final report body naresh fresh

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Contents:

Foreign exchange marketing

Currency Exchange Rate

Appreciation/depreciation of currency

Exchange rate regime

Foreign Exchange Market participants

Foreign exchange transactions

Global foreign exchange market turnover

Trading Hours

INDIAN FX MARKET

Stock markrting

Classification of financial marketing

BSE-SENSEX

NSE-NIFTY

TEST AND RESULTS

FINDINGS

CONCLUSION

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Introduction

Globalization and financial liberalization in India have brought about battery of changes inthe financial functioning of the economy, as a result of which, the resultant gain of the

global integration of domestic and foreign financial markets has thrown open new

opportunities but at the same time exposed the financial system to significant risks.

Consequently, it is important to understand the mutual relationship between the financial

markets from the standpoint of financial stability. Though the inception of the financial

sector reforms has taken place initiated in the beginning of the 1990s, particularly since

1997, there has been a dramatic change in the functioning of the financial sector of the

economy.

The recent emergence of new capital markets, the relaxation of foreign capital controls and

the adoption of more flexible exchange rate regimes have increased the interest of

academics and practitioners in studying the interactions between the stock and foreign

exchange markets. The gradual abolition of foreign exchange controls in emerging

economies like India has opened the possibility of international investment and portfolio

diversification. At the same time, the adoption of more flexible exchange rate regimes by

these countries in the late 1980s and early 1990s has increased the volatility of foreign

exchange markets and the risk associated with such investments.

The advent of floating exchange rates, opening up of current account, Liberalization of

capital account, reduction of customs duties, the development of 24-hour screen based

global trading, the increased use of national currencies outside the country of issue and

innovations in internationally traded financial products have led to the cross Country

linkages of capital markets and international integration of domestic economy.

Altogether, the whole gamut of institutional reforms, introduction of new instruments,

change in procedures, widening of network of participants, call for a reexamination of the

relationship between the stock market and the foreign sector of India.

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The process of economic liberalization and thrust on reforms in the financial sector and the

foreign exchange market in particular that was initiated in India in early nineties has

resulted into increasing integration of the Indian FX market with that of the global markets.

With a large number of foreign funds and foreign institutional investors now actively

participating in the Indian financial markets (foreign exchange reserves standing at about

USD118 bn), the style of functioning of the market itself has undergone a lot of change and

result of microstructure changes are visible. Today the Indian FX market, which was

insulated from outside impacts, has been getting integrated with the world markets.

An exchange rate has two effects on stock prices, a direct effect through Multi National

Firms and an indirect effect through domestic firms. In case of Multi National Firms

involved in exports, a change in rate will change the demand of its product in the

international market, which ultimately reflects in its B/S as profit or loss. Once the profit or

loss is declared, the stock price will also change for a domestic firm.

On the other hand, currency devaluation could either raise or decrease a firms stock prices.

This depends on the nature of the firms operations. A domestic firm that exports part of its

output will benefit directly from devaluation due to an increase in demand for its output. As

higher sales result in higher profits, local currency devaluation will cause firm stock price

to rise in general.

On the other hand, if the firm is a user of imported inputs, currency devaluation will raise

cost and lower profits. Thus, it will decrease the firms stock price.

Scope of the study

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The study includes only one currency pair i.e. INR/USD for the representation of the forex

market while the two major stock markets of India are covered. Thus the relation and

effects of other currencies is out of the preview of the research. The data set comprises of

daily closing price of Sensex, Nifty and INR/USD exchange rates obtained from the

respective Stock Exchange and Reserve Bank of India websites. The sample population of

the study comprises of daily closing price, for of BSE Sensex, CNX Nifty and exchange

rates of Rupee/Dollar are considered for analyzing.

Benefits

The determination of relationship between the foreign exchange market and stock market

would help the students to increase their understanding about these markets. It would also

provide a platform for participants to enhance their views about the relationship between

the two markets.

Limitations

Unavailability of intra-day minute to minute data of both the markets.

The study is limited to period of eight years.

Only one pair of USD/INR is used.

3.) Foreign Exchange Market:

The foreign exchange market exists wherever one currency is traded for another. It is by far

the largest market in the world, in terms of cash value traded, and includes trading between

large banks, central banks, currency speculators, multinational corporations, governments,

and other financial markets and institutions. The trade happening in the forex markets

across the globe currently exceeds US$1.9 trillion/day (on average). Retail traders

(individuals) are currently a very small part of this market and may only participate

indirectly through brokers or banks.

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The foreign exchange market provides the physical and institutional structure through

which the money of one country is exchanged for that of another country, the rate of

exchange between currencies is determined, and foreign exchange transactions are

physically completed.

The retail market for foreign exchange deals with transactions involving travelers and

tourists exchanging one currency for another in the form of currency notes or travelers

cheques. The wholesale market often referred to as the interbank market is entirely

different and the participants in this market are commercial banks, corporations and central

banks.

Currency Exchange Rate:

The Exchange rate or FX rate is the rate between two currencies specifies how much one

currency is worth in terms of the other. For example an exchange rate of 33 Indian Rupees

(IND, Rs.) to the United States Dollar (USD, $) means that IND 33 is worth the same as

USD 1. The foreign exchange market is one of the largest markets in the world. By some

estimates, about 2 trillion USD worth of currency changes hands every day.

The Spot exchange rate refers to the current exchange rate. The forward exchange rate

refers to an exchange rate that is quoted and traded today but for delivery and payment on a

specific future date.

Quotations

An exchange rate quotation is given by stating the number of units of a pricecurrency that

can be bought in terms of 1 unit currency (also called base currency). Ina quotation that

says the JPN/USD exchange rate is 120 (USD per JPN), the price currency is USD and the

unit currency is JPN.

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Quotes

Direct quote is a quote using a countrys home currency as the price currency (e.g.,Rs.33=

$ 1 in India) and is used by most countries.

Indirect quote is a quote using a countrys home currency as the unit currency (e.g, $ 0.03

= Rs. 1 in India) and is used in British newspapers and are also common in Australia, New

Zealand and Canada.

Appreciation/depreciation of currency:

While using direct quotation, if the home currency is strengthening (i.e., appreciating, or

becoming more valuable) then the exchange rate number decreases. Conversely if the

foreign currency is strengthening, the exchange rate number increases and the home

currency is depreciating.

Exchange rate regime:

The exchange rate regime is the way a country manages its currency in respect to foreign

currencies and the foreign exchange market. It is closely related to monetary policy and the

two are generally dependent.

A floating exchange rate or a flexible exchange rate is a type of exchange rate regime

wherein a currencys value is allowed to fluctuate according to the foreign exchange

market. A currency that uses a floating exchange rate is known as a floating currency.

A pegged float is pegged to some band or value, either fixed or periodically adjusted.

Pegged floats are Crawling bands, Crawling pegs and Pegged with horizontal bands.

Afixed rate is that rate that has direct convertibility towards another currency. Here, thecurrency is backed one to one by foreign reserves.

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Functions of foreign exchange market:

The foreign exchange market is the mechanism by which participants

Transfer purchasing power between countries,

Obtain or provide credit for international trade transactions, and

Minimize exposure to the risks of exchange rate changes

Foreign Exchange Market participants:

The foreign exchange market consists of two tiers:

the interbank or wholesale market and

The client or retail market.

Five broad categories of participants operate within these two tiers:

Bank and nonblank foreign exchange dealers:

Banks and a few nonblank foreign exchange dealers operate in both the interbank and clientmarkets. They profit from buying foreign exchange at a bid price and reselling it at a

slightly higher ask price. Dealers in the foreign exchange departments of large

international banks often function as market makers.

Currency trading is quite profitable for commercial and investment banks. Small to medium

sized banks are likely to participate but not as market makers in the interbank market.

Instead of maintaining significant inventory positions, they buy from and sell to large banks

to offset retail transactions with their own customers.

Individuals and firms conducting commercial or investment Transactions:

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Importers and exporters, international portfolio investors, Multi National Enterprises,

tourists, and others use the foreign exchange market to facilitate execution of commercial

or investment transactions. Some of these participants use the market to hedge foreign

exchange risk.

Speculators and arbitragers:

Speculators and arbitragers seek to profit from trading in the market itself. They operate in

their own interest, without a need or obligation to serve clients or to ensure a continuous

market. A large proportion of speculation and arbitrage is conducted on behalf of major

banks by traders employed by those banks. Thus banks act both as exchange dealers and as

speculators and arbitrages.

Central banks and treasuries:

Central bank and treasuries use the market to acquire or spend their countrys foreign

exchange reserves as well as to influence the price at which their own currency is traded.

They may act to support the value of their own currency because of policies adopted at the

national level or because of commitments entered into through membership in joint float

agreements.

Foreign exchange brokers:

Foreign exchange brokers are agents who facilitate trading between dealers. Brokers charge

small commission for the service provided to dealers. They maintain instant access to

hundreds of dealers world wide via open telephone lines.

Foreign exchange transactions

Transactions within the foreign exchange market are executed either on a spot basis,

requiring settlement two days after the transaction, or on a forwardorswap basis,which

requires settlement at some designated future date.

To be successful in the foreign exchange markets, one has to anticipate price changes by

keeping a close eye on world events and currency fluctuations.

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Global foreign exchange market turnover:

According to the Bank for International Settlements, average daily turnover in global

foreign exchange markets is estimated at $3.98 trillion as of April 2007. Trading in the

worlds main financial markets accounted for $3.21 trillion of this. This approximately

$3.21 trillion in main foreign exchange market turnover was broken down as follows:

Components are:

$621 billion in spot

$1.26 trillion in derivatives

$208 billion in outright forwards

$944 billion in forex swaps

$107 billion in FX options

Of the $3.98 trillion daily global turnover, trading in London accounted for around $1.36

trillion, or 34.1% of the total, making London by far the global center for foreign exchange.

In second and third places respectively, trading in New York accounted for 16.6%, andTokyo accounted for 6.0%.[4] In addition to traditional turnover, $2.1 trillion was traded

in derivatives.

Exchange-traded FX futures contracts were introduced in 1972 at the Chicago Mercantile

Exchange and are actively traded relative to most other futures contracts.

Several other developed countries also permit the trading of FX derivative products (like

currency futures and options on currency futures) on their exchanges. All these developed

countries already have fully convertible capital accounts. Most emerging countries do not

permit FX derivative products on their exchanges in view of prevalent controls on the

capital accounts. However, a few select emerging countries (e.g., Korea, South Africa, and

India) have already successfully experimented with the currency futures exchanges, despite

having some controls on the capital account.

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Source: BIS Triennial Survey 2007


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Structure

Decentralized interbank market

Main participants: Central Banks, commercial and investment banks, hedge funds,

corporations & private speculators

The free-floating currency system arose from the collapse of the Bretton Woods

agreement in 1971

Online trading began in the mid to late 1990s

Trading Hours

24 hour market

Sunday 5pm EST through Friday 4pm EST.

Trading begins in the Asia-Pacific region followed by the Middle East, Europe, and

America

Size

One of the largest financial markets in the world

$3.2 trillion average daily turnover, equivalent to:

o More than 10 times the average daily turnover of global equity markets1

o More than 35 times the average daily turnover of the NYSE2

o Nearly $500 a day for every man, woman, and child on earth3

o An annual turnover more than 10 times world GDP4

The spot market accounts for just under one-third of daily turnover

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1. About $280 billion - World Federation of Exchanges aggregate 2006

2. About $87 billion - World Federation of Exchanges 2006

3. Based on world population of 6.6 billion - US Census Bureau

4. About $48 trillion - World Bank 2006.

Major Markets

The US & UK markets account for just over 50% of turnover

Major markets: London, New York, Tokyo

Trading activity is heaviest when major markets overlap

Nearly two-thirds of NY activity occurs in the morning hours while European

markets are open

Average Daily Turnover by Geographic Location


Concentration in the Banking Industry

12 banks account for 75% of turnover in the U.K.

10 banks account for 75% of turnover in the U.S.

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3 banks account for 75% of turnover in Switzerland

9 banks account for 75% of turnover in Japan


Currencies

The US dollar is involved in over 80% of all foreign exchange transactions,

equivalent to over US$2.7 trillion per day

Currency Codes

USD = US Dollar

EUR = Euro

JPY = Japanese Yen

GBP = British Pound

CAD = Canadian Dollar

AUD = Australian Dollar

NZD = New Zealand Dollar

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Average Daily Turnover by Currency

N.B. Because two currencies are involved in each transaction, the sum of the percentage

shares of individual currencies totals 200% instead of 100%.


Currency Pairs

Majors: EUR/USD (Euro-Dollar), USD/JPY, GBP/USD - (commonly referred to as

the Cable), USD/CHF

Dollar bloc: USD/CAD, AUD/USD, NZD/USD

Major crosses: EUR/JPY, EUR/GBP, EUR/CHF

Average Daily Turnover by Currency Pair

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Factors affecting Exchange rates:

The prime factor that affects currency prices are supply and demand forces. The three

factors include:

Economic factors:

Government budget deficits or surpluses

Balance of trade levels and trends

Inflation levels and trends

Economic growth and health

Political conditions:

Political upheaval and political instability

Relation between two countries

Market psychology:

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Flights to quality

Economic numbers

Long-term trends

Indian FX Market

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India foreign exchange reserve is at $ 278.7 Billion USD (Feb 5, 2010)

NSE has witnessed healthy growth in the turnover and open interest positions during its

first completed month of currency futures trading in India. NSE commenced currency

futures trading in India on 29th August.

CDX (Currency Derivative Exchange), currency derivative segment of BSE (Bombay

Stock Exchange) commenced currency futures trading from 1st October. BSE on its very

first day of trading in currency futures clocked a turn over of about 65,000 contracts, which

is approximately Rs. 300 Crores.

With ever-growing global financial crisis, exchange rates are fluctuating widely. INR

exchange rate has touched 47 against USD. Currency futures trading in India has generatedhuge interest among Indian retail investors and traders. There is a strong demand for

information gathering about the intricacies of currency futures from small investors and

enterprises.

After over a year of introduction of exchange-traded currency futures in the USD-INR pair

on the stock exchanges in the country, the market regulators have now permitted trading of

Euro-INR, Japanese Yen-INR and Pound Sterling-INR on the exchange platform. This is a

move that the market had been demanding for a long time. This is an apt time to review

how the exchange-traded currency market has fared so far and what lies ahead as it

ventures further into new currency pairs.

The currency derivatives segment on the NSE and MCX has witnessed consistent growth

both in traded value and open interest since its inception. The total turnover in the segment

has increased incredibly from $3.4bn in October 2008 to $84bn in December 2009. The

average daily turnover reached $4bn in December 2009. Open interest in the segment on

the NSE and MCX stood at around 4 lakh contracts till end-December 2009.

India already has an active over-the-counter (OTC) market in currency derivatives where

the average daily turnover was $29bn in 2008 and $21bn in 2009 (till September 2009).

This market is being driven by its ability to meet the respective needs of participants. For

example, it is used by importers/exporters to hedge their payables/receivables; foreign

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institutional investors (FIIs) and NRIs use it to hedge their investments in India; borrowers

find it an effective way to hedge their foreign currency loans and resident Indians find it an

effective tool to hedge their investments offshore. Further, for arbitrageurs it presents an

opportunity to arbitrage between onshore and non-deliverable forward (NDF) markets.

The exchange-traded currency futures market is an extension of this already available OTC

market, but with added benefits of greater accessibility to potential participants; high price

transparency; high liquidity; standardised contracts; counterparty risk management through

clearing corporation and no requirement of underlying exposure in the currency. As the

market participants are realising these benefits of exchange-traded market in currency, they

are choosing this market over OTC.

However, it is too early to see a major shift in activity from OTC to exchange-traded

market as it has created a niche for itself and it would perhaps take some time for the

currency futures market to create one for itself. Globally, too, the foreign exchange market

is largely OTC in character. While the notional amount outstanding of OTC derivatives was

as high as $63trn in June 2008, the exchange-traded market is rather non-existent with

notional amount outstanding as end-June 2009 being only 0.5% of that in the OTC

segment.

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However, there is a renewed debate on the level of transparency and counterparty risk in

the OTC market kindled by the sub-prime mortgage crisis in the US and the need to

regulate OTC transactions effectively. This throws up certain important issues which, at

best, may need to be handled separately.

India has, in this light, embarked upon an experiment by attempting to make the exchange-

traded currency market popular and a first choice for investors. Though the market has not

been able to evince the kind of activity that the OTC market has witnessed as yet, the recent

phenomenal growth is a pointer towards better days ahead for this market. Some of the

issues plaguing the market at present include the fact that many corporates using currency

derivatives for hedging their foreign currency exposure find the requirement of margin and

settlement of daily mark-to-market differences cumbersome, especially since there is no

such requirement for OTC trades. It would conceivably take some time for them to realise

the concomitant benefits of these risk containment measures. Also, there is a perceived

resistance to change and switchover from OTC to exchange-traded framework following a

level of comfortability reached by market players with the OTC market framework.

Further, the market has been restricted in a number of ways. Till recently only USD-INR

futures contracts were permitted. One hopes to see more activity in the segment with more

currency pairs being added. Also to start with, FIIs have not been permitted to participate inthis market. This has in effect restricted the liquidity that FIIs could have otherwise created.

FIIs are already active in Dubai Gold and Commodity Exchange (DGCX). There is an

opportunity for business for domestic exchanges and intermediaries to be created in

bringing this market onshore. According to the latest release from DGCX, Indian rupee

futures volume rose 530% in 2009 to 66,346 contracts on the exchange. Volume in

December 2009 was 346% higher compared with the same period last year. Though small

in comparison to volumes being traded on Indian exchanges, there is still merit in getting

this market onshore. Additionally, the offshore NDF market in Indian rupee has also been

witnessing increasing volumes. The average daily volumes on the NDF rupee market have

increased from $38mn in 2003 Q1 to $800mn during 2008-09 (Asian Capital Markets

Monitor of ADB, April, 2009). Most of the major foreign banks offer NDFs, but Indian

banks are barred from doing so. These markets have evolved for the Indian rupee following

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foreign exchange convertibility restrictions. It is serving as an avenue for non-domestic

players, private companies and investors in India to hedge foreign currency exposure. It

also derives liquidity from non-residents wishing to speculate in the Indian rupee without

exposure to the currency and from arbitrageurs who try to exploit the differentials in the

prices in the onshore and offshore markets. Though foreign investors can now transact in

the onshore Indian forward markets with greater flexibility following various measures

taken by RBI in recent years, allowing them access to the exchange-traded currency futures

platform would further help in getting the volumes in the NDF market onshore and enhance

liquidity on domestic exchanges.

India has witnessed enhanced foreign investment inflows and trade flows in recent years.

The Indian currency is now becoming an important international currency. Though India

accounts for a very small proportion of the total foreign exchange market turnover in the

world as compared to other countries, its share has been slowly but continuously

increasing. According to BIS estimates, the percentage share of Indian rupee in total daily

average foreign exchange turnover has increased from 0.1% in 1998 to 0.2% in 2001 to

0.3% in 2004 and to 0.9% in April 2007 (updated data will be available in April 2010). All

of this implies greater need for hedging currency risk in Indian rupee, particularly given

that the exchange rate has been quite volatile during the last few years and hence increased

importance of exchange-traded currency market.

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Fluctuations in USD/INR over the years

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Table of year on year data of FIIs and Foreign exchange Reserve of India:-

Particulars FII Investment

(Rs. Cr)

SENSEX

(Annual increase

or decrease)

Foreign

Exchange

Reserve (billion

$)

2004 22789.3 730.21 125

2005 45337.9 2,771.44 127

2006 28092.6 4,364.42 160

2007 60057.2 6,459.22 260

2008 -53562.6 -10,677.96 245

2009 24574.3 4,773.29 265

Source: SEBI Website 2010

5.) Stock Market:

A stock market is a market for the trading of company stock and derivatives of

same; both of these are securities listed on a stock exchange as well as those only traded

privately.

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Functions of stock exchanges:

Most important source for companies to raise money

Provides liquidity to the investors

Acts as clearing house for transactions

Provides realistic value of companies

India has 22 stock exchanges and the important stock exchanges are Bombay Stock

Exchange and National Stock exchange at Mumbai. Established in 1875 BSE is one of the

oldest stock exchanges in Asia and has seen significant development ever since.

The regulatory agency which oversees the functioning of stock markets is the Securities

and Exchange Board of India (SEBI), which is also located in Bombay.

Classification of financial markets

i) Unorganized Markets

In these markets there a number of money lenders, indigenous bankers, traders etc. who

lend money to the public.

ii) Organized Market

In organized markets, there are standardized rules and regulations governing their financial

dealings. There is also a high degree of institutionalization and instrumentalization. These

markets are subject to strict supervision and control by the RBI or other regulatory bodies.

Organized markets can be further divided into capital market and Money market.

Capital market

Capital market is a market for financial assets which have a long or definite maturity.

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Which can be further divided into

Industrial Securities Market

Government Securities Market

Long Term Loans Market

Industrial Securities Market

It is a market where industrial concerns raise their capital or debt by issuing appropriate

Instruments. It can be subdivided into two. They are:

Primary Market or New Issues Market

Primary market is a market for new issues or new financial claims. Hence, it is also called

as New Issues Market. The primary market deals with those securities which are issued to

the public for the first time.

Secondary Market or Stock Exchange

Secondary market is a market for secondary sale of securities. In other words, securities

which have already passed through the new issues market are traded in this market.

Such securities are listed in stock exchange and it provides a continuous and regular market

for buying and selling of securities. This market consists of all stock exchanges recognized

by the government of India.

Importance of Capital Market

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Absence of capital market serves as a deterrent factor to capital formation and economic

growth. Resources would remain idle if finances are not funneled through capital market.

It provides incentives to saving and facilitates capital formation by offering suitable

rates of interest as the price of the capital

It serves as an important source for the productive use of the economys savings.

It provides avenue for investors to invest in financial assets.

It facilitates increase in production and productivity in the economy and thus

enhances the economic welfare of the society.

A healthy market consisting of expert intermediaries promotes stability in the value

of securities representing capital funds.

It serves as an important source for technological up gradation in the industrial

sector by utilizing the funds invested by the public.

The major stock indices also have a correlation with the currency rates. Three majorforces

affect the indices:

1) Corporate earnings, forecast and actual;

2) Interest rate expectations and

3) Global considerations.

Consequently, these factors channel their way through the local currency.

In an increasingly complex scenario of the financial world, it is of paramount importance

for the researchers, practitioners, market players and policy makers to understand the

working of the economic and the financial system and assimilate the mutual interlink ages

between the stock and foreign exchange markets in forming their expectations about the

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future policy and financial variables. The analysis of dynamic and strategic interactions

between stock and foreign exchange market came to the forefront because these two

markets are the most sensitive segments of the financial system and are considered as the

barometers of the economic growth through which the countrys exposure towards the

outer world is most readily felt.

The present study is an endeavor in this direction. Before going to discuss further about the

interlink ages between the stock and foreign exchange market, it is better to highlight the

evolutions and perspectives that are associated with both the markets since liberalization in

the Indian context.

In the literature, there is theoretical consensus neither on the existence of relationship

between stock prices and exchange rates nor on the direction of relationship. In theory there

are two approaches to exchange rate determination. They are-

Flow oriented -are considered as the traditional approach and assume that the exchange

rate is determined largely by countrys current account or trade balance performance. The

model posits that changes in exchange rates affect international competitiveness and trade

balance, thereby influencing real economic variables such as real income and output

(Dornbusch and Fisher, 1980). This model represents a positive relationship between stock

prices and exchange rates with direction of causation running from exchange rates to stock

prices.

Stock-oriented - models put much emphasis on the role of financial (formerly capital)

account in the exchange rate determination. These Models can be distinguished as portfolio

balance models and monetary models (Branson and Frankel, 1983). They postulate a

negative relationship between stock prices and exchange rates and come to the conclusion

that stock prices have an impact on exchange rates.

BSE - SENSEX

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Introduction

Bombay Stock Exchange is the oldest stock exchange in Asia with a rich heritage of over

133 years of existence. What is now popularly known as BSE was established as The

Native Share & Stock Brokers Association in 1875.

BSE is the first stock exchange in the country which obtained permanent recognition (in

1956) from the Government of India under the Securities Contracts (Regulation) Act

(SCRA) 1956. BSEs pivotal and pre-eminent role in the development of the Indian capital

market is widely recognized. It migrated from the open out-cry system to an online screen-

based order driven trading system in 1995. Earlier an Association Of Persons (AOP), BSE

is now a corporatised and demutualised entity incorporated under the provisions of the

Companies Act, 1956, pursuant to the BSE (Corporatisation and Demutualisation) Scheme,

2005 notified by the Securities and Exchange Board of India (SEBI). With demutualisation,

BSE has two of worlds prominent exchanges, Deutsche Brse and Singapore Exchange, as

its strategic partners.

Over the past 133 years, BSE has facilitated the growth of the Indian corporate sector by

providing it with cost and time efficient access to resources. There is perhaps no major

corporate in India which has not sourced BSEs services in raising resources from the

capital market.

Today, BSE is the worlds number 1 exchange in terms of the number of listed companies

and the worlds 5th in handling of transactions through its electronic trading system. The

companies listed on BSE command a total market capitalization of USDTrillion 1.06 as of

July, 2009. BSE reaches to over 400 cities and town nation-wide and has around 4,937

listed companies, with over 7745 scrips being traded as on 31st July 09.

The BSE Index, SENSEX, is Indias first and most popular stock market benchmark index.

Sensex is tracked worldwide. It constitutes 30 stocks representing 12 major sectors. The

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SENSEX is constructed on a free-float methodology, and is sensitive to market

movements and market realities. Apart from the SENSEX, BSE offers 23 indices, including

13 sectoral indices. It has entered into an index cooperation agreement with Deutsche Brse

and Singapore Stock Exchange. These agreements have made SENSEX and other BSE

indices available to investors across the globe. Moreover, Barclays Global Investors (BGI),

atHong Kong, the global leader in ETFs through its iShares brand, has created the

exchange traded fund (ETF) called iSharesBSE SENSEX India Tracker which tracks the

SENSEX. The ETF enables investors in Hong Kong to take an exposure to the Indian

equity market.

The exchange traded funds (ETF) on SENSEX, called SPIcE and Kotak SENSEX ETF

are listed on BSE. They bring to the investors a trading tool that can be easily used for the

purposes of investment, trading, hedging and arbitrage. These ETFs allow small investors

to take a long-term view of the market.

BSE provides an efficient and transparent market for trading in equity, debt instruments

and derivatives. It has always been at par with the international standards. The systems and

processes are designed to safeguard market integrity and enhance transparency in

operations. BSE is the first exchange in India and the second in the world to obtain an ISO

9001:2000 certification. It is also the first exchange in the country and second in the world

to receive Information Security Management System Standard BS 7799-2-2002

certification for its BSE On-line Trading System (BOLT).

Recently,BSE launched the BSE IPO index that will track the value of companies for two

years after listing. Also, as an investor friendly gesture, Bombay Stock Exchange has

commenced a facility of sending trade details to investors. Moving a step further a new

transaction fee structure for cash equity segment has also been introduced. BSE also

launched BSE StAR MF Mutual fund trading platform, would enable exchanges

members to use its existing infrastructure for transaction in MF schemes. It is an inclusive

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model with two depositories and industry wide participation. BSE also revamped its

website; the new website presents a wide range of new features like Live streaming

quotes for SENSEX companies, Advanced Stock Reach, Sensex View, Market

Galaxy, and Members.

SENSEX - The Barometer of Indian Capital Markets

Introduction

SENSEX, first compiled in 1986, was calculated on a Market Capitalization-Weighted

methodology of 30 component stocks representing large, well-established and financially

sound companies across key sectors. The base year of SENSEX was taken as 1978-79.

SENSEX today is widely reported in both domestic and international markets through print

as well as electronic media. It is scientifically designed and is based on globally accepted

construction and review methodology. Since September 1, 2003, SENSEX is being

calculated on a free-float market capitalization methodology. The free-float market

capitalization-weighted methodology is a widely followed index construction

methodology on which majority of global equity indices are based; all major index

providers like MSCI, FTSE, STOXX, S&P and Dow Jones use the free-float methodology.

The growth of the equity market in India has been phenomenal in the present decade. Right

from early nineties, the stock market witnessed heightened activity in terms of various bull

and bear runs. In the late nineties, the Indian market witnessed a huge frenzy in the TMT

sectors. More recently, real estate caught the fancy of the investors. SENSEX has captured

all these happenings in the most judicious manner. One can identify the booms and busts of

the Indian equity market through SENSEX. As the oldest index in the country, it provides

the time series data over a fairly long period of time (from 1979 onwards). Small wonder,

the SENSEX has become one of the most prominent brands in the country.

Index Specification:

Base Year 1978-79

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Base Index Value 100

Date of Launch 01-01-1986

Method of

calculation

Launched on full market capitalization method and effective September

01, 2003, calculation method shifted to free-float market capitalization.

Number of scripts 30

Index calculation

frequency

Real Time

SENSEX Calculation Methodology

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SENSEX is calculated using the Free-float Market Capitalization methodology, wherein,

the level of index at any point of time reflects the free-float market value of 30 component

stocks relative to a base period. The market capitalization of a company is determined by

multiplying the price of its stock by the number of shares issued by the company. This

market capitalization is further multiplied by the free-float factor to determine the free-float

market capitalization.

The base period of SENSEX is 1978-79 and the base value is 100 index points. This is

often indicated by the notation 1978-79=100. The calculation of SENSEX involves

dividing the free-float market capitalization of 30 companies in the Index by a number

called the Index Divisor. The Divisor is the only link to the original base period value of

the SENSEX. It keeps the Index comparable over time and is the adjustment point for all

Index adjustments arising out of corporate actions, replacement of scrips etc. During

market hours, prices of the index scrips, at which latest trades are executed, are used by the

trading system to calculate SENSEX on a continuous basis.

SENSEX - Scrip Selection Criteria

The general guidelines for selection of constituents in SENSEX are as follows:

Listed History: The scrip should have a listing history of at least 3 months at BSE.

Exception may be considered if full market capitalization of a newly listed company ranks

among top 10 in the list of BSE universe. In case, a company is listed on account of

merger/ demerger/ amalgamation, minimum listing history would not be required.

Trading Frequency: The scrip should have been traded on each and every trading day in

the last three months at BSE. Exceptions can be made for extreme reasons like scrip

suspension etc.

Final Rank: The scrip should figure in the top 100 companies listed by final rank. The

final rank is arrived at by assigning 75% weight age to the rank on the basis of three-month

average full market capitalization and 25% weight age to the liquidity rank based on three-

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month average daily turnover & three-month average impact cost.

Market Capitalization Weightage: The weight age of each scrip in SENSEX based on

three-month average free-float market capitalization should be at least 0.5% of the Index.

Industry/Sector Representation: Scrip selection would generally take into account a

balanced representation of the listed companies in the universe of BSE.

Track Record: In the opinion of the BSE Index Committee, the company should have an

acceptable track record.

BSE has designed a Free-float format, which is filled and submitted by all index companies

on a quarterly basis. BSE determines the Free-float factor for each company based on the

detailed information submitted by the companies in the prescribed format. Free-float factor

is a multiple with which the total market capitalization of a company is adjusted to arrive at

the Free-float market capitalization. Once the Free-float of a company is determined, it is

rounded-off to the higher multiple of 5 and each company is categorized into one of the 20

bands given below. A Free-float factor of say 0.55 means that only 55% of the market

capitalization of the company will be considered for index calculation.

Index Closure Algorithm

The closing SENSEX on any trading day is computed taking the weighted average of all

the trades on SENSEX constituents in the last 30 minutes of trading session. If a SENSEX

constituent has not traded in the last 30 minutes, the last traded price is taken forcomputation of the Index closure. If a SENSEX constituent has not traded at all in a day,

then its last days closing price is taken for computation of Index closure. The use of Index

Closure Algorithm prevents any intentional manipulation of the closing index value.

Maintenance of SENSEX

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One of the important aspects of maintaining continuity with the past is to update the base

year average. The base year value adjustment ensures that replacement of stocks in Index,

additional issue of capital and other corporate announcements like rights issue etc. do not

destroy the historical value of the index. The beauty of maintenance lies in the fact that

adjustments for corporate actions in the Index should not per se affect the index values.

The BSE Index Cell does the day-to-day maintenance of the index within the broad index

policy framework set by the BSE Index Committee. The BSE Index Cell ensures that

SENSEX and all the other BSE indices maintain their benchmark properties by striking a

delicate balance between frequent replacements in index and maintaining its historical

continuity. The BSE Index Committee comprises of capital market expert, fund managers,

market participants and members of the BSE Governing Board.

On-Line Computation of the Index

During trading hours, value of the Index is calculated and disseminated on real time basis.

This is done automatically on the basis of prices at which trades in Index constituents are

executed.

Index Review Frequency

The BSE Index Committee meets every quarter to discuss index related issues. In case of a

revision in the Index constituents, the announcement of the incoming and outgoing scrips is

made six weeks in advance of the actual implementation of the revision of the Index.

Constituents of BSE sensex-

1 Reliance Industries 16 DLF

2 ONGC 17 Hero Honda

3 Bharti Airtel 18 Dr Reddys

4 Tata Consultancy 19 Tata motors

5 Infosys tech 20 Tata steel

6 Reliance Communication 21 Bajaj auto

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7 Wipro 22 GAIL

8 ICICI Bank 23 Maruti udyog

9 ACC 24 Sun pharma

NSE - NIFTY

The Organization

The National Stock Exchange of India Limited has genesis in the report of the High

Powered Study Group on Establishment of New Stock Exchanges. It recommended

promotion of a National Stock Exchange by financial institutions (FIs) to provide access to

investors from all across the country on an equal footing. Based on the recommendations,

NSE was promoted by leading Financial Institutions at the behest of the Government of

India and was incorporated in November 1992 as a tax-paying company unlike other stock

exchanges in the country.

The following years witnessed rapid development of Indian capital market with

introduction of internet trading, Exchange traded funds (ETF), stock derivatives and the

first volatility index - IndiaVIX in April 2008, by NSE.

August 2008 saw introduction of Currency derivatives in India with the launch of Currency

Futures in USD INR by NSE. Interest Rate Futures was introduced for the first time in

India by NSE on 31st August 2009, exactly after one year of the launch of Currency

Futures.

With this, now both the retail and institutional investors can participate in equities, equity

derivatives, currency and interest rate derivatives, giving them wide range of products to

take care of their evolving needs.

NSEs mission is setting the agenda for change in the securities markets in India. The NSE

was set-up with the main objectives of:

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establishing a nation-wide trading facility for equities, debt instruments and

hybrids,

ensuring equal access to investors all over the country through an appropriate

communication network,

providing a fair, efficient and transparent securities market to investors using

electronic trading systems,

enabling shorter settlement cycles and book entry settlements systems, and

meeting the current international standards of securities markets.

The standards set by NSE in terms of market practices and technology have become

industry benchmarks and are being emulated by other market participants. NSE is more

than a mere market facilitator. Its that force which is guiding the industry towards new

horizons and greater opportunities.

NSE Milestones

November 1992 Incorporation

April 1993 Recognition as a stock exchange

April 1996 Launch of S&P CNX Nifty

June 1996 Establishment of Settlement Guarantee Fund

November 1996Setting up of National Securities Depository Limited, first depository

in India, co-promoted by NSE

February 2000 Commencement of Internet Trading

June 2000 Commencement of Derivatives Trading (Index Futures)

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June 2001 Commencement of trading in Index Options

July 2001 Commencement of trading in Options on Individual Securities

November 2001 Commencement of trading in Futures on Individual Securities

August 2008 Launch of Currency Derivatives

August 2009 Launch of Interest Rate Futures

November 2009 Launch of Mutual Fund Service System

S&P CNX Nifty

S&P CNX Nifty is a well diversified 50 stock index accounting for 22 sectors of the

economy. It is used for a variety of purposes such as benchmarking fund portfolios, index

based derivatives and index funds.

S&P CNX Nifty is owned and managed by India Index Services and Products Ltd. (IISL),

which is a joint venture between NSE and CRISIL. IISL is Indias first specializedcompany focused upon the index as a core product. IISL has a Marketing and licensing

agreement with Standard & Poors (S&P), who are world leaders in index services.

The total traded value for the last six months of all Nifty stocks is approximately 52% of

the traded value of all stocks on the NSE

Nifty stocks represent about 63% of the Free Float Market Capitalization as on Dec 31,

2009.

Impact cost of the S&P CNX Nifty for a portfolio size of Rs.2 crore is 0.10%

S&P CNX Nifty is professionally maintained and is ideal for derivatives trading

From June 26, 2009, S&P CNX Nifty is computed based on free float methodology.

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Constituents of Nifty-

1 Reliance Industries 26 Gujarat Ambuja

2 ONGC 27 Cipla

3 Bharti Airtel 28 Siemens

4 Tata Consultancy 29 Ranbaxy

5 Infosys tech 30 NTPC

6 Reliance Communication 31 ITC

7 Wipro 32 VSNL

8 ICICI Bank 33 Zee Entertainment

9 ACC 34 MTNL

10 BHEL 35 HPCL

11 SBI 36 Dabur

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12 Hindalco 37 IPCL

13 HLL 38 Jet Airways

14 L&T 39 Oriental Bank

15 HDFC 40 Glaxo Smith

16 Satyam computers 41 Tata power

17 Hero Honda 42 BPCL

18 Dr Reddys 43 Reliance energy

19 Tata motors 44 Punjab National Bank

20 Tata steel 45 ABB

21 Bajaj auto 46 Hindalco

22 GAIL 47 National Alu

23 Maruti udyog 48 M&M

24 Sun pharma 49 Seagrams

25 Grasim industries 50 HCL tech

Tests and Results

Test for Stationarity-

A time series is said to be stationary if its mean and variance are constant over time and the

value of the covariance between the two time periods depends only on the distance or gap

or lag between the two time periods and not the actual time at which the covariance is

computed. Tests for stationarity are routinely applied to highly persistent time series.

Following Kwiatkowski, Phillips, Schmidt and Shin (1992), standard stationarity employs a

rescaling by an estimator of the long-run variance of the (potentially) stationary series. Test

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for stationarity is important in case of time series data because a nonstationary time series

will have time varying mean or a time-varying variance or both. Hence the results cannot

be extrapolated for the entire population.

The test for stationarity can be done using Unit Root Test. It is due to the fact that = 1. If

however, || 1, that is if the absolute value of is less than one, then it can be shown that

the time series is stationary.

Given that in most situations only one observation is available at a given time, stationarity

ensures that all parts of the series are like the other parts, which allows us to estimate the

needed parameters. Therefore, the mean, the variance and the covariance of the series are

not functions of time and depend rather on the lag between the observations (the difference

between the times at which two observations were recorded). To summarize, if Xt is a

discrete time series, its distribution is described by its first two ments, which under

stationarity must depend only on the lag:

Since all time series data sets contain either deterministic or stochastic trends (or both), unit

root tests and stationarity tests are a way of determining which kind of trends are present in

the data. If only deterministic trends are present, then the series can be seen as being

generated by some non-random, pre-determined function of time with

some random error thrown in. On the other hand, if stochastic trends are present, then the

generating model of the series combines a starting value and a sequence of random

innovations with zero mean and constant variance, which forms a more dynamic structure.

In this case, each observation depends on its history of past random innovations, which

greatly impact its current value. Thus, in the case of stochastic trends the value of a future

observation depends on the values of present and past observations.

Augmented Dickey Fuller Test-

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An augmented Dickey-Fuller test is a test for a unit root in a time series sample. An

augmented Dickey-Fuller test is a 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.

Under the Dickey-Fuller test the null hypothesis that = 0, the estimated t value of the

coefficient of Yt-1 in follows the (tau) statistic. The values are arrived from Monte Carlo

simulation. This test is conducted by augmenting the preceding three equations by adding

the lagged values of the dependent variable Yt.

So the required regression is:

Yt= 1 + 2 t + Yt-1+ i Yt-i + t

Where is a constant, the coefficient on a time trend and p the lag order of the

autoregressive process. Imposing the constraints = 0 and = 0 corresponds to modeling a

random walk and using the constraint = 0 corresponds to modeling a random walk with a

drift.

By including lags of the order p the ADF formulation allows for higher-order

autoregressive processes. This means that the lag length p has to be determined when

applying the test. One possible approach is to test down from high orders and examine the

t-values on coefficients. An alternative approach is to examine information criteria such as

the Akaike information criterion, Bayesian information criterion or the Hannon Quinn

criterion.

The unit root test is then carried out under the null hypothesis = 1 against the alternative

hypothesis of < 1. Once a value for the test statistic

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Computed it can be compared to the relevant critical value for the Dickey-Fuller Test. If the

test statistic is less than the critical value then the null hypothesis of = 1 is rejected and no

unit root is present

Where t is a pure white noise error term and the number of lagged difference terms to

include is often determined empirically, the idea being to include enough terms so that the

error term in is serially uncorrelated. Dickey and Fuller (1979) found that the distributions

of the t-statistics for the models given above are skewed to the left and have critical values

that are quite large and negative. That means that if the standard t-distributions were used

during testing; we would tend to over-reject the null hypothesis.

One important element in the ADF test is the number of lags present in the model. It has

been observed that the number of lagged factors has a great impact on the size and power

properties of the ADF test and therefore it is important to precisely determine how many

should be included in the model.

Some advocate starting with a large number of lags, estimating their coefficients and

eliminating the ones than are statistically insignificant at the chosen level. This process

would continue until no insignificant terms are left in the model. we can include

deterministic trends in the models (linear or non-linear) and the analysis goes along the

same lines as in the case of the DF variants. The only modification is, once again, the

presence of the lagged terms, which has to be determined with relatively high accuracy for

the unit root tests to be effective.

Then the test statistic T*(bOLS -1) has a known, documented distribution. Its value in a

particular sample can be compared to that distribution to determine a probability that the

original sample came from a unit root autoregressive process; that is, one in which b=1.

Properties and Characteristics of Unit Root Processes

Shocks to a unit root process have permanent effects, they do not decay

Non-stationary processes have no long-run means to revert to after a shock

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Their variance is time dependent and it goes to infinity as it goes to infinity

I(1) processes can be rendered stationary and used for OLS estimation by taking

their first differences yt = yt y

First the stationarity has been checked for the closing values of stock markets and

settlement price of exchange market. If they are not stationary, then they are converted in

logarithmic values in order to make them in a continuous form. And if yet the time series

are not stationary, then daily returns are identified as the difference in the natural logarithm

of the closing index value for the two consecutive trading days .It can be presented as:

or

Where is logarithmic daily return at time t. and are daily prices of an asset at

two successive days, t-1 and t respectively.

In order to do time series analysis, transformation of original series is required depending

upon the type of series when the data is in the level form. The series of return was

transformed by taking natural logarithm. There are two advantages of this kind oftransformation of the series. First it eliminates the possible dependence of changes in stock

price index on the price level of the index. Second, the change in the log of the stock price

index yields continuously compounded series.

In the sample time series data i.e., BSE Sensex, NSE Nifty and Exchange Rate data have

been tested for their stationarity and the results are as follows

NIFTY-

LagsADF T

Statistic

1% Significance

value

5% Significance

value

10%

Significance

value

1 -47.87083 -2.566521 -1.941037 -1.616556

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2 -40.58591 -2.566521 -1.941037 -1.616556

3 -31.87316 -2.566521 -1.941037 -1.616556

4 -28.52705 -2.566521 -1.941037 -1.616556

5 -25.85936 -2.566521 -1.941037 -1.616556

6 -24.74234 -2.566521 -1.941037 -1.616556

7 -24.03868 -2.566521 -1.941037 -1.616556

0 -62.48830 -2.566521 -1.941037 -1.616556

In the analysis we find that the calculated Tau statistic is significant even at 1%

significant level

Hence we can conclude that the data set (NSE Nifty) is stationary at first difference.

Exchange Rate-

LagsADF T

Statistic

1% Significance

value

5% Significance

value

10%

Significance

value

1 -57.28325 -2.566521 -1.941037 -1.616556

2 -47.72631 -2.566521 -1.941037 -1.616556

3 -41.01345 -2.566521 -1.941037 -1.616556

4 -35.61721 -2.566521 -1.941037 -1.616556

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5 -30.38839 -2.566521 -1.941037 -1.616556

6 -28.16267 -2.566521 -1.941037 -1.616556

7 -27.03275 -2.566521 -1.941037 -1.616556

0 -84.83897 -2.566521 -1.941037 -1.616556

The log naturals of Exchange rate is found to be stationary at 1% significance level

using Augmented Dickey Fuller test

The regression equation showed that the variables are stationary at 1% critical

value.

SENSEX

LagsADF T

Statistic

1% Significance

value

5% Significance

value

10%

Significance

value

1 -47.43808 -2.566521 -1.941037 -1.616556

2 -40.74394 -2.566521 -1.941037 -1.616556

3 -32.83651 -2.566521 -1.941037 -1.616556

4 -28.26082 -2.566521 -1.941037 -1.616556

5 -26.08505 -2.566521 -1.941037 -1.616556

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6 -24.84825 -2.566521 -1.941037 -1.616556

7 -24.23758 -2.566521 -1.941037 -1.616556

0 -60.11125 -2.566521 -1.941037 -1.616556

The data set of BSE Sensex is tested for stationarity using Augmented Dickey

Fuller test

The Test showed that the data is Stationary at 1st difference

The test is stationary at 1% critical value.

Testing for the distribution

A frequency distribution is a list of the values that a variable takes in a sample. It is

usually a list, ordered by quantity, showing the number of times each value appears.

Frequency distribution is said to be skewed when its mean and median are different. The

kurtosis of a frequency distribution is the concentration of scores at the mean, or how

peaked the distribution appears if depicted graphicallyfor example, in a histogram. If the

distribution is more peaked than the normal distribution it is said to be leptokurtic; if less

peaked it is said to be platykurtic.

Normal distribution

The importance of the normal distribution as a model of quantitative phenomena in the

natural and behavioral sciences is due to the central limit theorem. Many psychological

measurements and physical phenomena (like photon counts and noise) can be approximated

well by the normal distribution. While the mechanisms underlying these phenomena are

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often unknown, the use of the normal model can be theoretically justified by assuming that

many small, independent effects are additively contributing to each observation.

The normal distribution also arises in many areas of statistics. For example, the sampling

distribution of the sample mean is approximately normal, even if the distribution of the

population from which the sample is taken is not normal. In addition, the normal

distribution maximizes information entropy among all distributions with known mean and

variance, which makes it the natural choice of underlying distribution for data summarized

in terms of sample mean and variance. The normal distribution is the most widely used

family of distributions in statistics and many statistical tests are based on the assumption of

normality. In probability theory, normal distributions arise as the limiting distributions of

several continuous and discrete families of distributions.

While statisticians and mathematicians uniformly use the term normal distribution for

this distribution, physicists sometimes call it a Gaussian distribution and, because of its

curved flaring shape, social scientists refer to it as the bell curve. Feller (1968) uses the

symbol for in the above equation, but then switches to in Feller (1971).

De Moivre developed the normal distribution as an approximation to the binomial

distribution, and it was subsequently used by Laplace in 1783 to study measurement errors

and by Gauss in 1809 in the analysis of astronomical data.

The normal distribution is implemented in Mathematica as Normal Distribution [mu,

sigma]. The so-called standard normal distribution is given by taking =0 and 2=1 in a

general normal distribution. An arbitrary normal distribution can be converted to a standard

normal distribution by changing variables to , yielding

Normal distributions have many convenient properties, so random variates with unknown

distributions are often assumed to be normal, especially in physics and astronomy.

Although this can be a dangerous assumption, it is often a good approximation due to a

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surprising result known as the central limit theorem. This theorem states that the mean of

any set of variates with any distribution having a finite mean and variance tends to the

normal distribution. Many common attributes such as test scores, height, etc., follow

roughly normal distributions, with few members at the high and low ends and many in the

middle.

Because they occur so frequently, there is an unfortunate tendency to invoke normal

distributions in situations where they may not be applicable. As Lippmann stated,

Everybody believes in the exponential law of errors: the experimenters, because they think

it can be proved by mathematics; and the mathematicians, because they believe it has been

established by observation (Whittaker and Robinson 1967, p. 179).

Among the amazing properties of the normal distribution are that the normal sum

distribution and normal difference distribution obtained by respectively adding and

subtracting variates X and Y from two independent normal distributions with arbitrary

means and variances are also normal!

The data is tested for the distribution that it follows and the results are as follows

Testing data for distribution- BSE Sensex

Statistics

N 1982

Mean -0.000871787

Median -0.001574715

Std. Deviation 0.013910821

Variance 0.000193511

Skewness 0.697783705

Kurtosis 5.990656093

Minimum -0.079310971

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Maximum 0.11809177

Sum -1.29896276

Testing data for distribution- Exchange rate

Statistics

N 1982

Mean 4.86383E-05

Median 4.32641E-05


Variance 5.72045E-05



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Minimum -0.07349314

Maximum 0.064866809

Sum 0.072471089

Testing data for distribution- NSE Nifty

Statistics

N 1982

Mean -0.00081295

Median -0.000747853


Variance 0.00020997


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Minimum -0.10247349

Maximum 0.13053862

Sum -1.211295894

Interpretation-

As can be seen from the above results the data sets of BSE Sensex, NSE Nifty and

Exchange Rate follows normal distribution. Hence the data is capable for further

testing.

The variances of the data sets are 00 which confirms the data as to its stationarity.

Test for Co-integration Johnsons Co-integration test

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Co integration is an econometric technique for testing the correlation between stationary

time series variables. If two or more series are themselves stationary, and a linear

combination of them is stationary, then the series are said to be co integrated. For instance,

a stock market index and the price of its associated futures contract move through time,

each roughly following a random walk. Testing the hypothesis that there is a statistically

significant connection between the futures price and the spot price could now be done by

finding a co integrating vector. (If such a vector has a low order of integration it can signify

an equilibrium relationship between the original series, which are said to be co integrated

of an order below one).

It is often said that co integration is a means for correctly testing hypotheses concerning the

relationship between two variables having unit roots (i.e. integrated of order one). Series is

said to be integrated of order d if one can obtain a stationary series by differencing the

term d times. Such that:

C = Y dX (1)

is stationary, where the parameterdis the co integrating parameter that links the two time

series together. Further, the relationship Y = dX is considered to be a long-run, or

equilibrium, relationship suggested by economic theory. Under such circumstances these

markets are said to be co integrated. In contrast, lack of co integration implies that the

aforementioned variables have no link in the long-run. If two, or more series, are co

integrated, then there exist common factors that affect both and their permanent or secular

trends, and so the series will eventually adjust to equilibrium. The implications for

diversification are that even if, in the short-term the covariance between two series

indicates portfolio benefits, in the long-run

such benefits are spurious as the two series will eventually adjust to an equilibrium

relationship.

Hence, the existence of an equilibrium relationship between two or more variables,

assuming that they all are integrated individually to the same degree, requires that the co

integration between them is of a lower degree. That is if both X and Y are stationary I(1)

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the co integration vector must be stationary I(0). However, if X and Y are integrated to

different degrees, there will not be any parameterdthat satisfies Equation (1). Thus a long-

run relationship implies the requirement that the two variables should be (i) integrated to

the same degree and (ii) a linear combination of the two variables should exist which is

integrated to a lower degree than the individual variables.

Testing for co integration involves two steps.

1. Determine the degree of integration in each of the series, a unit root analysis.

2. Estimate the co integration regression and test for integration.

Assuming that each series has the same number of unit roots, the co integration test can

commence. Engle and Granger (1987) proposed seven tests for examining the hypothesis

that two time series are not co integrated. In co integration tests, the null hypothesis is non-

co integration. Only two are used here both based on the using an OLS regression in the

following form:

Y = a + bX + m (3)

where b is the estimator for the equilibrium parameter, d; a is the intercept; and m is the

disturbance term. The first of the two tests of co integration is based on the Co integrating

Regression Durbin-Watson (CRDW) statistic. As a simple rule of thumb for a quick

evaluation of the co integration hypothesis Banerjee et al (1986) proposed that: if the

CRDW statistic is smaller than the coefficient of determination (R2) the co integration

hypothesis is likely to be false; otherwise, when CRDW> R2, co integration may occur.

Alternatively the CRDW statistic can be evaluated against critical values developed by

Engle and Granger (1987), if the CRDW statistic exceeds the critical value, the null

hypothesis of non-co integration is rejected. Suggesting that the series are not co integrated.

The test for co integration involves the significance of the estimated l1 coefficient. Again

the null hypothesis is that the error terms are nonstationary and acceptance of this

hypothesis indicates that the series under investigation are not integrated. If the t-statistic

on the l1 coefficient exceeds the critical value, the m residuals from the co integration

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regression equation (3) are stationary and the variables X and Y are co integrated. Critical

values for this t statistic are given in Mackinnon (1991).

Test: Johnsons Co-integration test

Sample: 1 1982

Included observations: 1982

Series: 1. Exchange rate and NSE

2. Exchange rate and BSE

Lags interval: 1 to 4

Eigenvalue

Likelihood ratio

5% Critical

Value

1% Critical

Value

Hypothesized

No. of CE(s)

0.169822854329 540.15012665 19.96 24.60** None

At most 10.150445830265 252.22840788 9.24 12.97**

** indicates the rejection of integration between series at 1% and 5% significance

level

Interpretation-

As per Johnsons Co integration Test there exists no relationship between the two

series i.e., Exchange rate and NSE Nifty and Exchange rate and BSE Sensex

Through this test we can conclude that there is no long term relationship between exchange

rate and stock indices.

Test for Cross correlation

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Cross correlation is a standard method of estimating the degree to which two series are

correlated. Consider two series x(i) and y(i) where i=0,1,2...N-1. The cross correlation r at

delay d is defined as

Where mx and my are the means of the corresponding series. If the above is computed for

all delays d=0, 1, 2, N-1 then it results in a cross correlation series of twice the length as the

original series.

There is the issue of what to do when the index into the series is less than 0 or greater than

or equal to the number of points. (i-d < 0 or i-d >= N) The most common approaches are to

either ignore these points or assuming the series x and y are zero for i < 0 and i >= N. In

many signal processing applications the series is assumed to be circular in which case the

out of range indexes are wrapped back within range, ie: x(-1) = x(N-1), x(N+5) = x(5)

etc.

The period for cross correlation has been decreased to 1 st January 2004 to 30th June 2009.

This time period has been further segmented into duration of six months i.e. two equal

halfs of a year to find out if there is any short run relation or spillover from one variable to

another in this time period. 12 days lag is considered to see the lead/lag relation between

the two variables.

Abbreviations

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ER: Exchange rate

Sensex: BSE Sensex

Nifty: CNX Nifty

NEGATIVE LAG POSITIVE LAG

ER INDEX LEADING LAGGING

INDEX ER LAGGING LEADING

0.0910 0.0920 0.0889 0.0910 0.0910 0.1034

Correlation and T values of ER and SENSEX for the period 2004 to 2009

Lag

I half of 2004

Correlation

II half of

2004

Correlation

I half of

2005

Correlation

II half of

2005

Correlation

I half of

2006

Correlation

-12

0.042

(0.438)

-0.011

(0.117)

0.126

(1.326)

-0.011

(0.110)

0.038

(0.400)

-11

0.033

(0.344)

0.087

(0.935)

-0.093

(0.979)

0.123

(1.242)

-0.001

(0.011)

-10

0.085

(0.895)

0.099

(1.065)

-0.017

(0.181)

0.108

(1.091)

0.012

(0.128)

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-9

0.003

(0.032)

0.148

(1.609)

-0.235

(2.500)*

0.051

(0.520)

0.11

(1.170)

-8

-0.038

(0.404)

-0.081

(0.880)

-0.121

(1.287)

-0.022

(0.224)

0.063

(0.677)

-7

0.092

(0.979)

0.116

(1.261)

0.101

(1.086)

0.103

(1.062)

0.154

(1.656)

-6

0.064

(0.681)

0.066

(0.725)

0.072

(0.774)

-0.039

(0.402)

0.134

(1.457)

-5

0.182

(1.957)

0.102

(1.121)

0.069

(0.750)

-0.158

(1.646)

0.028

(0.304)

-4

0.065

(0.699)

-0.205

(2.253)*

0.023

(0.250)

-0.176

(1.833)

-0.108

(1.174)

-3

0.119

(1.293)

0.037

(0.411)

0.209

(2.272)*

-0.009

(0.095)

0.032

(0.352)

-2

0.011

(0.120)

-0.2

(2.222)*

0.111

(1.220)

-0.121

(1.274)

-0.172

(1.911)

-1

0.055

(0.598)

-0.002

(0.022)

-0.205

(2.253)*

-0.409

(4.351)*

0.01

(0.110)

0

0.002

(0.022)

-0.162

(1.820)

-0.076

(0.835)

-0.328

(3.489)*

-0.068

(0.756)

1 0.071 -0.055 -0.169 -0.307 -0.002

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2

-0.096

(1.043)

0.123

(1.367)

0.012

(0.132)

-0.079

(0.832)

0.023

(0.253)

3

0.136

(1.478)

-0.091

(1.011)

-0.004

(0.043)

-0.169

(1.779)

0.094

(1.033)

4

-0.029

(0.312)

0.044

(0.484)

0.019

(0.207)

-0.057

(0.594)

-0.084

(0.913)

5

-0.071

(0.763)

-0.03

(0.330)

-0.024

(0.261)

-0.139

(1.448)

-0.03

(0.326)

6

-0.086

(0.915)

0.069

(0.758)

-0.067

(0.720)

-0.052

(0.536)

-0.087

(0.946)

7

-0.049

(0.521)

-0.275

(2.989)*

0.008

(0.086)

-0.014

(0.144)

-0.084

(0.903)

8

-0.147

(1.564)

0.021

(0.228)

-0.035

(0.372)

-0.051

(0.520)

-0.031

(0.333)

9

0.01

(0.105)

-0.149

(1.620)

-0.075

(0.798)

-0.028

(0.286)

-0.039

(0.415)

10

-0.067

(0.705)

0.074

(0.796)

0.009

(0.096)

-0.136

(1.374)

-0.05

(0.532)

11

-0.116

(1.208)

-0.163

(1.753)

0.033

(0.347)

-0.054

(0.545)

-0.042

(0.447)

12-0.174 -0.034 -0.044 0.034 0.04

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(1.813) (0.362) (0.463) (0.340) (0.421)

L

a

g

II half

2006

Correla

tion

I half

2007

Correla

tion

II half

2007

Correla

tion

I half

2008

Correla

tion

II half

2008

Correla

tion

I half

2009

Correlat

ion

-

1

2

-0.047

(0.495)

0.024

(0.247)

-0.024

(0.255)

-0.006

(0.063)

-0.031

(0.326)

0.14

(1.273)

-

1

1

0.109

(1.147)

0.085

(0.885)

0.085

(0.914)

-0.09

(0.947)

-0.013

(0.137)

-0.063

(0.573)

-

1

0

-0.07

(0.745)

-0.111

(1.156)

-0.158

(1.699)

-0.039

(0.411)

0.076

(0.809)

0.006

(0.055)

-9 -0.081

(0.862)

0.003

(0.032)

0.066

(0.717)

0.054

(0.574)

-0.001

(0.011)

0.001

(0.009)

-8 -0.073

(0.777)

-0.056

(0.589)

0.049

(0.533)

-0.049

(0.521)

0.068

(0.723)

-0.107

(0.991)

-7 -0.007

(0.075)

-0.081

(0.862)

-0.025

(0.272)

0.044

(0.468)

0.036

(0.387)

-0.068

(0.636)

-6 0.006 -0.066 0.033 0.023 -0.147 -0.106

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(0.065) (0.702) (0.363) (0.247) (1.581) (0.991)

-5

0.06

(0.652)

0.114

(1.213)

-0.034

(0.374)

-0.224

(2.409)

*

0.053

(0.576)

0.14

(1.321)

-4 -0.024

(0.261)

-0.131

(1.409)

0.168

(1.846)

-0.143

(1.554)

0.151

(1.641)

-0.113

(1.076)

-3 -0.082

(0.891)

-0.014

(0.151)

-0.042

(0.467)

0.021

(0.228)

-0.103

(1.120)

-0.012

(0.114)

-2

-0.04

(0.440)

-0.092

(1.000)

-0.198

(2.176)

*

-0.183

(1.989)

-0.009

(0.099)

-0.05

(0.481)

-1

0.137

(1.505)

-0.076

(0.826)

-0.123

(1.382)

-0.165

(1.813)

0.032

(0.352)

-0.251

(2.413)

*

0

-0.123

(1.352)

0.12

(1.304)

-0.012

(0.135)

-0.111

(1.220)

-0.188

(2.066)

*

-0.009

(0.087)

1 -0.065

(0.714)

-0.017

(0.185)

-0.09

(1.011)

0.121

(1.330)

-0.004

(0.044)

0.022

(0.212)

2

-0.011

(0.121)

-0.106

(1.152)

-0.062

(0.689)

-0.023

(0.250)

-0.192

(2.110)

*

-0.107

(1.029)

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3 0.126

(1.370)

0.049

(0.527)

-0.16

(1.778)

0.02

(0.217)

0.08

(0.870)

-0.05

(0.476)

4 -0.01

(0.109)

-0.151

(1.624)

0.049

(0.538)

0.03

(0.326)

-0.003

(0.033)

0.081

(0.771)

5 0.063

(0.685)

-0.079

(0.840)

-0.048

(0.527)

0.036

(0.387)

0.014

(0.152)

0.138

(1.302)

6 0.027

(0.290)

0.086

(0.915)

0.032

(0.352)

0.068

(0.731)

0.065

(0.699)

0.063

(0.589)

7 -0.027

(0.290)

-0.099

(1.053)

-0.09

(0.978)

-0.006

(0.064)

-0.051

(0.548)

-0.004

(0.037)

8

0.073

(0.777)

-0.089

(0.937)

0.006

(0.065)

0.008

(0.085)

0.225

(2.394)

*

0.046

(0.426)

9 -0.127

(1.351)

0.009

(0.095)

0.101

(1.098)

0.01

(0.106)

-0.014

(0.149)

-0.139

(1.287)

1

0

-0.086

(0.915)

-0.009

(0.094)

0.025

(0.269)

-0.058

(0.611)

-0.095

(1.011)

0.194

(1.780)

1

1

-0.065

(0.684)

0.04

(0.094)

0.122

(1.312)

0.183

(1.926)

-0.027

(0.284)

0.054

(0.491)

1

2

0.174

(1.832)

-0.002

(0.021)

0.171

(1.819)

-0.015

(0.156)

0.028

(0.295)

0.083

(0.755)

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Numbers with in brackets indicate T values = correlation/ standard error * indicates t

values greater than 2, @ 5% significance level

Interpretation:

From the above table, it is clear that in first half of 2004, T value for all the leads and

lags is not statistically significant. So there is no impact of ER on sensex and vice

versa.

In the second half of 2004 T value at -2 lag and at -4 lag is significant. This shows that

ER at zero date has an inverse effect on second and fourth days share prices and T

value at + 7 lag is also significant. So SENSEX inversely affects the ER.

In the first half of 2005 SENSEX is affected by ER on first, third and ninth day. In the

second half of 2005 on the same day and next day there was an inverse affect on Index

due to fluctuations in ER. And there was cyclical relationship between the variables

during this period.

In the year 2006 and in first half of 2007, ER and SENSEX are not affected by each

other. In the second half 2007 ER affects SENSEX on the second day.

In the first half of 2008 ER leads SENSEX at five day lag and SENSEX leads ER at

five day lag. In the second half of 2008 SENSEX leads ER at the same day and at +2

and +8 days lag.

In the first half of 2009 fluctuations in ER are reflected in SENSEX on the next day.

So finally we can find that there is no systematic pattern of lead or lag between the

variables in this period.

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Correlation and T values of ER and NIFTY for the period 2004 to 2009

La

gI half of

2004

Correlatio

n

II half of

final report body naresh fresh

Documents