Index and Futures and five highly
traded scripts of Nifty i.e. Reliance Industries Ltd, Infosys Technologies Ltd, DLF Ltd, Tata
Steel Ltd and ICICI Bank Ltd based on selected time period.

Benefits

One of the core benefits of a market for derivative products of any asset class is the additional
information content that may be extracted out of the prices evolving in this market. Thus it is
said that besides the traditional role of risk sharing assigned to futures market, this market also
play an important role in the aggregation of information and price discovery.

The explicit determination of lead-lag relationship between the spot and the future market would
help the players to increase their potential profits/returns.

Limitations

The assumptions of this study form the part of the limitations, too.

a) Each new future contract is purchased only on the expiry of the previous contract.
b) In extension to the above assumption, the trading time for the futures contract is taken from
    1st day of the calendar month to the last expiry date of the same month. This limits data from
    being in the continuous form for subsequent months.
c) Unavailability of intra-day minute to minute data of stock specific.




                                                                                                26
Methodology

In order to get standard estimation the time series, they are converted into stationary stochastic
process. A stochastic process is said to be stationary if its mean is zero and variance are constant
over time and the value of the covariance between two time periods depends only on the distance
or gap or lag between the two time periods and not the actual time at which the covariance is
computed. In order to transform the stochastic process into stationary, the testing of stationarity
is required. Therefore, the analysis is begun with the stationarity testing i.e. Graphical Analysis,
Autocorrelation Function & correlogram and unit root testing.

First the stationarity has been checked for the closing price of cash market and settlement price
of futures market. If they are not stationary, then they are converted in logarithmic values in
order to make them in a continuous form. And if yet the time series are not stationary, then daily
returns are identified as the difference in the natural logarithm of the closing index value for the
two consecutive trading days .It can be presented as:

               �������� = ������������ �������� ��������−1                              (����
                                           or �������� = ������������ �������� − log⁡ ����−1 )


Where �������� is logarithmic daily return at time t. ��������−1 and �������� are daily prices of an asset at two
successive days, t-1 and t respectively.

In order to do time series analysis, transformation of original series is required depending upon
the type of series when the data is in the level form. The series of return was transformed by
taking natural logarithm. There are two advantages of this kind of transformation of the series.
First it eliminates the possible dependence of changes in stock price index on the price level of
the index. Second, the change in the log of the stock price index yields continuously
compounded series.

In examining the lead-lag relationship between cash and futures market, the first common but
important practice is to determine the maximum length of leads or lags which are assumed to be
significant in the present context. Here as the market is efficient it will process the information as
soon as it comes, so there may not be existence of lead-lag relationship at the higher lags. So the
length of leads and lags are taken as five, as there are five trading dates in a week. In order to get


                                                                                                   27
the lag (i.e.,����−���� ) and the lead (i.e.,����+���� ) coefficients the cross correlation function has been
undertaken. Cross correlation coefficient is correlation coefficient between the current cash
returns ( ��������,���� ) and past futures return ( ��������,����−���� ), and between the past cash return ( ��������,����+���� ) and
current futures return ( �������� ,���� ). It is to be noted here that the asymptotic standard errors for the
cross-correlation coefficients is approximated as the square root of the reciprocal of the number
of observations included in the sample.

After determining the lead-lag length, the next step is to examine the lead-lag behavior between
the cash and futures markets by estimating the following regression equations:

The model applied to investigate the lead-lag relation among the spot and the futures market in
terms of returns is such that

                                      ����
                     ��������,���� = ���� +   ���� →−����   �������� ��������,����+���� + ������������−1 + ��������


Where��������,���� , and ��������,���� , are cash and futures index returns at time t which have been collected at
each one minute interval. The coefficients with negative subscripts (i.e., ����−1 , ����−2 ,…, ����−���� ) are
lag coefficients and those with positive subscripts (i.e., ����+1 , ����+2 , …, ����+���� ) are lead coefficients.
If the lag coefficients become significant, then it can be inferred that the independent variable
lags dependent variable, or in other words, dependent variable leads the independent variable. In
the other way, if the lead coefficients will significant, then it can be proved that independent
variable leads the dependent variable. If both the lead and lag coefficients are found to be
significant, then neither market can be said to significantly lead the other and therefore both the
markets (spot and futures) are proved to be informational efficient. Moreover if such relationship
is not found, then Granger Causality test has been performed to check the cointegration in two
series i.e. cointegration between cash series and future series. Apart from this, the efforts have
been made to examine such relation between futures and individual component stocks. The
analysis has been performed using MS-Excel and E-view.




                                                                                                            28
                                         4.1 NIFTY
Table 4.1.1: Descriptive Statistics of Intra Day Price NIFTY Spot And NIFTY Futures



                                          NIFTY_FUT             NIFTY_SPOT
               Mean                         5037.881              5037.589
               Median                       5045.000              5049.100
               Maximum                      5296.100              5297.000
               Minimum                      4668.600              4676.550
               Std. Dev.                    172.1669              170.1119
               Skewness                    -0.109025             -0.106941
               Kurtosis                     1.645878              1.648441

               Jarque-Bera                  1705.223               1697.305
               Probability                  0.000000               0.000000

               Sum                          1.10E+08              1.10E+08
               Sum Sq. Dev.                 6.45E+08              6.30E+08

               Observations                  21755                  21755




Interpretation:

Descriptive statistics of closing price of NIFTY spot and NIFTY futures market are presented in
Table 1 above. The descriptive statistics for the series are mean, median, maximum, minimum,
standard deviation, skewness, kurtosis, Jarque-Bera & probability. Here as the skewness is not
zero for both the series, but negative. So both the series are negatively skewed. Moreover
Kurtosis statistics are less than 3, so both the series are platykurtic. And also Jarque Bera
statistics are greater than zero which rejects the hypothesis of normal distribution for both the
series.




                                                                                              29
Figure: 4.1.1 Intra Day Price of NIFTY Spot index (Dec-2009 to Feb-2010)



                                       NIFTY_SPOT
              5,400

              5,300

              5,200

              5,100

              5,000

              4,900

              4,800

              4,700

              4,600
                              5000       10000       15000       20000




Figure:4.1.2 Intra Day Price of NIFTY Futures (Dec-2009 to Feb-2010)




                                        NIFTY_FUT
              5,400

              5,300

              5,200

              5,100

              5,000

              4,900

              4,800

              4,700

              4,600
                              5000       10000       15000       20000




                                                                           30
Interpretation:

In the above diagram, Figure 1 and Figure 2 represents the per minute closing price of NIFTY
cash index and NIFTY futures.These plots gives an initial clue about the likely nature of the time
series. In Figure 1 and 2, it has been seen that over the period of study closing prices of NIFTY
cash and futures respectively have been increasing or decreasing, that is, showing an upward
trend or downward trend, suggesting perhaps that the mean of both the series has beeen
changing. This perhaps suggests that both the series are not stationary.

Moreover, here both the plots are minutely observed so we can easily infer that, both the series
move in the same direction and somewhat with the same magnitude. So, by establishing the lead-
lag relationship we may predict the change in prices within the particular trading period in either
way.




                                                                                                31
Autocorrelation Function And Correlogram

If one plot a diagramme for autocorrelation function, then the solid vertical line in this
diagramme represents the zero axis; observations above the line are positive and those below the
line are nagative values. For a purely white noise process the autocorrelations at various lags
hover around zero.This is the picture of a correlogram of a stationary time series. Thus, if the
correlogram of an actual time series resembles the correlogram of a white noise time series, we
can say that time series is probably stationary.

Table 4.1.2: Correlogram of Daily Closing Price of NIFTY Cash



            Date: 03/14/10 Time: 17:39
            Sample: 1 21755
            Included observations: 21755

               Autocorrelation    Partial Correlation        AC       PAC     Q-Stat   Prob

                |*******             |*******            1   1.000   1.000    21751.   0.000
                |*******            *|     |             2   1.000   -0.076   43496.   0.000
                |*******             |    |              3   0.999   0.006    65234.   0.000
                |*******             |    |              4   0.999   0.035    86966.   0.000
                |*******             |    |              5   0.999   0.020    108693   0.000
                |*******             |    |              6   0.999   -0.002   130413   0.000
                |*******             |    |              7   0.999   0.025    152128   0.000
                |*******             |    |              8   0.999   0.007    173838   0.000
                |*******             |    |              9   0.999   0.014    195542   0.000
                |*******             |    |             10   0.998   0.005    217242   0.000
                |*******             |    |             11   0.998   0.005    238936   0.000
                |*******             |    |             12   0.998   -0.007   260625   0.000
                |*******             |    |             13   0.998   0.002    282310   0.000
                |*******             |    |             14   0.998   -0.005   303989   0.000
                |*******             |    |             15   0.998   0.004    325662   0.000
                |*******             |    |             16   0.998   -0.010   347331   0.000
                |*******             |    |             17   0.997   -0.008   368994   0.000
                |*******             |    |             18   0.997   -0.002   390652   0.000
                |*******             |    |             19   0.997   0.007    412304   0.000
                |*******             |    |             20   0.997   0.004    433951   0.000
                |*******             |    |             21   0.997   -0.017   455593   0.000
                |*******             |    |             22   0.997   -0.004   477229   0.000




                                                                                               32
Table 4.1.3: Correlogram of Intra Day Price of NIFTY Futures



            Date: 03/14/10 Time: 17:42
            Sample: 1 21755
            Included observations: 21755

               Autocorrelation      Partial Correlation        AC       PAC     Q-Stat   Prob

                |*******             |*******             1    1.000   1.000    21751.   0.000
                |*******             |    |               2    1.000   -0.002   43497.   0.000
                |*******             |    |               3    1.000   -0.002   65236.   0.000
                |*******             |    |               4    0.999   0.005    86970.   0.000
                |*******             |    |               5    0.999   0.016    108698   0.000
                |*******             |    |               6    0.999   -0.012   130421   0.000
                |*******             |    |               7    0.999   0.022    152138   0.000
                |*******             |    |               8    0.999   0.001    173849   0.000
                |*******             |    |               9    0.999   0.010    195556   0.000
                |*******             |    |               10   0.998   -0.002   217257   0.000
                |*******             |    |               11   0.998   -0.001   238953   0.000
                |*******             |    |               12   0.998   -0.003   260644   0.000
                |*******             |    |               13   0.998   -0.001   282329   0.000
                |*******             |    |               14   0.998   -0.013   304008   0.000
                |*******             |    |               15   0.998   0.009    325683   0.000
                |*******             |    |               16   0.998   -0.015   347351   0.000
                |*******             |    |               17   0.997   -0.001   369014   0.000
                |*******             |    |               18   0.997   -0.007   390671   0.000
                |*******             |    |               19   0.997   -0.004   412323   0.000
                |*******             |    |               20   0.997   0.002    433969   0.000
                |*******             |    |               21   0.997   -0.006   455609   0.000
                |*******             |    |               22   0.997   -0.016   477243   0.000




Interpretation:

Table 2 and 3 represents the correlogram of intra day closing prices of NIFTY cash market and
NIFTY futures market respectively. Here the length of lag is considered 22, as there are 22
trading days in a month. In above tables the autocorrelation coefficients starts at very high value
at lag 1(1.0000 for NIFTY cash and NIFTY futures each)and declines very slowly. Thus it seems
that both the time series are nonstationary.

The AC statistics presented in Table 2 and 3 shows that the autocorrelation and partial
autocorrelation are statistically significant as they fall outside the the asymptotic bounds 2���� −0.5 .




                                                                                                     33
Unit root Test

A unit root test is a statistical test for detecting the presence of stationarity in the series. The
early and pioneering work on testing for a unit root in a time series was done by Dickey and
Fuller(Dickey and Fuller 1979,1981). If the variables in the regression model are not stationary,
then it can be shown that the standard assumptions for asymptotic analysis will not be valid. In
other words, the usual ―t-ratios‖ will not follow a t-distribution, so we cannot validly undertake
hypothesis tests about the regression parameters.

The presence of the unit root in a time series is tested with the help of Augmented Dickey-Fuller
Test. It tests for a unit root in the univariate representation of time series. For a return series ���� ���� ,
the ADF test consist of a regression of the first difference of the series against the series lagged k
times as follows :

                                                      ����
                          ∆�������� = ���� + ������������−1 +     ����=1 �������� ∆��������−����   + ��������


                                                           (����
                          ∆�������� = �������� − ��������−1 ; �������� = ln⁡ ���� )


The null hypothesis is �������� : δ=0 and ����1 : ���� < 1 . The acceptance of null hypothesis implies
nonstationarity.

One can transform the nonstationary time series to stationary time series either by differencing or
by detrending. The transformation depends upon whether the series are difference stationary or
trend stationary.




                                                                                                        34
Table 4.1.4:Unit Root Testing of Intra Day Price of NIFTY Spot


            Null Hypothesis: NIFTY_SPOT has a unit root
            Exogenous: Constant, Linear Trend
            Lag Length: 0 (Fixed)

                                                                   t-Statistic    Prob.*

            Augmented Dickey-Fuller test statistic                 -1.834484     0.6880
            Test critical values:  1% level                        -3.958456
                                   5% level                        -3.410009
                                   10% level                       -3.126725

            *MacKinnon (1996) one-sided p-values.


            Augmented Dickey-Fuller Test Equation
            Dependent Variable: D(NIFTY_SPOT)
            Method: Least Squares
            Date: 03/15/10 Time: 19:31
            Sample (adjusted): 2 21755
            Included observations: 21754 after adjustments

            Variable                 Coefficient     Std. Error    t-Statistic   Prob.

            NIFTY_SPOT(-1)           -0.000277       0.000151      -1.834484     0.0666
            C                        1.454890        0.791218      1.838799      0.0660
            @TREND(1)                -5.99E-06       4.09E-06      -1.463099     0.1435

            R-squared                0.000160          Mean dependent var        -0.006415
            Adjusted R-squared       0.000068          S.D. dependent var        2.861682
            S.E. of regression       2.861585          Akaike info criterion     4.940766
            Sum squared resid        178111.7          Schwarz criterion         4.941868
            Log likelihood           -53737.71         Hannan-Quinn criter.      4.941125
            F-statistic              1.741985          Durbin-Watson stat        1.839837
            Prob(F-statistic)        0.175197




                                                                                             35
Table 4.1.5: Unit Root Testing of Intra Day Price of NIFTY Futures


            Null Hypothesis: NIFTY_FUT has a unit root
            Exogenous: Constant, Linear Trend
            Lag Length: 0 (Automatic based on SIC, MAXLAG=46)

                                                                     t-Statistic     Prob.*

            Augmented Dickey-Fuller test statistic                  -1.883004       0.6633
            Test critical values:      1% level                     -3.958456
                                       5% level                     -3.410009
                                      10% level                     -3.126725

            *MacKinnon (1996) one-sided p-values.


            Augmented Dickey-Fuller Test Equation
            Dependent Variable: D(NIFTY_FUT)
            Method: Least Squares
            Date: 03/15/10 Time: 19:30
            Sample (adjusted): 2 21755
            Included observations: 21754 after adjustments

                     Variable         Coefficient    Std. Error      t-Statistic     Prob.

                  NIFTY_FUT(-1)        -0.000291     0.000155       -1.883004       0.0597
                       C                1.530254     0.810454        1.888144       0.0590
                   @TREND(1)           -6.31E-06     4.24E-06       -1.487228       0.1370

            R-squared                   0.000168        Mean dependent var         -0.005519
            Adjusted R-squared          0.000076        S.D. dependent var         2.961857
            S.E. of regression          2.961745        Akaike info criterion      5.009572
            Sum squared resid           190798.3         Schwarz criterion         5.010674
            Log likelihood             -54486.11        Hannan-Quinn criter.       5.009931
            F-statistic                 1.828074        Durbin-Watson stat         1.997068
            Prob(F-statistic)           0.160748




Interpretation:

Stationarity conditions of the Intra Day price of NIFTY cash and futures were tested by
Augmented Dickey Fuller Test. The results of this test reported in Table 4 and 5. ADF statistics
of both the series i.e. NIFTY SPOT in Table 4 and FUTURE in Table 5 shows presence of unit
root in both the markets as their ���������������� exceeds the ���������������� (i.e. 1.834484 < 3.410009 for cash and
1.883004 < 3.410009 for futures) at 5% significant level. So the null hypothesis is accepted that
both the series have unit root (i.e. δ=0). So both the series are non-stationary. Moreover trend
coefficients of both the series are statistically insignificant as their Mackinnon‘s value do not

                                                                                                   36
exceed the critical value at 5% level (p=0.1435> 0.05 for cash and p=0.1370 > 0.05 for futures).
This suggests the absence of trend in both the markets.

Table 4.1.6:Unit Root Testing of Logarithmic Series of Intra Day Price of NIFTY Spot



            Null Hypothesis: LN_NIFTY_SPOT has a unit root
            Exogenous: Constant, Linear Trend
            Lag Length: 0 (Fixed)

                                                                   t-Statistic     Prob.*

            Augmented Dickey-Fuller test statistic                 -1.850756      0.6798
            Test critical values:      1% level                    -3.958456
                                       5% level                    -3.410009
                                      10% level                    -3.126725

            *MacKinnon (1996) one-sided p-values.


            Augmented Dickey-Fuller Test Equation
            Dependent Variable: D(LN_NIFTY_SPOT)
            Method: Least Squares
            Date: 03/15/10 Time: 19:36
            Sample (adjusted): 2 21755
            Included observations: 21754 after adjustments

                    Variable          Coefficient    Std. Error    t-Statistic     Prob.

             LN_NIFTY_SPOT(-1)         -0.000284     0.000154     -1.850756       0.0642
                    C                   0.002434     0.001314      1.851402       0.0641
                @TREND(1)              -1.21E-09     8.28E-10     -1.463984       0.1432

            R-squared                  0.000162       Mean dependent var         -1.28E-06
            Adjusted R-squared         0.000070       S.D. dependent var         0.000576
            S.E. of regression         0.000576       Akaike info criterion      -12.08224
            Sum squared resid          0.007206         Schwarz criterion        -12.08114
            Log likelihood             131421.5       Hannan-Quinn criter.       -12.08188
            F-statistic                1.764487        Durbin-Watson stat        1.841559
            Prob(F-statistic)          0.171299




                                                                                             37
Table 4.1.7: Unit Root Testing of Logarithmic Series of Intra Day Price of NIFTY Futures



          Null Hypothesis: LN_NIFTY_FUT has a unit root
          Exogenous: Constant, Linear Trend
          Lag Length: 0 (Fixed)

                                                                 t-Statistic     Prob.*

          Augmented Dickey-Fuller test statistic                 -1.898544      0.6552
          Test critical values:     1% level                     -3.958456
                                    5% level                     -3.410009
                                   10% level                     -3.126725

          *MacKinnon (1996) one-sided p-values.


          Augmented Dickey-Fuller Test Equation
          Dependent Variable: D(LN_NIFTY_FUT)
          Method: Least Squares
          Date: 03/15/10 Time: 19:37
          Sample (adjusted): 2 21755
          Included observations: 21754 after adjustments

                  Variable          Coefficient    Std. Error    t-Statistic     Prob.

            LN_NIFTY_FUT(-1)         -0.000298     0.000157     -1.898544       0.0576
                   C                  0.002556     0.001346      1.899296       0.0575
              @TREND(1)              -1.28E-09     8.58E-10     -1.487040       0.1370

          R-squared                  0.000170       Mean dependent var         -1.10E-06
          Adjusted R-squared         0.000078       S.D. dependent var          0.000596
          S.E. of regression         0.000596       Akaike info criterion      -12.01280
          Sum squared resid          0.007724         Schwarz criterion        -12.01170
          Log likelihood             130666.3       Hannan-Quinn criter.       -12.01245
          F-statistic                1.849968        Durbin-Watson stat         1.996934
          Prob(F-statistic)          0.157267




                                                                                           38
Interpretation:

Stationarity conditions of the logarithmic series of intra-day prices of NIFTY cash and futures
were tested by Augmented Dickey Fuller Test. The results of this test reported in Table 6 and 7.
ADF statistics of both the series i.e. LNIFTYCP in Table 6 and LFUTURECP in Table 7 shows
presence of unit root in both the markets as their ���������������� exceeds the ���������������� (i.e. 1.850756 < 3.410009
for cash and 1.898544 < 3.410009 for futures) at 5% significant level. So the null hypothesis is
accepted that both the series have unit root (i.e. δ=0). So both the series are nonstationary.
Moreover trend coefficients of both the series are statistically insignificant as their Mackinnon‘s
value do not exceed the critical value at 5% level (p=0.1432 > 0.05 for cash and p=0.1370 > 0.05
for futures). This suggests the absence of trend in both the markets.




                                                                                                       39
Table 4.1.8:Unit Root Testing of Logarithmic Return Series of Intra Day Price of NIFTY
Cash



          Null Hypothesis: DLN_NIFTY_SPOT has a unit root
          Exogenous: Constant, Linear Trend
          Lag Length: 0 (Fixed)

                                                                 t-Statistic     Prob.*

          Augmented Dickey-Fuller test statistic                 -136.2455      0.0001
          Test critical values:     1% level                     -3.958456
                                    5% level                     -3.410009
                                   10% level                     -3.126725

          *MacKinnon (1996) one-sided p-values.


          Augmented Dickey-Fuller Test Equation
          Dependent Variable: D(DLN_NIFTY_SPOT)
          Method: Least Squares
          Date: 03/15/10 Time: 19:40
          Sample (adjusted): 2 21754
          Included observations: 21753 after adjustments

                  Variable          Coefficient    Std. Error    t-Statistic     Prob.

          DLN_NIFTY_SPOT(-1)         -0.920935     0.006759      -136.2455      0.0000
                  C                   9.23E-07     7.78E-06       0.118647      0.9056
              @TREND(1)              -1.91E-10     6.20E-10      -0.308313      0.7578

          R-squared                  0.460470       Mean dependent var          5.01E-08
          Adjusted R-squared         0.460420       S.D. dependent var         0.000781
          S.E. of regression         0.000574       Akaike info criterion      -12.08835
          Sum squared resid          0.007162         Schwarz criterion        -12.08725
          Log likelihood             131482.0       Hannan-Quinn criter.       -12.08799
          F-statistic                9281.414        Durbin-Watson stat        1.998230
          Prob(F-statistic)          0.000000




                                                                                           40
Table 4.1.9: Unit Root Testing of Logarithmic Return Series of Intra Day Price of NIFTY
Futures


           Null Hypothesis: DLN_NIFTY_FUT has a unit root
           Exogenous: Constant, Linear Trend
           Lag Length: 0 (Fixed)

                                                                  t-Statistic     Prob.*

           Augmented Dickey-Fuller test statistic                 -147.2754      0.0001
           Test critical values:     1% level                     -3.958456
                                     5% level                     -3.410009
                                    10% level                     -3.126725

           *MacKinnon (1996) one-sided p-values.


           Augmented Dickey-Fuller Test Equation
           Dependent Variable: D(DLN_NIFTY_FUT)
           Method: Least Squares
           Date: 03/15/10 Time: 19:42
           Sample (adjusted): 2 21754
           Included observations: 21753 after adjustments

                   Variable          Coefficient    Std. Error    t-Statistic     Prob.

             DLN_NIFTY_FUT(-1)        -0.998612     0.006781      -147.2754      0.0000
                    C                  1.13E-06     8.08E-06       0.139688      0.8889
                @TREND(1)             -2.04E-10     6.43E-10      -0.316309      0.7518

           R-squared                  0.499310       Mean dependent var          2.70E-08
           Adjusted R-squared         0.499264       S.D. dependent var         0.000842
           S.E. of regression         0.000596       Akaike info criterion      -12.01262
           Sum squared resid          0.007725         Schwarz criterion        -12.01151
           Log likelihood             130658.2       Hannan-Quinn criter.       -12.01226
           F-statistic                10845.02        Durbin-Watson stat        1.999885
           Prob(F-statistic)          0.000000




Interpretation:

In Table 8 and 9, ADF statistics of both the series shows absence of unit root (i.e. δ=0) in both
the series i.e. DLFUTURECP and DLNIFTYCP as their ���������������� exceeds the ���������������� ( 136.2455 &
147.2754 > 3.410009). Thus both the series are now stationary. And trend coefficients of both
the series are also statistically insignificant, that shows the absence of trend in both the series.




                                                                                                       41
Lead-Lag Relationship Analysis of Return Series of NIFTY Cash and Futures (Whole Period-
From Dec 01,2010 to Feb 28,2010)

Table-4.1.10: Descriptive Statistics of Intra Day Logarithmic Returns


                                       DLN_NIFTY_SPOT DLN_NIFTY_FUT
                  Mean                      -0.000392            -0.000392
                  Median                     0.000000             0.000000
                  Maximum                    0.015491             0.013374
                  Minimum                   -8.502252            -8.504300
                  Std. Dev.                  0.057647             0.057661
                  Skewness                  -147.4635            -147.4620
                  Kurtosis                   21748.66             21748.35


                  Jarque-Bera                4.29E+11            4.29E+11
                  Probability                0.000000             0.000000


                  Sum                       -8.530188            -8.528331
                  Sum Sq. Dev.               72.29215             72.32751

                  Observations                21755                21755




Interpretation:

Descriptive statistics on NIFTY spot and NIFTY futures market returns are presented in Table 10
above. The descriptive statistics for the return series are mean, median, maximum, minimum,
standard deviation, skewness, kurtosis, Jarque-Bera & probability. If we look into the summary
statistics of NIFTY Cash and NIFTY Futures index, then it can be seen that the mean returns has
been found to be zero reverting. The difference between the maximum and minimum value of
return is more or less same in both the market that leads to same standard deviation in both the
market. Skewness and Kurtosis measure the shape of the probability distribution. Skewness
measures the degree of asymmetry, with symmetry implying zero skewness. Here the returns are
negatively skewed, indicating the relatively long left tail compared to the right tail, so the
distribution is non-symmetric. Kurtosis indicates the extent to which probability is concentrated

                                                                                              42
in the centre and especially at the tail of the distribution rather than in the shoulders which are
the regions between center and the tails. Every normal distribution has a Skewness equal to zero
and Kurtosis of 3. Kurtosis in excess of 3 indicates the leptokurtosis. Here, it can be found that
all the figures are positive and greater than 3, therefore all the return distributions are said to be
leptokurtic. The more the value of the kurtosis of the return in a market, the more destabilize is
the market‘s return. In statistics, the Jarque-Bera test is a goodness-of-fit measure of departure
from normality, based on the sample kurtosis and skewness JB=���� 6 (���� 2 − ���� − 3 2 4), where
n is the number of observations (or degrees of freedom in general); S is the sample skewness, K
is the sample kurtosis. The statistic JB has an asymptotic chi-square distribution with two
degrees of freedom and can be used to test the null hypothesis that the data are from a normal
distribution. The null hypothesis is a joint hypothesis of the skewness being zero and the excess
kurtosis being 0, since samples from a normal distribution have an expected skewness of 0 and
an expected excess kurtosis of 0 (which is the same as a kurtosis of 3). As the definition of JB
shows, any deviation from this increases the JB statistic. In this study, the higher value of Jarque-
Bera indicates that all the return series in all the time periods are non-normal.




                                                                                                   43
Table 4.1.11: Cross-correlation

              Date: 03/15/10 Time: 18:35
              Sample: 1 21755
              Included observations: 21754
              Correlations are asymptotically consistent approximations


              DLN_NIFTY_FUT,DLN_NIF DLN_NIFTY_FUT,DLN_NIFT
                   TY_SPOT(-i)            Y_SPOT(+i)       i                     lag      lead

                    |*********|                  |*********|              0    0.8557    0.8557
                    |      |                     |*      |                1    0.0349    0.1379
                    |      |                     |      |                 2    -0.0101   0.0155
                    |      |                     |      |                 3    -0.0163   -0.0203
                    |      |                     |      |                 4    -0.0156   -0.0253
                    |      |                     |      |                 5    0.0100    0.0059
                    |      |                     |      |                 6    -0.0152   -0.0250
                    |      |                     |      |                 7    -0.0096   -0.0042
                    |      |                     |      |                 8    -0.0125   -0.0120
                    |      |                     |      |                 9    -0.0069   -0.0058
                    |      |                     |      |                 10   0.0041    -0.0037



Interpretation

Using the cross-correlation function the lead and the lag coefficients have been found out in
Table 11 up to 10 order.




                                                                                                   44
Table 4.1.12: Lead-lag Relationship among the Spot and the Futures Markets Returns

Panel A:


             Dependent Variable: DLN_NIFTY_SPOT
             Method: Least Squares
             Date: 03/15/10 Time: 18:37
             Sample (adjusted): 11 21744
             Included observations: 21734 after adjustments

                     Variable         Coefficient    Std. Error     t-Statistic     Prob.

                      C                -1.40E-06     3.86E-06       -0.361720      0.7176
              DLN_NIFTY_FUT(-10)       -0.004430     0.006485       -0.683098      0.4946
              DLN_NIFTY_FUT(-9)        -0.003292     0.006485       -0.507578      0.6118
              DLN_NIFTY_FUT(-8)        -0.011055     0.006485       -1.704700      0.0883
              DLN_NIFTY_FUT(-7)        -0.001366     0.006485       -0.210635      0.8332
              DLN_NIFTY_FUT(-6)       -0.026136*     0.006484       -4.030759      0.0001
              DLN_NIFTY_FUT(-5)         0.008732     0.006485        1.346342      0.1782
              DLN_NIFTY_FUT(-4)       -0.024028*     0.006486       -3.704633      0.0002
              DLN_NIFTY_FUT(-3)       -0.018759*     0.006487       -2.892000      0.0038
              DLN_NIFTY_FUT(-2)       0.013875*      0.006486        2.139043      0.0324
              DLN_NIFTY_FUT(-1)       0.133577*      0.006487        20.59076      0.0000
               DLN_NIFTY_FUT(1)       0.034351*      0.006487        5.295162      0.0000
               DLN_NIFTY_FUT(2)        -0.009118     0.006487       -1.405583      0.1599
               DLN_NIFTY_FUT(3)       -0.013710*     0.006487       -2.113455      0.0346
               DLN_NIFTY_FUT(4)       -0.016645*     0.006486       -2.566182      0.0103
               DLN_NIFTY_FUT(5)       0.012919*      0.006486        1.991822      0.0464
               DLN_NIFTY_FUT(6)       -0.015230*     0.006484       -2.348781      0.0188
               DLN_NIFTY_FUT(7)        -0.006656     0.006485       -1.026252      0.3048
               DLN_NIFTY_FUT(8)        -0.012089     0.006484       -1.864409      0.0623
               DLN_NIFTY_FUT(9)        -0.006002     0.006484       -0.925569      0.3547
              DLN_NIFTY_FUT(10)         0.002494     0.006484        0.384614      0.7005

                   R-squared           0.023698        Mean dependent var         -1.46E-06
               Adjusted R-squared      0.022799        S.D. dependent var         0.000576
                S.E. of regression     0.000569        Akaike info criterion      -12.10429
               Sum squared resid       0.007031          Schwarz criterion        -12.09657
                  Log likelihood       131558.3        Hannan-Quinn criter.       -12.10178
                    F-statistic        26.35223         Durbin-Watson stat        2.146052
                 Prob(F-statistic)     0.000000




                                                                                              45
Panel B:


              Dependent Variable: DLN_NIFTY_FUT
              Method: Least Squares
              Date: 03/15/10 Time: 18:40
              Sample (adjusted): 11 21744
              Included observations: 21734 after adjustments

                      Variable         Coefficient    Std. Error     t-Statistic     Prob.

                       C                -1.23E-06     4.00E-06      -0.307912       0.7582
              DLN_NIFTY_SPOT(-10)        0.002640     0.006978       0.378272       0.7052
              DLN_NIFTY_SPOT(-9)        -0.005118     0.006999      -0.731251       0.4646
              DLN_NIFTY_SPOT(-8)        -0.009843     0.006999      -1.406384       0.1596
              DLN_NIFTY_SPOT(-7)        -0.005482     0.007003      -0.782795       0.4338
              DLN_NIFTY_SPOT(-6)       -0.016498*     0.007002      -2.355986       0.0185
              DLN_NIFTY_SPOT(-5)       0.016362*      0.007004       2.335969       0.0195
              DLN_NIFTY_SPOT(-4)       -0.015916*     0.007004      -2.272359       0.0231
              DLN_NIFTY_SPOT(-3)        -0.011858     0.007002      -1.693388       0.0904
              DLN_NIFTY_SPOT(-2)        -0.007855     0.007007      -1.120983       0.2623
              DLN_NIFTY_SPOT(-1)       0.036698*      0.006987       5.252321       0.0000
               DLN_NIFTY_SPOT(1)       0.141117*      0.006987       20.19805       0.0000
               DLN_NIFTY_SPOT(2)         0.00620      0.007007       0.886091       0.3756
               DLN_NIFTY_SPOT(3)       -0.019386*     0.007002      -2.768424       0.0056
               DLN_NIFTY_SPOT(4)       -0.021094*     0.007004      -3.011604       0.0026
               DLN_NIFTY_SPOT(5)       0.014323*      0.007005       2.044670       0.0409
               DLN_NIFTY_SPOT(6)       -0.028201*     0.007003      -4.027073       0.0001
               DLN_NIFTY_SPOT(7)         0.001422     0.007003       0.203052       0.8391
               DLN_NIFTY_SPOT(8)        -0.010309     0.006999      -1.472996       0.1408
               DLN_NIFTY_SPOT(9)        -0.003149     0.006998      -0.449939       0.6528
              DLN_NIFTY_SPOT(10)        -0.004154     0.006978      -0.595355       0.5516

              R-squared                 0.023045        Mean dependent var         -1.32E-06
              Adjusted R-squared        0.022145        S.D. dependent var         0.000596
              S.E. of regression        0.000589        Akaike info criterion      -12.03484
              Sum squared resid         0.007536          Schwarz criterion        -12.02713
              Log likelihood            130803.6        Hannan-Quinn criter.       -12.03233
              F-statistic               25.60866         Durbin-Watson stat        2.297313
              Prob(F-statistic)         0.000000




Interpretation:

Table 12 shows the lead lag relationship between the Cash and Futures market returns on minute
basis. Here by using Linear Regression Equation the lead and lag coefficients have been found
out up to 10th orders. In Panel A logarithmic return series of NIFTY cash DLNIFTYSPOT is
taken as dependent variable and null hypothesis was set as NIFTY futures leads/lags NIFTY cash


                                                                                               46
and in Panel B logarithmic return series of NIFTY futures i.e. DLNNIFTYFUT is taken as
dependent variable and null hypothesis was set as NIFTY cash leads/lags NIFTY futures.

The t-statistics for both the hypothesis are significant between +6 to -6 at 5% confidence level.
(Significant coefficients are shown using *) This suggests that both cash and futures markets
would react simultaneously to much of the information. It is to be noted here that any strong
generalization can‘t be made by looking in to the specific results found in this study, because
such results may be restricted only for the specific time period considered in this study and
therefore may be time-variant in nature. As far as the whole study period is concerned, the leads
as well as lag coefficients in the futures market are found to be significant up to 6 lags. This
suggests that the futures market leads or lags the cash market 4-6 minutes, while the reverse is
possible up to 6 lags that cash market leads or lags the futures market for 4-6 minutes, depending
on the time period. The regression results, using the cash return innovations, exhibits that neither
the lead, nor the lag coefficients are found to be significant beyond 6 leads or lags. This suggests
the contemporaneous bi-directional lead-lag relationship between these two market, and the flow
of information is also simultaneous between cash and futures market.




                                                                                                 47
Table 4.1.13: Pair wise Granger Causality Tests



Pairwise Granger Causality Tests
Date: 03/12/10 Time: 22:49
Sample: 1 21755
Lags: 10

Null Hypothesis:                                      Obs     F-Statistic   Prob.

DLN_NIFTY_FUT does not Granger Cause DLN_NIFTY_SPOT   21745   2.86444*      0.0014
DLN_NIFTY_SPOT does not Granger Cause DLN_NIFTY_FUT           2.36931*      0.0085




Pairwise Granger Causality Tests
Date: 03/12/10 Time: 22:50
Sample: 1 21755
Lags: 9

Null Hypothesis:                                      Obs     F-Statistic   Prob.

DLN_NIFTY_FUT does not Granger Cause DLN_NIFTY_SPOT   21746   3.17648*      0.0008
DLN_NIFTY_SPOT does not Granger Cause DLN_NIFTY_FUT           2.64457*      0.0046




Pairwise Granger Causality Tests
Date: 03/12/10 Time: 22:51
Sample: 1 21755
Lags: 8

Null Hypothesis:                                      Obs     F-Statistic   Prob.

DLN_NIFTY_FUT does not Granger Cause DLN_NIFTY_SPOT   21747   3.55711*      0.0004
DLN_NIFTY_SPOT does not Granger Cause DLN_NIFTY_FUT           2.77997*      0.0045




Pairwise Granger Causality Tests
Date: 03/12/10 Time: 22:51
Sample: 1 21755
Lags: 7

Null Hypothesis:                                      Obs     F-Statistic   Prob.

DLN_NIFTY_FUT does not Granger Cause DLN_NIFTY_SPOT   21748   3.86394*      0.0003
DLN_NIFTY_SPOT does not Granger Cause DLN_NIFTY_FUT           2.99772*      0.0038




                                                                                     48
Pairwise Granger Causality Tests
Date: 03/12/10 Time: 22:52
Sample: 1 21755
Lags: 6

Null Hypothesis:                                      Obs     F-Statistic   Prob.

DLN_NIFTY_FUT does not Granger Cause DLN_NIFTY_SPOT   21749   4.40578*      0.0002
DLN_NIFTY_SPOT does not Granger Cause DLN_NIFTY_FUT           3.43749*      0.0021




Pairwise Granger Causality Tests
Date: 03/12/10 Time: 22:53
Sample: 1 21755
Lags: 5

Null Hypothesis:                                      Obs     F-Statistic   Prob.

DLN_NIFTY_FUT does not Granger Cause DLN_NIFTY_SPOT   21750   5.22613*      8.E-05
DLN_NIFTY_SPOT does not Granger Cause DLN_NIFTY_FUT           4.06200*      0.0011




Pairwise Granger Causality Tests
Date: 03/12/10 Time: 22:53
Sample: 1 21755
Lags: 4

Null Hypothesis:                                      Obs     F-Statistic   Prob.

DLN_NIFTY_FUT does not Granger Cause DLN_NIFTY_SPOT   21751   6.48761*      3.E-05
DLN_NIFTY_SPOT does not Granger Cause DLN_NIFTY_FUT           5.05933*      0.0005




Pairwise Granger Causality Tests
Date: 03/12/10 Time: 22:54
Sample: 1 21755
Lags: 3

Null Hypothesis:                                      Obs     F-Statistic   Prob.

DLN_NIFTY_FUT does not Granger Cause DLN_NIFTY_SPOT   21752   8.28000*      2.E-05
DLN_NIFTY_SPOT does not Granger Cause DLN_NIFTY_FUT           6.10476*      0.0004




                                                                                     49
Pairwise Granger Causality Tests
Date: 03/12/10 Time: 22:54
Sample: 1 21755
Lags: 2

Null Hypothesis:                                                   Obs     F-Statistic   Prob.

DLN_NIFTY_FUT does not Granger Cause DLN_NIFTY_SPOT                21753    0.71268      0.4903
DLN_NIFTY_SPOT does not Granger Cause DLN_NIFTY_FUT                         1.00438      0.3663




Pairwise Granger Causality Tests
Date: 03/12/10 Time: 22:54
Sample: 1 21755
Lags: 1

Null Hypothesis:                                                   Obs     F-Statistic   Prob.

DLN_NIFTY_FUT does not Granger Cause DLN_NIFTY_SPOT                21754    1.13137      0.2875
DLN_NIFTY_SPOT does not Granger Cause DLN_NIFTY_FUT                         1.22408      0.2686



Interpretation:

Granger Causality Test checks the existence of relationship between two variables. In other
words it checks the dependence of one variable on other variable or the direction of influence. If
variable X (Granger) causes variable Y, then changes in X should precede changes in Y.
Therefore, in a regression of Y on other variables (including its own lagged values) if we include
past or lagged values of X and it significantly improves the prediction of Y, then we can say that
X (Granger) causes Y. A similar definition applies if Y (Granger) causes X. This causality
relationship between two variables can have 3 forms - (1) Unidirectional causality-if from both
variables either variable significantly causes changes in other variable (2) Bilateral causality-is
suggested when the sets of both time series coefficients are statistically significantly different
from zero in both the regressions. (3) Independence-is suggested when the sets of both the time
series coefficients are not statistically significant in both the regression. In Table 13 the
dependence or causality relationship of NIFTY cash and NIFTY futures return is checked for 10
lags. From the above table, one can easily infer that the F- statistics for all 3-10 lags in both the
regression, i.e. regression of NIFTY cash returns on NIFTY futures return and regression of
NIFTY futures return on NIFTY cash return are statistically significant at 5% level. So it
suggests both the variables are dependent and there is bilateral causality between them
(Significant coefficients are shown using *).

                                                                                                  50
                                            4.2 DLF

Preliminary Analysis

Table 4.2.1: Descriptive Statistics of Daily Closing Price and Settlement Price of DLF
SPOT And DLF Futures respectively


                                                DLF_FUT          DLF_SPOT
                    Mean                         336.9366          338.0603
                    Median                       365.4250          364.8000
                    Maximum                      470.4500          471.9500
                    Minimum                      122.4000          136.6500
                    Std. Dev.                    81.20752          78.99383
                    Skewness                    -0.969066         -0.922891
                    Kurtosis                     3.123610          3.036218

                    Jarque-Bera                  38.03064          34.36624
                    Probability                  0.000000          0.000000

                    Sum                          81538.65          81810.60
                    Sum Sq. Dev.                 1589313.          1503846.

                    Observations                    242               242

Interpretation

Descriptive statistics on DLF spot and DLF futures closing and settlement prices respectively are
presented in Table 1 above. The descriptive statistics series are mean, median, maximum,
minimum, standard deviation, skewness, kurtosis, Jarque-Bera & probability. Here as the
skewness is not zero for both the series, but negative. So both the series are negatively skewed.
Kurtosis suggests the leptokurtic series. Moreover Jarque Bera statistics are greater than zero
which rejects the hypothesis of normal distribution for both the series.




                                                                                              51
Graphical Analysis

Figure: 4.2.1 Daily closing price of DLF SPOT index (MARCH-2009 to FEB-2010)




                                  DLF_SPOT
    480

    440

    400

    360

    320

    280

    240

    200

    160

    120
              25     50    75    100   125    150   175    200   225




Figure:4.2.2 Daily settlement price of DLF futures (MARCH-2009 to FEB-2010)




                                   DLF_FUT
    500

    450

    400

    350

    300

    250

    200

    150

    100
              25     50    75    100   125    150   175    200   225




                                                                               52
Interpretation

In the above diagram, Figure 1 and Figure 2 represents the daily closing price of DLF cash index
and daily settlement price of DLF futures.These plots gives an initial clue about the likely nature
of the time series. In Figure 1 and 2, it has been seen that over the period of study closing price
and the settlement price of DLF cash and futures respectively have been increasing or
decreasing, that is, showing an upward trend or downward trend, suggesting perhaps that the
mean of both the series has beeen changing. This perhaps suggests that both the series are not
stationary.

Moreover, if both the plots are minutely observed than one can easily infer that, both the series
move in the same direction and somewhat with the same magnitude. So, by establishing the lead-
lag relationship one may predict the change in prices within the particular trading period in either
way.




                                                                                                 53
Table 4.2.2: Correlogram of Daily Closing Price of DLF SPOT


            Date: 03/17/10 Time: 11:57
            Sample: 1 242
            Included observations: 242

               Autocorrelation   Partial Correlation         AC     PAC      Q-Stat   Prob

                .|*******          .|*******           1    0.972   0.972    231.58   0.000
                .|*******          .|. |               2    0.942   -0.057   449.92   0.000
                .|*******          .|. |               3    0.911   -0.032   654.91   0.000
                .|******|          .|. |               4    0.880   -0.004   847.17   0.000
                .|******|          .|. |               5    0.849   -0.027   1026.8   0.000
                .|******|          .|. |               6    0.818   -0.016   1194.3   0.000
                .|******|          .|. |               7    0.789   0.026    1350.8   0.000
                .|***** |          .|. |               8    0.761   -0.010   1496.9   0.000
                .|***** |          .|. |               9    0.733   -0.006   1633.2   0.000
                .|***** |          *|. |               10   0.702   -0.082   1758.7   0.000
                .|***** |          .|. |               11   0.676   0.074    1875.5   0.000
                .|***** |          .|. |               12   0.652   0.023    1984.5   0.000
                .|***** |          .|. |               13   0.626   -0.052   2085.6   0.000
                .|**** |           .|. |               14   0.602   0.015    2179.4   0.000
                .|**** |           .|. |               15   0.580   0.031    2266.9   0.000
                .|**** |           .|. |               16   0.559   -0.008   2348.6   0.000
                .|**** |           *|. |               17   0.536   -0.068   2423.9   0.000
                .|**** |           .|. |               18   0.511   -0.019   2492.7   0.000
                .|**** |           .|. |               19   0.484   -0.050   2554.9   0.000
                .|*** |            .|. |               20   0.460   0.011    2611.1   0.000
                .|*** |            .|. |               21   0.438   0.047    2662.4   0.000
                .|*** |            .|* |               22   0.421   0.074    2710.0   0.000




                                                                                              54
Table 4.2.3: Correlogram of Daily Closing Price of DLF FUTURE


              Date: 03/17/10 Time: 11:57
              Sample: 1 242
              Included observations: 242

                 Autocorrelation   Partial Correlation         AC     PAC      Q-Stat   Prob

                  .|*******          .|*******           1    0.971   0.971    230.94   0.000
                  .|*******          .|. |               2    0.940   -0.038   448.50   0.000
                  .|*******          .|. |               3    0.909   -0.025   652.78   0.000
                  .|******|          .|. |               4    0.879   0.001    844.56   0.000
                  .|******|          .|. |               5    0.849   -0.025   1024.0   0.000
                  .|******|          .|. |               6    0.817   -0.037   1191.0   0.000
                  .|******|          .|. |               7    0.788   0.037    1347.1   0.000
                  .|***** |          .|. |               8    0.761   0.006    1493.2   0.000
                  .|***** |          .|. |               9    0.735   -0.000   1630.0   0.000
                  .|***** |          *|. |               10   0.704   -0.094   1756.1   0.000
                  .|***** |          .|. |               11   0.678   0.067    1873.5   0.000
                  .|***** |          .|. |               12   0.654   0.034    1983.4   0.000
                  .|***** |          .|. |               13   0.630   -0.037   2085.8   0.000
                  .|**** |           .|. |               14   0.607   0.002    2181.2   0.000
                  .|**** |           .|. |               15   0.586   0.030    2270.4   0.000
                  .|**** |           .|. |               16   0.565   -0.007   2353.9   0.000
                  .|**** |           .|. |               17   0.543   -0.057   2431.3   0.000
                  .|**** |           .|. |               18   0.519   -0.032   2502.2   0.000
                  .|**** |           .|. |               19   0.492   -0.055   2566.3   0.000
                  .|*** |            .|. |               20   0.467   0.001    2624.2   0.000
                  .|*** |            .|. |               21   0.445   0.047    2677.1   0.000
                  .|*** |            .|* |               22   0.428   0.085    2726.3   0.000



Interpretation

Table 2 and 3 represents the correlogram of daily closing price and settlament price of DLF cash
market and DLF futures market respectively. Here the length of lag is considered 22, as there are
22 trading days in a month. In above tables the autocorrelation coefficients starts at very high
value at lag 1(0.972 for DLF cash and 0.971 for DLF futures)and declines gradually. Thus it
seems that both the time series are nonstationary.

The AC statistics presented in Table 2 and 3 shows that the autocorrelation and partial
autocorrelation are statistically significant as they fall outside the the asymptotic bounds 2���� −0.5
(±0.089).




                                                                                                  55
Table 4.2.4:Unit Root Testing of Daily Closing Price of DLF SPOT



             Null Hypothesis: DLF_SPOT has a unit root
             Exogenous: Constant, Linear Trend
             Lag Length: 0 (Fixed)

                                                                    t-Statistic    Prob.*

             Augmented Dickey-Fuller test statistic                 -1.623922      0.7809
             Test critical values:     1% level                     -3.996592
                                       5% level                     -3.428581
                                      10% level                     -3.137711

             *MacKinnon (1996) one-sided p-values.


             Augmented Dickey-Fuller Test Equation
             Dependent Variable: D(DLF_SPOT)
             Method: Least Squares
             Date: 03/17/10 Time: 11:59
             Sample (adjusted): 2 242
             Included observations: 241 after adjustments

                     Variable          Coefficient    Std. Error    t-Statistic    Prob.

                 DLF_SPOT(-1)           -0.021981     0.013535     -1.623922       0.1057
                      C                  9.237636     3.960552      2.332411       0.0205
                  @TREND(1)             -0.009778     0.015361     -0.636553       0.5250

             R-squared                   0.025011      Mean dependent var         0.620124
             Adjusted R-squared          0.016818      S.D. dependent var         13.87363
             S.E. of regression          13.75647      Akaike info criterion      8.093266
             Sum squared resid           45039.23        Schwarz criterion        8.136645
             Log likelihood             -972.2385      Hannan-Quinn criter.       8.110742
             F-statistic                 3.052706       Durbin-Watson stat        1.875349
             Prob(F-statistic)           0.049085




                                                                                             56
Table 4.2.5:Unit Root Testing of Daily Closing Price of DLF FUT


             Null Hypothesis: DLF_FUT has a unit root
             Exogenous: Constant, Linear Trend
             Lag Length: 0 (Fixed)

                                                                     t-Statistic    Prob.*

             Augmented Dickey-Fuller test statistic                  -1.721053      0.7391
             Test critical values:     1% level                      -3.996592
                                       5% level                      -3.428581
                                      10% level                      -3.137711

             *MacKinnon (1996) one-sided p-values.


             Augmented Dickey-Fuller Test Equation
             Dependent Variable: D(DLF_FUT)
             Method: Least Squares
             Date: 03/17/10 Time: 11:59
             Sample (adjusted): 2 242
             Included observations: 241 after adjustments

                     Variable          Coefficient    Std. Error     t-Statistic    Prob.

                  DLF_FUT(-1)           -0.023318     0.013548      -1.721053       0.0865
                      C                  9.769753     3.924138       2.489656       0.0135
                  @TREND(1)             -0.010043     0.015807      -0.635367       0.5258

             R-squared                   0.027832       Mean dependent var         0.694191
             Adjusted R-squared          0.019662       S.D. dependent var         14.18189
             S.E. of regression          14.04177       Akaike info criterion      8.134320
             Sum squared resid           46926.78         Schwarz criterion        8.177699
             Log likelihood             -977.1856       Hannan-Quinn criter.       8.151797
             F-statistic                 3.406807        Durbin-Watson stat        1.930147
             Prob(F-statistic)           0.034772




                                                                                              57
Interpretation

Stationarity conditions of the daily closing price and settlement price of DLF cash and futures
were tested by Augmented Dickey Fuller Test. The results of this test reported in Table 4 and 5.
ADF statistics of both the series i.e. DLF SPOT in Table 4 and FUTURE in Table 5 shows
presence of unit root in both the markets as their ���������������� exceeds the ���������������� (i.e. 1.623922 < 3.428581
for cash and 1.721053 < 3.428581 for futures) at 5% significant level. So the null hypothesis is
accepted that both the series have unit root (i.e. δ=0). So both the series are nonstationary.
Moreover trend coefficients of both the series are statistically insignificant as their Mackinnon‘s
value do not exceed the critical value at 5% level (p=0.5250> 0.05 for cash and p=0.5258 > 0.05
for futures). This suggests the absence of trend in both the markets.




                                                                                                       58
Table 4.2.6:Unit Root Testing of Logarithmic Series of Daily Closing Price of DLF SPOT


             Null Hypothesis: LN_DLF_SPOT has a unit root
             Exogenous: Constant, Linear Trend
             Lag Length: 0 (Fixed)

                                                                    t-Statistic     Prob.*

             Augmented Dickey-Fuller test statistic                 -1.830141      0.6871
             Test critical values:     1% level                     -3.996592
                                       5% level                     -3.428581
                                      10% level                     -3.137711

             *MacKinnon (1996) one-sided p-values.


             Augmented Dickey-Fuller Test Equation
             Dependent Variable: D(LN_DLF_SPOT)
             Method: Least Squares
             Date: 03/17/10 Time: 12:00
             Sample (adjusted): 2 242
             Included observations: 241 after adjustments

                     Variable          Coefficient    Std. Error    t-Statistic     Prob.

               LN_DLF_SPOT(-1)          -0.022152     0.012104     -1.830141       0.0685
                     C                   0.135655     0.066779      2.031408       0.0433
                 @TREND(1)              -3.75E-05     4.91E-05     -0.764444       0.4454

             R-squared                  0.034915       Mean dependent var         0.002882
             Adjusted R-squared         0.026805       S.D. dependent var         0.043298
             S.E. of regression         0.042713       Akaike info criterion      -3.456236
             Sum squared resid          0.434216         Schwarz criterion        -3.412857
             Log likelihood             419.4765       Hannan-Quinn criter.       -3.438760
             F-statistic                4.305238        Durbin-Watson stat        1.853904
             Prob(F-statistic)          0.014564




                                                                                              59
Table 4.2.7:Unit Root Testing of Logarithmic Series of Daily Closing Price of DLF FUT




             Null Hypothesis: LN_DLF_FUT has a unit root
             Exogenous: Constant, Linear Trend
             Lag Length: 0 (Fixed)

                                                                    t-Statistic     Prob.*

             Augmented Dickey-Fuller test statistic                 -2.158723      0.5100
             Test critical values:     1% level                     -3.996592
                                       5% level                     -3.428581
                                      10% level                     -3.137711

             *MacKinnon (1996) one-sided p-values.


             Augmented Dickey-Fuller Test Equation
             Dependent Variable: D(LN_DLF_FUT)
             Method: Least Squares
             Date: 03/17/10 Time: 12:00
             Sample (adjusted): 2 242
             Included observations: 241 after adjustments

                     Variable          Coefficient    Std. Error    t-Statistic     Prob.

                LN_DLF_FUT(-1)          -0.026544     0.012296     -2.158723       0.0319
                      C                  0.161234     0.067551      2.386865       0.0178
                  @TREND(1)             -3.57E-05     5.24E-05     -0.680996       0.4965

             R-squared                  0.043233       Mean dependent var         0.003427
             Adjusted R-squared         0.035193       S.D. dependent var         0.046134
             S.E. of regression         0.045315       Akaike info criterion      -3.337970
             Sum squared resid          0.488729         Schwarz criterion        -3.294591
             Log likelihood             405.2254       Hannan-Quinn criter.       -3.320493
             F-statistic                5.377166        Durbin-Watson stat        1.950775
             Prob(F-statistic)          0.005199




                                                                                              60
Interpretation

Stationarity conditions of the intra-day price series of DLF cash and futures were tested by
Augmented Dickey Fuller Test. The results of this test reported in Table 6 and 7. ADF statistics
of both the series i.e. LN_DLF_SPOT in Table 6 and LN_DLF_FUT in Table 7 shows absence
of unit root in both the markets as their ���������������� exceeds the ���������������� (i.e. 1.830141 < 3.428581 for cash
and 2.158723 < 3.428581 for futures) at 5% significant level. So the null hypothesis is accepted
that both the series have unit root (i.e. δ=0). So both the series are nonstationary. Moreover trend
coefficients of both the series are statistically insignificant as their Mackinnon‘s value do not
exceed the critical value at 5% level (p=0.4454 > 0.05 for cash and p=0.4965 > 0.05 for futures).
This suggests the absence of trend in both the markets.




                                                                                                             61
Table 4.2.8:Unit Root Testing of Logarithmic Return Series of Daily Closing Price of DLF
SPOT

             Null Hypothesis: DLN_DLF_SPOT has a unit root
             Exogenous: Constant, Linear Trend
             Lag Length: 0 (Fixed)

                                                                    t-Statistic     Prob.*

             Augmented Dickey-Fuller test statistic                 -14.42521      0.0000
             Test critical values:     1% level                     -3.996754
                                       5% level                     -3.428660
                                      10% level                     -3.137757

             *MacKinnon (1996) one-sided p-values.


             Augmented Dickey-Fuller Test Equation
             Dependent Variable: D(DLN_DLF_SPOT)
             Method: Least Squares
             Date: 03/17/10 Time: 12:01
             Sample (adjusted): 2 241
             Included observations: 240 after adjustments

                     Variable          Coefficient    Std. Error    t-Statistic     Prob.

               DLN_DLF_SPOT(-1)         -0.936368     0.064912     -14.42521       0.0000
                      C                  0.013170     0.005644      2.333283       0.0205
                  @TREND(1)             -8.66E-05     4.05E-05     -2.136120       0.0337

             R-squared                  0.467523       Mean dependent var         0.000145
             Adjusted R-squared         0.463029       S.D. dependent var         0.058681
             S.E. of regression         0.043000       Akaike info criterion      -3.442806
             Sum squared resid          0.438216         Schwarz criterion        -3.399298
             Log likelihood             416.1367       Hannan-Quinn criter.       -3.425275
             F-statistic                104.0447        Durbin-Watson stat        1.996562
             Prob(F-statistic)          0.000000




                                                                                              62
Table 4.2.9:Unit Root Testing of Logarithmic Return Series of Daily Closing Price of DLF
FUT

               Null Hypothesis: DLN_DLF_FUT has a unit root
               Exogenous: Constant, Linear Trend
               Lag Length: 0 (Fixed)

                                                                      t-Statistic     Prob.*

               Augmented Dickey-Fuller test statistic                 -15.12741      0.0000
               Test critical values:     1% level                     -3.996754
                                         5% level                     -3.428660
                                        10% level                     -3.137757

               *MacKinnon (1996) one-sided p-values.


               Augmented Dickey-Fuller Test Equation
               Dependent Variable: D(DLN_DLF_FUT)
               Method: Least Squares
               Date: 03/17/10 Time: 12:01
               Sample (adjusted): 2 241
               Included observations: 240 after adjustments

                       Variable          Coefficient    Std. Error    t-Statistic     Prob.

                  DLN_DLF_FUT(-1)         -0.983778     0.065033     -15.12741       0.0000
                        C                  0.015756     0.006030      2.612742       0.0096
                    @TREND(1)             -0.000103     4.33E-05     -2.376519       0.0183

               R-squared                  0.491245       Mean dependent var          9.09E-05
               Adjusted R-squared         0.486952       S.D. dependent var         0.064002
               S.E. of regression         0.045843       Akaike info criterion      -3.314773
               Sum squared resid          0.498072         Schwarz criterion        -3.271265
               Log likelihood             400.7728       Hannan-Quinn criter.       -3.297242
               F-statistic                114.4215        Durbin-Watson stat        1.994949
               Prob(F-statistic)          0.000000



Interpretation

In Table 8 and 9, ADF statistics of both the series shows absence of unit root (i.e. δ=0) in both
the series i.e. DLN_DLF_SPOT and DLN_DLF_FUT as their ���������������� exceeds the ���������������� ( 14.42521 &
15.12741 > 3.410009). Thus both the series are now stationary. And trend coefficients of both
the series are also statistically insignificant, that shows the absence of trend in both the series.




                                                                                                       63
Table 4.2.10: Descriptive Statistics of Daily Logarithmic Returns


                                       DLN_DLF_SPOT             DLN_DLF_FUT
               Mean                       0.002882                0.003427
               Median                     0.000408                0.001674
               Maximum                    0.223415                0.256301
               Minimum                   -0.111233               -0.122799
               Std. Dev.                  0.043298                0.046134
               Skewness                   0.804189                0.809027
               Kurtosis                   6.310293                7.107421

               Jarque-Bera                  136.0136               195.7021
               Probability                  0.000000               0.000000

               Sum                          0.694656               0.825911
               Sum Sq. Dev.                 0.449926               0.510813

               Observations                    241                    241



Interpretation

The descriptive statistics for the return of series DLF are mean, median, maximum, minimum,
standard deviation, skewness, kurtosis, Jarque-Bera & probability, which are shown in Table 1.
DLN_DLF_SPOT is the logarithmic return series of futures market of script DLF, where as
DLN_DLF_FUT is the logarithmic return series of cash market of script DLF. The mean return
of both the series are positive. There is not much difference in the std. deviation of both the
series. Both the return series are positively skewed and as the kurtosis value is more than 3, both
the series are leptokurtic. Moreover the Jarque-Bera statistics are also high, which rejects the
hypothesis of normal distribution.




                                                                                                64
Figure 4.2.3: Daily Return on DLF SPOT Market


                                          DLN_DLF_SPOT
              .24

              .20

              .16

              .12

              .08

              .04

              .00

             -.04

             -.08

             -.12
                        25    50     75    100    125   150    175    200    225




Figure 4.2.4: Daily Return on DLF FUTURE Market




                                          DLN_DLF_FUT
            .30

            .25

            .20

            .15

            .10

            .05

            .00

           -.05

           -.10

           -.15
                      25     50     75     100    125    150    175    200    225




Interpretation

From the above Figure 1 & 2, it can be seen that returns of both future series and cash series for
DLF are mean reverting and close to zero. So both the series may be stationary.




                                                                                               65
Table 4.2.11: Unit Root Test for DLN_DLF_SPOT


            Null Hypothesis: DLN_DLF_SPOT has a unit root
            Exogenous: Constant, Linear Trend
            Lag Length: 0 (Fixed)

                                                                   t-Statistic     Prob.*

            Augmented Dickey-Fuller test statistic                 -14.42521      0.0000
            Test critical values:     1% level                     -3.996754
                                      5% level                     -3.428660
                                     10% level                     -3.137757

            *MacKinnon (1996) one-sided p-values.


            Augmented Dickey-Fuller Test Equation
            Dependent Variable: D(DLN_DLF_SPOT)
            Method: Least Squares
            Date: 03/16/10 Time: 20:42
            Sample (adjusted): 2 241
            Included observations: 240 after adjustments

                    Variable          Coefficient    Std. Error    t-Statistic     Prob.

              DLN_DLF_SPOT(-1)         -0.936368     0.064912     -14.42521       0.0000
                     C                  0.013170     0.005644      2.333283       0.0205
                 @TREND(1)             -8.66E-05     4.05E-05     -2.136120       0.0337

            R-squared                  0.467523       Mean dependent var         0.000145
            Adjusted R-squared         0.463029       S.D. dependent var         0.058681
            S.E. of regression         0.043000       Akaike info criterion      -3.442806
            Sum squared resid          0.438216         Schwarz criterion        -3.399298
            Log likelihood             416.1367       Hannan-Quinn criter.       -3.425275
            F-statistic                104.0447        Durbin-Watson stat        1.996562
            Prob(F-statistic)          0.000000




                                                                                             66
Table 4.2.12: Unit Root Test for DLN_DLF_FUT

               Null Hypothesis: DLN_DLF_FUT has a unit root
               Exogenous: Constant, Linear Trend
               Lag Length: 0 (Fixed)

                                                                      t-Statistic     Prob.*

               Augmented Dickey-Fuller test statistic                 -15.12741      0.0000
               Test critical values:     1% level                     -3.996754
                                         5% level                     -3.428660
                                        10% level                     -3.137757

               *MacKinnon (1996) one-sided p-values.


               Augmented Dickey-Fuller Test Equation
               Dependent Variable: D(DLN_DLF_FUT)
               Method: Least Squares
               Date: 03/16/10 Time: 20:43
               Sample (adjusted): 2 241
               Included observations: 240 after adjustments

                       Variable          Coefficient    Std. Error    t-Statistic     Prob.

                  DLN_DLF_FUT(-1)         -0.983778     0.065033     -15.12741       0.0000
                        C                  0.015756     0.006030      2.612742       0.0096
                    @TREND(1)             -0.000103     4.33E-05     -2.376519       0.0183

               R-squared                  0.491245       Mean dependent var          9.09E-05
               Adjusted R-squared         0.486952       S.D. dependent var         0.064002
               S.E. of regression         0.045843       Akaike info criterion      -3.314773
               Sum squared resid          0.498072         Schwarz criterion        -3.271265
               Log likelihood             400.7728       Hannan-Quinn criter.       -3.297242
               F-statistic                114.4215        Durbin-Watson stat        1.994949
               Prob(F-statistic)          0.000000




Interpretation

Table 2 and 3 shows that the ADF t-statistic exceeds the critical value of series
DLN_DLF_SPOT in Table 2 and of DLN_DLF_FUT in Table 3 i.e. ���������������� exceeds the ����������������
(3.428660 < 14.42521 & 15.12741) significantly. So the null hypothesis of unit root has been
rejected. So both the return series are stationary.




                                                                                                67
Table 4.2.13: Cross Correlation

             Date: 03/16/10 Time: 20:44
             Sample: 1 242
             Included observations: 241
             Correlations are asymptotically consistent approximations

             DLN_DLF_FUT,DLN_DLF_ DLN_DLF_FUT,DLN_DLF_S
                   SPOT(-i)              POT(+i)        i                       lag      lead

                   .|**********                .|**********              0    0.9757    0.9757
                   .|.     |                   .|*      |                1    0.0262    0.1107
                   .|.     |                   .|.     |                 2    0.0289    0.0162
                   *|.      |                  *|.      |                3    -0.0632   -0.0663
                   .|*      |                  .|.     |                 4    0.0583    0.0280
                   *|.      |                  .|.     |                 5    -0.0479   -0.0031
                   *|.      |                  *|.      |                6    -0.0650   -0.0852
                   .|.     |                   .|.     |                 7    0.0424    0.0382
                   .|.     |                   .|.     |                 8    0.0137    0.0196
                   .|*      |                  .|**     |                9    0.1431    0.1671
                   *|.      |                  *|.      |                10   -0.0831   -0.0755




                                                                                                  68
Table 4.2.14: Lead-lag Relationship among the Spot and the Futures Returns on DLF on
Daily basis

Panel A:


              Dependent Variable: DLN_DLF_SPOT
              Method: Least Squares
              Date: 03/17/10 Time: 16:23
              Sample (adjusted): 6 236
              Included observations: 231 after adjustments

                      Variable          Coefficient   Std. Error     t-Statistic     Prob.

                      C                  0.002681     0.002975       0.901353       0.3684
                DLN_DLF_FUT(-5)         -0.021103     0.064135      -0.329048       0.7424
                DLN_DLF_FUT(-4)          0.055023     0.063769       0.862844       0.3892
                DLN_DLF_FUT(-3)         -0.075752     0.062822      -1.205833       0.2292
                DLN_DLF_FUT(-2)          0.015437     0.063436       0.243341       0.8080
                DLN_DLF_FUT(-1)          0.119752     0.063463       1.886953       0.0605
                DLN_DLF_FUT(1)           0.039023     0.063879       0.610878       0.5419
                DLN_DLF_FUT(2)           0.049079     0.065663       0.747432       0.4556
                DLN_DLF_FUT(3)          -0.075434     0.065337      -1.154524       0.2495
                DLN_DLF_FUT(4)           0.072259     0.066524       1.086202       0.2786
                DLN_DLF_FUT(5)          -0.033223     0.067356      -0.493250       0.6223

              R-squared                  0.035397       Mean dependent var         0.003208
              Adjusted R-squared        -0.008448       S.D. dependent var         0.043994
              S.E. of regression         0.044179       Akaike info criterion      -3.354687
              Sum squared resid          0.429392         Schwarz criterion        -3.190762
              Log likelihood             398.4663       Hannan-Quinn criter.       -3.288570
              F-statistic                0.807321        Durbin-Watson stat        2.147124
              Prob(F-statistic)          0.621834




                                                                                               69
Panel B:




               Dependent Variable: DLN_DLF_FUT
               Method: Least Squares
               Date: 03/17/10 Time: 16:24
               Sample (adjusted): 6 236
               Included observations: 231 after adjustments

                       Variable          Coefficient   Std. Error     t-Statistic     Prob.

                        C                 0.003310     0.003131       1.057104       0.2916
                 DLN_DLF_SPOT(-5)        -0.057350     0.071635      -0.800585       0.4242
                 DLN_DLF_SPOT(-4)         0.088882     0.071621       1.241000       0.2159
                 DLN_DLF_SPOT(-3)        -0.094453     0.070822      -1.333652       0.1837
                 DLN_DLF_SPOT(-2)         0.038690     0.071057       0.544495       0.5867
                 DLN_DLF_SPOT(-1)         0.032831     0.070733       0.464151       0.6430
                 DLN_DLF_SPOT(1)          0.121254     0.070824       1.712055       0.0883
                 DLN_DLF_SPOT(2)          0.043060     0.072073       0.597444       0.5508
                 DLN_DLF_SPOT(3)         -0.080916     0.071978      -1.124178       0.2622
                 DLN_DLF_SPOT(4)          0.061096     0.072832       0.838865       0.4025
                 DLN_DLF_SPOT(5)         -0.012067     0.073406      -0.164388       0.8696

               R-squared                  0.035625       Mean dependent var         0.003762
               Adjusted R-squared        -0.008210       S.D. dependent var         0.046455
               S.E. of regression         0.046645       Akaike info criterion      -3.246041
               Sum squared resid          0.478672         Schwarz criterion        -3.082117
               Log likelihood             385.9178       Hannan-Quinn criter.       -3.179925
               F-statistic                0.812710        Durbin-Watson stat        2.173938
               Prob(F-statistic)          0.616668



Interpretation

In Panel A and B of Table 5, the Regression Equation has been used to find out the lead and lag
relationship up to 5th orders. In Panel A DLN_DLF_SPOT is taken as dependent variable, where
as in Panel B DLN_DLF_FUT is taken as dependent variable. In table if we look at the
coefficient‘s values in both the Panels, then they are not significant at any lags. That means one
can not predict any lead-lag relationship from this analysis.




                                                                                                70
                           4.3 INFOSYS (Infosys Technologies Ltd.)

Table 4.3.1: Descriptive Statistics of Daly Returns


                                      DLN_INFY_SPOT          DLN_INFY_FUT
                   Mean                   0.003146              0.003185
                   Median                 0.001335              0.002062
                   Maximum                0.122349              0.089336
                   Minimum               -0.143830             -0.108005
                   Std. Dev.              0.022700              0.020935
                   Skewness              -0.161523              0.156505
                   Kurtosis               12.28393              7.056186

                   Jarque-Bera             866.5522              166.1958
                   Probability             0.000000              0.000000

                   Sum                     0.758189              0.767609
                   Sum Sq. Dev.            0.123666              0.105182

                   Observations               241                    241



Interpretation

The descriptive statistics for the return of series INFOSYS are mean, median, maximum,
minimum, standard deviation, skewness, kurtosis, Jarque-Bera & probability, which are shown in
Table 1. DLN_INFY_SPOT is the logarithmic return series of cash market of script INFOSYS,
where as DLN_INFY_FUT is the logarithmic return series of futures market of script INFOSYS.
The mean return of both the series are negative. There is not much difference in the std.
deviation of both the series. Both the return series are negatively skewed and as the kurtosis
value is more than 3, both the series are leptokurtic. Moreover the Jarque-Bera statistics are also
exceeds zero, which rejects the hypothesis of normal distribution.




                                                                                                71
Figure 4.3.1: Daily Return on INFOSYS SPOT Market


                                          DLN_INFY_SPOT
             .15


             .10


             .05


             .00


            -.05


            -.10


            -.15
                       25     50     75    100    125   150    175    200    225




Figure 4.3.2: Daily Return on INFOSYS FUTURE Market



                                          DLN_INFY_FUT
              .12


              .08


              .04


              .00


             -.04


             -.08


             -.12
                        25    50     75    100    125   150    175   200     225




Interpretation

From the above Figure 1 & 2, it can be seen that returns of both future series and cash series for
INFOSYS are fluctuating around zero. So both the series may be stationary.




                                                                                               72
Table 4.3.2: Unit Root Test for DLN_INFY_SPOT

            Null Hypothesis: DLN_INFY_SPOT has a unit root
            Exogenous: Constant, Linear Trend
            Lag Length: 0 (Fixed)

                                                                   t-Statistic     Prob.*

            Augmented Dickey-Fuller test statistic                 -15.82531      0.0000
            Test critical values:     1% level                     -3.996754
                                      5% level                     -3.428660
                                     10% level                     -3.137757

            *MacKinnon (1996) one-sided p-values.


            Augmented Dickey-Fuller Test Equation
            Dependent Variable: D(DLN_INFY_SPOT)
            Method: Least Squares
            Date: 03/16/10 Time: 22:07
            Sample (adjusted): 2 241
            Included observations: 240 after adjustments

                    Variable          Coefficient    Std. Error    t-Statistic     Prob.

             DLN_INFY_SPOT(-1)         -1.025444     0.064798     -15.82531       0.0000
                    C                   0.006352     0.002967      2.140603       0.0333
                @TREND(1)              -2.52E-05     2.12E-05     -1.187833       0.2361

            R-squared                  0.513809       Mean dependent var          4.94E-05
            Adjusted R-squared         0.509706       S.D. dependent var         0.032462
            S.E. of regression         0.022730       Akaike info criterion      -4.717838
            Sum squared resid          0.122447         Schwarz criterion        -4.674330
            Log likelihood             569.1406       Hannan-Quinn criter.       -4.700308
            F-statistic                125.2312        Durbin-Watson stat        2.010333
            Prob(F-statistic)          0.000000




                                                                                             73
Table 4.3.3: Unit Root Test for DLN_INFY_FUT



                Null Hypothesis: DLN_INFY_FUT has a unit root
                Exogenous: Constant, Linear Trend
                Lag Length: 0 (Fixed)

                                                                       t-Statistic     Prob.*

                Augmented Dickey-Fuller test statistic                 -14.92905      0.0000
                Test critical values:     1% level                     -3.996754
                                          5% level                     -3.428660
                                         10% level                     -3.137757

                *MacKinnon (1996) one-sided p-values.


                Augmented Dickey-Fuller Test Equation
                Dependent Variable: D(DLN_INFY_FUT)
                Method: Least Squares
                Date: 03/16/10 Time: 22:08
                Sample (adjusted): 2 241
                Included observations: 240 after adjustments

                        Variable          Coefficient    Std. Error    t-Statistic     Prob.

                  DLN_INFY_FUT(-1)        -0.966816      0.064761     -14.92905       0.0000
                         C                 0.006105      0.002739      2.229165       0.0267
                    @TREND(1)             -2.44E-05      1.96E-05     -1.248174       0.2132

                R-squared                  0.484668       Mean dependent var          5.51E-05
                Adjusted R-squared         0.480319       S.D. dependent var         0.029049
                S.E. of regression         0.020941       Akaike info criterion      -4.881816
                Sum squared resid          0.103928         Schwarz criterion        -4.838308
                Log likelihood             588.8179       Hannan-Quinn criter.       -4.864286
                F-statistic                111.4489        Durbin-Watson stat        1.986657
                Prob(F-statistic)          0.000000



Interpretation

Table 2 and 3 shows that the ADF t-statistic exceeds the critical value of series
DLN_INFY_SPOT in Table 2 and of DLN_INFY_FUT in Table 3 i.e. ���������������� exceeds the ����������������
(3.428660 < 15.82531 & 14.92905. So the null hypothesis of unit root has been rejected. So both
the return series are stationary.




                                                                                                 74
Table 4.3.4: Cross Correlation




             Date: 03/16/10 Time: 22:08
             Sample: 1 242
             Included observations: 241
             Correlations are asymptotically consistent approximations

             DLN_INFY_FUT,DLN_INFY DLN_INFY_FUT,DLN_INFY_
                    _SPOT(-i)             SPOT(+i)        i                     lag      lead

                   .|**********                 .|**********             0    0.9863    0.9863
                   .|.     |                    .|.     |                1    0.0223    0.0072
                  **|.      |                  **|.      |               2    -0.1607   -0.1766
                   *|.      |                   *|.      |               3    -0.1276   -0.1181
                   .|*      |                   .|*      |               4    0.1090    0.1144
                   *|.      |                   *|.      |               5    -0.0591   -0.0793
                   *|.      |                   *|.      |               6    -0.0579   -0.0473
                   .|.     |                    .|.     |                7    0.0114    0.0073
                   .|.     |                    .|.     |                8    -0.0165   -0.0104
                   .|.     |                    .|.     |                9    0.0170    0.0113
                   .|*      |                   .|*      |               10   0.0542    0.0826




                                                                                                  75
Table 4.3.5: Lead-lag Relationship among the Spot and the Futures Returns on INFOSYS
on Daily basis

Panel A:

                 Dependent Variable: DLN_INFY_SPOT
                 Method: Least Squares
                 Date: 03/17/10 Time: 16:18
                 Sample (adjusted): 6 236
                 Included observations: 231 after adjustments

                         Variable          Coefficient   Std. Error     t-Statistic     Prob.

                          C                 0.005158     0.001652       3.121493       0.0020
                   DLN_INFY_FUT(-5)       -0.142703*     0.069606      -2.050164       0.0415
                   DLN_INFY_FUT(-4)         0.109733     0.069837       1.571279       0.1176
                   DLN_INFY_FUT(-3)       -0.163964*     0.070255      -2.333849       0.0205
                   DLN_INFY_FUT(-2)       -0.176553*     0.071031      -2.485577       0.0137
                   DLN_INFY_FUT(-1)         0.038319     0.072592       0.527865       0.5981
                   DLN_INFY_FUT(1)          0.044669     0.072704       0.614393       0.5396
                   DLN_INFY_FUT(2)        -0.152693*     0.072636      -2.102180       0.0367
                   DLN_INFY_FUT(3)        -0.188209*     0.071834      -2.620053       0.0094
                   DLN_INFY_FUT(4)          0.121348     0.071406       1.699404       0.0907
                   DLN_INFY_FUT(5)         -0.114523     0.071344      -1.605232       0.1099

                 R-squared                 0.137654        Mean dependent var         0.003226
                 Adjusted R-squared        0.098456        S.D. dependent var         0.022978
                 S.E. of regression        0.021817        Akaike info criterion      -4.765788
                 Sum squared resid         0.104718          Schwarz criterion        -4.601864
                 Log likelihood            561.4486        Hannan-Quinn criter.       -4.699672
                 F-statistic               3.511791         Durbin-Watson stat        2.171756
                 Prob(F-statistic)         0.000256




                                                                                                  76
Panel B:



               Dependent Variable: DLN_INFY_FUT
               Method: Least Squares
               Date: 03/17/10 Time: 16:19
               Sample (adjusted): 6 236
               Included observations: 231 after adjustments

                       Variable          Coefficient   Std. Error     t-Statistic     Prob.

                        C                 0.005127     0.001514       3.385325       0.0008
                 DLN_INFY_SPOT(-5)       -0.090995     0.059306      -1.534341       0.1264
                 DLN_INFY_SPOT(-4)        0.071394     0.059416       1.201596       0.2308
                 DLN_INFY_SPOT(-3)      -0.149474*     0.059458      -2.513924       0.0127
                 DLN_INFY_SPOT(-2)      -0.141427*     0.060338      -2.343904       0.0200
                 DLN_INFY_SPOT(-1)        0.026957     0.061722       0.436753       0.6627
                 DLN_INFY_SPOT(1)         0.021468     0.061797       0.347400       0.7286
                 DLN_INFY_SPOT(2)       -0.160444*     0.061466      -2.610294       0.0097
                 DLN_INFY_SPOT(3)       -0.157953*     0.060500      -2.610770       0.0097
                 DLN_INFY_SPOT(4)         0.085081     0.060494       1.406438       0.1610
                 DLN_INFY_SPOT(5)        -0.112711     0.060553      -1.861355       0.0640

               R-squared                 0.136498        Mean dependent var         0.003245
               Adjusted R-squared        0.097248        S.D. dependent var         0.021146
               S.E. of regression        0.020092        Akaike info criterion      -4.930546
               Sum squared resid         0.088811          Schwarz criterion        -4.766621
               Log likelihood            580.4780        Hannan-Quinn criter.       -4.864429
               F-statistic               3.477651         Durbin-Watson stat        1.985119
               Prob(F-statistic)         0.000287




Interpretation

In Panel A and B of Table 5, the Regression Equation has been used to find out the lead and lag
relationship up to 5th orders. In Panel A DLN_INFY_SPOT is taken as dependent variable,
where as in Panel B DLN_INFY_FUT is taken as dependent variable. In table if we look at the
coefficient‘s values in both the Panels, then they are significant at (-3), (-2), (2) and (3) lags.
That means lead lag relationship exists in both the ways, i.e. Futures price can lead or lag the
cash price by 2-3 days and cash price can also lead or lag the Futures price by 2-3 days.




                                                                                                77
                          4.4 RIL (RELIANCE INDUSTRIES LTD.)

Table 4.4.1: Descriptive Statistics of Daily Returns

                                       DLN_RIL_SPOT           DLN_RIL_FUT
                  Mean                   -0.000933              -0.000905
                  Median                 -0.000190              -4.90E-05
                  Maximum                 0.193667               0.197074
                  Minimum                -0.724245              -0.724185
                  Std. Dev.               0.053969               0.054040
                  Skewness               -9.845910              -9.793234
                  Kurtosis                135.8412               135.1591

                  Jarque-Bera               181096.8              179240.3
                  Probability               0.000000              0.000000

                  Sum                      -0.224746             -0.218070
                  Sum Sq. Dev.              0.699039              0.700867

                  Observations                 241                    241

Interpretation

The descriptive statistics for the return of future and cash series of RELIANCE are mean,
median, maximum, minimum, standard deviation, skewness, kurtosis, Jarque-Bera & probability,
which are shown in Table 1. DLN_RIL_FUT is the logarithmic return series of futures market of
script RELIANCE, where as DLN_RIL_SPOT is the logarithmic return series of cash market of
script RELIANCE. The mean return of both the series are negative. There is not much difference
in the std. deviation of both the series. Both the return series are negatively skewed and as the
kurtosis value is more than 3, both the series are leptokurtic. Moreover the Jarque-Bera statistics
are also high, which rejects the hypothesis of normal distribution.




                                                                                                78
Figure 4.4.1: Daily Return on RIL SPOT Market




                                        DLN_RIL_SPOT
           .4


           .2


           .0


          -.2


          -.4


          -.6


          -.8
                     25     50     75     100    125    150     175    200    225




Figure 4.4.2: Daily Return on RIL FUTURE Market



                                         DLN_RIL_FUT
          .4


          .2


          .0


          -.2


          -.4


          -.6


          -.8
                    25      50     75     100    125    150     175    200     225




Interpretation

From the above Figure 1 & 2, it can be seen that returns of both future series and cash series for
RELIANCE are mean reverting and close to zero. So both the series may be stationary.




                                                                                               79
Table 4.4.2: Unit Root Test for DLN_RIL_SPOT


            Null Hypothesis: DLN_RIL_SPOT has a unit root
            Exogenous: Constant, Linear Trend
            Lag Length: 0 (Fixed)

                                                                   t-Statistic     Prob.*

            Augmented Dickey-Fuller test statistic                 -15.35208      0.0000
            Test critical values:     1% level                     -3.996754
                                      5% level                     -3.428660
                                     10% level                     -3.137757

            *MacKinnon (1996) one-sided p-values.


            Augmented Dickey-Fuller Test Equation
            Dependent Variable: D(DLN_RIL_SPOT)
            Method: Least Squares
            Date: 03/16/10 Time: 22:16
            Sample (adjusted): 2 241
            Included observations: 240 after adjustments

                    Variable          Coefficient    Std. Error    t-Statistic     Prob.

              DLN_RIL_SPOT(-1)        -0.996972      0.064941     -15.35208       0.0000
                     C                 0.009680      0.007013      1.380260       0.1688
                @TREND(1)             -8.73E-05      5.06E-05     -1.725201       0.0858

            R-squared                  0.498611       Mean dependent var         0.000167
            Adjusted R-squared         0.494379       S.D. dependent var         0.075865
            S.E. of regression         0.053945       Akaike info criterion      -2.989281
            Sum squared resid          0.689686         Schwarz criterion        -2.945773
            Log likelihood             361.7138       Hannan-Quinn criter.       -2.971751
            F-statistic                117.8432        Durbin-Watson stat        1.998503
            Prob(F-statistic)          0.000000




                                                                                             80
Table 4.4.3: Unit Root Test for DLN_RIL_FUT



              Null Hypothesis: DLN_RIL_FUT has a unit root
              Exogenous: Constant, Linear Trend
              Lag Length: 0 (Fixed)

                                                                     t-Statistic     Prob.*

              Augmented Dickey-Fuller test statistic                 -15.46799      0.0000
              Test critical values:     1% level                     -3.996754
                                        5% level                     -3.428660
                                       10% level                     -3.137757

              *MacKinnon (1996) one-sided p-values.


              Augmented Dickey-Fuller Test Equation
              Dependent Variable: D(DLN_RIL_FUT)
              Method: Least Squares
              Date: 03/16/10 Time: 22:17
              Sample (adjusted): 2 241
              Included observations: 240 after adjustments

                      Variable          Coefficient    Std. Error    t-Statistic     Prob.

                 DLN_RIL_FUT(-1)         -1.004826     0.064962     -15.46799       0.0000
                       C                  0.009749     0.007024      1.387955       0.1665
                   @TREND(1)             -8.78E-05     5.07E-05     -1.733517       0.0843

              R-squared                  0.502371       Mean dependent var         0.000162
              Adjusted R-squared         0.498172       S.D. dependent var         0.076262
              S.E. of regression         0.054024       Akaike info criterion      -2.986351
              Sum squared resid          0.691709         Schwarz criterion        -2.942843
              Log likelihood             361.3622       Hannan-Quinn criter.       -2.968821
              F-statistic                119.6294        Durbin-Watson stat        1.998720
              Prob(F-statistic)          0.000000



Interpretation

Table 2 and 3 shows that the ADF t-statistic exceeds the critical value of series DLN_RIL_SPOT
in Table 2 and of DLF_RIL_FUT in Table 3 i.e. ���������������� exceeds the ���������������� (3.428660 < 15.35208 &
15.46799). So the null hypothesis of unit root has been rejected. So both the return series are
stationary.




                                                                                               81
Table 4.4.4: Cross Correlation




             Date: 03/16/10 Time: 22:18
             Sample: 1 242
             Included observations: 241
             Correlations are asymptotically consistent approximations

             DLN_RIL_SPOT,DLN_RIL_ DLN_RIL_SPOT,DLN_RIL_F
                     FUT(-i)                UT(+i)        i                     lag      lead

                   .|**********                .|**********              0    0.9990    0.9990
                   .|.     |                   .|.     |                 1    0.0145    0.0098
                   .|.     |                   .|.     |                 2    -0.0106   -0.0091
                   *|.      |                  *|.      |                3    -0.0688   -0.0724
                   .|.     |                   .|.     |                 4    -0.0159   -0.0057
                   .|.     |                   .|.     |                 5    -0.0076   -0.0134
                   .|.     |                   .|.     |                 6    0.0013    0.0032
                   .|*      |                  .|*      |                7    0.0571    0.0550
                   .|.     |                   .|.     |                 8    -0.0287   -0.0304
                   .|.     |                   .|.     |                 9    0.0032    0.0068
                   .|.     |                   .|.     |                 10   0.0152    0.0160




                                                                                                  82
Table 4.4.5: Lead-lag Relationship among the Spot and the Futures Returns on
ICICIBANK on Daily Basis

Panel A:

            Dependent Variable: DLN_RIL_SPOT
            Method: Least Squares
            Date: 03/17/10 Time: 16:14
            Sample (adjusted): 6 236
            Included observations: 231 after adjustments

                    Variable          Coefficient   Std. Error     t-Statistic     Prob.

                     C                -0.000862     0.003684      -0.234127       0.8151
               DLN_RIL_FUT(-5)        -0.010493     0.067217      -0.156111       0.8761
               DLN_RIL_FUT(-4)        -0.010110     0.067215      -0.150407       0.8806
               DLN_RIL_FUT(-3)        -0.068311     0.067053      -1.018770       0.3094
               DLN_RIL_FUT(-2)        -0.013142     0.067481      -0.194753       0.8458
               DLN_RIL_FUT(-1)         0.015489     0.067332       0.230045       0.8183
               DLN_RIL_FUT(1)          0.012342     0.067406       0.183094       0.8549
               DLN_RIL_FUT(2)         -0.012289     0.067752      -0.181383       0.8562
               DLN_RIL_FUT(3)         -0.069941     0.067528      -1.035734       0.3015
               DLN_RIL_FUT(4)          0.001763     0.067681       0.026048       0.9792
               DLN_RIL_FUT(5)         -0.012542     0.067726      -0.185184       0.8533

            R-squared                  0.010724       Mean dependent var         -0.000691
            Adjusted R-squared        -0.034244       S.D. dependent var         0.054957
            S.E. of regression         0.055890       Akaike info criterion      -2.884423
            Sum squared resid          0.687207         Schwarz criterion        -2.720498
            Log likelihood             344.1508       Hannan-Quinn criter.       -2.818306
            F-statistic                0.238475        Durbin-Watson stat        2.018955
            Prob(F-statistic)          0.992060




                                                                                             83
Panel B:


               Dependent Variable: DLN_RIL_FUT
               Method: Least Squares
               Date: 03/17/10 Time: 16:15
               Sample (adjusted): 6 236
               Included observations: 231 after adjustments

                       Variable          Coefficient   Std. Error     t-Statistic     Prob.

                        C                -0.000844     0.003687      -0.228956       0.8191
                 DLN_RIL_SPOT(-5)        -0.016712     0.067344      -0.248156       0.8042
                 DLN_RIL_SPOT(-4)        -0.000315     0.067372      -0.004676       0.9963
                 DLN_RIL_SPOT(-3)        -0.070763     0.067223      -1.052663       0.2937
                 DLN_RIL_SPOT(-2)        -0.012318     0.067630      -0.182145       0.8556
                 DLN_RIL_SPOT(-1)         0.011375     0.067482       0.168564       0.8663
                 DLN_RIL_SPOT(1)          0.016125     0.067550       0.238713       0.8116
                 DLN_RIL_SPOT(2)         -0.012629     0.067884      -0.186043       0.8526
                 DLN_RIL_SPOT(3)         -0.066830     0.067657      -0.987783       0.3243
                 DLN_RIL_SPOT(4)         -0.007368     0.067806      -0.108669       0.9136
                 DLN_RIL_SPOT(5)         -0.006800     0.067853      -0.100222       0.9203

               R-squared                  0.010674       Mean dependent var         -0.000672
               Adjusted R-squared        -0.034296       S.D. dependent var         0.055010
               S.E. of regression         0.055945       Akaike info criterion      -2.882449
               Sum squared resid          0.688564         Schwarz criterion        -2.718525
               Log likelihood             343.9229       Hannan-Quinn criter.       -2.816333
               F-statistic                0.237357        Durbin-Watson stat        2.032186
               Prob(F-statistic)          0.992208




Interpretation

In Panel A and B of Table 5, the Regression Equation has been used to find out the lead and lag
relationship up to 5th orders. In Panel A DLN_RIL_SPOT is taken as dependent variable, where
as in Panel B DLN_RIL_FUT is taken as dependent variable. In table if we look at the
coefficient‘s values in both the Panels, then they are not significant at any lags. This suggests
lack of lead-lag relationship on daily basis.




                                                                                                84
                                     4.5 TATA STEEL

Table 4.5.1: Descriptive Statistics of Daily Returns


                          DLN_TATA_STEEL_FUT DLN_TATA_STEEL_SPOT
         Mean                   0.005355            0.005328
         Median                 0.007216            0.007405
         Maximum                0.152051            0.157035
         Minimum               -0.130537           -0.128169
         Std. Dev.              0.040513            0.040414
         Skewness              -0.018125           -0.027191
         Kurtosis               3.825273            4.054963

         Jarque-Bera               6.852334                       11.20554
         Probability               0.032511                       0.003688

         Sum                       1.290571                       1.284020
         Sum Sq. Dev.              0.393921                       0.391987

         Observations                 241                            241



Interpretation

The descriptive statistics for the return of series TATASTEEL are mean, median, maximum,
minimum, standard deviation, skewness, kurtosis, Jarque-Bera & probability, which are shown in
Table 1. DLN_TATA_STEEL_FUT is the logarithmic return series of futures market of script
TATA STEEL, where as DLN_TATA_STEEL_SPOT is the logarithmic return series of cash
market of script TATA STEEL. The mean return of both the series are equal and positive. There
is not much difference in the std. deviation of both the series. Both the return series are
negatively skewed and as the kurtosis value is more than 3, both the series are leptokurtic.
Moreover the Jarque-Bera statistics are also high, which rejects the hypothesis of normal
distribution.




                                                                                           85
Figure 4.5.1: Daily Return on TATA STEEL SPOT Market


                                      DLN_TATA_STEEL_SPOT
                   .20


                   .15


                   .10


                   .05


                   .00


                  -.05


                  -.10


                  -.15
                           25    50    75   100   125   150   175   200   225




Figure 4.5.2: Daily Return on TATA STEEL Future Market


                                      DLN_TATA_STEEL_FUT
                  .20


                  .15


                  .10


                  .05


                  .00


                 -.05


                 -.10


                 -.15
                           25   50     75   100   125   150   175   200   225




Interpretation

From the above Figure 1 & 2, it can be seen that returns of both future series and cash series for
TATA STEEL are mean reverting and close to zero. So both the series may be stationary.


                                                                                               86
Table 4.5.2: Unit Root Test for DLN_ICICI_SPOT


          Null Hypothesis: DLN_TATA_STEEL_SPOT has a unit root
          Exogenous: Constant, Linear Trend
          Lag Length: 0 (Fixed)

                                                                      t-Statistic     Prob.*

          Augmented Dickey-Fuller test statistic                      -15.58599      0.0000
          Test critical values:             1% level                  -3.996754
                                            5% level                  -3.428660
                                           10% level                  -3.137757

          *MacKinnon (1996) one-sided p-values.


          Augmented Dickey-Fuller Test Equation
          Dependent Variable: D(DLN_TATA_STEEL_SPOT)
          Method: Least Squares
          Date: 03/16/10 Time: 22:29
          Sample (adjusted): 2 241
          Included observations: 240 after adjustments

                     Variable             Coefficient   Std. Error    t-Statistic     Prob.

          DLN_TATA_STEEL_SPOT(-1) -1.010645             0.064843      -15.58599      0.0000
                    C              0.014385             0.005295       2.716744      0.0071
                @TREND(1)         -7.37E-05             3.78E-05      -1.947866      0.0526

          R-squared                       0.506176       Mean dependent var         0.000138
          Adjusted R-squared              0.502009       S.D. dependent var         0.057110
          S.E. of regression              0.040302       Akaike info criterion      -3.572424
          Sum squared resid               0.384942         Schwarz criterion        -3.528916
          Log likelihood                  431.6909       Hannan-Quinn criter.       -3.554893
          F-statistic                     121.4642        Durbin-Watson stat        1.999289
          Prob(F-statistic)               0.000000




                                                                                                87
Table 4.5.3: Unit Root Test for DLN_ICICI_FUT


             Null Hypothesis: DLN_TATA_STEEL_FUT has a unit root
             Exogenous: Constant, Linear Trend
             Lag Length: 0 (Fixed)

                                                                      t-Statistic     Prob.*

             Augmented Dickey-Fuller test statistic                   -16.14162      0.0000
             Test critical values:          1% level                  -3.996754
                                            5% level                  -3.428660
                                           10% level                  -3.137757

             *MacKinnon (1996) one-sided p-values.


             Augmented Dickey-Fuller Test Equation
             Dependent Variable: D(DLN_TATA_STEEL_FUT)
             Method: Least Squares
             Date: 03/16/10 Time: 22:30
             Sample (adjusted): 2 241
             Included observations: 240 after adjustments

                       Variable           Coefficient   Std. Error    t-Statistic     Prob.

              DLN_TATA_STEEL_FUT(-
                       1)                 -1.045906     0.064796      -16.14162      0.0000
                       C                   0.014978     0.005305       2.823420      0.0052
                   @TREND(1)              -7.69E-05     3.79E-05      -2.030603      0.0434

             R-squared                    0.523673       Mean dependent var          9.83E-05
             Adjusted R-squared           0.519654       S.D. dependent var         0.058245
             S.E. of regression           0.040368       Akaike info criterion      -3.569145
             Sum squared resid            0.386207         Schwarz criterion        -3.525637
             Log likelihood               431.2973       Hannan-Quinn criter.       -3.551614
             F-statistic                  130.2788        Durbin-Watson stat        1.994377
             Prob(F-statistic)            0.000000




Interpretation

Table 2 and 3 shows that the ADF t-statistic exceeds the critical value of series
DLN_TATA_STEEL_SPOT in Table 2 and of DLN_TATA_STEEL_FUT in Table 3 i.e.
���������������� exceeds the ���������������� (3.428660 < 15.58599 & 16.14162) significantly. So the null hypothesis of
unit root has been rejected. So both the return series are stationary.




                                                                                                    88
Table 4.5.4: Cross Correlation


             Date: 03/16/10 Time: 22:28
             Sample: 1 242
             Included observations: 241
             Correlations are asymptotically consistent approximations

             DLN_TATA_STEEL_FUT,D DLN_TATA_STEEL_FUT,DL
             LN_TATA_STEEL_SPOT(-i) N_TATA_STEEL_SPOT(+i) i                     lag      lead

                   .|**********                .|**********              0    0.9927    0.9927
                   .|.     |                   .|.     |                 1    -0.0141   -0.0088
                   .|*      |                  .|*      |                2    0.0950    0.0671
                   .|.     |                   .|.     |                 3    -0.0122   -0.0002
                   .|.     |                   *|.      |                4    -0.0334   -0.0548
                   *|.      |                  *|.      |                5    -0.0873   -0.0905
                   .|.     |                   .|.     |                 6    -0.0379   -0.0214
                   .|.     |                   .|.     |                 7    0.0385    0.0283
                   .|*      |                  .|*      |                8    0.0843    0.0952
                   .|**     |                  .|**     |                9    0.1652    0.1772
                   *|.      |                  *|.      |                10   -0.0448   -0.0410




                                                                                                  89
Table 4.5.5: Lead-lag Relationship among the Spot and the Futures Returns on TATA
STEEL on Daily basis

Panel A:

           Dependent Variable: DLN_TATA_STEEL_SPOT
           Method: Least Squares
           Date: 03/17/10 Time: 16:08
           Sample (adjusted): 6 236
           Included observations: 231 after adjustments

                     Variable           Coefficient   Std. Error   t-Statistic     Prob.

                      C                 0.005874      0.002929      2.005477      0.0461
            DLN_TATA_STEEL_FUT(-5)      -0.098327     0.067983     -1.446347      0.1495
            DLN_TATA_STEEL_FUT(-4)      -0.051498     0.068204     -0.755047      0.4510
            DLN_TATA_STEEL_FUT(-3)      0.025010      0.067545      0.370267      0.7115
            DLN_TATA_STEEL_FUT(-2)      0.076291      0.066778      1.142463      0.2545
            DLN_TATA_STEEL_FUT(-1)      -0.006687     0.067093     -0.099666      0.9207
            DLN_TATA_STEEL_FUT(1)       -0.012678     0.067141     -0.188830      0.8504
            DLN_TATA_STEEL_FUT(2)       0.104205      0.067248      1.549551      0.1227
            DLN_TATA_STEEL_FUT(3)       0.016851      0.067885      0.248223      0.8042
            DLN_TATA_STEEL_FUT(4)       -0.021652     0.068555     -0.315838      0.7524
            DLN_TATA_STEEL_FUT(5)       -0.086376     0.068592     -1.259275      0.2093

           R-squared                    0.034063       Mean dependent var        0.005641
           Adjusted R-squared           -0.009843      S.D. dependent var        0.041031
           S.E. of regression           0.041232       Akaike info criterion     -3.492737
           Sum squared resid            0.374024        Schwarz criterion        -3.328812
           Log likelihood               414.4111       Hannan-Quinn criter.      -3.426620
           F-statistic                  0.775821       Durbin-Watson stat        1.956234
           Prob(F-statistic)            0.652056




                                                                                             90
Panel B:



            Dependent Variable: DLN_TATA_STEEL_FUT
            Method: Least Squares
            Date: 03/17/10 Time: 16:10
            Sample (adjusted): 6 236
            Included observations: 231 after adjustments

                       Variable            Coefficient     Std. Error    t-Statistic     Prob.

                       C                    0.005925       0.002934       2.019620      0.0446
             DLN_TATA_STEEL_SPOT(-5)       -0.089588       0.068278      -1.312105      0.1909
             DLN_TATA_STEEL_SPOT(-4)       -0.025079       0.068475      -0.366251      0.7145
             DLN_TATA_STEEL_SPOT(-3)        0.013703       0.067840       0.201989      0.8401
             DLN_TATA_STEEL_SPOT(-2)        0.097703       0.067045       1.457279      0.1465
             DLN_TATA_STEEL_SPOT(-1)       -0.017572       0.067292      -0.261130      0.7942
             DLN_TATA_STEEL_SPOT(1)        -0.010733       0.067259      -0.159579      0.8734
             DLN_TATA_STEEL_SPOT(2)         0.076185       0.067374       1.130774      0.2594
             DLN_TATA_STEEL_SPOT(3)         0.024271       0.067977       0.357048      0.7214
             DLN_TATA_STEEL_SPOT(4)        -0.038354       0.068629      -0.558868      0.5768
             DLN_TATA_STEEL_SPOT(5)        -0.092607       0.068703      -1.347932      0.1791

            R-squared                       0.032496        Mean dependent var         0.005665
            Adjusted R-squared             -0.011482        S.D. dependent var         0.041077
            S.E. of regression              0.041312        Akaike info criterion      -3.488871
            Sum squared resid               0.375473          Schwarz criterion        -3.324946
            Log likelihood                  413.9646        Hannan-Quinn criter.       -3.422754
            F-statistic                     0.738922         Durbin-Watson stat        2.001448
            Prob(F-statistic)               0.687314




Interpretation

In Panel A and B of Table 5, the Regression Equation has been used to find out the lead and lag
relationship up to 5th orders. In Panel A DLN_TATA_STEEL_SPOT is taken as dependent
variable, where as in Panel B DLN_TATA_STEEL_FUT is taken as dependent variable. In table
if we look at the coefficient‘s values in both the Panels, then they are not significant at any lags.
It suggests the lack of lead-lag relationship on daily basis.




                                                                                                   91
                              4.6 ICICI BANK (ICICI Bank Ltd.)

Table 4.6.1: Descriptive Statistics of Daily Returns


                                   DLN_ICICI_SPOT          DLN_ICICI_FUT
                  Mean                 0.000114               0.004378
                  Median               2.08E-05               0.002722
                  Maximum              0.159904               0.227129
                  Minimum             -0.178257              -0.128010
                  Std. Dev.            0.048812               0.036384
                  Skewness            -0.032488               0.804400
                  Kurtosis             4.763355               8.902096

                  Jarque-Bera            31.26618              375.7890
                  Probability            0.000000              0.000000

                  Sum                    0.027367              1.055158
                  Sum Sq. Dev.           0.571830              0.317708

                  Observations             241                    241

Interpretation

The descriptive statistics for the return of series ICICIBANK are mean, median, maximum,
minimum, standard deviation, skewness, kurtosis, Jarque-Bera & probability, which are shown in
Table 1. DLN_ICICI_FUT is the logarithmic return series of futures market of script
ICICIBANK, where as DLN_ICICI_SPOT is the logarithmic return series of cash market of
script ICICIBANK. The mean return of both the series are positive. There is not much difference
in the std. deviation of both the series. The DLN_ICICI_SPOT return series is negatively skewed
where as DLN_ICICI_FUT series is positively skewed, and as the kurtosis value is more than 3,
both the series are leptokurtic. Moreover the Jarque-Bera statistics are also high, which rejects
the hypothesis of normal distribution.




                                                                                              92
Figure 4.6.1: Daily Return on ICICIBANK Cash Market


                                         DLN_ICICI_SPOT
            .20

            .15

            .10

            .05

            .00

           -.05

           -.10

           -.15

           -.20
                      25     50     75     100    125    150    175    200    225




Figure 4.6.2: Daily Return on ICICIBANK Futures Market


                                         DLN_ICICI_FUT
          .25

          .20

          .15

          .10

          .05

          .00

          -.05

          -.10

          -.15
                     25      50     75    100     125    150    175     200    225




Interpretation

From the above Figure 1 & 2, it can be seen that returns of both future series and cash series for
ICICIBANK are mean reverting and close to zero. So both the series may be stationary.




                                                                                               93
Table 4.6.2: Unit Root Test for DLN_ICICI_SPOT


            Null Hypothesis: DLN_ICICI_SPOT has a unit root
            Exogenous: Constant, Linear Trend
            Lag Length: 0 (Fixed)

                                                                   t-Statistic     Prob.*

            Augmented Dickey-Fuller test statistic                 -23.60279      0.0000
            Test critical values:     1% level                     -3.996754
                                      5% level                     -3.428660
                                     10% level                     -3.137757

            *MacKinnon (1996) one-sided p-values.


            Augmented Dickey-Fuller Test Equation
            Dependent Variable: D(DLN_ICICI_SPOT)
            Method: Least Squares
            Date: 03/16/10 Time: 22:42
            Sample (adjusted): 2 241
            Included observations: 240 after adjustments

                    Variable          Coefficient    Std. Error    t-Statistic     Prob.

             DLN_ICICI_SPOT(-1)        -1.403507     0.059464     -23.60279       0.0000
                     C                  0.000871     0.005820      0.149654       0.8812
                @TREND(1)              -5.26E-06     4.19E-05     -0.125511       0.9002

            R-squared                  0.701548       Mean dependent var         -6.24E-05
            Adjusted R-squared         0.699030       S.D. dependent var         0.081922
            S.E. of regression         0.044943       Akaike info criterion      -3.354418
            Sum squared resid          0.478712         Schwarz criterion        -3.310910
            Log likelihood             405.5302       Hannan-Quinn criter.       -3.336888
            F-statistic                278.5493        Durbin-Watson stat        2.253694
            Prob(F-statistic)          0.000000




                                                                                             94
Table 4.6.3: Unit Root Test for DLN_ICICI_FUT


               Null Hypothesis: DLN_ICICI_FUT has a unit root
               Exogenous: Constant, Linear Trend
               Lag Length: 0 (Fixed)

                                                                      t-Statistic     Prob.*

               Augmented Dickey-Fuller test statistic                 -14.17029      0.0000
               Test critical values:     1% level                     -3.996754
                                         5% level                     -3.428660
                                        10% level                     -3.137757

               *MacKinnon (1996) one-sided p-values.


               Augmented Dickey-Fuller Test Equation
               Dependent Variable: D(DLN_ICICI_FUT)
               Method: Least Squares
               Date: 03/16/10 Time: 22:42
               Sample (adjusted): 2 241
               Included observations: 240 after adjustments

                       Variable          Coefficient    Std. Error    t-Statistic     Prob.

                  DLN_ICICI_FUT(-1)       -0.915986     0.064641     -14.17029       0.0000
                         C                 0.011614     0.004745      2.447650       0.0151
                    @TREND(1)             -6.19E-05     3.39E-05     -1.825802       0.0691

               R-squared                  0.458655       Mean dependent var         0.000214
               Adjusted R-squared         0.454087       S.D. dependent var         0.048889
               S.E. of regression         0.036122       Akaike info criterion      -3.791392
               Sum squared resid          0.309242         Schwarz criterion        -3.747884
               Log likelihood             457.9670       Hannan-Quinn criter.       -3.773861
               F-statistic                100.3992        Durbin-Watson stat        1.985190
               Prob(F-statistic)          0.000000



Interpretation

Table 2 and 3 shows that the ADF t-statistic exceeds the critical value of series
DLN_ICICI_SPOT in Table 2 and of DLN_ICICI_FUT in Table 3 i.e. ���������������� exceeds the ����������������
(3.428660 < 23.60279 & 14.17029) significantly. So the null hypothesis of unit root has been
rejected. So both the return series are stationary.




                                                                                                95
Table 4.6.4: Cross Correlation

             Date: 03/16/10 Time: 22:39
             Sample: 1 242
             Included observations: 241
             Correlations are asymptotically consistent approximations

             DLN_ICICI_FUT,DLN_ICICI DLN_ICICI_FUT,DLN_ICICI_
                    _SPOT(-i)                SPOT(+i)         i                 lag      lead

               *******|.      |             *******|.       |            0    -0.6720   -0.6720
                   .|******* |                  *|.     |                1    0.6694    -0.1319
                   .|*      |                   .|.     |                2    0.1287    0.0031
                   .|.     |                    .|*     |                3    -0.0075   0.1167
                   *|.      |                   *|.     |                4    -0.1169   -0.1406
                   .|*      |                   .|.     |                5    0.1381    -0.0147
                   .|.     |                    .|*     |                6    0.0204    0.0709
                   *|.      |                   .|.     |                7    -0.0648   -0.0032
                   .|.     |                    .|*     |                8    0.0058    0.0923
                   *|.      |                   .|.     |                9    -0.0930   -0.0405
                   .|.     |                    .|*     |                10   0.0434    0.0528




                                                                                                  96
Table 4.6.5: Lead-lag Relationship among the Spot and the Futures Returns on
ICICIBANK on Daily Basis

Panel A:

            Dependent Variable: DLN_ICICI_SPOT
            Method: Least Squares
            Date: 03/17/10 Time: 16:04
            Sample (adjusted): 6 236
            Included observations: 231 after adjustments

                    Variable          Coefficient   Std. Error     t-Statistic     Prob.

                     C                -0.005350     0.002544      -2.103420       0.0366
              DLN_ICICI_FUT(-5)        0.142584     0.065003       2.193496       0.0293
              DLN_ICICI_FUT(-4)       -0.127452     0.065551      -1.944325       0.0531
              DLN_ICICI_FUT(-3)        0.116326     0.065497       1.776059       0.0771
              DLN_ICICI_FUT(-2)        0.071122     0.066159       1.075011       0.2835
              DLN_ICICI_FUT(-1)       -0.123271     0.066298      -1.859335       0.0643
              DLN_ICICI_FUT(1)       0.872349*      0.067105       12.99978       0.0000
              DLN_ICICI_FUT(2)         0.067914     0.067504       1.006084       0.3155
              DLN_ICICI_FUT(3)         0.106393     0.067386       1.578846       0.1158
              DLN_ICICI_FUT(4)       -0.146561*     0.067179      -2.181645       0.0302
              DLN_ICICI_FUT(5)         0.120434     0.067239       1.791140       0.0746

            R-squared                 0.498086        Mean dependent var         -0.000338
            Adjusted R-squared        0.475272        S.D. dependent var         0.049046
            S.E. of regression        0.035528        Akaike info criterion      -3.790536
            Sum squared resid         0.277694          Schwarz criterion        -3.626612
            Log likelihood            448.8069        Hannan-Quinn criter.       -3.724420
            F-statistic               21.83221         Durbin-Watson stat        2.251252
            Prob(F-statistic)         0.000000




                                                                                             97
Panel B:

               Dependent Variable: DLN_ICICI_FUT
               Method: Least Squares
               Date: 03/17/10 Time: 16:06
               Sample (adjusted): 6 236
               Included observations: 231 after adjustments

                       Variable          Coefficient   Std. Error     t-Statistic     Prob.

                         C               0.004703      0.000990       4.748465       0.0000
                 DLN_ICICI_SPOT(-5)      0.186930*     0.024961       7.488794       0.0000
                 DLN_ICICI_SPOT(-4)      0.321701*     0.030314       10.61218       0.0000
                 DLN_ICICI_SPOT(-3)      0.501780*     0.031488       15.93560       0.0000
                 DLN_ICICI_SPOT(-2)      0.673062*     0.030295       22.21683       0.0000
                 DLN_ICICI_SPOT(-1)      0.809408*     0.025561       31.66565       0.0000
                 DLN_ICICI_SPOT(1)       0.000738      0.026091       0.028292       0.9775
                 DLN_ICICI_SPOT(2)       0.027216      0.031447       0.865468       0.3877
                 DLN_ICICI_SPOT(3)       0.048550      0.032943       1.473740       0.1420
                 DLN_ICICI_SPOT(4)       0.028310      0.031561       0.897011       0.3707
                 DLN_ICICI_SPOT(5)       0.037487      0.025769       1.454734       0.1472

               R-squared                 0.839015        Mean dependent var         0.004984
               Adjusted R-squared        0.831697        S.D. dependent var         0.036686
               S.E. of regression        0.015050        Akaike info criterion      -5.508393
               Sum squared resid         0.049832          Schwarz criterion        -5.344468
               Log likelihood            647.2194        Hannan-Quinn criter.       -5.442276
               F-statistic               114.6586         Durbin-Watson stat        0.449083
               Prob(F-statistic)         0.000000




Interpretation

In Panel A and B of Table 5, the Regression Equation has been used to find out the lead and lag
relationship up to 5th orders. In Panel A DLN_ICICI_SPOT is taken as dependent variable,
where as in Panel B DLN_ICICI_FUT is taken as dependent variable. In table if we look at the
coefficient‘s values in both the Panels, then they are significant at (-1), (-2) (-3), (-4) and (-5)
lags in panel B and at (1) and (4) in panel A. That means strong leading role is played by futures
market, i.e. Futures price can lead cash price by 1-5 days.




                                                                                                 98
                                            FINDINGS

An attempt has been made to investigate lead-lag relationship, by using Linear Regression
Equation and Granger Causality Test. The results for the NIFTY index and for the five scripts
are as follow:

1. NIFTY
The lead-lag relationship for the NIFTY cash and NIFTY futures has been found and this
relationship exists which is contemporaneous and bi-directional. Here NIFTY Cash leads or lags
the NIFTY Futures by 4-6 minutes. Also NIFTY Futures leads or lags the NIFTY cash by 4-6
minutes.


2. STOCK
   For the first script i.e. DLF, this relationship does not exists for the specific time period.
   For INFOSYS, this relationship exists which is contemporaneous and bi-directional. Here
    INFOSYS Cash/Future leads or lags the INFOSYS Futures/Cash by 2-3 days.
   In 3rd script i.e. RELIANCE, the lead lag relationship does not exist for the specific time
    period.
   For the TATA STEEL script, the lead lag relationship does not exist for the specific time
    period.
   And for the last script ICICIBANK, there exist lead-lag relationship which uncovers that
    Futures market lead the Cash market by 1-5 days.


3. For Market efficiency
It can be inferred from the analysis that for the given time period as bi-directional lead-lag
relationship exists between the NIFTY Index and Futures, so the market is not equally efficient
in processing the information.

But as the lead-lag relationship has not been found out for three scripts, one can conclude that
script based market is equally efficient in processing the information.




                                                                                                     99
                                       CONCLUSION

By using intraday (here minute-by-minute) data from December 2009 to February 2010, an effort
has been made to investigate the possible lead-lag relationship among the NIFTY spot index and
index futures market in India. As far as the regression results on the lead-lag relationship
between spot and futures index return is concerned, it revealed that there is a strong
contemporaneous and bi-directional relationship among the spot and futures market in India in
disseminating information available to the market. We have got almost the same results even for
some underlying NIFTY stocks that are very actively traded in the market. As far as our
knowledge is concerned, the possible explanation behind such more or less symmetric lead-lag
relationship among Indian spot and futures markets may be the joint efficiency of both the
markets.


As we know that one of the main objective of introducing derivatives product, such as index
futures, in Indian market is to enhance the informational efficiency of the underlying cash
market. Therefore by looking into such results, one can easily conclude that the informational
efficiency of the Indian cash market has really been increased due to the onset of derivative
trading, as claimed by the Indian regulators.


As far as our research is concerned, it may not be feasible to make any strong generalization on
the possible lead-lag relationship among the spot and futures market in India by looking at these
results. Though our evidence proves that new market information disseminates (may not be
equally) in both the spot and futures market and therefore serve an important role in the matter of
price discovery, we can get some more strong and reliable results through investigating such
relationship for a longer period of time within which the problem (if any) of any periodic effect
will be disappeared. Apart from this, a comparison among the results longer (at lease one year)
periods can also exhibit whether there is any change in the informational efficiency of the
markets over a period of time. Therefore, a further research in those lines can strongly focus
whether there is any real change in the informational efficiency of Indian cash market after the
introduction of derivative trading.




                                                                                               100

								
To top