CHAPTER 1: INTRODUCTION

In a perfectly functioning world, every piece of information should be reflected

simultaneously in the underlying spot market and its futures markets. However, in reality,

information can be disseminated in one market first and then transmitted to other markets due

to market imperfections. A large part of the literature on derivatives markets concerns the

effect that the introduction and existence of these markets have on the stability of the

underlying cash markets. Such effects include the impact of the introduction of derivatives

trading on the cash price volatility, market depth, information assimilation, price discovery

and risk transfer, amongst others. More specifically, how well the two markets are linked

together and relationship between price movements of stock index futures returns and

underlying cash market returns. Both futures and cash index prices reflect the aggregate

values of the underlying stocks.

Over the years, the Indian capital market has evolved into a dynamic segment of the Indian

financial system. From the historical perspective, the Indian capital market can be divided

into four stages since independence. In the first stage of its development, it was strengthened

through the establishment of a network of financial institutions such as IFCI (1948), ICICI

(1955), IDBI and UTI (1964). In the second stage, it introduced the Foreign Exchange

Regulation Act. The third stage of development has been initiated with the emergence of

several specialized institutions such as SEBI, CRISIL, CARE, ICRA, SHCIL, IL&FS and

OTCEI. Further, during this phase, several committees and working groups have been set up

to look after the development and working of the Indian capital market. The fourth stage of

development of the Indian capital market refers to the economic reforms initiatives of 1990-

91. This phase is termed as a period of change, signifying the widening and deepening of the

market. One of the significant reforms during this period was the setting up of the National

Stock Exchange (NSE). Another significant development of this phase was marked by the

introduction of derivatives trading based on the recommendations of L C Gupta Committee

Report.

With the introduction of derivatives in the equity markets in the late 1990s in the major world

markets, the volatility behaviour of the market has further got complicated as the derivatives

opens new avenues for hedging and speculation. The derivatives were launched mainly with

1

the twin objective of risk transfer and to increase liquidity thereby ensuring better market

efficiency.

The spot and futures markets provide investors with an opportunity to trade in the same

underlying security. It is quite logical, therefore, to anticipate a trading induced dynamic

relationship between the two markets.

There are several ways in which opening of the futures trading can increase efficiency and

smoothen price variations in a cash market. It has been argued that the introduction of

derivatives would cause some of the informed and speculative trading to shift from the

underlying cash market to the derivative market, given that these investors view derivatives

as superior investment instruments. This superiority stems from their inherent leverage and

lower transaction costs. In addition, it could also be argued that the migration of speculators

would cause a decrease in the volatility of the underlying cash market by reducing the

amount of noise trading. Most importantly, futures markets provide a mechanism for those

who buy and sell actual security to hedge themselves against unfavourable price changes.

Through the futures market, risk can be spread across a large number of investors, and

transferred away from those hedging spot positions to professional speculators, who are more

willing and able to bear it. This risk transfer may substantially improve the functioning of the

spot market because it reduces the need to incorporate risk premium in cash market

transactions to compensate for the risk of price fluctuations. Futures markets may also

increase the informational efficiency of the cash markets. There are certain inherent

characteristics of the futures market system that make it efficient. First, the index based

derivative, can be traded through a single contract, unlike spot market where one has to

simultaneously trade in a number of securities that comprise the market portfolio. Second,

investment in futures necessitates smaller initial outlay as one can enter into a futures contract

by paying a small proportion of the total value of the asset. As a result there would be greater

number of buyers and sellers and greater volumes traded - the typical conditions for an

efficient market. `

2

1.1 Types of financial markets on the basis of:

The entire stock market movements in the index represent the average returns obtained by the

investors. Stock market index is sensitive to the news of:

Company specific

Country specific

Thus the movement in the stock index is also the reflection of the expectation of the future

performance of the companies listed on the exchange.

3

financial markets

types of claims

debtequity markets

maturitymoney marketcapital market

tradespot marketdelivery market

deals in financial claims

primary marketsecondary market

1.2 Indian Capital Markets

The cash market is a buying strategy in which the buyer makes an immediate payment that is

equal to the current market price for commodities and other types of securities. Upon the

receipt of the payment, the seller relinquishes all claims to the property and bestows

ownership upon the buyer. In a sense, any type of retail transaction such as the purchase of

groceries could be considered a cash market, as the goods are received by the buyer upon

rendering cash payment for the products. One of the characteristics that set the

cash market apart from a futures market is this immediate satisfaction and transfer of

ownership. Futures markets involve a longer period for the transaction to be considered

complete. With a cash market, the investor immediately assumes ownership and is free to do

with the commodity or security as he or she wishes. While both approaches are capable of

helping an investor realize a return on an investment, the cash market approach may offer a

level of speed and excitement that will attract investors who prefer to be constantly on the

move with the investment portfolio.

One of the common designations for a cash market is "spot market." Spot markets get their

name from the fact that business deals are initiated and completed on the spot, rather than

requiring an extended period of time to resolve. Cash markets tend to be somewhat fast

paced, since the turnaround time on a transaction is so short. Many investors may purchase a

commodity on the cash market this morning, see a rise in the value by this afternoon, and sell

before closing and make a significant profit. Many physical commodities are bought and sold

in this type of market. Metals are one example of a commodity that is often sold in a

cash market. Grains like corn or wheat are also commodities traded in this type of market.

Even meats such as pork bellies are often sold in a cash market. In addition, some securities

as well as some underlying equities and bond s may also be sold in a cash market

environment The spot market is a securities or commodities market where goods, both

perishable and non-perishable, are sold for cash and delivered immediately or within a short

period of time. Contracts sold on a spot market are also effective immediately. The spot

market is also known as the “cash market” or “physical market.” Purchases are settled in cash

at the current prices set by the market, as opposed to the price at the time of delivery. An

example of a spot market commodity that is regularly sold is crude oil; it is sold at the current

prices, and physically delivered later.

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A commodity is a basic good which is interchangeable with other like-kind commodities.

Some examples of commodities are grains, beef, oil, gold, silver, electricity, and natural gas.

Technology has entered the market with commodities such as cell phone minutes and

bandwidth. Commodities are standardized, and must meet specific standards to be sold on the

spot market. The world spot market, or foreign currency trading (Forex), is a huge spot

market. It is the simultaneous exchange of one nation’s currency for another’s. The way it

works is through an investor selecting a currency pair. Great Britain (GBP) and the United

State (USD) currency is a common pair that is bought and sold on the world spot market. If

the GBP is gaining strength against the USD, the investor buys. If it is weak, he sells. The

benefit of foreign currency is that it is very liquid; an investor can enter and exit the market as

he chooses. The spot market differs from the futures market in that the price in the futures

market is affected by the cost of storage and future price movements. In the spot market,

prices can be affected by current supply and demand, which tends to make the prices more

volatile. Another factor that affects spot market prices is whether the commodity is perishable

or non-perishable. A non-perishable commodity such as gold or silver will sell at a price

which reflects future price movements. A perishable commodity such as grain or fruit will be

affected by supply and demand. For example, tomatoes bought in July will reflect the current

surplus of the commodity and will be less expensive than in January, when demand for a

smaller crop drives costs up. An investor cannot purchase tomatoes for a January delivery at

July’s prices, making tomatoes a perfect example of a spot market commodity.

The history of the Indian capital markets and the stock market, in particular can be traced

back to 1861 when the American Civil War began. The opening of the Suez Canal during the

1860s led to a tremendous increase in exports to the United Kingdom and United States.

Several companies were formed during this period and any banks came to the fore to handle

the finances relating to these trades. With many of these registered under the British

Companies Act, the Stock Exchange, Mumbai, came into existence in 1875.

It was an unincorporated body of stockbrokers, which started doing business in the city under

a banyan tree. Business was essentially confined to company owners and brokers, with very

little interest evinced by the general public. There had been much fluctuation in the stock

market on account of the American war and the battles in Europe. Sir Premchand Roychand

remained a kingpin for many years. Sir Phiroze Jeejeebhoy was another who dominated the

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stock market scene from 1946 to 1980. His word was law and he had a great deal of influence

over both brokers and the government. He was a good regulator and many crises were averted

due to his wisdom and practicality.

The BSE building, icon of the Indian capital markets, is called P.J. Tower in his memory. The

planning process started in India. The planning process started in India in 1951, with

importance being given to the formation of institutions and markets The Securities Contract

Regulation Act 1956 became the parent regulation after the Indian Contract Act 1872, a basic

law to be followed by security markets in India. To regulate the issue of share prices, the

Controller of Capital Issues Act (CCI) was passed in 1947.

The stock markets have had many turbulent times in the last 140 years of their existence. The

imposition of wealth and expenditure tax in 1957 by Mr. T.T. Krishnamachari, the then

finance minister, led to a huge fall in the markets. The dividend freeze and tax on bonus

issues in 1958-59 also had a negative impact. War with China in 1962 was another

memorably bad year, with the resultant shortages increasing prices all round. This led to a

ban on forward trading in commodity markets in 1966, which was again a very bad period,

together with the introduction of the Gold Control Act in 1963.

The markets have witnessed several golden times too. Retail investors began participating in

the stock markets in a small way with the dilution of the FERA in 1978. Multinational

companies, with operations in India, were forced to reduce foreign share holding to below a

certain percentage, which led to a compulsory sale of shares or issuance of fresh stock. Indian

investors, who applied for these shares, encountered a real lottery because those were the

days when the CCI decided the price at which the shares could be issued. There was no free

pricing and their formula was very conservative. The next big boom and mass participation

by retail investors happened in 1980, with the entry of Mr. Dhirubhai Ambani. Dhirubhai can

be said to be the father of modern capital markets. The Reliance public issue and subsequent

issues on various Reliance companies generated huge interest. The general public was so

unfamiliar with share certificates that Dhirubhai is rumoured to have distributed them to

educate people. Mr. V.P. Singh’s fiscal budget in 1984 was path breaking for it started the era

of liberalization. The removal of estate duty and reduction of taxes led to a swell in the new

issue market and there was a deluge of companies in 1985. Mr. Manmohan Singh as Finance

Minister came with a reform agenda in 1991 and this led to a resurgence of interest in the

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capital markets, only to be punctured by the Harshad Mehta scam in 1992. The mid-1990s

saw a rise in leasing company shares, and hundreds of companies, mainly listed in Gujarat,

and got listed in the BSE. The end- 1990s saw the emergence of Ketan Parekh and the

information; communication and entertainment companies came into the limelight. This

period also coincided with the dotcom bubble in the US, with software companies being the

most favoured stocks.

There was a meltdown in software stock in early 2000. Mr. P Chidambaram continued the

liberalization and reform process, opening up of the companies, lifting taxes on long-term

gains and introducing short-term turnover tax. The markets have recovered since then and we

have witnessed a sustained rally that has taken the index over 13000. Several systemic

changes have taken place during the short history of modern capital markets. The setting up

of the Securities and Exchange Board (SEBI) in 1992 was a landmark development. It got its

act together, obtained the requisite powers and became effective in early 2000. The setting up

of the National Stock Exchange in 1984, the introduction of online trading in 1995, the

establishment of the depository in 1996, trade guarantee funds and derivatives trading in

2000, have made the markets safer. The introduction of the Fraudulent Trade Practices Act,

Prevention of Insider Trading Act, Takeover Code and Corporate Governance Norms, are

major developments in the capital markets over the last few years that has made the markets

attractive to foreign institutional investors. This history shows us that retail investors are yet

to play a substantial role in the market as long-term investors. Retail participation in India is

very limited considering the overall savings of households. Investors who hold shares in

limited companies and mutual fund units are about 20-30 million. Those who participated in

secondary markets are 2-3 million. Capital markets will change completely if they grow

beyond the cities and stock exchange centres reach the Indian villages. Both SEBI and retail

participants should be active in spreading market wisdom and empowering investors in

planning their finances and understanding the markets.

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1.3 Derivatives Markets in India

A derivative is a financial instrument, whose value depends on the values of basic underlying

variable. In the sense, derivatives is a financial instrument that offers return based on the

return of some other underlying asset, i.e. the return is derived from another instrument.

Derivatives play a very important role in the price discovery process and risk management.

Spot and future are two different interlinked markets. As there is a same underline asset with

different delivery period, there will be some relationship between spot and future indices.

Derivative products initially emerged as a hedging device against fluctuations in commodity

prices, and commodity linked derivatives remained the sole form of such products for almost

three hundred years. It was primarily used by the farmers to protect themselves against

fluctuations in the price of their crops. From the time it was sown to the time it was ready for

harvest, farmers would face price uncertainties. Through the use of simple derivative

products, it was possible for the farmers to partially or fully transfer price risks by locking in

asset prices.

From hedging devices, derivatives have grown as major trading tool. Traders may execute

their views on various underlings by going long or short on derivatives of different types.

Financial derivatives are financial instruments whose prices are derived from the prices of

other financial instruments. Although financial derivatives have existed for a considerable

period of time, they have become a major force in financial markets only since the early

1970s. In the class of equity derivatives, futures and options on stock indices have gained

more popularity than on individual stocks, especially among institutional investors, who are

major users of index-linked derivatives. Even small investors find these useful due to high

correlation of the popular indices with various portfolios and ease of use.

A Spot contract is an agreement between two parties to buy or sell a specified quantity and

defined quality of a commodity at a certain time as specified in the contract as settlement

cycle. The spot contract is of one day duration and the open position at the end of the trading

session results into the compulsory delivery.

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Volume in Futures and Options Segment of NSE For The Fiscal Year 2010–2011

(1.3. Volume of future and option)

The spot

and

futures

markets

provide investors with an

opportunity to trade in the same underlying security. It is quite logical,

therefore, to

anticipate a trading induced dynamic

relationship between the two markets. Financial market is a market where

financial instruments are exchanged or traded and helps in determining the prices of the

assets that are traded in and is also called the price discovery process.

1.3.1 History

Derivatives markets in India have been in existence in one form or the other for a long time.

In the area of commodities, the Bombay Cotton Trade Association started futures trading way

back in 1875. In 1952, the Government of India banned cash settlement and options trading.

9

Total Of All Indices815,662,210 227,221,2035,088,941 100.00496,336,138132,991,9562,718,266 100.00

Total of NiftyIndex Futures AndOptions

782,700,769 220,498,4354,938,375 95.96 480,656,460 129,958,1912,656,257

Indices/ PeriodNo Of

ContractsTradedValue(̀ Mn.)

TradedValue

(Us $ Mn.)

PercentageOf

ContractsTo Total

Contracts(%)

No OfContracts

Traded Value(̀ Mn.)

TradedValue

(Us $ Mn.)

PercentageOf

ContractsTo Total

Contracts(%)

2010-2011 April - September 2011

Index Futures

Nifty 133,368,752 37,184,645 832,803 16.35 57,212,398 15,340,099 313,541 11.53

Minifty 14,658,741 1,626,215 36,421 1.80 6,980,517 751,194 15,354 1.41

Banknifty 16,927,993 4,733,010 106,002 2.08 7,606,559 2,006,688 41,015 1.53

Cnxit 66,951 23,249 521 0.01 34,025 10,582 216 0.01

Nftymcap50 1,216 427 10 0.00 188 39 1 0.00

Djia * * * * 48,003 13,375 273 0.01

S&P500 * * * * 22,910 6,711 137 0.00

Index Options

Nifty 649,332,017 183,313,7904,105,572 79.61423,444,062 114,618,0922,342,716 85.31

Minifty 165,856 18,800 421 0.02 112,011 12,453 255 0.02

Banknifty 1,102,592 313,348 7,018 0.14 853,488 226,257 4,625 0.17

Cnxit 1,237 426 10 0.00 0 0 0 0.00

Nftymcap50 36,855 7,294 163 0.00 1,725 343 7 0.00

S&P500 * * * * 20,252 6,124 125 0.00

Total Of All Indices815,662,210 227,221,2035,088,941 100.00496,336,138132,991,9562,718,266 100.00

Total of NiftyIndex Futures AndOptions

Derivatives trading shifted to informal forwards markets. In recent years, government policy

has shifted in favour of an increased role of market-based pricing and less suspicious

derivatives trading. The first step towards introduction of financial derivatives trading in

India was the promulgation of the Securities Laws (Amendment) Ordinance, 1995. It

provided for withdrawal of prohibition on options in securities. The last decade, beginning

the year 2000, saw lifting of ban on futures trading in many commodities. Around the same

period, national electronic commodity exchanges were also set up. Derivatives trading

commenced in India in June 2000 after SEBI granted the final approval to this effect in May

2001 on the recommendation of L. C Gupta committee. Securities and Exchange Board of

India (SEBI) permitted the derivative segments of two stock exchanges, NSE3 and BSE4,

and their clearing house/corporation to commence trading and settlement in approved

derivatives contracts.

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. In June 2003, NSE introduced Interest Rate Futures which were

subsequently banned due to pricing issue.

Table 1.3.1. Total Option & Future

  Total Average Daily

Turnover (Rs in cr.)Year No. of contracts Turnover (Rs in

cr.)

2012-13 973093873 26878890 121623.9

2011-12 1205045464 31349732 125902.5

2010-11 1034212062 29248221 115150.5

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2009-10 679293922 17663665 72392.07

2008-09 657390497 11010482 45310.63

2007-08 425013200 13090478 52153.3

2006-07 216883573 7356242 29543

2005-06 157619271 4824174 19220

2004-05 77017185 2546982 10107

2003-04 56886776 2130610 8388

2002-03 16768909 439862 1752

2001-02 4196873 101926 410

2000-01 90580 2365 11

Initially, SEBI approved trading in index futures contracts based on various stock market

indices such as, S&P CNX, Nifty and Sensex. Subsequently, index-based trading was

permitted in options as well as individual securities.

1.3.1:- global volume

34%

28%

15%

12%

4%3%3% 1%

Global Option And Future Volume by category (2011)

Equity Index Individual EquityInterest Rate CurrencyAgricultural EnergyMetals Others

Asia Pacific40%

North America33%

Europe20%

Latin America6%

Other1%

Global Futures And Options Volume By Region(2011)

1.3.2 Commodity Derivatives in India

Commodity derivatives in India were established by the Cotton Trade Association in 1875,

since then the market has suffered from liquidity problems and several regulatory dogmas.

However in the recent times the commodity trade has grown significantly and today there are

25 derivatives exchanges in India which include four national commodity exchanges;

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National Commodity and Derivatives Exchange (NCDEX), National Multi Commodity

Exchange of India (NCME), National Board of Trade (NBOT) and Multi Commodity

Exchange (MCX)

NCDEX

It is the largest commodity derivatives exchange in India and is the only commodity

exchange promoted by national level institutions. NCDEX was incorporated in 2003 under

the Companies Act, 1956 and is regulated by the Forward Market Commission in respect of

the futures trading in commodities. NCDEX is located in Mumbai

MCX

MCX is recognised by the government of India and is amongst the world’s top three bullion

exchanges and top four energy exchanges. MCX’s headquarter is in Mumbai and facilitates

online trading, clearing and settlement operations for the commodities futures market in the

country. Since its inception in June 2000, derivatives market has exhibited exponential

growth both in terms of volume and number of traded contracts. The market turn-over has

grown from Rs.2365 crore in 2000-2001 to Rs. 11010482.20 crore in 2008-2009. Within a

short span of eight years, derivatives trading in India has surpassed cash segment in terms of

turnover and number of traded contracts.

1.3.3 Regulation of Derivatives Trading in India

The regulatory framework in India is based on the L.C. Gupta Committee Report, and the

J.R. Varma Committee Report. It is mostly consistent with the IOSCO5 principles and

addresses the common concerns of investor protection, market efficiency and integrity and

financial integrity. The L.C. Gupta Committee Report provides a perspective on division of

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regulatory responsibility between the exchange and the SEBI. It recommends that SEBI’s

role should be restricted to approving rules, bye laws and regulations of a derivatives

exchange as also to approving the proposed derivatives contracts before commencement of

their trading. It emphasises the supervisory and advisory role of SEBI with a view to

permitting desirable flexibility, maximizing regulatory effectiveness and minimizing

regulatory cost. Regulatory requirements for authorization of derivatives brokers/dealers

include relating to capital adequacy, net worth, certification requirement and initial

registration with SEBI. It also suggests establishment of a separate clearing corporation,

maximum exposure limits, mark to market margins, margin collection from clients and

segregation of clients’ funds, regulation of sales practice and accounting and disclosure

requirements for derivatives trading. The J. R. Varma committee suggests a methodology for

risk containment measures for index-based futures and options, stock options and single stock

futures. The risk containment measures include calculation of margins, position limits,

exposure limits and reporting and disclosure.

In India, trading in derivatives started in June 2000 with the introduction of futures contracts

in the BSE and the NSE. Derivatives trading on individual stocks began on November 9,

2001. Since then the Futures and Options (F&O) segment has been continuously growing in

terms of new products and contracts, volume, and value. At present, the NSE has established

itself as the market leader in this segment in the country with more than 99.5% market share.

The F&O segment of the NSE outperformed the cash market segment with an average daily

turnover of Rs. 191.44 bn as against Rs. 90.09 bn of cash segment in the year 2005-06. It

shows the importance of derivatives in the capital market sector of the economy.

The National Stock Exchange (NSE), located in Bombay is the first screen based automated

stock exchange. It was set up in 1993 to encourage stock exchange reform through system

modernization and competition. It opened for trading in mid- 1994 and today accounts for

99% market shares of derivatives trading in India. 4 Bombay Stock Exchange (BSE), which

is Asia's Oldest Broking House, was established in 1875 in Mumbai. It is also called as Dalal

Street. The BSE Index, called the Sensex, is calculated by Free Float Method by including

scripts of top 30 companies selected on the market capitalization criterion.

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1.4 Future

A future is an important instrument for risk exposure through hedging, portfolio

diversification, and price discovery.

Futures contract is a standardized transaction taking place on the futures exchange. Futures

market was designed to solve the problems that exist in forward market. A futures contract is

an agreement between two parties, to buy or sell an asset at a certain time in the future at a

certain price, but unlike forward contracts, the futures contracts are standardized and

exchange traded To facilitate liquidity in the futures contracts, the exchange specifies certain

standard quantity and quality of the underlying instrument that can be delivered, and a

standard time for such a settlement. Futures’ exchange has a division or subsidiary called a

clearing house that performs the specific responsibilities of paying and collecting daily gains

and losses as well as guaranteeing performance of one party to other. A futures' contract can

be offset prior to maturity by entering into an equal and opposite transaction. More than 99%

of futures transactions are offset this way.

Yet another feature is that in a futures contract gains and losses on each party’s position is

credited or charged on a daily basis, this process is called daily settlement or marking to

market. Any person entering into a futures contract assumes a long or short position, by a

small amount to the clearing house called the margin money.

The standardized items in a futures contract are:

Quantity of the underlying

Quality of the underlying

The date and month of delivery

The units of price quotation and minimum price change

Location of settlement

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Stock futures contract is a contractual agreement to trade in stock/ shares of a company on a

future date. Some of the basic things in a futures trade as specified by the exchange are:

Contract size

Expiration cycle

Trading hours

Last trading day

Margin requirement

1.4.1 Advantages of stock futures trading

Investing in futures is less costly as there is only initial margin money to be deposited

A large array of strategies can be used to hedge and speculate, with smaller cash

outlay there is greater liquidity

Diversification of the risks as the investor is not investing in a particular stock

Flexibility of changing the portfolio and adjusting the exposures to particular stock

index, market or industry

1.4.2 Disadvantages of stock futures trading

The risk of losses is greater than the initial investment of margin money

The futures contract does not give ownership or voting rights in the equity in which it

is trading

There is greater vigilance required because futures trades are marked to market daily

1.4.3 Factors Affecting Futures

Any investor with an exposure to the futures market needs a grasp of the various factors that

affect futures. Here is an overview:

1.4.3.1 General Factors

15

As with any investment, the general economic condition of the country plays an important

role in establishing the futures market sentiment. A booming economy is the basis for

expectation of price rise. Futures traders may opt to go long in a flourishing economy to

make profits when prices rise in future. Political stability or uncertainty can have a major

impact on futures prices as these directly affect the economy of the country. The growth

prospects for a particular sector of the economy should also be a consideration before making

an investment in futures.

1.4.3.2 Factors Influencing Commodity Futures

Commodities form an important segment of the futures markets. Any factors affecting the

supply or cost of production of a particular commodity affects its futures contracts. For

example, unfavourable weather can have a major effect on the futures of an agricultural

commodity. Traders will expect supply to dry up in coming months causing the price to go

up. Most traders will want to go long on the commodity, expecting price to rise. This will

push the price up for futures of the commodity. Export import policies and restrictions may

have a bearing on how futures trade when the goods are actually deliverable. Considering that

many futures trades are often cross border transactions, complicated export import formalities

can lower prices.

1.4.3.3 Factors Influencing Currency Futures

Currency futures are influenced by many factors, most important being the policies of the

Federal Reserve and the US Treasury regarding money supply. Government policies

regarding taxation and other decisions to bring down inflation will also have an effect on

currency futures.

The recent performance of the dollar versus the opposite currency in the contract plays an

important role in determining the price at which a futures contract can be struck. GDP growth

and trade deficit should also be considered when trading in currency futures.

1.4.3.4 Factors Influencing Index Futures and Single Stock Futures (SSFs)

16

Index and single stock futures are influenced by many of the same factors as the delivery

based stock market. High interest rates, changes in taxation policies, market sentiment, GDP

growth etc affect the prices of these futures. SSFs move largely in line with the current price

movement of that stock in the market, with some premium or discount based on the expected

direction that the stock price will move in.

Major factors that affect stock prices

corporate result Political situations

Buyback news from good companies. Fear of war

Tax benefits Good monsoon rains

Mergers and Demerger GDP

Splitting Industrial growths

Change of groups for eg. From group B1 to

A

favourable industrial policies from govt

New projects or contracts got by companies Inflation

Listing of companies in Nasdaq,Nyse etc.

and their performance there.

Interest rates

Short and long covering Crude oil

Take Over of competitors business. change in the value of rupee

Before rights and public issues. FII

winning or losing a case of suit RBI monetary policies

strikes continuous holidays

Demand for products of the company Terrorist attacks

Availability of raw material Political situations

1.5 Status Report on the Developments in the Derivatives

Market

17

1.5.1 Equity Derivatives Segment

At the end of

March 2012, NSE had derivatives (futures and options) on 217 stocks and 9 indices while

BSE had derivatives on 218 stocks and 9 indices. During January-March, 2012, 2 securities

were added for equity derivatives trading at NSE and BSE whereas 11 securities were

excluded from equity derivatives trading at NSE and BSE.

During January-March, 2012, the average daily turnover at NSE was INR 123,008 crore

whereas average daily turnover at BSE was INR 11,442 crore As the majority of equity

derivatives trading takes place at NSE, the analysis for the same is based on the derivative

transactions at NSE.

1.5.2 Derivative Maturity profile

Average daily volume in longer-dated (contracts with maturity of more than three months and

upto 5 years) derivative contracts on NIFTY was 6,009 contracts and average daily turnover

was INR 163 crore; both volume and turnover have increased by 34% and 41% respectively

over the previous quarter.

Average daily volume in shorter-dated (contracts with maturity upto 3 months) derivative

contracts on indices and stocks decreased by 12% to 4,648,661 contracts whereas average

daily turnover decreased by 4% to INR 122,845 crore over the previous quarter.

1.5.3 Mini Nifty Contract

18

Global Futures and Options Volume(Based on the number of contracts traded and/or cleared at 75 exchanges worldwide)

Jan-Jun 2010 Jan-Jun 2011 % Change

Futures 5,699,350,602 6,098,780,118 7.0%Options 5,554,279,670 6,303,756,977 13.5%Combined 11,253,630,27212, 402, 537, 09 10.2%

Average daily volume in Mini Nifty (contracts with minimum lot size of INR 1 lakh) was

54,688 contracts and average daily turnover was INR 576 crore which decreased by 20%

(volume) and 15% (turnover) over the previous quarter. Further, Normal Nifty average daily

volume decreased by 16% while average daily turnover of Normal Nifty decreased by 11% in

the current quarter over the previous quarter.

1.5.4 Volume and Turnover Analysis

During the quarter under review, average daily volume (no. of contracts) decreased by 12%

to 4,654,670 contracts while average daily turnover decreased by 4% to INR 123,008 crore

over October-December, 2011.

Table 1.5.1: Index Futures & stock future

  Index Futures Stock Futures

Year No. of

contracts

Turnover (Rs in

cr.)

No. of contracts Turnover (Rs in

cr.)

2012-13 85426125 2216122.34 130172394 3698303.29

2011-12 146188740 3577998.41 158344617 4074670.73

2010-11 165023653 4356754.53 186041459 5495756.7

2009-10 178306889 3934388.67 145591240 5195246.64

2008-09 210428103 3570111.4 221577980 3479642.12

2007-08 156598579 3820667.27 203587952 7548563.23

2006-07 81487424 2539574 104955401 3830967

2005-06 58537886 1513755 80905493 2791697

2004-05 21635449 772147 47043066 1484056

2003-04 17191668 554446 32368842 1305939

2002-03 2126763 43952 10676843 286533

2001-02 1025588 21483 1957856 51515

2000-01 90580 2365 - -

19

Futures (Index Futures + Stock Futures) constituted 26.24% of the total number of contracts

traded in the equity derivatives segment. Contracts traded in Stock Futures and Index Futures

accounted for 14.28% and 11.96% respectively.

Options constituted 73.76% of the total volume. This mainly comprised of trading in Index

Options (69.89%). Stock options contributed the rest of the options trading volume (3.87%).

Turnover in the equity derivatives segment was 9.50 times that of the turnover in the cash

market segment, during January-March, 2012 as compared to 13.12 times in the previous

quarter. The turnover in the cash market increased by 42% while turnover of equity

derivatives increased by 2% during the current quarter as compared to the previous quarter.

In this context, it may be stated that, equity derivatives (Futures and options) turnover is

reported on a notional basis whereas for trading and settlement of options, only option

premium is taken into account.

As premium value is merely about 1% of notional value, therefore, reporting on the basis of

notional value inflates the equity derivatives turnover.

State Bank of India, Tata Motors Limited, ICICI Bank, Reliance Industries Ltd and Infosys

Technologies Ltd were the most actively traded securities in terms of number of contracts

(both on futures and options) in the equity derivatives segment. They together contributed

24% of derivatives turnover on individual stocks.

Client trading constituted 38.71%, Proprietary trading constituted 42.92% and FII

(Proprietary and sub-account) trading constituted remaining 18.37% of the total equity

derivatives turnover. While FII trading increased by 10%, both Client trading and Proprietary

trading decreased by 2% in the current quarter as compared to the previous quarter.

Table 1.5.2: Index Option & Stock Option

20

  Index Options Stock Options

Year No. of contracts Notional Turnover (Rs

in cr.)

No. of contracts Notional

Turnover (Rs in

cr.)

2012-13 699691942 19236746.55 57803412 1727718.31

2011-12 864017736 22720031.64 36494371 977031.13

2010-11 650638557 18365365.76 32508393 1030344.21

2009-10 341379523 8027964.2 14016270 506065.18

2008-09 212088444 3731501.84 13295970 229226.81

2007-08 55366038 1362110.88 9460631 359136.55

2006-07 25157438 791906 5283310 193795

2005-06 12935116 338469 5240776 180253

2004-05 3293558 121943 5045112 168836

2003-04 1732414 52816 5583071 217207

2002-03 442241 9246 3523062 100131

2001-02 175900 3765 1037529 25163

2000-01 - - - -

1.5.5 Volatility Analysis

During the quarter under review, average daily volatility in the underlying S&P CNX Nifty

decreased to 1.27% in March 2012 from 1.36% in January 2012. India stands 1st in Stock

Futures, 13th in Index Futures, 4th in Stock Options and 4th in Index Options in World

Derivatives Market (in terms of turnover) at the end of March 2012.

1.5.6 Short-collection/non-collection of client margin

At the end of March 2012, the value of margin shortfall in the equity derivatives segment at

NSE was INR 215 crore. During February 2011, value of margin shortfall in the equity

derivatives segment of NSE was INR 3,864 crore. In the currency derivatives segment of

NSE, value of margin shortfall increased to INR 10.8 crore in March 2012 as compared to

INR 0.09 crore in January 2012.

CHAPTER 2: LITERATURE REVIEW

21

Y.P.Singh and Shalini Bhatia (2006) examines the Futures Trading Impact Spot Market

Volatility (Evidence from Indian Financial Markets) using daily data from October 1995 to

March 2005 by using the (1, 1) variant of the Generalised Auto Regressive Conditional

Heteroskedasticity (GARCH 1, 1) model. The findings reveal that there has been a small yet

statistically significant decline in daily volatility of the NIFTY index after the introduction of

futures and spot market volatility decline on expiration days.

Pretimaya Samanta and Pradeepta Kumar Samanta (2007) investigate Impact of Futures

Trading on the Underlying Spot Market Volatility using daily closing price returns of S&P

CNX Nifty, Nifty Junior, and S&P 500 index from October 4, 1995 to December 31, 2006.

By using univariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH)

model the study suggests that there is no significant change in the volatility of the spot market

of the S&P CNX Nifty Index, but the structure of the volatility has changed to some extent.

However, some interesting results in case of introduction of stock futures suggest that it has

mixed results in spot market volatility in the case of ten individual stocks.

Sibani Prasad Sarangi and Uma Shankar Patnaik (2007) apply the family of Generalized

Autoregressive Conditional Heteroskedasticity (GARCH) techniques to capture the time-

varying nature of volatility and volatility clustering phenomenon in the daily closing price

returns of 28 individual stocks listed on S&P CNX Nifty, Nifty Junior index and S&P index

from October 4, 1995 to March 31, 2007. The empirical evidence suggests that in most of the

stocks, there is no significant change in the volatility of the spot market. But with regard to

the information flow to the spot market, futures’ trading has changed the nature of the

volatility which is reflected by the change in the news coefficient and persistent coefficient.

Manolis G. Kavussanos, Ilias D. Visvikis and Panayotis D. Alexakis (2008) examines the

lead-lag relationship between cash and stock index futures using data sets of daily closing

cash and futures prices of the FTSE/ATHEX-20 and FTSE/ATHEX Mid-40 markets from

February 2000 to June 2003 by using Augmented Dickey-Fuller (ADF, 1981) and Phillips

and Perron (PP, 1988) and Johansen (1988). Empirical results show that there is a bi-

directional relationship between cash and futures prices. However, futures lead the cash index

returns, by responding more rapidly to economic events than stock prices. This speed is much

higher in the more liquid FTSE/ATHEX-20 market. Moreover, results indicate that futures

22

volatilities spill information over to the corresponding cash market volatilities in both

investigated futures markets, but volatilities in the cash markets have no effect on the

volatilities of futures markets. Overall, it seems that new market information is disseminated

faster in the futures market compared to the stock market. This implies that the futures

markets can be used as price discovery vehicles, providing further evidence those derivatives

markets contribute to completing and stabilising capital markets in Greece. A further finding

of this study is that futures volume and disequilibrium effects between cash and futures

Dhananjay Sahu (2007) examines the effect of futures introduction on spot market volatility

and informational efficiency in Indian stock market by using daily return of CNX Nifty Index

from October 05, 1995 to March 31, 2007. The Generalized Autoregressive Conditional

Heteroskedasticity (GARCH) (1, 1) model applied to study market volatility by using Nifty

Junior index return and lagged S&P500 index return. The results indicate that the

introduction of futures trading has had no impact on market volatility and after the

introduction of futures trading, the spot market has become more efficient owing to the

diminishing importance of old news and faster incorporation of recent news in prices.

Sangeeta Wats and K.K.Misra (2009) examines whether prices in the futures market help

to determine the prices in spot market. The daily closing prices of the near month contracts

for NSE index futures and ten stock futures for the period from June 12, 2000 till December

31, 2007 is taken into account and by using various models of econometrics like Johansen’s

Co-integration Test, Causality test, Impulse Response Analysis Variance Decomposition

Test, The study concludes that price discovery ensues primarily in the futures market with the

spot market contributing an almost insignificant role.

Madhusudan Karmakar (2009) investigates the lead-lag relationship in the first moment as

well as the second moment between the S&P CNX Nifty and the Nifty future and how much

and how fast these movements transfer between these markets. The daily S&P CNX Nifty

spot and the Nifty futures data from June 12, 2000 to March 29, 2007 and Multivariate Co

integration tests, Vector Error Correction Model (VECM) and Bivariate BEKK model used in

this study. The VECM results show that the Nifty futures dominate the cash market in price

discovery. The bivariate BEKK model shows that although the persistent volatility spills over

from one market to another market bi-directionally, past innovations originating in future

market have the unidirectional significant effect on the present volatility of the spot market.

23

The findings of the study thus suggest that the Nifty future is more informational efficient

than the underlying spot market.

Satya Swarup Debashis (2008) investigate the effect of futures trading on the volatility and

operating efficiency of the underlying Indian stock market by taking a sample of 15

individual stocks. The daily data of S&P 500 futures and S&P 500 stock index from June

1995 to June 2008 and two tailed t-test used in this study. The result shows that the

introduction of Nifty index futures trading in India is associated with both reduction in spot

price volatility and reduced trading efficiency in the underlying stock market. The results of

this study are crucial to investors, stock exchange officials and regulators. Derivatives play a

very important role in the price discovery process and in completing the market. Their role in

risk management for institutional investors and mutual fund managers need hardly be

overemphasized. This role as a tool for risk management clearly assumes that derivatives

trading do not increase market volatility and risk.

Anver Sadath and B Kamaiah (2009) examines the bid-ask spread of underlying stocks

around the introduction of Single Stock Futures (SSF) in the National Stock Exchange (NSE),

in order to ascertain whether SSF trading has any liquidity effect on the underlying stocks.

Using both high frequency and daily data from January 1, 2001 to December 31, 2002 on a

dataset consisting of 28 stocks on which stocks futures were traded from November 9, 2001

in the NSE, the study shows that the liquidity of underlying stocks has increased as there is

considerable decline in both spread and return variance in the post-futures period. This

decline in spread may be attributed to the SSF trading, as existence of futures market prompts

informed traders to migrate to futures market so as to capitalize on the trading flexibilities

available there. Consequently, the dealers in the spot market reduce spread as they need not

incur any adverse selection cost for trading with informed traders. Besides, with shift of well-

informed traders to futures markets, better information is incorporated into the prices. This

leads to reduction in volatility of spot market. This decline in volatility helps dealers to

reduce spread as inventory risk associated with maintaining balanced inventory decreases.

Thus, it can be concluded that introduction of SSF in the NSE has resulted in improvement of

liquidity in the cash market.

Srinivasan (2009) examines the causal relationship between Nifty spot index and index

futures market in India. The daily data series from June 12, 2000 to September 12, 2008. The

24

Vector Error Correction Model (VECM), ADF and Phillips-Perron tests used in this study.

The results reveal that there exists a long-run relationship between Nifty spot and Nifty

futures prices. Further, the results confirm the presence of a bidirectional relationship

between the Nifty spot and Nifty futures market prices in India. It can, therefore, be

concluded that both the spot and futures markets play the leading role through price discovery

process in India and said to be informational efficient and react more quickly to each other.

Pratap Chandra Pati and Purna Chandra Padhan (2009) investigate the price discovery

process and lead-lag relationship between NSE S&P CNX Nifty stock index futures and its

underlying spot index, using daily data from January 1, 2004 to December 31, 2008.By using

Johansen- co integration test, Vector Error Correction Model (VECM), impulse response

functions, variance decomposition, Granger non-causality tests, The results reveal that futures

price leads spot price and performs the price discovery function. The results of variance

decomposition indicate that the futures market shocks dominate over spot market in

explaining the variation in spot market. However, disturbance originating from spot market

contributes very less percentage variability to futures market. To conclude, futures price leads

spot price and performs the price discovery function. The obtained results have important

implications for traders, regulatory bodies and practitioners.

P Srinivasan (2009) employed Johansen’s co integration technique followed by the Vector

Error Correction Model (VECM) to examine the causal relationship between National Stock

Exchange (NSE) spot and futures markets prices of selected nine oil and gas industry stocks

of India. The study used daily data series from May 12, 2005 to January 29, 2009. The

analysis reveals that there exists a long-run relationship between spot and futures prices of

each of the selected individual securities. Besides, the study also indicates a bidirectional

relationship between spot and futures markets prices in the case of four oil industry stocks,

spot leading the futures price in the case of three stocks, and the futures leading the spot price

in the case of two selected gas and oil industry stocks.

P Sakthivel and B Kamaiah (2010) investigates the role of information in price discovery

function and volatility spill over in Nifty and S&P CNX Nifty futures. By employing two-

step TGARCH procedures, Engle-Granger co integration and error correction model, the

results of show that there is long-run equilibrium relationship between spot and futures

25

markets, and there is a bidirectional volatility spill over between spot and near, middle and

far month futures.

T Mallikarjunappa and Afsal E M (2010) attempts to determine the lead-lag relationship

between spot and futures markets in the Indian context by using high frequency price data of

twelve individual stocks, observed at one-minute interval. The study applies the concept of

co-integration, Vector Error Correction Model (VECM) and EGARCH models. The key

results of the study are; there is a contemporaneous and bi-directional lead-lag relationship

between the spot and futures markets, a feedback mechanism of short life is functional

between the two markets, price discovery occurs in both the markets simultaneously, there

exists short-term disequilibrium that could be corrected in the next period, volatility spill over

from spot market to futures market is present in such a way that a decrease in spot volatility

leads to a decrease in futures volatility, volatility shocks are asymmetric and persistent in

both the markets, spill over from futures market to spot market is not significant, neither spot

nor futures assume a considerable leading role and neither of the markets is supreme in price

discovery. in the case of 33.33 per cent of spot values and 33.33 per cent of futures values,

there exists short-term disequilibrium that could be corrected in the next period by decreasing

the prices, spot market volatility spills over to futures market in most of the cases (66.66 %)

and a decrease in spot volatility brings about a decrease in futures volatility in 50 per cent of

the cases, spill over effect from futures to spot market is present and significant in 91.66 per

cent of stocks and is more than the spill over effect from spot to futures (50% valid cases),

the markets are highly integrated, a symmetric behaviour of volatility shocks is mixed in both

the markets, asymmetric volatility is detected in 50 per cent of the cases of spot market and

58.33 per cent cases of futures market, stocks exhibiting asymmetric volatility show more

sensitivity to negative shocks, and there are no cases of market becoming more volatile in

response to good news.

Pratap Chandra Pati and Prabina Rajib (2011) investigate the relationship between the

National Stock Exchange (NSE) S&P CNX Nifty futures and its underlying spot index in

terms of both return and volatility. The data consist of 5-min transaction prices for National

Stock Exchange (NSE) S&P CNX Nifty futures and spot index from 1 March 2007 to 31

January 2008. By applying Johansen–Juselius (J–J) co integration, Vector Error Correction

Model (VECM), Granger causality test and Generalized Autoregressive Conditional

Heteroskedasticity (GARCH) models, the study find evidence of single common stochastic

26

trend, to which spot and futures prices move together in a long-run equilibrium path and there

is unidirectional causality running from futures to spot market. The study finds evidence that

both the prices move together in a long-run equilibrium path, suggesting a violation of weak

form of market efficiency. There is unidirectional causality from futures to spot market. In

addition, the study finds bidirectional volatility transmission. However, there is pronounced

spill over effect of a previous shock and volatility from the futures market to spot market.

Chiao-Yi Chang (2011) examines the informational content of the basis under positive and

negative prior shocks, and its linkage to the relationship between the Indian stock index spots

and futures contracts. This result reflects the fact that investors’ perceived uncertainty of

‘negative prior shocks’ will change the original connection of futures and spot returns,

considering the strengthening basis. This study used daily SGX CNX Nifty (India) Index

Futures data from 25 September 2000 to 31 December 2008, traded on the Singapore Stock

Exchange. This result reflects the fact that investors’ perceived uncertainty of ‘negative prior

shocks’ will change the original connection of futures and spot returns, considering the

strengthening basis. The study fails to find that the spot returns lead the futures prices.

Santhosh Kumar and M A Lagesh (2011) investigates price volatility and hedging

behaviour of four notional commodity futures indices which represent the relevant sectors

like Agriculture (AGRI), Energy (ENER), Metal (META) and an aggregate of Agricultural,

Energy and Metal commodities (COMDX), retrieved from the commodity futures exchange

market, Multi Commodity Exchange (MCX), of India. The daily closing prices over the

period of June 8, 2005 to August 31, 2010 and Generalized Autoregressive Conditional

Heteroskedasticity (GARCH) (1, 1), DVECH-GARCH, BEKK-GARCH and CCC-GARCH

models have been used. The empirical evidence confirms that all the models were able to

reduce the exposure to spot market as perfectly as possible in comparison to the unhedged

portfolio. It is seen from the optimal hedge ratios obtained from different econometric models

and their variance reduction analysis that the hedge ratios have reduced the exposure to spot

market as perfectly as possible.

Sathya Swaroop Debasish (2011) investigate whether there has been significant change in

relative volatility of the underlying spot return and futures return in the Indian stock market

due to the introduction of futures trading. The study has used data on daily opening, low, high

and closing prices of the selected indices and individual stocks traded in the spot market. The

27

futures data include the near month prices of daily opening, low, high and closing. The spot

prices and the one-month futures prices of the selected stocks and indices are taken for the

study. The futures time series analyzed here uses data on the near month contract as they are

most heavily traded. The study used data on daily opening, low. high and closing prices of

the selected indices and individual stocks traded in the spot market. The futures data include

the near-month prices of daily opening, low, high and closing. The study used univariate

Generalized Autoregressive Conditional Heteroskedasticity (GARCH). E-GARCH family

models and three stock indices of NSE namely Nifty, CNX IT and CNX Bank and four

measures of volatility found that for the three NSE indices, the study rejects the null

hypothesis of 'no significant change in relative inter-day volatility between spot prices and

futures prices' over daily opening, low, high and closing prices for the entire period 2000-

2007, but cannot reject the hypothesis fully for all the individual years.

Anver Sadath and Bandi Kamaiah (2011) investigate the effects of individual stock futures

expiration on the underlying stock market in the NSE. Using daily data of 42 sample stocks

of high market capitalization, this study has found positive abnormal return and also

abnormal volume on days prior to the expiration day. That futures expiration has resulted in

positive price and volume effects during the days leading to the expiration date. This result is

at variance with the findings of studies on the US, where negative price effect was found

before the expiration day. The reported expiration day effects may be due to the unwinding of

arbitrage positions in the spot market. While cash settlement feature of stock futures contracts

allows futures positions to be self-closed, spot positions must be closed through trades in the

spot market.

Sangeeta Wats (2011) examine the repercussions on the underlying spot market volatility

due to the introduction of futures operations in Nifty. The study period analyzed was from

October 1995 to December 2007 and Generalized Autoregressive Conditional

Heteroskedasticity (GARCH) technique used to analyzed data. The results show that the

introduction of futures trading has reduced the underlying spot market volatility and has

contributed towards enhancement in market efficiency. The major observations on evaluating

the impact of futures introduction on the spot market volatility is that S&P CNX futures have

a stabilizing effect on the underlying stock market, thereby supporting the ‘market

completion’ hypothesis. It is established that the introduction of index futures has reduced

spot market volatility. There is a significant ARCH effect; there is a significant decrease in

28

the influence of domestic and global factors on the underlying spot market; day-of-the-week

effect which existed in pre-futures period is not present in post-futures; and there is a change

in unconditional variance and persistence of volatility in the post-futures period.

Dr Shailesh Rastogi (2011) examine the impact of introduction of exchange traded currency

derivatives on the spot exchange rate volatility using Generalized Autoregressive Conditional

Heteroskedasticity(GARCH) (1, 1) model and the impact of introduction of currency

derivatives on market efficiency of the spot exchange rate. The study used data of Currency

futures from 1 January 2005 to 31 May 2010. The result for this study is that the introduction

of currency derivatives has significantly impacted the Indian currency spot market in terms of

conditional volatility. The currency derivatives started in August 2008 in India has

significantly impacted the volatility of the spot market of foreign exchange of dollars in terms

of rupees (Rs/$). The presence of currency futures in the Indian foreign market has made the

market more dynamic and persistent in terms of volatility where changes last longer during

post-future period. The spot rate market of the exchange rate market of dollars in terms of

rupees (Rs/$) has been found to be Weak-form efficient Moreover, weak-form market

efficiency of spot foreign exchange market of dollars in terms of rupees (Rs/$) has not shown

any significant change after the introduction of currency futures market in India.

Tanupa Chakraborty (2012) examines the resilience displayed by the spot indices S&P

CNX Nifty, and two sectoral indices—CNX IT and Bank Nifty—of National Stock Exchange

(NSE), one of the major stock exchanges in India, versus their respective futures contracts

using Value-at-Risk (VaR) concept during dotcom and subprime mortgage crises over 2000-

10 period. Therefore a close look at the indices values during the two phases of crises

suggests that dot com crisis was felt during March 12, 2001 to August 7, 2003 in S&P CNX

Nifty, while subprime crisis had impacted S&P CNX Nifty between March 3, 2008 and

November 19, 2009, CNX IT from August 1, 2007 to July 22, 2009, and Bank Nifty during

March 4, 2008 to September 14, 2009. The study finds that losses based on one-day VaR at

95% confidence interval have been greater in the futures market than in their respective

underlying spot markets, thereby implying that Indian derivatives market displays less

resilience than its equity market. By summarizing the results, it may be inferred that losses

(as indicated by the VaR measure) have been greater in the futures market than in the spot

market for each of the three indices during both the crises. Although the percentage

29

difference between VaR in futures and in spot may be small (i.e., at most 0.33% of portfolio

value), such a small fraction may turn into huge losses when the portfolio value is large.

Kedar nath Mukherjee and R. K. Mishra (2004) investigate the possible lead-lag

relationship, both in terms of return and volatility, among the NIFTY spot index and index

futures market in India. By using intraday data from April to September 2004 and cross-

correlation test, the study suggests that though there is a strong contemporaneous and bi-

directional relationship among the returns in the spot and futures market. There is also

interdependence (in both direction) and therefore more or less symmetric spill over among

the stock return volatility in the spot and futures market. The results relating to the

informational effect on the lead-lag relationship exhibit that though the leading role of the

futures market wouldn’t strengthen even for major market-wide information releases, the role

of the futures market in the matter of price discovery tends to weakens and sometime

disappear after the release of major firm-specific announcements.

Dr.Hiren M Maniar (2009) studies the effect of expiration day of the Index futures and

Options on the trading volume, variance and price of the underlying shares. The study use

both daily and high frequency (5 minutes and 10 minutes) data on S&P CNX Nifty Index and

Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. The finding

using intra-day data is that while there is no pressure – downward or upward - on index

returns, the volatility is indeed significantly affected by the expiration of contracts. This

effect, however, doesn’t show up in daily data.

O.P. GUPTA (2002) examines impact of introduction of index futures on the underlying

stock market volatility in India and how does the futures market volatility compare to stock

market volatility? The study utilized daily price data (high, low, open and close) for BSE

Sensex and S&P CNX Nifty Index from June 1998 to June 2002. Similar data from June 9,

2000 to March 31, 2002 have also been used for BSE Index Futures and from June 12, 2000

to June 30, 2002 for the Nifty Index Futures. The study shows that the volatility of the BSE

Index and Nifty index seems to have declined post introduction of index futures for all the

window periods in respect of all the three measures. The empirical results reported here

indicate that the over-all volatility of the stock market has declined after the introduction of

the index futures for both of the indices.

30

P. Sakthivel (2008) investigates the impact of introduction of index futures trading on

volatility of Nifty. The study employed Generalized Autoregressive Conditional

Heteroskedasticity (GARCH) (1, 1) model to capture the time varying nature of the volatility

and volatility clustering phenomena using daily closing price of the Nifty from January 3,

1992 to 31st may, 2007. The results showed that after introduction of the futures trading

reduced stock market volatility, due to increase market efficiency. There is a changes

structure in spot market volatility after introduction futures trading. Specifically, there is

evidence that the increased impact on recent news and reduced effect of the uncertainty

originating from the old news. The study finally observed that the introduction of the

derivatives contract improved the market efficiency and reduced the asymmetric information.

Dr. Premalata Shenbagaraman (2003) assesses the impact of introducing index futures and

options contracts on the volatility of the underlying stock index in India. Daily closing prices

for the period 5th Oct 1995 to 31st Dec 2002 for the SNX Nifty and the Nifty Junior and

Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model has been used

in this study. The empirical evidence is mixed and most suggest that the introduction of

derivatives do not destabilize the underlying market. The studies also show that the

introduction of derivative contracts improves liquidity and reduces informational

asymmetries in the market. The futures and options trading have not led to a change in the

volatility of the underlying stock index, but the nature of volatility seems to have changed

post-futures. There is a no evidence of any link between trading activity variables in the

futures market and spot market volatility.

SUCHISMITA BOSE (2007) analyse whether the Indian Stock Index Futures market plays

an important role in the assimilation of information and price discovery in the stock market.

This study used Granger causality test and VECTOR ERROR CORRECTION MODEL

(VECM) model and daily closing prices of the futures contract on the S&P CNX Nifty index

and the underlying index values available from the NSE. For the analysis it concentrates on

data from the period March 2002 through September 2006. the study find that there is

significant information flow from the futures to the spot market and futures prices/returns

have predictive power for the spot prices. If the long run relation between the two price series

is taken into consideration, then it finds clear bidirectional information flows or feedback

between the markets. The contributions of the two markets to the price discovery process are

also almost equal with the futures showing a marginal edge over the spot market, as the

31

information flow into the stock prices from the futures is slightly higher than the price

information flows to the futures market from the spot market. The futures market also

readjusts faster to market-wide information and thus absorbs much of the volatility induced

by flow of new information.

Snehal Bandivadekar and Saurabh Ghosh (2003) investigate the impact of introduction of

index futures on spot market volatility on both S&P CNX Nifty and BSE Sensex using

ARCH/Generalized Autoregressive Conditional Heteroskedasticity (GARCH) technique. The

study used sample from January 1997 to March 2003. The empirical analysis points towards

a decline in spot market volatility after the introduction of index futures due to increased

impact of recent news and reduced effect of uncertainty originating from the old news.

SILVIA GERBER and PETER SIMMONS (1993) analyse how the presence of a futures

market gives risk-averse dealers in the spot asset opportunities for arbitrage that reduce the

spot market bid-ask spread through reducing the dealers' risk exposure.

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

derivatives on the stock market in India using S&P CNX IT index and found that clustering

and persistence of volatility in different degrees before and after derivatives and the listing in

futures has increased the market volatility.

Debasis Bagchi (2009) this paper investigate the nature of dynamic relationship that exists

amongst selected futures indexes in American, European and Asian continents. A total of

nine futures indexes are selected for investigation. The data on futures indexes (closing

values) are collected over the period between April 1, 2002 and March 31, 2008 on daily

basis, from Reuters. The correlations among the future indexes on regional account are found

to be strongly positive which is suggestive that the indexes are affected more on regional

news rather on world news. The futures indexes are found to be non-stationary and American

and Asian futures markets are not co integrated, while European futures markets are found to

be co integrated. It implies that diversification and risk reduction is possible in American and

Asian futures markets, but not likely in European futures markets on individual regional

basis. However, the futures markets are co integrated on inter-region basis, meaning thereby

that long-term dynamic equilibrium relationship exists amongst the inter-region futures

indexes, for instance, American and European, American and Asian, Asian and European

32

futures markets. The results suggest risk diversification is less possible between regions, yet

arbitrage opportunity may exist due to short-term deviation from the long-term equilibrium.

Granger Casualty test reveals that directional relationship exists amongst various futures

markets. The Vector Auto regression shows that error correction term is significant but small

and close to zero. It signifies that the long run equilibrium is affected by short-run deviations.

The impulse response analysis documents that emerging market in American continent, i.e.,

Mexico has a reflective effect on US Futures market while in Europe; the FTSE 100 Futures

index has a predominating character. For the European futures, the France and UK futures

indexes are dynamically deviating on short-run period as the shock is found to transmit in a

powerful manner over the time horizon, while it is found to be low for S&P MIB (Italian

futures index), revealing short-term deviations are less in this case. In Asian region, Kospi

200 Futures is found to response comparatively higher with respect to Nifty Futures and

MSCI SGX Futures.

33

CHAPTER: 3. DATA AND METHODOLOGY

3.1 Research Objective

To study short run relationship between Future and Spot market

To study long run relationship between Future and Spot market

To Study Causal Relationship among Future and Spot Equity Market

To develop the model to forecast the Future and Spot price

3.2 Sample and Period of study

Research : - Quantitative

Sample size : - The daily closing price of S&P CNX Nifty and S&P CNX nifty

Future from 12 June 2000 to 30 march 2012

Total Number

of observation : - 2950

34

3.1. Indices:-

BSE

• Sensex

• BSE Teck Index

• BSE PSU Index

• Capital Goods

• BSE FMCG Index

• BSE Healthcare

• BSE CD Index

• BSE IT Index

• Bankex

• BSE Auto

• BSE Metal

• BSE Oil & Gas

• BSE Realty

• BSE Power Index

• BSE IPO

• Tasis Shariah 50

NSE

• S&P CNX NIFTY

• Nifty Midcap 50

• CNX NIFTY JUNIOR

• S&P CNX DEFTY

• CNX IT

• BANK Nifty

• CNX Realty

• CNX Infra

• CNX Energy

• CNX FMCG

• CNX MNC

• CNX Pharma

• CNX PSE

• CNX PSU Bank

• CNX Service

• CNX Media

• CNX Metal

• CNX Auto

35

3.2. Companies in the S&P CNX NIFTY:-

ACC Infosys

Ambuja Cements ITC

Asian Paints Jaiprakash Asso

Axis Bank Jindal Steel

Bajaj Auto Kotak Mahindra

Bank of Baroda Larsen

Bharti Airtel Lupin

BHEL Mah and Mah

BPCL Maruti Suzuki

Cairn India NMDC

Cipla NTPC

Coal India ONGC

DLF PNB

Dr Reddys Labs Power Grid Corp

GAIL Ranbaxy Labs

Grasim Reliance

HCL Tech Reliance Infra

HDFC SBI

HDFC Bank Sesa Goa

Hero Motocorp Sun Pharma

Hindalco Tata Motors

HUL Tata Power

ICICI Bank Tata Steel

IDFC TCS

IndusInd Bank UltraTechCement

3.3 Methodology

36

3.3.1 Descriptive Statistics

Descriptive statistics is the discipline of quantitatively describing the main features of a

collection of data. Descriptive statistics are distinguished from inferential

statistics (or inductive statistics), in that descriptive statistics aim to summarize a sample,

rather than use the data to learn about the population that the sample of data is thought to

represent. This generally means that descriptive statistics, unlike inferential statistics, are not

developed on the basis of probability theory. Even when a data analysis draws its main

conclusions using inferential statistics, descriptive statistics are generally also presented.

Descriptive statistics is also a set of brief descriptive coefficients that summarizes a given

data set that represents either the entire population or a sample. The measures that describe

the data set are measures of central tendency and measures of variability or dispersion.

Measures of central tendency include the mean, median and mode, while measures of

variability include the standard deviation (or variance), the minimum and maximum

variables, kurtosis and skewness.

Descriptive statistics provides simple summaries about the sample and about the observations

that have been made. Such summaries may be either quantitative, i.e. summary statistics, or

visual, i.e. simple-to-understand graphs. These summaries may either form the basis of the

initial description of the data as part of a more extensive statistical analysis, or they may be

sufficient in and of themselves for a particular investigation. it can be use in :

Univariate analysis

Univariate analysis involves describing the distribution of a single variable, including its

central tendency (including the mean, median, and mode) and dispersion (including

the range and quantiles of the data-set, and measures of spread such as

the variance and standard deviation).

Bivariate analysis

When a sample consists of more than one variable, descriptive statistics may be used to

describe the relationship between pairs of variables. In this case, descriptive statistics include:

Cross-tabulations and contingency tables

Graphical representation via scatterplots

37

Quantitative measures of dependence

Descriptions of conditional distributions

Descriptive statistics includes:

Standard deviation

Standard deviation is a measure of how spread out the data points are. A set

with a low standard deviation has most of the data points centered around

the average. A set with a high standard deviation has data points that are

not so clustered around the average. 

Skewness

Skewness is a measure of the extent to which a probability distribution of a real-

valued random variable "leans" to one side of the mean. The skewness value can be positive

or negative, or even undefined. Many models assume normal distribution; i.e., data are

symmetric about the mean. The normal distribution has a skewness of zero. But in reality,

data points may not be perfectly symmetric. So, an understanding of the skewness of the

dataset indicates whether deviations from the mean are going to be positive or negative. It’s

look like as below.

In this study, both indices are positively skewed, it means that The right tail is longer; the

mass of the distribution is concentrated on the left of the figure. It has relatively few high

values. The distribution is said to be right-skewed, right-tailed, or skewed to the right.

Kurtosis

Kurtosis means “A statistical measure used to describe the distribution of observed data

around the mean”. It is sometimes referred to as the "volatility of volatility”. There are three

types of kurtosis; Leptokurtic, Mesokurtic and platykurtic. If a distributions kurtosis

coefficient is greater than 3, the distribution is leptokurtic, and if platykurtic less than 3, the

38

distribution is platykurtic, and if platykurtic equal to 3, the distribution is Mesokurtic. The

graphical example for the same is below:

Kurtosis gauges the level of fluctuation within a distribution. High levels of kurtosis represent

a low level of data fluctuation, as the observations cluster about the mean. Lower values of

kurtosis mean that data has a larger degree of variance. For example, If the Data follow

Platykurtic distribution, which means that compared to a normal distribution, a platykurtic

data set has a flatter peak around its mean, which causes thin tails within the distribution. The

flatness results from the data being less concentrated around its mean, due to large variations

within observations.

3.3.2 Correlation Analysis:

There can be both short-run and long-run relationships between financial time series.

Correlation coefficients are used for examining short-run co-movements and multi co-

linearity among the variables. If correlation coefficient is greater than 0.8, it indicates that

multi co-linearity exists. The population correlation coefficient, p, (-1 ≤ p ≤ 1) measures the

degree of linear association between two variables.

Sir Francis Galton pioneered correlation. In 1877, Galton unveiled reversion, the earliest

ancestor of correlation, and described it like this: “Reversion is the tendency of that ideal

mean type to depart from the parent type, reverting towards what may be roughly and perhaps

fairly described as the average ancestral type”. Karl Pearson, Galton's colleague and friend,

and father of Egon Pearson, pursued the refinement of correlation with such vigor that the

statistic r, a statistic Galton called the index of co-relation and Pearson called the Galton

39

coefficient of reversion, is known today as Pearson's r. Formula for correlation is given

below:

r=∑ ( xi−x ) ( yi− y )

√∑ ( xi−x )2 ( yi− y )2

Where xi and yi are the values of x and y for observation i and where x,  and ȳ are the sample

means of x and y

The primary objective of correlation analysis is to measure the strength or degree of linear

association between two variables. Values of the correlation coefficient are always between -

1 and +1. A correlation coefficient of +1 indicates that two variables are perfectly related in a

positive linear sense; a correlation coefficient of -1 indicates that two variables are perfectly

related in a negative linear sense, and a correlation coefficient of 0 indicates that there is no

linear relationship between the two variables.

3.3.3 Unit Root Test:

3.3.3.1 Correlogram

It is a test for detecting the presence of stationarity in the series. Correlogram which are

simply the plots of autocorrelation function (ACF) and partial autocorrelation function

(PACF) against the lag length. Partial autocorrelation measure correlation between (time

series) observation that are k time periods apart after controlling for correlation at

intermediate lags (i.e. lags less than k). In other words, partial autocorrelation is the

correlation between Yt and Yt - k after removing the effect of the intermediate Y’s. For Non

stationarity series autocorrelation starts at very high level and decline very slowly. Second,

PACF shows that if series become stationarity at first lag so after first lag, the PACF drops

dramatically and all PACF after 1 lag are statistically insignificant.

40

A useful aid in interpreting a set of autocorrelation coefficients is a graph called a

correlograme in which is plotted against the lag (k); where is the autocorrelation coefficient at

lag(k). A correlograme can be used to get a general understanding on the following aspects of

our time series:

A random series: if a time series is completely random then for Large (N), will be

approximately zero for all non-zero values of (k).

Short-term correlation: stationary series often exhibit short-term correlation

characterized by a fairly large value of followed by (2) or (3) more coefficients which,

while significantly greater than zero, tend to get successively smaller.

Non-stationary series: if a time series contains a trend, then the values of will not

come to zero except for very large values of the lag.

Seasonal fluctuations: correlogrames are used in the model identification stage

for Box–Jenkins autoregressive moving average time series models. Autocorrelations

should be near-zero for randomness; if the analyst does not check for randomness,

then the validity of many of the statistical conclusions becomes suspect. The

correlogram is an excellent way of checking for such randomness.

The randomness assumption is critically important for the following three reasons:

Most standard statistical tests depend on randomness. The validity of the test

conclusions is directly linked to the validity of the randomness assumption.

Many commonly-used statistical formulae depend on the randomness assumption, the

most common formula being the formula for determining the standard deviation of the

sample mean:

Where, s is the standard deviation of the data. Although heavily used, the results from

using this formula are of no value unless the randomness assumption holds.

For univariate data, the default model is

If the data are not random, this model is incorrect and invalid, and the estimates for the

parameters (such as the constant) become nonsensical and invalid.

41

3.3.3.2 Augmented Dickey-Fuller (ADF) Test

Augmented Dickey-Fuller (ADF) test is employed to test the validity of market integration

hypothesis. A unit root test is a statistical test for the proposition that in an autoregressive

statistical model of a time series, the autoregressive parameter is one. It is a test for detecting

the presence of stationarity in the series. The early and pioneering work on testing for a unit

root in time series was done by Dickey and Fuller (Dickey and Fuller 1979 and 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; hence they are inappropriate to undertake hypothesis tests about

the regression parameters.

Stationarity time series is one whose mean, variance and covariance are unchanged by time

shift. Nonstationary time series have time varying mean or variance or both. If a time series is

nonstationary, we can study its behaviour only for a time period under consideration. It is not

possible to generalize it to other time periods. It is, therefore, not useful for forecasting

purpose.

Therefore, it behaves like AR (1) process with ρ = 1. Dickey Fuller test is designed to

examine if ρ = 1. The complete model with deterministic terms such as intercepts and trends

is shown in equation (1):

Δ y t=α +π+δy t−1+∑i=1

m

β i Δ y t−1+ε t (1)

The presence of 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. The ADF unit root

test is based on the null hypothesis Ho is, series has a unit root. If the calculated ADF statistic

is less than the critical value, then the null hypothesis is rejected; otherwise accepted. If the

variable is non-stationary at level, the ADF test will be performed at the first difference.

3.3.4 Co-integration Test:

Once variable have been classified as integrated of order I(0), I(1), I(2) etc. is possible to set

up models that lead to stationary relations among the variables, and where standard inference

is possible. The necessary criteria for stationarity among non-stationary variables are called

42

co-integration. Testing for co-integration is necessary step to check if modeling empirically

meaningful relationships. If variables have different trends processes, they cannot stay in

fixed long-run relation to each other, implying that you cannot model the long-run, and there

is usually no valid base for inference based on standard distributions. If you do no not find

co-integration it is necessary to continue to work with variables in differences instead.

There are several tests of co-integration. The Johansen test is the most fundamental test.

Engle and Granger (1987) formulated one of the first tests of co-integration (or common

stochastic trends). This test has the advantage that it is intuitive, easy to perform and once

you master it you will also realize it limitations and why there are other tests. The intuition

behind the test motivates it role as the first cointegration test to learn. Start by estimating the

so called co-integrating regression (the first step),

x1 , t=β 1+β 2x 2 ,t +...+β p x p , t+ut

Where, p is the number of variables in the equation. In this regression we assume that all

variables are I(1) and might cointegrate to form a stationary relationship, and thus a

stationary residual term ˆ ut=x 1 ,t−β 1−β 2 x 2, t−...−βpxp ,t

(In the tabulated critical values p = n). This equation represents the assumed economically

meaningful (or understandable) steady state or equilibrium relationship among the variables.

If the variables are co-integrating, they will share a common trend and form a stationary

relationship in the long run. Furthermore, under co-integration, due to the properties of super

converge, the estimated parameters can be viewed as correct estimates of the long-run steady

state parameters, and the residual (lagged once) can be used as an error correction term in an

error correction model. (Observe that the estimated standard errors from this model are

generally useless when the variables are integrated. Thus, no inference using standard

distribution is possible. Do not print the standard errors or the t-statistics from this model).

Johansen's co-integration test (Johansen and Juselius, 1990) has been applied to check

whether the long run equilibrium relationship exists between the variables. The Johansen

approach to cointegration test is based on two test statistics, viz., trace statistic, and maximum

eigenvalue statistic. The trace statistic can be specified as:

Trace(r , k )=−T ∑ ln(1−λ i) (2)

43

Where λi is the i th largest eigenvalue of matrix Π and T is the number of observations. In the

trace test, the null hypothesis is that the number of distinct cointegrating vector(s) is less than

or equal to the number of cointegration relations (r). From the above, it is clear that λ trace

equals

Zero when all λ= 0. The maximum eigenvalue test examines the null hypothesis of exactly r

cointegrating relations against the alternative of r + 1 cointegrating relations with the test

statistic:

λ max (r , r+1)=−T ln(1−λ r+1) (3)

3.3.5 Granger Causality test:

The Granger causality test is a statistical hypothesis test for determining whether one time

series is useful in forecasting another. Ordinarily, regressions reflect "mere" correlations,

but Clive Granger, who won a Nobel Prize in Economics, argued that a certain set of tests

reveal something about causality.

A time series X is said to Granger-cause Y if it can be shown, usually through a series of t-

tests and F-tests on lagged values of X (and with lagged values of Y also included), that

those X values provide statistically significant information about future values of Y.

In statistics and econometrics, an augmented Dickey–Fuller test (ADF) is a test for a unit

root in a time series sample. It is an augmented version of the Dickey–Fuller test for a larger

and more complicated set of time series models. The augmented Dickey–Fuller (ADF)

statistic, used in the test, is a negative number. The more negative it is, the stronger the

rejections of the hypothesis that there is a unit root at some level of confidence.

The null hypothesis is series has a unit root. If the ADF test statistic values are higher than

the critical values at 5% significance level, null hypothesis accepted and If ADF test statistic

values are less than critical value then reject the null hypothesis.

At the end, the Granger Causality test (Engle and Granger, 1987) has been used to find out

the direction of causality between the variables. To test for Granger Causality, the following

bivariate regression model can be used:

44

y t=α 0+∑i=1

m

α iY t−1+∑j=1

m

β j X t−1+ε t

(4)

x t=ω0+∑i=1

m

γ iY t−1+∑j=1

n

θ j X t−1+ε t

(5)

The null hypothesis is H0: ∑ βj = 0 in the first regression equation of y i.e. lagged X terms do

not belong in the regression means X does not cause y.

If all the coefficients of x in the first regression equation of y, i.e. β j for j = 1...... are

significant, then the null hypothesis that x does not cause y is rejected.

3.3.6 Vector Error Correction Model (VECM)

VECMs may be estimated by Stata’s vec command. These models are employed because

many economic time series appear to be ‘first-difference stationary,’ with their levels

exhibiting unit root or non-stationary behavior. Conventional regression estimators, including

VARs, have good properties when applied to covariance-stationary time series, but encounter

difficulties when applied to non-stationary or integrated processes.

These difficulties were illustrated by Granger and Newbold (J. Econometrics, 1974) when

they introduced the concept of spurious regressions. If you have two independent random

walk processes, a regression of one on the other will yield a significant coefficient, even

though they are not related in any way.

This insight, and Nelson and Plosser’s findings (J. Mon. Ec., 1982) that unit roots might be

present in a wide variety of macroeconomic series in levels or logarithms, gave rise to the

45

industry of unit root testing, and the implication that variables should be rendered stationary

by differencing before they are included in an econometric model.

Further theoretical developments by Granger and Engle in their celebrated paper

(Econometrics, 1987) raised the possibility that two or more integrated, non-stationary time

series might be co-integrated, so that some linear combination of these series could be

stationary even though each series is not.

If two series are both integrated (of order one, or I(1)) we could model their interrelationship

by taking first differences of each series and including the differences in a VAR or a

structural model. However, this approach would be suboptimal if it was determined that these

series are indeed co-integrated. In that case, the VAR would only express the short-run

responses of these series to innovations in each series. This implies that the simple regression

in first differences is misspecified.

If the series are co-integrated, they move together in the long run. A VAR in first differences,

although properly specified in terms of covariance-stationary series, will not capture those

long-run tendencies. Accordingly, the VAR concept may be extended to the vector error-

correction model, or VECM, where there is evidence of co-integration among two or more

series. The model is fit to the first differences of the non-stationary variables, but a lagged

error-correction term is added to the relationship.

In the case of two variables, this term is the lagged residual from the co-integrating

regression, of one of the series on the other in levels. It expresses the prior disequilibrium

from the long-run relationship, in which that residual would be zero. In the case of multiple

variables, there is a vector of error-correction terms, of length equal to the number of co-

integrating relationships, or co-integrating vectors, among the series.

In terms of economic content, we might expect that there is some long-run value of the

dividend/price ratio for common equities. During market ‘bubbles’, the stock price index may

be high and the ratio low, but we would expect a market correction to return the ratio to its

long-run value. A similar rationale can be offered about the ratio of rents to housing prices in

a housing market where there is potential to construct new rental housing as well as single-

family homes.

46

To extend the concept to more than two variables, we might rely on the concept of

purchasing power parity (PPP) in international trade, which defines a relationship between

the nominal exchange rate and the price indices in the foreign and domestic economies. We

might find episodes where a currency appears over- or undervalued, but in the absence of

central bank intervention and effective exchange controls, we expect that the ‘law of one

price’ will provide some long-run anchor to these three measures’ relationship.

Consider two series, yt and xt , that obey the following equations:

yt +βxt=ϵt ;ϵt=ϵt−1+ωt

yt +αxt=Vt ;Vt=ρVt−1+H t ;|ρ|<1

Assume that ωt and ℌt are i.i.d. disturbances, correlated with each other. The random-walk

nature of ϵ t implies that both yt and xt are also I(1), or non-stationary, as each side of the

equation must have the same order of integration. By the same token, the stationary nature of

the Vt process implies that the linear combination (yt + _xt ) must also be stationary, or I(0).

Thus yt and xt cointegrate, with a cointegrating vector (1; α).

We can rewrite the system as

∆ Yt=βδZt−1+ῃ 1t

∆ Xt=−δZt−1+ῃ 2 t

Where,

δ=(1−ρ )/(α−β )

Zt=Yt +αXt

and the errors (ῃ1t ; ῃ2t ) are stationary linear combinations of (ωt ; ℌt ).

47

When yt and xt are in equilibrium, zt = 0. The coefficients on zt indicate how the system

responds to disequilibrium. A stable dynamic system must exhibit negative feedback: for

instance, in a functioning market, excess demand must cause the price to rise to clear the

market.

In the case of two non-stationary (I(1)) variables yt and xt , if there are two nonzero values (a;

b) such that ayt + bxt is stationary, or I(0), then the variables are co-integrated. To identify

the co-integrating vector, we set one of the values (a; b) to 1 and estimate the other. As

Granger and Engle showed, this can be done by a regression in levels. If the residuals from

that ‘Granger–Engle’ regression are stationary, co-integration is established.

In the general case of K variables, there may be 1, 2,. . . ,(K-1) co-integrating vectors

representing stationary linear combinations. That is, if yt is a vector of I(1) variables and

there exists a vector β such that βyt is a vector of I(0) variables, then the variables in yt are

said to be co-integrated with co-integrating vector β. In that case we need to estimate the

number of co-integrating relationships, not merely whether co-integration exists among these

series.

For a K-variable VAR with p lags,

Yt=V + A 1Yt−1+…+ ApYt−p+ϵ t

Let ϵ t be i.i.d. normal over time with covariance matrix ∑. We may rewrite the VAR as a

VECM:

∆ Yt=V +ῃYt−1+∑i−1

p−1

ri ∆Yt−i+ϵt

Where,

ῃ=∑j=1

j=p

Aj−Ik

and

ri=− ∑j=i+1

j=p

Aj

48

If all variables in yt are I(1), the matrix ῃ has rank 0 < r < K, where r is the number of linearly

independent co-integrating vectors. If the variables are co-integrated (r > 0) the VAR in first

differences is misspecified as it excludes the error correction term. If the rank of ῃ = 0, there

is no co-integration among the non-stationary variables, and a VAR in their first differences

is consistent. If the rank of ῃ = K, all of the variables in yt are I(0) and a VAR in their levels

is consistent.

If the rank of ῃ is r > 0, it may be expressed as ῃ =αβ, where α and β are (K* r) matrices of

rank r. We must place restrictions on these matrices’ elements in order to identify the system.

Stata’s implementation of VECM modeling is based on the maximum likelihood framework

of Johansen (J. Ec. Dyn. Ctrl., 1988 and subsequent works). In that framework, deterministic

trends can appear in the means of the differenced series, or in the mean of the co-integrating

relationship. The constant term in the VECM implies a linear trend in the levels of the

variables. Thus, a time trend in the equation implies quadratic trends in the level data.

Writing the matrix of coefficients on the vector error correction term yt-1 as ῃ =αβ, we can

incorporate a trend in the co-integrating relationship and the equation itself as

∆ yt=α ( βyt−1+μ+ρt )+∑i=1

p−1

ri ∆ yt−i+γ+τt+ϵt

Johansen spells out five cases for estimation of the VECM:

Unrestricted trend: estimated as shown, co-integrating equations are trend

stationary

Restricted trend, ҭ = 0: co-integrating equations are trend stationary, and trends in

levels are linear but not quadratic

Unrestricted constant: ҭ = ҏ = 0: co-integrating equations are stationary around

constant means, linear trend in levels

Restricted constant: ҭ = ҏ = ӳ = 0: co-integrating equations are stationary around

constant means, no linear time trends in the data

49

No trend: ҭ = ҏ = ӳ = µ = 0: co-integrating equations, levels and differences of the

data have means of zero

To consistently test for co-integration, choosing the appropriate lag length is necessary. By

using the vecrank command to test for co-integration via Johansen’s max-eigenvalue statistic

and trace statistic.

CHAPTER 4: EMPIRICAL ANALYSIS

50

4.1 Descriptive Statistics

First, descriptive statistics like Mean, Standard Deviation, Skewness, Kurtosis, Jarque-Bera

Statistic, and Probability Value are calculated for S&P CNX NIFTY and S&P CNX NIFTY

FUTURE. Results of the same are presented in Table 4.1.1

Table 4.1.1: Descriptive Statistics of S&P CNX Nifty and S&P CNX Nifty Future

NIFTY FUTURE NIFTY

Mean 3095.018 3095.498

Median 2860.150 2868.550

Maximum 6333.450 6312.450

Minimum 855.4000 854.2000

Std. Dev. 1726.384 1723.259

Skewness 0.222768 0.217366

Kurtosis 1.526239 1.520991

Jarque-Bera 291.3706 292.1067

Probability 0.000000 0.000000

Sum 9130303. 9131720.

Sum Sq. Dev. 8.79E+09 8.76E+09

Observations 2950 2950

From the Table 4.1.1, it is clear that both indices are positively skewed. Kurtosis values

reveal that if kurtosis value greater than 3, indices follow Leptokurtic distribution or if

kurtosis value less than 3, it follow Platykurtic distribution and If the value of skewness is

zero and kurtosis is three, the data is said to be normally distributed. In the present study,

both indices follow Platykurtic distribution. Jarque-Bera statistics tests the null hypothesis

that is data follow normal distribution. By using probability values of Jarque-Bera statistic,

value is statistically significant at 1% level of significance indicating that the distribution of

the selected.

4.2 Correlation Analysis

51

Table 4.2.1: Correlation Analysis

Correlation FUTURE  SPOT 

FUTURE  1 0.999975

SPOT  0.999975 1

Correlation analysis results between stock market indices are reported in Table 4.2. It

indicates that S&P CNX NIFTY and NIFTY FUTURE are highly positively correlated to

each other.

4.3 Correlograme:

52

Table 4.3.1: Correlograme of S&P CNX NIFTY

Autocorrelation Partial Correlation AC PAC Q-Stat Prob

|******* |******* 1 0.999 0.999 2947.1 0.000

|******* | | 2 0.998 -0.021 5889.1 0.000

|******* | | 3 0.997 -0.006 8825.9 0.000

|******* | | 4 0.996 0.008 11758. 0.000

|******* | | 5 0.995 0.015 14685. 0.000

|******* | | 6 0.994 0.014 17607. 0.000

|******* | | 7 0.993 0.034 20524. 0.000

|******* | | 8 0.992 -0.039 23438. 0.000

|******* | | 9 0.991 -0.018 26346. 0.000

|******* | | 10 0.990 -0.010 29249. 0.000

|******* | | 11 0.989 -0.015 32147. 0.000

|******* | | 12 0.988 0.018 35039. 0.000

|******* | | 13 0.987 -0.020 37927. 0.000

|******* | | 14 0.986 -0.008 40809. 0.000

|******* | | 15 0.985 -0.024 43685. 0.000

|******* | | 16 0.983 -0.008 46556. 0.000

|******* | | 17 0.982 -0.003 49422. 0.000

|******* | | 18 0.981 -0.022 52281. 0.000

|******* | | 19 0.980 0.006 55134. 0.000

|******* | | 20 0.979 -0.001 57982. 0.000

|******* | | 21 0.978 0.025 60824. 0.000

|******* | | 22 0.977 0.001 63660. 0.000

|******* | | 23 0.975 0.001 66491. 0.000

|******* | | 24 0.974 0.011 69316. 0.000

|******* | | 25 0.973 0.005 72137. 0.000

|******* | | 26 0.972 -0.043 74951. 0.000

|******* | | 27 0.971 -0.008 77759. 0.000

|******* | | 28 0.970 0.018 80562. 0.000

|******* | | 29 0.969 -0.020 83359. 0.000

|******* | | 30 0.967 0.005 86149. 0.000

|******* | | 31 0.966 0.006 88934. 0.000

|******* | | 32 0.965 0.015 91714. 0.000

|******* | | 33 0.964 0.015 94487. 0.000

|******* | | 34 0.963 0.006 97256. 0.000

|******* | | 35 0.962 -0.016 100019 0.000

|******* | | 36 0.961 0.017 102777 0.000

53

Table 4.3.2: Correlograme of S&P CNX NIFTY FUTURE

Autocorrelation Partial Correlation AC PAC Q-Stat Prob

|******* |******* 1 0.999 0.999 2946.7 0.000

|******* | | 2 0.998 0.006 5888.2 0.000

|******* | | 3 0.997 -0.011 8824.4 0.000

|******* | | 4 0.996 0.008 11755. 0.000

|******* | | 5 0.995 0.016 14681. 0.000

|******* | | 6 0.994 0.014 17603. 0.000

|******* | | 7 0.993 0.031 20519. 0.000

|******* | | 8 0.992 -0.035 23431. 0.000

|******* | | 9 0.991 -0.022 26338. 0.000

|******* | | 10 0.990 -0.003 29240. 0.000

|******* | | 11 0.989 -0.018 32136. 0.000

|******* | | 12 0.988 0.013 35027. 0.000

|******* | | 13 0.987 -0.021 37912. 0.000

|******* | | 14 0.985 -0.007 40792. 0.000

|******* | | 15 0.984 -0.027 43667. 0.000

|******* | | 16 0.983 -0.008 46535. 0.000

|******* | | 17 0.982 -0.003 49398. 0.000

|******* | | 18 0.981 -0.020 52254. 0.000

|******* | | 19 0.979 0.009 55104. 0.000

|******* | | 20 0.978 0.001 57949. 0.000

|******* | | 21 0.977 0.020 60787. 0.000

|******* | | 22 0.976 0.004 63620. 0.000

|******* | | 23 0.975 -0.005 66447. 0.000

|******* | | 24 0.974 0.014 69269. 0.000

|******* | | 25 0.973 0.002 72085. 0.000

|******* | | 26 0.971 -0.044 74895. 0.000

|******* | | 27 0.970 -0.002 77699. 0.000

|******* | | 28 0.969 0.021 80497. 0.000

|******* | | 29 0.968 -0.022 83289. 0.000

|******* | | 30 0.966 0.007 86074. 0.000

|******* | | 31 0.965 0.003 88854. 0.000

|******* | | 32 0.964 0.020 91628. 0.000

|******* | | 33 0.963 0.011 94397. 0.000

|******* | | 34 0.962 0.005 97160. 0.000

|******* | | 35 0.961 -0.013 99918. 0.000

|******* | | 36 0.960 0.015 102670 0.000

54

4.4 Augmented Dickey-Fuller Unit Root Test:

In statistics and econometrics, an augmented Dickey–Fuller test (ADF) is a test for a unit

root in a time series sample. It is an augmented version of the Dickey–Fuller test for a larger

and more complicated set of time series models. The augmented Dickey–Fuller (ADF)

statistic, used in the test, is a negative number. The more negative it is, the stronger the

rejections of the hypothesis, that there is a unit root at some level of confidence.

The null hypothesis is series has a unit root. If the ADF test statistic values are higher than

the critical values at 5% significance level, null hypothesis accepted and If ADF test statistic

values are less than critical value then reject the null hypothesis. ADF unit root test has been

applied four times to each indices and the result of the same given below. Thus, all the stock

markets indices are stationary and integrated of the first order, i.e. I (1).

4.4.1 Level-Intercept

Table 4.4.1.1: ADF test at Level-Intercept for Nifty future

Test for the unit root : LEVEL

Null Hypothesis: FUTURE has a unit root

Exogenous: Constant

Lag Length: 0 (Automatic - based on SIC, maxlag=14)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -0.758506  0.8298

Test critical values: 1% level -3.432377

5% level -2.862321

10% level -2.567230

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

As the ADF statistic value i.e. -0.758506, is higher than the critical value at 5% significance

level i.e. -2.862321, null hypothesis accepted. The S&P CNX Nifty future has unit root at

level in constant model. It means that an S&P CNX Nifty future index is non-stationary at

level in constant model.

55

Table 4.4.1.2: ADF test at Level-Intercept for Nifty

Test for the unit root : LEVEL

Null Hypothesis: SPOT has a unit root

Exogenous: Constant

Lag Length: 0 (Automatic - based on SIC, maxlag=14)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -0.785689  0.8225

Test critical values: 1% level -3.432378

5% level -2.862322

10% level -2.567230

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

As the ADF statistic value i.e. -0.785689, is higher than the critical value at 5% significance

level i.e. -2.862322, null hypothesis accepted. The S&P CNX Nifty has unit root at level in

constant model. It means that an S&P CNX Nifty index is non-stationary at level in constant

model.

4.4.2 Level – Trend & Intercept

Table 4.4.2.1: ADF test at Level- Trend & Intercept for Nifty future

Test for the unit root : LEVEL

Null Hypothesis: FUTURE has a unit root

Exogenous: Constant, Linear Trend

Lag Length: 0 (Automatic - based on SIC, maxlag=14)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -2.690711  0.2405

Test critical values: 1% level -3.961154

5% level -3.411331

10% level -3.127509

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

56

As the ADF statistic value i.e. -2.690711, is higher than the critical value at 5% significance

level i.e. -3.411331, null hypothesis accepted. The S&P CNX Nifty future has unit root at

level in trend & constant model. It means that an S&P CNX Nifty future index is non-

stationary at level in trend & constant model.

Table 4.4.2.1: ADF test at Level- Trend & Intercept for Nifty

Test for the unit root : LEVEL

Null Hypothesis: SPOT has a unit root

Exogenous: Constant, Linear Trend

Lag Length: 0 (Automatic - based on SIC, maxlag=14)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -2.695966  0.2383

Test critical values: 1% level -3.961155

5% level -3.411331

10% level -3.127510

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

As the ADF statistic value i.e. -2.695966, is higher than the critical value at 5% significance

level i.e. -3.411331, null hypothesis accepted. The S&P CNX Nifty has unit root at level in

trend & constant model. It means that an S&P CNX Nifty index is non-stationary at level in

trend & constant model.

4.4.3 1st Difference – Intercept

57

Table 4.4.2.1: ADF test at 1st Dif. – Intercept for Nifty future

Test for the unit root: 1st difference

Null Hypothesis: D(FUTURE) has a unit root

Exogenous: Constant

Lag Length: 0 (Automatic - based on SIC, maxlag=14)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -53.23561  0.0001

Test critical values: 1% level -3.432378

5% level -2.862322

10% level -2.567230

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

As the ADF statistic value i.e. -53.23561, is lower than the critical value at 5% significance

level i.e. -2.862322, null hypothesis rejected. The S&P CNX Nifty future has not unit root at

1st difference in constant model. It means that an S&P CNX Nifty future index is stationary at

1st difference in constant model.

Table 4.4.2.1: ADF test at 1st Dif. – Intercept for Nifty

Test for the unit root: 1st difference

Null Hypothesis: D(SPOT) has a unit root

Exogenous: Constant

Lag Length: 0 (Automatic - based on SIC, maxlag=14)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -51.04938  0.0001

Test critical values: 1% level -3.432378

5% level -2.862322

10% level -2.567230

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

58

As the ADF statistic value i.e. -51.04938, is lower than the critical value at 5% significance

level i.e. -2.862322, null hypothesis rejected. The S&P CNX Nifty has not unit root at 1st

difference in constant model. It means that an S&P CNX Nifty index is stationary at 1st

difference in constant model.

4.4.4 1st Difference –Intercept & Trend

Table 4.4.4.1: ADF test at 1st Dif. – Intercept & Trend for Nifty future

Test for the unit root: 1st difference

Null Hypothesis: D(FUTURE) has a unit root

Exogenous: Constant

Lag Length: 0 (Automatic - based on SIC, maxlag=14)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -53.22818  0.0000

Test critical values: 1% level -3.961155

5% level -3.411331

10% level -3.127510

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

As the ADF statistic value i.e. -53.22818, is lower than the critical value at 5% significance

level i.e. -3.411331, null hypothesis rejected. The S&P CNX Nifty future has not unit root at

1st difference in trend & constant model. It means that an S&P CNX Nifty future index is

stationary at 1st difference in trend & constant model.

59

Table 4.4.4.2: ADF test at 1st Dif. – Intercept & Trend for Nifty

Test for the unit root: 1st difference

Null Hypothesis: D(SPOT) has a unit root

Exogenous: Constant

Lag Length: 0 (Automatic - based on SIC, maxlag=14)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -51.04215  0.0000

Test critical values: 1% level -3.961155

5% level -3.411331

10% level -3.127510

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

As the ADF statistic value i.e. -51.04215, is lower than the critical value at 5% significance

level i.e. -3.411331, null hypothesis rejected. The S&P CNX Nifty has not unit root at 1st

difference in trend & constant model. It means that an S&P CNX Nifty index is stationary at

1st difference in trend & constant model.

60

4.5 Johansen's Co-integration Test:

In the next step, the co-integration between non-stationary variables has been tested by the

Johansen's Trace and Maximum Eigenvalue tests. The results of these tests are shown in

Table. First part of the co-integration results i.e. Table 4.4 (A) the trace test, indicate that

there exist one co-integrating vectors at 5% level. Second part of the co-integration results i.e.

Table 4.4 (B) the Maximum Eigenvalue test also indicate that there exist one co-integrating

vectors at 5% level. Thus, Johansen cointegration test concluded that long run equilibrium

relationship exist between stock market indices.

When running the Johansen test in Eviews one has to select options/assumptions concerning

the deterministic trend in VAR equations and cointegrating equations (CE).

Practical guide:

Use case 1 only if you know that all series have zero mean (unusual in empirical

studies),

Use case 2 if none of the series appear to have a trend,

Use case 3 if series are trending and you believe all trends are stochastic,

Use case 4 if series are trending and you believe some of them are trend stationary,

Case 5 may provide a good fit in-sample but will produce implausible forecasts out-of

sample,

Use case 6 if you are not certain which trend assumption to use (E views will help you

determine the choice of the trend assumption).

Most often for macroeconomic/financial data it will be sensible to assume option 3, so that

the trend is stochastic and that option selected in this study.

Note that variable lags in co-integration test apply to differenced series in auxiliary regression

and not to levels.

61

Table 4.5.1: Johansen's Cointegration Test Table

Trend assumption: Linear deterministic trend

Series: FUTURE SPOT

Lags interval (in first differences): 1 to 4

Unrestricted Cointegration Rank Test (Trace)

Hypothesized No. of

CE(s)

Eigenvalu

e Trace Statistic

0.05 Critical

Value

Prob.*

*

None *  0.036575  110.2640  15.49471

 0.000

1

At most 1  0.000181  0.532174  3.841466

 0.465

7

Trace test indicates 1 cointegrating eqn(s) at the 0.05 level

* denotes rejection of the hypothesis at the 0.05 level

**MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized No. of

CE(s)

Eigenvalu

e

Max-Eigen

Statistic

0.05 Critical

Value

Prob.*

*

None *  0.036575  109.7318  14.26460

 0.000

1

At most 1  0.000181  0.532174  3.841466

 0.465

7

Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level

* denotes rejection of the hypothesis at the 0.05 level

**MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I):

FUTURE SPOT

-0.09571  0.095874

-0.00109  0.001674

Unrestricted Adjustment Coefficients (alpha):

D(FUTURE)  3.658188 -0.77915

62

D(SPOT)  2.117350 -0.76122

1 Cointegrating Equation(s): Log likelihood -26238.22

Normalized cointegrating coefficients (standard error in parentheses)

FUTURE SPOT

 1.000000 -1.00168

 

 (0.00057

)

Adjustment coefficients (standard error in parentheses)

D(FUTURE) -0.35014

 

 (0.10764

)

D(SPOT) -0.20266

 

 (0.10188

)

   

63

4.6 Granger Causality Tests:

The Granger causality test is a statistical hypothesis test for determining whether one time

series is useful in forecasting another. Ordinarily, regressions reflect "mere" correlations,

but Clive Granger, who won a Nobel Prize in Economics, argued that a certain set of tests

reveal something about causality.

A time series X is said to Granger-cause Y if it can be shown, usually through a series of t-

tests and F-tests on lagged values of X (and with lagged values of Y also included), that

those X values provide statistically significant information about future values of Y.

Now, the pair-wise Granger Causality test is performed between all possible pairs of indices

to determine the direction of causality. If the probability values are less than 0.05 the reject

the null hypothesis i.e. X does not granger cause to Y. Rejected hypotheses are reported in

Bold Format in Table. The result shows that S&P CNX NIFTY Granger Cause S&P CNX

NIFTY FUTURE.

Table 4.6.1: Pair wise Granger Causality Tests

Pair wise Granger Causality Tests

Lags: 5

 Null Hypothesis: Obs F-Statistic Prob. 

 SPOT does not Granger Cause FUTURE  2945  2.63689 0.0219

 FUTURE does not Granger Cause SPOT  1.95342 0.0825

64

4.7 Vector Error Correction Model

Vector Error Correction (VEC) model is multivariate generalization of ECM model known

from the previous classes. You can see it also as VAR model designed for use with non-

stationary time series that are known to be co-integrated. The specification of VEC models

contains the co-integration relations, so it assumes that the economy converges to the long-

run relationships. On the other hand, it allows also for the short-run adjustment dynamics.

Remember that testing for co-integration is only sensible in case of non-stationary series,

integrated of the same order. Therefore in the first step of the analysis one should test for

integration level of the analyzed variables. Here both variable are non-stationary and

integrated of order I(1). The existence of co-integration between (selected) variables in VAR

model means that it can be represented in a form of error correction mechanism – in that case

Vector Error Correction (VEC).

Table 4.7.1: Vector Error Correction Estimates

 Vector Error Correction Estimates

 Standard errors in ( ) & t-statistics in [ ]

Co-integrating Eq:  CointEq1

FUTURE(-1)  1.000000

SPOT(-1) -1.001714

 (0.00050)

[-2006.89]

C  5.806340

Error Correction: D(FUTURE) D(SPOT)

CointEq1 -0.354910 -0.185706

 (0.10241)  (0.09693)

[-3.46563] [-1.91579]

D(FUTURE(-1))  0.092668  0.409083

65

 (0.15741)  (0.14899)

[ 0.58871] [ 2.74563]

D(FUTURE(-2)) -0.001874  0.089084

 (0.14222)  (0.13462)

[-0.01317] [ 0.66174]

D(SPOT(-1)) -0.061325 -0.360597

 (0.16458)  (0.15579)

[-0.37261] [-2.31469]

D(SPOT(-2))  0.021523 -0.073302

 (0.14699)  (0.13913)

[ 0.14643] [-0.52686]

C  1.253230  1.229450

 (1.12417)  (1.06407)

[ 1.11481] [ 1.15542]

 R-squared  0.005092  0.006618

 Adj. R-squared  0.003401  0.004930

 Sum sq. resids  10942181  9803645.

 S.E. equation  60.99643  57.73594

 F-statistic  3.010594  3.918856

 Log likelihood -16293.18 -16131.29

 Akaike AIC  11.06154  10.95167

 Schwarz SC  11.07373  10.96386

 Mean dependent  1.317866  1.309824

 S.D. dependent  61.10042  57.87878

 Determinant resid covariance (dof adj.)  190645.1

 Determinant resid covariance  189869.6

 Log likelihood -26272.28

66

 Akaike information criterion  17.83935

 Schwarz criterion  17.86780

CHAPTER 5: CONCLUSION

This project examined Co-movement between S&P CNX NIFTY index and S&P CNX NIFTY FUTURE index. Descriptive statistics is a set of brief descriptive coefficients that summarizes a given data set that represents either the entire population or a sample. From the Table 4.1, the skewness value of NIFTY FUTURE and NIFTY is 0.222768 and 0.217366 respectively. It means that both indices are positively skewed. Skewness is a measure of the extent to which a probability distribution of a real-valued random variable "leans" to one side of the mean. The skewness value can be positive or negative, or even undefined. Many models assume normal distribution; i.e., data are symmetric about the mean. The normal distribution has a skewness of zero. But in reality, data points may not be perfectly symmetric. So, an understanding of the skewness of the dataset indicates whether deviations from the mean are going to be positive or negative.

In this study, both indices are positively skewed, it means that The right tail is longer; the mass of the distribution is concentrated on the left of the figure. It has relatively few high values. The distribution is said to be right-skewed, right-tailed, or skewed to the right.

Kurtosis values of NIFTY FUTURE and NIFTY are 1.526239 and 1.520991 respectively. It means that both indices follow Platykurtic distribution. Kurtosis means “A statistical measure used to describe the distribution of observed data around the mean”. It is sometimes referred to as the "volatility of volatility”. there are three types of kurtosis; Leptokurtic, Mesokurtic and platykurtic. If a distributions kurtosis coefficient is greater than 3, the distribution is leptokurtic, and if platykurtic less then 3, the distribution is platykurtic, and if platykurtic equal to 3, the distribution is Mesokurtic.

Kurtosis gauges the level of fluctuation within a distribution. High levels of kurtosis represent a low level of data fluctuation, as the observations cluster about the mean. Lower values of

67

kurtosis mean that data has a larger degree of variance. In this study both indices follow Platykurtic distribution, which means that compared to a normal distribution, a platykurtic data set has a flatter peak around its mean, which causes thin tails within the distribution. The flatness results from the data being less concentrated around its mean, due to large variations within observations.

The correlation between NIFTY FUTURE and NIFTY is 0.999975. It means that both are highly correlated. Values of the correlation coefficient are always between -1 and +1. A correlation coefficient of +1 indicates that two variables are perfectly related in a positive linear sense; a correlation coefficient of -1 indicates that two variables are perfectly related in a negative linear sense, and a correlation coefficient of 0 indicates that there is no linear relationship between the two variables. The degree of linear association between two indices is very high.

Results of the Correlograme indicate that Autocorrelation of both indices series starts at a very high value and decline very slowly, i.e. the pattern of Non stationary series. Looking at Partial Correlation (PAC) we can say that series become stationery at first difference. Correlograme of both indices, series become stationary at first difference.

An augmented Dickey–Fuller test (ADF) is a test for a unit root in a time series sample. This test is use to check stationary of the data. In this study, both indices data become stationary at I(1). It means that at the 1st difference data become stationary. Time series stationary is a statistical characteristic of a series’ mean and variance over time. If both are constant over time, then the series is said to be a stationary process (i.e. is not a random walk/has no unit root), otherwise, the series is described as being a non-stationary process (i.e. a random walk/has unit root). Differencing techniques are normally used to transform a time series from a non-stationary to stationary by subtracting each datum in a series from its predecessor. As such, the set of observations that correspond to the initial time period (t) when the measurement was taken describes the series’ level. Differencing a series using differencing operations produces other sets of observations such as the first differenced values, the second-differenced values and so on.

X level - Xt

X 1st -differenced value - Xt - Xt -1

X 2nd -differenced value - Xt - Xt -2

If a series is stationary without any differencing it is designated as I(0), or integrated of order 0. On the other hand, a series that has stationary first differences is designated I(1), or integrated of order 1. Stationary of a series is an important phenomenon because it can influence its behaviour. The model hypotheses of interest are: The Series is

HO: Non-stationary

68

HA: Stationary

ADF Statistics is compared to Critical values to draw conclusions about Stationarity. The null hypothesis is series has a unit root. If the ADF test statistic values are higher than the critical values at 5% significance level, null hypothesis accepted and If ADF test statistic values are less than critical value then reject the null hypothesis.

In this study the result for the ADF test is below:

Indices LEVEL 1ST DIFFERENCE Intercept Intercept &

Trend Intercept  Intercept &

Trend SPOT -0.785689 -2.695966 -51.04938* -51.04215*FUTURE -0.758506 -2.690711 -53.23561* -53.22818*

*, **, *** indicates ADF test value is significant at 1%, 5% and 10% level of significance respectively.

For constant model, critical values at 1%, 5% and10% levels of significance are -3.432377, -2.862321 and -2.567230 respectively.

For constant and trend model, critical values at 1%, 5% and 10% level of significance are -3.961154, -3.411331 and -3.127509 respectively.

In the next step, the co-integration between non-stationary variables has been tested by the

Johansen's Trace and Maximum Eigen value tests. First part of the co-integration results i.e.

The trace test, indicate that there exist one co-integrating vectors at 5% level. Second part of

the co-integration results i.e. The Maximum Eigen value test also indicate that there exist one

co-integrating vectors at 5% level. Thus, Johansen co-integration test concluded that long run

equilibrium relationship exist between stock market indices.

Now, the pair-wise Granger Causality test is performed between all possible pairs of indices to determine the direction of causality. If the probability values are less than 0.05 the reject the null hypothesis i.e. X does not granger cause to Y. The result of the study shows that SPOT Granger Cause FUTURE.

Granger causality is a statistical concept of causality that is based on prediction. According to Granger causality, As a SPOT "Granger-causes" (or "G-causes") a  FUTURE, it means that

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past values of SPOT should contain information that helps predict FUTURE above and beyond the information contained in past values of FUTURE alone. Its mathematical formulation is based on linear regression modelling of stochastic processes (Granger 1969). More complex extensions to nonlinear cases exist, however these extensions are often more difficult to apply in practice.

By using Vector Error Correction Estimates, the model given below is developed.

Estimation Proc:===============================EC(C,1) 1 2 FUTURE SPOT

VAR Model:===============================D(FUTURE) = A(1,1)*(B(1,1)*FUTURE(-1) + B(1,2)*SPOT(-1) + B(1,3)) + C(1,1)*D(FUTURE(-1)) + C(1,2)*D(FUTURE(-2)) + C(1,3)*D(SPOT(-1)) + C(1,4)*D(SPOT(-2)) + C(1,5)

D(SPOT) = A(2,1)*(B(1,1)*FUTURE(-1) + B(1,2)*SPOT(-1) + B(1,3)) + C(2,1)*D(FUTURE(-1)) + C(2,2)*D(FUTURE(-2)) + C(2,3)*D(SPOT(-1)) + C(2,4)*D(SPOT(-2)) + C(2,5)

VAR Model - Substituted Coefficients:===============================D(FUTURE) = - 0.354910391045*( FUTURE(-1) - 1.00171402383*SPOT(-1) + 5.80634019765 ) + 0.0926675266643*D(FUTURE(-1)) - 0.00187377787031*D(FUTURE(-2)) - 0.0613253921801*D(SPOT(-1)) + 0.0215230647757*D(SPOT(-2)) + 1.25323006289

D(SPOT) = - 0.185706016952*( FUTURE(-1) - 1.00171402383*SPOT(-1) + 5.80634019765 ) + 0.409082576826*D(FUTURE(-1)) + 0.0890842955516*D(FUTURE(-2)) - 0.360597229337*D(SPOT(-1)) - 0.0733024793809*D(SPOT(-2)) + 1.22945025604

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CHAPTER 6: REFERENCE AND BIBLIOGRAPHY

1. BOSE, S. (2007). Contribution of Indian Index Futures to Price Formation in the Stock Market. Icrabulletin: Money & Finance, 39-56.

2. Chakraborty, T. (2012).Resilience of Indian Equity Versus Derivatives Markets: An Analysis Using VaR Approach. The IUP Journal of Applied Finance, 18(3), 95-108.

3. Chang, C. Y. (2011). The Basis under Negative Shock and the Price Discovery in Futures Market. Applied Financial Economics, 21, 755–761.

4. Debashis, S. S. ().Financial Engineering and the Impact of Index Futures Trading on Spot Market in India. Pranjana, 11(2), 27-38.

5. Debasish S. S. (2011). Spot and Futures: Market Relative Volatility. SCMS Journal of Indian Management, 94-104.

6. Dr Rastogi, S.(2011). Impact of Currency Futures on Spot Market Volatility: An Empirical Study. The Indian Journal of Management, 4(2), 3-8.

7. Dr. Maniar, H. M.(2009). Expiration Hour Effect of Futures and Options Markets on Stock Market - A Case Study on NSE. International Review of Economics and Finance, 18(3), 363-538.

8. Dr. Shenbagaraman, P. ().Do Futures and Options trading increase stock market volatility? 1-22.

9. Gupta O.P. (2002), Effect of Introduction of Index Futures on Stock Market Volatility: The Indian Evidence, Department of Financial Studies, University of Delhi South Campus, New Delhi (India), 1-28.

10. Karmakar, M. (2009).Price Discoveries and Volatility Spillovers in S&P CNX Nifty Future and its Underlying Index CNX Nifty. Vikalpa, 34(2), 41-56.

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11. Kavussanos, M. G., Visvikis, I. D. & Alexakis, P. D. (2008). The Lead-Lag Relationship Between Cash and Stock Index Futures in a New Market. European Financial Management, 14(5), 1007–1025.

12. Kedar nath Mukherjee, K. N. & Mishra, R. K. () Lead-Lag Relationship between Equities and Stock Index Futures Market and its Variation around Information Release: Empirical Evidence from India. 1-33.

13. Kumar, S. & Lagesh, M. A. (2011). Spot Return Volatility and Hedging with Futures Contract: Empirical Evidence from the Notional Commodity Futures Indices of India. The IUP Journal of Behavioral Finance, 8(2), 70-85.

14. Mallikarjunappa, T., & Afsal, E. M. (2007). “Futures trading and market volatility in Indian equity market: A study of CNX IT index”. Asian Academy of Management Journal of Accounting and Finance, Vol.3, No.1, pp.59–76.

15. P Sakthivel, P. & Kamaiah, B. (2010). Price Discovery and Volatility Spillover Between Spot and Futures Markets: Evidence from India. The IUP Journal of Applied Economics, 9(2), 81-97.

16. Pati, P. C. & Padhan, P. C. (2009). Information, Price Discovery and Causality in the Indian Stock Index Futures Market. The IUP Journal of Financial Risk Management, 6(3 & 4), 7-21.

17. Pati, P. C. & Rajib, P. (2011). Intraday return dynamics and volatility spillovers between NSE S&P CNX Nifty stock index and stock index futures. Applied Economics Letters, 18, 567–574.

18. Sadath, A. & Kamaiah, B. (2009).Liquidity Effect of Single Stock Futures on the Underlying Stocks: A Case of NSE. The IUP Journal of Applied Economics, 8(5 & 6), 142-160.

19. Sadath, A. & Kamaiah, B. (2011). Expiration Effects of Stock Futures on the Price and Volume of Underlying Stocks: Evidence from India. The IUP Journal of Applied Economics, 10(3), 25-38.

20. Sahu, D. (). Does the Index Trading Influence Spot Market Volatility? Evidence From Indian Stock Market

21. Sakthivel, P. (). The Effect of Futures Trading on the Underlying Volatility: Evidence from the Indian Stock Market. 1-22.

22. Samanta, P. & Samanta, P. K. Impact of Futures Trading on the Underlying Spot Market Volatility. The ICFAI Journal of Applied Finance, 13(10), 52-65.

23. Sarangi, S. P. & Patnaik, U. S. (2007). Futures Trading and Volatility:A Case of S&P CNX Nifty Stocks and Stock Futures. The ICFAI Journal of Derivatives Markets, 4(4), 65-87.

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24. Singh, Y. P. & Bhatia, S. (2006). Does Futures Trading Impact Spot Market Volatility? Evidence from Indian Financial Markets. Decision, 33(2), 41-62.

25. Srinivasan, P. (2009).An Empirical Analysis of Price Discovery in the NSE Spot and Futures Markets of India. The IUP Journal of Applied Finance, 15(11), 24-36.

26. Srinivasan, P. (2009).Price Discovery in NSE Spot and Futures Markets of Selected Oil and Gas Industries in India: What Causes What? The IUP Journal of Financial Risk Management, 6(3 & 4), 22-37.

27. T Mallikarjunappa, T. & Afsal, E. M.(2010). Price Discovery Process and Volatility Spillover in Spot and Futures Markets: Evidences of Individual Stocks. Vikalpa, 35(2), 49-62.

28. Wats, S. & Misra, K.K. (). Price Discovery Efficiency of the Indian Futures Market, 39-50.

29. Wats, S. (2011). Repercussions of Futures Trading on Spot Market: The NSE Saga. The IUP Journal of Applied Finance, 17(3), 68-85.

Websites:

www.yahoo finance.com

www.bseindia.com

www.econstats.com

www.wikipedia.com

www.nseindia.com

www.investopedia.com

Book:

Basic Econometrics by Damodar Gujarati

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