final gp
TRANSCRIPT
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.
4
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
5
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
6
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.
7
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.
8
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
10
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;
11
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
12
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.
13
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
14
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
69
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
70
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.
71
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.
72
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
73