1.) RESEARCH METHODOLOGY

Problem Statement

In the last two decades, globalization, interlinkages of the capital markets, gradual

eradication of capital inflow barriers and the implementation of more flexible exchange

rate mechanism in developed as well as transition economies, created a systematic

interdependency between and within the stock and foreign exchange markets. The

individual have very vague idea about such relationship between two markets. Thus,

investigating the relationship between stock prices and exchange rates has received

unprecedented attention in the literature. A number of studies have empirically examined

the relationship between the stock and foreign exchange markets. This study explores the

evidence of relationship between exchange rates and stock prices and also lead lag

relationship between exchange rates and stock prices. We use a three-step

framework for examining dynamic relationships between exchange rates and stock index.

Literature Review

Apte (2001) investigated the relationship between the volatility of the stock market and

the nominal exchange rate of India by using the EGARCH specifications on the daily

closing USD/INR exchange rate, BSE 30 (Sensex) and NIFTY-50 over the period 1991

to 2000. The study suggests that there appears to be a spillover from the foreign exchange

market to the stock market but not the reverse.

Bhattacharya and Mukharjee (2002) studied the nature of causal relation between the

stock market, exchange rate, foreign exchange reserves and value of trade balance in

India from 1990 to 2001 by applying the co-integration and long-run Granger Non-

causality tests. The study suggests that there is no causal linkage between stock prices

and the three variables under consideration.

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To examine the dynamic linkages between the foreign exchange and stock markets for

India, Nath and Samanta (2003) employed the Granger causality test on daily data during

the period March 1993 to December 2002. The empirical findings of the study suggest

that these two markets did not have any causal relationship. When the study extended its

analysis to verify if liberalization in both the markets brought them together, it found no

significant causal relationship between the exchange rate and stock price movements,

except for the years 1993, 2001 and 2002 during when a unidirectional causal influence

from stock index return to return in forex market is detected and a very mild causal

influence in the reverse direction is found in some years such as 1997 and 2002.

Yamini Karmarkar and G Kawadia tried to investigate the relationship between RS/$

exchange rate and Indian stock markets. Five composite indices and five sectoral indices

were studied over the period of one year: 2000. the results indicated that exchange rate

has high correlation with the movement of stock markets.

Research Objectives

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

i) Investigating the relationship between the foreign exchange market and stock market

in India. To see that weather there is a significant relationship or dynamic linkage

between the two markets.

ii) To find out which variable is leading and which variable is lagging. The lead-lag relationship illustrates how well the two markets are linked, and how fast one market reflects new information from the other. If relation between foreign exchange market and stock market exist, then it is possible that investor may use this information to predict the exchange rate movement or indices movement.

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Hypothesis

H0: There is no significant relation between stock prices and exchange rates

H1: There is significant relation between stock prices and exchange rates

Research Design

The study type is Descriptive because this research helps to find out the meaning out of

the secondary data, but not the cause-and-effect (causal) linkages among its different

elements.

The Sample

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.

Sources of Data

Primary Data-nil

Secondary sources- The study is based on the secondary data collected from the official

website of BSE, NSE and Exchange Rate data from exchangeate.com.

Period of the Study

Daily closing values of BSE Sensex, CNX Nifty and exchange rates of Rupee/Dollar are

considered from 1-1-2001 to 31-3-2009.

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Statistical Tools used in the study

Augmented Dickey-Fuller test (Unit Root Test for stationarity of data)

Johnson co integration test (Test for long run relationship)

Cross Correlation (Test for short run relationship)

Scope of the study

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.

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.

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Data and Methodology

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 series span the period from 1st January 2001 to 31st March 2009. The daily stock

index and INR/USD returns are continuously compounded rate of return, computed as the

first difference of the natural logarithm of the daily stock index and INR/USD exchange

rate value.

The stationary status of series should be tested when investigating the relationship

between exchange rate and stock market price. In order to test the unit roots i.e.

stationarity in the Sensex, Nifty and INR/USD exchange rates, the study employ

augmented Dickey and Fuller (ADF) test. If the findings of ADF test suggests that the

series are integrated of order one, Johansen co integration tests methodologies would be

used to determine whether any co integration between stock and exchange market

variables exists or not.

Further Cross Correlation method would be used on fragmented data, each of size six

months, from 2004 to 2009 to determine any lead or lag relation. The analysis has been

performed using MS-Excel and E-view.

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2.) Introduction

Globalization and financial liberalization in India have brought about battery of changes

in the 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 1980’s and early 1990’s 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.

In the present scenario, interesting results are emerging particularly for the developing

countries where the markets are experiencing new relationships between money markets,

forex markets, capital markets, international events, oil prices, WTO agreements etc

which were not perceived earlier. The analysis on stock markets is important as it is

considered as the most sensitive segment of the economy and through this segment the

country’s exposure to the outer world is most readily felt. The impact of fluctuation in

exchange rate on domestic companies, companies importing or exporting and on multi

national corporations with the degree of exposure is increasing in each case respectively.

The movements in exchange rate indirectly affect the value and hence the stock prices of

these companies. The value of the company is affected due to the forex exposures namely

Transaction exposures, translation exposure and economic exposure.

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 it’s 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.

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On the other hand, currency devaluation could either raise or decrease a firm’s stock

prices. This depends on the nature of the firm’s 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 firm’s stock price.

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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.

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.

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Quotations

An exchange rate quotation is given by stating the number of units of a price currency

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

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

and the unit currency is JPN.

Quotes

Direct quote is a quote using a country’s 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 country’s 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 currency’s 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.

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A fixed rate is that rate that has direct convertibility towards another currency. Here, the

currency is backed one to one by foreign reserves.

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

client markets. 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.

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• Individuals and firms conducting commercial or investment Transactions:

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 country’s 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 forward or swap basis, which

requires settlement at some designated future date.

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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.

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

world’s 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%, and Tokyo 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,

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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

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 1990’s

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

Source: BIS Triennial Survey 2007

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.

3 banks account for 75% of turnover in Switzerland

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9 banks account for 75% of turnover in Japan

Source: BIS Triennial Survey 2007

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

Average Daily Turnover by Currency

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

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

Source: BIS Triennial Survey 2007

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

Source: BIS Triennial Survey 2007

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

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• Inflation levels and trends

• Economic growth and health

Political conditions:

• Political upheaval and political instability

• Relation between two countries

Market psychology:

• Flights to quality

• Economic numbers

• Long-term trends

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4.) Indian FX Market

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

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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

generated huge 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

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

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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 in this 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

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foreign banks offer NDFs, but Indian banks are barred from doing so. These markets

have evolved for the Indian rupee following 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 FII’s 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

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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.

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.

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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.

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.

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Importance of Capital Market

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 economy’s 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 major

forces 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

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

the 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 country’s

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 country’s 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.

31

6.) BSE - SENSEX

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. BSE’s 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 world’s prominent exchanges, Deutsche

Börse 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 BSE’s services in raising resources from the

capital market.

Today, BSE is the world’s number 1 exchange in terms of the number of listed

companies and the world’s 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 31 st July

09.

32

The BSE Index, SENSEX, is India’s first and most popular stock market benchmark

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

sectors. The 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 Börse 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 ‘iShares®BSE 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).

 

33

 

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 exchange’s

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

inclusive 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

34

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

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

35

SENSEX Calculation Methodology

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

36

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-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.

 

37

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

for computation of the Index closure. If a SENSEX constituent has not traded at all in a

day, then its last day’s 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

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

38

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

7 Wipro 22 GAIL

8 ICICI Bank 23 Maruti udyog

9 ACC 24 Sun pharma

10 BHEL 25 Grasim industries

39

11 SBI 26 Gujarat Ambuja

12 Hindalco 27 Cipla

13 HLL 28 Siemens

14 L&T 29 Ranbaxy

15 HDFC 30 NTPC

7.) NSE - NIFTY

40

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.

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

NSE was set-up with the main objectives of:

• 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,

41

• 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. It’s 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)

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

42

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 India’s first

specialized company focused upon the index as a core product. IISL has a Marketing and

licensing agreement with Standard & Poor’s (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.

43

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

44

11 SBI 36 Dabur

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

8.) Tests and Results

Test for Stationarity-

45

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 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 ρ = 1.

If however, |ρ| ≤ 1, that is if the absolute value of ρ 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:

E[Xt] = μt = μ,

V ar(Xt) = σ2t = σ2,

Cov(Xt, Xt−s) = E[(Xt − μt)(Xt−s − μt−s)] = σt,t−s = σ|s|,

Corr(Xt, Xt−s) = σt,t−s σ2 = ρt,t−s.

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

46

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-

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 ť (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 = β1 + β2 t + §Yt-1+ α i Σ Yt-i + ε t

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

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

a random walk and using the constraint β = 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

47

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 γ = 1 against the alternative

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

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 γ = 1 is rejected

and no unit root is present

Where ε 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.

48

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

• 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 of

transformation of the series. First it eliminates the possible dependence of changes in

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

stock price index yields continuously compounded series.

49

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

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-

Lags ADF T 1%

Significance

5%

Significance

10%

Significance

50

Statistic value value 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

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 –

51

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

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.

Interpretation:

52

In Table Nifty, Exchange rate and Sensex, ADF statistics of the series shows absence of

unit root (i.e. δ=0) in the series exceeds the .

1. Nifty 0 lag (-62.48830 > -2.566521) and 7 lag (-24.03868 > -2.566521)

2. Exchange rate 0 lag (-84.83897 > -2.566521) and 7 lag (-27.03275 > -2.566521)

3. Sensex 0 lag (-60.11125 > -2.566521) and 7 lag (-24.23758 > -2.566521)

Thus the series are now stationary. And trend coefficients of both the series are also

statistically insignificant, that shows the absence of trend in both the series.

53

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 graphically—for 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 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

54

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 μ=0 and σ2=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

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

55

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

Maximum 0.11809177

Sum -1.29896276

56

Testing data for distribution- Exchange rate

Statistics

N 1982

Mean 4.86383E-05

Median 4.32641E-05

Std. Deviation 0.007563368

Variance 5.72045E-05

Skewness 0.012718054

Kurtosis 24.49873234

Minimum -0.07349314

Maximum 0.064866809

57

Sum 0.072471089

Testing data for distribution- NSE Nifty

Statistics

N 1982

Mean -0.00081295

Median -0.000747853

Std. Deviation 0.014490351

Variance 0.00020997

Skewness 0.953871738

58

Kurtosis 9.916419963

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 – Johnson’s Co-integration test

59

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 parameter d is 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)

60

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

different degrees, there will not be any parameter d that 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

61

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

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: Johnson’s 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 Johnson’s 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

62

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 1st January 2004 to 30th June 2009.

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

half’s 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

63

ER: Exchange rate

Sensex: BSE Sensex

Nifty: CNX Nifty

NEGATIVE LAG POSITIVE LAG

ER – INDEX LEADING LAGGING

INDEX – ER LAGGING LEADING

Std. Error for each half at 0 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

0.0909 0.0890 0.0910 0.0940 0.0899

II half

2006

Correlat

ion

I half

2007

Correlat

ion

II half

2007

Correlat

ion

I half

2008

Correlat

ion

II half

2008

Correlat

ion

I half

2009

Correlat

ion

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

64

Lag

I half of

2004

Correlati

on

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)

-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)

65

-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

(0.772)

-0.055

(0.618)

-0.169

(1.857)

-0.307

(3.266)*

-0.002

(0.022)

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 -0.03 -0.024 -0.139 -0.03

66

(0.763) (0.330) (0.261) (1.448) (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

(1.813)

-0.034

(0.362)

-0.044

(0.463)

0.034

(0.340)

0.04

(0.421)

La II half I half II half I half II half I half

67

g 2006

Correl

ation

2007

Correl

ation

2007

Correla

tion

2008

Correla

tion

2008

Correla

tion

2009

Correla

tion

-12-0.047

(0.495

)

0.024

(0.247

)

-0.024

(0.255)

-0.006

(0.063)

-0.031

(0.326)

0.14

(1.273)

-110.109

(1.147

)

0.085

(0.885

)

0.085

(0.914)

-0.09

(0.947)

-0.013

(0.137)

-0.063

(0.573)

-10 -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

68

(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)

69

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)

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 -0.089 0.006 0.008 0.225 0.046

70

(0.777

)

(0.937

) (0.065) (0.085)

(2.394)

* (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)

10 -0.086

(0.915

)

-0.009

(0.094

)

0.025

(0.269)

-0.058

(0.611)

-0.095

(1.011)

0.194

(1.780)

11 -0.065

(0.684

)

0.04

(0.094

)

0.122

(1.312)

0.183

(1.926)

-0.027

(0.284)

0.054

(0.491)

12 0.174

(1.832

)

-0.002

(0.021

)

0.171

(1.819)

-0.015

(0.156)

0.028

(0.295)

0.083

(0.755)

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.

71

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 day’s 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.

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

72

La

g

I half

of

2004

Correl

ation

II half of

2004

Correlatio

n

I half of

2005

Correlatio

n

II half of

2005

Correlatio

n

I half of

2006

Correlatio

n

-12 0.039

(0.406

)

-0.011

(0.117)

0.101

(1.063)

0.004

(0.040)

0.047

(0.495)

-11 0.043

(0.448

)

0.119

(1.280)

-0.071

(0.747)

0.113

(1.141)

0.013

(0.138)

-10 0.1

(1.053

)

0.098

(1.054)

-0.032

(0.340)

0.112

(1.131)

-0.003

(0.032)

-9 -0.027

(0.284

)

0.135

(1.467)

-0.232

(2.468)*

0.041

(0.418)

0.094

(1.000)

-8 -0.01

(0.106

)

-0.078

(0.848)

-0.096

(1.021)

-0.012

(0.122)

0.072

(0.774)

-7 0.067

(0.713

)

0.108

(1.174)

0.104

(1.118)

0.117

(1.206)

0.171

(1.839)

73

-6 0.096

(1.021

)

0.075

(0.824)

0.049

(0.527)

-0.024

(0.247)

0.148

(1.609)

-5 0.17

(1.828

)

0.065

(0.714)

0.071

(0.772)

-0.16

(1.667)

0.031

(0.337)

-4 0.055

(0.591

)

-0.181

(1.989)

0.028

(0.304)

-0.179

(1.865)

-0.112

(1.217)

-3 0.146

(1.587

)

0.02

(0.222)

0.203

(2.207)*

0.004

(0.042)

0.013

(0.143)

-2 0.042

(0.457

)

-0.155

(1.722)

0.105

(1.154)

-0.146

(1.537)

-0.189

(2.100)*

-1 0.033

(0.359

)

-0.03

(0.337)

-0.165

(1.813)

-0.42

(4.468)*

0.012

(0.132)

0 0.036

(0.396

)

-0.17

(1.910)

-0.099

(1.088)

-0.365

(3.883)*

-0.072

(0.800)

1 0.04 -0.068 -0.152 -0.335 -0.002

74

(0.435

) (0.764) (1.670) (3.564)* (0.022)

2 -0.053

(0.576

)

0.129

(1.433)

0.018

(0.198)

-0.105

(1.105)

0.027

(0.297)

3 0.132

(1.435

)

-0.112

(1.244)

-0.009

(0.098)

-0.17

(1.789)

0.079

(0.868)

4 -0.046

(0.495

)

0.069

(0.758)

0.011

(0.120)

-0.075

(0.781)

-0.084

(0.913)

5 -0.051

(0.548

)

-0.026

(0.286)

-0.016

(0.174)

-0.149

(1.552)

-0.021

(0.228)

6 -0.069

(0.734

)

0.029

(0.319)

-0.063

(0.677)

-0.048

(0.495)

-0.074

(0.804)

7 -0.07

(0.745

)

-0.263

(2.859)

-0.002

(0.022)

-0.015

(0.155)

-0.118

(1.269)

75

8 -0.113

(1.202

)

0.03

(0.326)

-0.021

(0.223)

-0.041

(0.418)

-0.052

(0.559)

9 0.001

(0.011

)

-0.151

(1.641)

-0.084

(0.894)

-0.032

(0.327)

-0.03

(0.319)

10 -0.096

(1.011

)

0.069

(0.742)

0.01

(0.106)

-0.139

(1.404)

-0.05

(0.532)

11 -0.081

(0.844

)

-0.169

(1.817)

0.05

(0.526)

-0.063

(0.636)

-0.057

(0.606)

12 -0.161

(1.677

)

-0.053

(0.564)

-0.055

(0.579)

0.038

(0.380)

0.035

(0.368)

76

La

g

II half

2006

Correl

ation

I half

2007

Correl

ation

II half

2007

Correla

tion

I half

2008

Correla

tion

II half

2008

Correl

ation

I half

2009

Correla

tion

-11 0.109

(1.147

)

0.078

(0.813

)

0.053

(0.570)

-0.092

(0.968)

-0.016

(0.168

)

0.008

(0.073)

-10 -0.069

(0.734

)

-0.093

(0.969

)

-0.139

(1.495)

-0.045

(0.474)

0.091

(0.968

)

-0.004

(0.037)

-9 -0.079

(0.840

)

-0.002

(0.021

)

0.109

(1.185)

0.056

(0.596)

-0.002

(0.021

)

0.006

(0.056)

-8 -0.057

(0.606

)

-0.053

(0.558

)

0.063

(0.685)

-0.026

(0.277)

0.043

(0.457

)

-0.113

(1.046)

-7 -0.004

(0.043

)

-0.078

(0.830

)

-0.025

(0.272)

0.055

(0.585)

0.039

(0.419

)

-0.019

(0.178)

-6 -0.027

(0.290

)

-0.074

(0.787

)

0.008

(0.088)

0.018

(0.194)

-0.158

(1.699

)

-0.148

(1.383)

-5 0.073 0.087 -0.009 -0.241 0.081 0.159

77

(0.793

)

(0.926

) (0.099)

(2.591)

*

(0.880

) (1.500)

-4 -0.067

(0.728

)

-0.088

(0.946

)

0.146

(1.604)

-0.141

(1.533)

0.126

(1.370

)

-0.107

(1.019)

-3 -0.102

(1.109

)

-0.008

(0.086

)

-0.038

(0.422)

0.001

(0.011)

-0.113

(1.228

)

-0.047

(0.448)

-2 0.088

(0.967

)

-0.102

(1.109

)

-0.235

(2.582)

*

-0.181

(1.967)

0.007

(0.077

)

-0.024

(0.231)

-1 0.093

(1.022

)

-0.075

(0.815

)

-0.112

(1.258)

-0.172

(1.890)

0.043

(0.473

)

-0.222

(2.135)

*

0 -0.111

(1.220

)

0.103

(1.120

)

-0.014

(0.157)

-0.101

(1.110)

-0.169

(1.857

)

-0.063

(0.612)

1 -0.112

(1.231

)

0.004

(0.043

)

-0.091

(1.022)

0.132

(1.451)

-0.014

(0.154

)

0.003

(0.029)

2 0.035 -0.082 -0.083 -0.003 -0.154 -0.035

78

(0.385

)

(0.891

) (0.922) (0.033)

(1.692

) (0.337)

3 0.095

(1.033

)

0.039

(0.419

)

-0.145

(1.611)

0.027

(0.293)

0.076

(0.826

)

-0.032

(0.305)

4 0.029

(0.315

)

-0.141

(1.516

)

0.016

(0.176)

0.045

(0.489)

-0.02

(0.217

)

0.038

(0.362)

5 0.045

(0.489

)

-0.099

(1.053

)

-0.022

(0.242)

0.034

(0.366)

0.019

(0.207

)

0.179

(1.689)

6 -0.028

(0.301

)

0.063

(0.670

)

0.018

(0.198)

0.069

(0.742)

0.068

(0.731

-0.014

(0.131)

7 0.017

(0.183

)

-0.093

(0.989

)

-0.096

(1.043)

0.006

(0.064)

-0.038

(0.409

)

0.08

(0.748)

8 0.124

(1.319

)

-0.086

(0.905

)

0.061

(0.663)

-0.009

(0.096)

0.173

(1.840

)

0.022

(0.204)

79

9 -0.134

(1.426

)

0.025

(0.263

)

0.119

(1.293)

-0.002

(0.021)

-0.009

(0.096

)

-0.128

(1.185)

10 -0.058

(0.617

)

-0.028

(0.292

)

0.035

(0.376)

-0.022

(0.232)

-0.093

(0.989

)

0.179

(1.642)

11 -0.049

(0.516

)

0.018

(0.188

)

0.119

(1.280)

0.163

(1.716)

-0.018

(0.189

)

0.044

(0.400)

12 0.175

(1.842

)

0.012

(0.124

)

0.195

(2.074)

*

-0.008

(0.083)

0.021

(0.221

)

0.116

(1.055)

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

values greater than 2, @ 5% significance level.

Interpretation:

From the above tables, it is clear that in the year 2004, there is no relationship

between the variables. In the year 2005 there was cyclical relation between the

80

variables. In the year 2006 and first half 2007 there was no significant relationship

between the variables. In the second half 2007 ER affects NIFTY on the second day.

In the first half of 2008 ER leads NIFTY at five day length and NIFTY leads ER at

five day length. In the first half of 2009 fluctuations in ER are reflected in NIFTY 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. This also shows that SENSEX and NIFTY are moving in the

same direction.

9.) Findings-

Theory says that exchange rates should have a direct impact on the companies with

heavy import or export activities and thus affecting the profitability and hence the

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

Multi National Firms and an indirect effect through domestic firms.

81

As the index is nothing but weighted average of the share prices of various companies

from different sectors, the sensex has been considered to see the impact of ER on it.

Both Sensex and Nifty are considered to see where they move in the same direction

or not.

1.) After analyzing the data by using Johnson Co integration, we found that in the

long run Exchange Rate does not affect the share prices. The results show that there

was no significant relationship between the Exchange Rate and any index.

The possible reasons for such behavior could be as follows:

• It can be said that because of using only a single variable, namely exchange rate,

the impact on stock prices was not felt. If more of independent variables like

interest rates, money supply etc. could be added, then possibly a very good

relation could have been established.

• In reality, stock prices and exchange rate are affected by a myriad of factors such

as fiscal and monetary policy, interest rates, inflation, money supply, political

factors, international events, fundamental performance, forex reserves, BOP,

exchange control, etc.

• The non-existence of relationship may also be because of Indian markets not yet

being highly integrated or sensitive to the new information. Also the Indian

companies comparatively may not be exposed to a lot of forex exposure, like

companies in developed countries.

• Alternatively Indian managers are highly cautious and hedge to a good extent of

their forex exposure.

• High volatility introduced in the exchange market due to floating rate regime

nurtures the speculative activities, makes it difficult to pinpoint the precise effect

of exchange rates on stock prices.

• Another very important reason can be that Indian stocks are highly sentiment

driven and stocks of certain companies may start soaring for no reason. There are

82

few qualitative factors that influence stock prices like speculation and investor

confidence level.

2.) An attempt has been made to investigate lead-lag relationship, by using cross

correlation. The results for the both index and for exchange rate are as follow:

In the second half of 2005, there was very significant relationship between the

exchange rates and all the indices. During this period, all the indices have also

influenced the exchange rates at one day lag. There was also a very good cyclical

relationship of lead and lag between the variables.

The lead-lag relationship occurred between the variables during different periods

is because in India, though stock market investment does not constitute a very

significant portion of total household savings compared to other form of financial

assets, it may have a significant impact of exchange rate movement as FII

investment has played a dominant role. The control measures of RBI have shown

its influence from time to time and disrupted the relation between the two

variables.

For the market efficiency, it can be inferred from the analysis that for the given time

period a spillover effect or influence from foreign exchange market is seen on the stock

market, so it is not efficient in processing the information.

10.) Conclusions-

By using daily data, we have examined the long-run and short-run dynamics between

stock prices and exchange rates in India. Our main concerns were to examine whether

these links were affected by the existence of foreign exchange controls, floating rates

and raising value of Rupee and raising indices in India.

The following conclusions have been derived from our analysis:

83

• There is no significant cause and effect relationship between the two variables. As

the relationship occurred between the variables during different periods is because

of chance factor and not because of cause factor.

• Thus the results provide the evidence for the presence goods market or portfolio

approach.

Hence, we can reject the hypothesis that there is relationship between the exchange

rate and stock indices and the two are affected by various factors in spite of the

increasing integration between the two markets.

The outcome of the study is consistent with the findings of Apte (2001) and it also in

agreement with Nath and Samanta (2003).

In conclusion, in the era of increasing integration in financial markets one should take

sufficient care while implementing exchange rate policies. Furthermore, indications

are that the existence of foreign exchange restrictions does not isolate the domestic

capital markets. The general increase in international trade and the resultant increase

in economic integration have also increased financial integration and reduced the

benefit of international diversification.

10.) BIBLOGRAPHY -

Text Books

Basic Econometrics,

Damodar N. Gujarati and S Sangeetha, (Fourth Edition)

84

Research Methodology

Donald Cooper and Pamela Schindler , (Eighth Edition)

Financial markets and services

Gordon and Natrajan, (Second Edition)

Websites

• www.investopedia.com

• www.nseindia.com

• www.bseindia.com

• www.exchangerate.com

• www.bsi.com

• www.sebi.gov.in

• www.iciciresearch.com

• www.easy-forex.com

Reference:

• “Integration between Foreign Exchange and Capital Markets in India: An

empirical exploration" by Golka C Nath and G P Samanta, the ICFAI Journal of

Applied Finance vol. 9 No. 6, Pg. 29 to 40

85

• “Stock Prices and Exchange Rates interlinkages in emerging financial markets:

the Indian perspective” by Alok Kumar Mishra the ICFAI Journal of Applied

Finance vol.11 No.4,Pg. 31 to 48

• Dickey, D.A., and Fuller, W.A. (1981). Likelihood ratio statistics for

autoregressive time series with a unit root. Econometrica 49, 1057-1072.

• Johansen, S. and K. Juselius, 1990, “Maximum Likelihood Estimation and

Inference on Co integration - With Application to the Demand for Money,”

Oxford Bulletin of Economics and Statistics 52, 169-210.

Annexure

Sensex closing values for the period 2005

DATE CLOSING P1/P0

Log Normal ofP1/P0

31/5/2005 6715.11 1.0077 0.007730/5/2005 6663.55 0.9934 -0.006627/5/2005 6707.72 1.0055 0.005526/5/2005 6670.78 1.0111 0.011025/5/2005 6597.6 1.0049 0.004924/5/2005 6565.37 1.0039 0.003923/5/2005 6539.83 1.0062 0.0062

86

20/5/2005 6499.5 1.0032 0.003219/5/2005 6478.94 1.0050 0.004918/5/2005 6447 0.9971 -0.002917/5/2005 6466 0.9905 -0.009516/5/2005 6528.03 1.0119 0.011813/5/2005 6451.54 0.9992 -0.000812/5/05 6456.82 1.0018 0.001811/5/05 6445.13 0.9985 -0.001510/5/05 6454.71 0.9959 -0.00419/5/05 6481.35 1.0145 0.01446/5/05 6388.48 1.0045 0.00455/5/05 6359.65 1.0111 0.01114/5/05 6289.55 1.0117 0.01163/5/05 6216.77 1.0035 0.00352/5/05 6195.15 1.0066 0.0066

29/4/2005 6154.44 0.9794 -0.020928/4/2005 6284.2 1.0009 0.000927/4/2005 6278.5 0.9903 -0.009726/4/2005 6339.98 0.9941 -0.006025/4/2005 6377.85 1.0049 0.004922/4/2005 6346.57 1.0075 0.007521/4/2005 6299.2 1.0089 0.008820/4/2005 6243.74 1.0177 0.017619/4/2005 6134.86 0.9964 -0.003618/4/2005 6156.78 0.9853 -0.014815/4/2005 6248.34 0.9661 -0.034513/4/2005 6467.92 1.0005 0.000512/4/05 6464.61 1.0105 0.010411/4/05 6397.52 0.9873 -0.01278/4/05 6479.54 0.9899 -0.01017/4/05 6545.64 0.9908 -0.00926/4/05 6606.41 1.0086 0.00855/4/05 6550.29 0.9918 -0.00824/4/05 6604.42 1.0172 0.0170

31/3/2005 6492.82 1.0175 0.017330/3/2005 6381.4 1.0021 0.002129/3/2005 6367.86 0.9781 -0.022228/3/2005 6510.74 1.0105 0.010524/3/2005 6442.87 0.9982 -0.001823/3/2005 6454.46 0.9876 -0.012522/3/2005 6535.45 0.9818 -0.018421/3/2005 6656.69 0.9935 -0.0065

18/3/2005 6700.34 1.0046 0.004617/3/2005 6669.52 0.9885 -0.011516/3/2005 6746.88 0.9992 -0.000815/3/2005 6752.45 0.9915 -0.008514/3/2005 6810.04 0.9936 -0.006411/3/05 6853.73 0.9922 -0.007810/3/05 6907.65 1.0022 0.00219/3/05 6892.82 0.9968 -0.00328/3/05 6915.09 1.0052 0.00527/3/05 6878.98 1.0043 0.00434/3/05 6849.48 1.0095 0.00953/3/05 6784.72 1.0146 0.01452/3/05 6686.89 1.0054 0.00541/3/05 6651.08 0.9906 -0.0094

87

28/2/2005 6713.86 1.0219 0.021725/2/2005 6569.72 0.9993 -0.000724/2/2005 6574.21 0.9987 -0.001323/2/2005 6582.5 0.9990 -0.001022/2/2005 6589.41 1.0084 0.008321/2/2005 6534.68 0.9925 -0.007618/2/2005 6584.32 0.9992 -0.000817/2/2005 6589.29 0.9972 -0.002816/2/2005 6607.78 0.9907 -0.009415/2/2005 6670.06 0.9986 -0.001414/2/2005 6679.33 1.0069 0.006811/2/05 6633.76 1.0085 0.008510/2/05 6577.83 0.9976 -0.00249/2/05 6593.53 1.0075 0.00748/2/05 6544.77 1.0015 0.00157/2/05 6535.17 0.9874 -0.01264/2/05 6618.23 0.9997 -0.00033/2/05 6619.97 1.0138 0.01372/2/05 6530.06 0.9966 -0.00341/2/05 6552.47 0.9995 -0.0005

31/1/2005 6555.94 1.0213 0.021128/1/2005 6419.09 1.0288 0.028427/1/2005 6239.43 1.0124 0.012325/1/2005 6162.98 1.0093 0.009224/1/2005 6106.43 0.9876 -0.012520/1/2005 6183.24 1.0016 0.001619/1/2005 6173.32 0.9969 -0.003118/1/2005 6192.35 0.9997 -0.000317/1/2005 6194.07 1.0033 0.003314/1/2005 6173.82 0.9924 -0.007613/1/2005 6221.06 1.0194 0.019212/1/05 6102.74 0.9807 -0.019511/1/05 6222.87 0.9864 -0.013710/1/05 6308.54 0.9826 -0.01767/1/05 6420.46 1.0083 0.00836/1/05 6367.39 0.9858 -0.01435/1/05 6458.84 0.9711 -0.0293

Exchange Rates for the year 2005

DATE Rs/US$ P1/P0Log Normal of

P1/P04-Jan 43.493645-Jan 43.63293 1.0032 0.00326-Jan 43.78201 1.0034 0.00347-Jan 43.65672 0.9971 -0.0029

10-Jan 43.84261 1.0043 0.004211-Jan 43.72259 0.9973 -0.002712-Jan 43.56188 0.9963 -0.003713-Jan 43.53863 0.9995 -0.000514-Jan 43.58271 1.0010 0.0010

88

18-Jan 43.58263 1.0000 0.000020-Jan 43.75279 1.0039 0.003924-Jan 43.70195 0.9988 -0.001225-Jan 43.72211 1.0005 0.000526-Jan 43.66984 0.9988 -0.001227-Jan 43.67276 1.0001 0.000128-Jan 43.66156 0.9997 -0.00031-Feb 43.71259 1.0012 0.00122-Feb 43.33247 0.9913 -0.00873-Feb 43.32255 0.9998 -0.00027-Feb 43.44269 1.0028 0.00288-Feb 43.65185 1.0048 0.00489-Feb 43.65189 1.0000 0.000010-Feb 43.71111 1.0014 0.001411-Feb 43.74456 1.0008 0.000814-Feb 43.70121 0.9990 -0.001015-Feb 43.73176 1.0007 0.000716-Feb 43.70216 0.9993 -0.000717-Feb 43.71217 1.0002 0.000218-Feb 43.74031 1.0006 0.000622-Feb 43.67757 0.9986 -0.001423-Feb 43.62018 0.9987 -0.001324-Feb 43.6291 1.0002 0.000225-Feb 43.63162 1.0001 0.000128-Feb 43.60753 0.9994 -0.00061-Mar 43.67468 1.0015 0.00152-Mar 43.65224 0.9995 -0.00053-Mar 43.66786 1.0004 0.00044-Mar 43.64942 0.9996 -0.00047-Mar 43.6569 1.0002 0.00028-Mar 43.60364 0.9988 -0.00129-Mar 43.60555 1.0000 0.000010-Mar 43.52817 0.9982 -0.001811-Mar 43.48405 0.9990 -0.001014-Mar 43.49608 1.0003 0.000315-Mar 43.52628 1.0007 0.000716-Mar 43.56387 1.0009 0.000917-Mar 43.6074 1.0010 0.001018-Mar 43.59846 0.9998 -0.000221-Mar 43.66779 1.0016 0.001622-Mar 43.67592 1.0002 0.000223-Mar 43.7356 1.0014 0.001424-Mar 43.70206 0.9992 -0.000825-Mar 43.7043 1.0001 0.000128-Mar 43.73386 1.0007 0.000729-Mar 43.7519 1.0004 0.000430-Mar 43.74548 0.9999 -0.000131-Mar 43.67049 0.9983 -0.00171-Apr 43.68055 1.0002 0.00024-Apr 43.69338 1.0003 0.00035-Apr 43.76521 1.0016 0.00166-Apr 43.70378 0.9986 -0.00147-Apr 43.72772 1.0005 0.00058-Apr 43.67612 0.9988 -0.0012

11-Apr 43.66781 0.9998 -0.000212-Apr 43.7005 1.0007 0.0007

89

13-Apr 43.70595 1.0001 0.000114-Apr 43.69394 0.9997 -0.000315-Apr 43.77805 1.0019 0.001918-Apr 43.70308 0.9983 -0.001719-Apr 43.73003 1.0006 0.000620-Apr 43.65527 0.9983 -0.001721-Apr 43.72473 1.0016 0.001622-Apr 43.69043 0.9992 -0.000825-Apr 43.66869 0.9995 -0.000526-Apr 43.65923 0.9998 -0.000227-Apr 43.65038 0.9998 -0.000228-Apr 43.70642 1.0013 0.001329-Apr 43.52396 0.9958 -0.00422-May 43.50566 0.9996 -0.00043-May 43.58325 1.0018 0.00184-May 43.45578 0.9971 -0.00295-May 43.37845 0.9982 -0.00186-May 43.39546 1.0004 0.00049-May 43.47917 1.0019 0.001910-May 43.3979 0.9981 -0.001911-May 43.24308 0.9964 -0.003612-May 43.35403 1.0026 0.002613-May 43.39835 1.0010 0.001016-May 43.43531 1.0009 0.000917-May 43.4499 1.0003 0.000318-May 43.48287 1.0008 0.000819-May 43.48538 1.0001 0.000120-May 43.39281 0.9979 -0.002123-May 43.39335 1.0000 0.000024-May 43.39922 1.0001 0.000126-May 43.49058 1.0021 0.002127-May 43.49993 1.0002 0.000231-May 43.6672 1.0038 0.0038

90

91