Relationship Between Exchange Rate and Stock Indices

8/7/2019 Relationship Between Exchange Rate and Stock Indices

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RESEARCH PROJECT

On

Relationship between Exchange rate and Stock Indices

Submitted in partial fulfillment of the requirement for MBA

Degree of Bangalore University

BY

Girija K N

Registration Number

05XQCM6026

Under the guidance of

Dr. Nagesh Malavalli

M.P. Birla Institute of Management

Associate Bharatiya Vidya Bhavan

Bangalore-5600012005-2007


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DECLARATION

I hereby declare that the research project titled Dynamic

Relationship between Exchange rate and Stock Indicesis prepared under

the guidance of Dr. Nagesh Malavalli in partial fulfillment of MBA degree

of Bangalore University, and is my original work.

This project does not form a part of any report submitted for

degree or diploma under Bangalore University or any other university.

Place: Bangalore Girija K. N.


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GUIDES CERTIFICATE

I hereby declare that the research work embodied in this

dissertation entitled Dynamic Relationship between Exchange rates and

Stock Indices has been undertaken and completed by Ms Girija K.N. under

my guidance and supervision.

I also certify that she has fulfilled all the requirements under the

covenant governing the submission of dissertation to the Bangalore

university for the award of MBA Degree.

Place: Bangalore Dr. Nagesh MalavalliDate: Research Guide

MPBIM, Bangalore


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PRINCIPALS CERTIFICATE

This is to certify that Ms. Girija K. N., bearing Registration No:

05XQCM6026 has done a research project on Dynamic relationship

between Exchange Rates and Stock Indicesunder the guidance of

Dr. Nagesh Malavalli, M P Birla Institute of Management, Bangalore.

This has not formed a basis for the award of any degree/diploma for any

other university.

Place: Bangalore Dr. N. S. MALLAVALLIDate: PRINCIPAL

MPBIM, Bangalore


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ACKNOWLEDGEMENT

I am thankful to Dr. N. S. Malavalli, Principal, M.P.Birla

institute of management, Bangalore, who has given his valuable support

during my project.

I am extremely thankful to Prof. T. V. Narasimha Rao,

M.P.Birla institute of Management, Bangalore, who has guided me to do this

project by giving valuable suggestions and advice.

Finally, I express my sincere gratitude to all my friends and well

wishers who helped me to do this project.

Girija K. N.


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Table of Contents

Chapter No. Particulars Page number

I Introductiono Backgroundo Purpose of the studyo Problem statemento Objectives of the studyo Hypothesiso Limitations of the studyo Theoretical frame work

09-22

II Review of Literatureo Theoretical literatureo Empirical literature

23-27

III Data analysis andInterpretation

28-50

IV DISCUSSIONS ANDCONCLUSIONS

51-55

V BIBLIOGRAPHY 56-58

VI ANNEXURE 59-64


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List of tables

Table number Particulars Page no

1 Global Forex Turnover 17

2 Constituents of BSE 32

3 Constituents of NSE 33

4 Unit root test NSE 38

5 Unit root test Exchange rate 39

6 Unit root test BSE 40

7 Test for distribution BSE 44

8 Test for distribution Exchange 45

9 Test for distribution Nifty 46

10 Test for co integration 50

11 Regression BSE and Exchange 59

12 Regression NSE and Exchange 60

13 Stationarity test BSE 61

14 Stationarity test NSE 62

15 Stationarity test Exchange rate 64


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Executive Summary

Stock market and foreign exchange market are the barometers of the Economy

and both the markets are sensitive segments of the economy. Any changes in the policies

of the country are quickly reflected in these markets. There are different factors, which

affect the stock markets like interest rates, company performance, future growth

prospects, inflation, political stability, exchange rates etc. There are different factors,

which affect the Exchange rates are like the flow of capital between nations, inflation,

interest rates, faith in government's ability to protect the value of currency, speculation

etc.

But in this era of financial integration, there is a lot of movement of funds between

the markets and have ushered in a sea change in the financial architecture of the Indian

economy.

This study attempts to analyze the interlinkages between exchange rates and stock

prices. The study is conducted by considering exchange rates and various indices form

2001 to 2006. This is analyzed by using statistical tools like Augmented Dickey Fuller

Test and Johnsons Co-integration test by taking 4 day lag. From the results it is clear that

there is no significant relationship between the exchange rates and index values.


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CHAPTER I

INTRODUCTION


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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 thefinancial 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 tothe cross Country linkages of capital markets and international integration of domestic

economy.


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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.

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 countrys 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.


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

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

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

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

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

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

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

part of its output will benefit directly from devaluation due to an increase in demand for

its output. As higher sales result in higher profits, local currency devaluation will cause

firm stock price to rise in general.

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

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

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 countrys home currency as the price currency (e.g.,Rs.33

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

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

0.03 = Rs. 1 in India) and is used in British newspapers and are also common in

Australia, New Zealand and Canada.

Appreciation/depreciation of currency:

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

appreciating, or becoming more valuable) then the exchange rate number decreases.

Conversely if the foreign currency is strengthening, the exchange rate number increases

and the home currency is depreciating.

Exchange rate regime:

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

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

policy and the two are generally dependent.

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

regime wherein a 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.


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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.

A fixed rate is that rate that have direct convertibility towards another currency.

Here, the currency is backed one to one by foreign reserves.

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.


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Functions of foreign exchange market:

The foreign exchange market is the mechanism by which participants

Transfer purchasing power between countries,

Obtain or provide credit for international trade transactions, and

Minimize exposure to the risks of exchange rate changes

Foreign Exchange Market participants:

The foreign exchange market consists of two tiers:

the interbank or wholesale market and

the client or retail market.

Five broad categories of participants operate within these two tiers:

Bank and nonblank foreign exchange dealers:

Banks and a few nonblank foreign exchange dealers operate in both the interbank

and 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 oflarge 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 countrys

foreign exchange reserves as well as to influence the price at which their own currency istraded. 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.


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Foreign exchange transactions

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

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

which requires settlement at some designated future date.

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

changes by keeping a close eye on world events and currency fluctuations.

Global foreign exchange market turnover:

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


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Factors affecting Exchange rates:

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

three factors include:

Economic factors:

Government budget deficits or surpluses

Balance of trade levels and trends

Inflation levels and trends

Economic growth and health

Political conditions:

Political upheaval and political instability

Relation between two countries

Market psychology:

Flights to quality

Long-term trends

Economic numbers


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Stock Exchange:

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 serves as an important source for the productive use of the economys

savings.

It provides incentives to saving and facilitates capital formation by offering

suitable rates of interest as the price of the capital

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 themutual interlinkages 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


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considered as the barometers of the economic growth through which the countrys

exposure towards the outer world is most readily felt.

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

about the interlinkages between the stock and foreign exchange market, it is better to

highlight the evolutions and perspectives that are associated with both the markets since

liberalization in the Indian context.

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

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

In theory there are two approaches to exchange rate determination. They are-

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

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

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

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

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

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

to stock prices.

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

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

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

postulate a negative relationship between stock prices and exchange rates and come to the

conclusion that stock prices have an impact on exchange rates.


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CHAPTER II

LITERATURE SURVEY


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Literature survey:

The possible interlinkages between stock prices and exchange rates suggested by

several arguments/hypothesis, particularly those identified in goods market approaches

explaining likely impact of exchange rate on stock prices and portfolio balance

approaches for justifying impact in reverse direction.

The arguments provided in goods market approaches flow that,

as many companies borrow in foreign currencies to fund their operations, a change in

exchange rate affects the cost of funds and value of earnings of many firms, which in turn

affect the competitiveness of a firm and its stock prices a depreciation (appreciation) of

local currency makes exporting goods more (less) attractive to foreigners, which resultsin increase (decrease) of foreign demand for goods, which in turn raises (reduces) the

revenue of the firm, value of firms appreciates(depreciates) and thus stock prices increase

(decrease).

The sensitivity of an importing firm to a change in exchange rate is just opposite

to that of an exporting firm. Therefore, on a macro basis the impact of exchange rate

fluctuations on stock market seems to depend on both the importance of a countrys

international trade in its economy and the degree of the trade imbalance.

To complete the linkage, influence in reverse direction can be justified by

portfolio balance approaches under the exchange rate regime that allows exchange rate

to be determined by market mechanism (i.e. the demand and supply conditions). A

glooming stock market would attract capital flows from foreign investors, which may

cause an increase in the demand for a countrys currency. Thus, local currency

appreciates.


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The reverse would happen in case of fallen stock prices where the investors would

try to sell their stocks to avoid further losses and would convert their money in to foreign

currency to move out of the country. There would be demand for foreign currency in

exchange of local currency. As a result rising (declining) stock prices would lead to an

appreciation (depreciation) in local currency.

Moreover, foreign investment in domestic equities could increase over time due

to benefits of international diversification that foreign investors would gain.

Further more, movements in stock prices may influence exchange rates (and

money demand) because investors wealth (and liquidity demand) could depend on the

performance of the stock market.

Empirical results:

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.

Ajayi, R A and Mongone M (1996) applied Error Correction Model for the two

variables namely; stock indices and exchange rates to simultaneously estimate the short

and long run dynamics of the variables. The tests revealed significant short and long run

feedback relations between the two financial markets.

Abhay Pethe and Ajit Karnik (2000), Basabi Bhattacharya and Jaydeep

Mukherjee (2002), Golaka C Nath and GP Samanta (1999), Naeem Muhammad and

Abdul Rasheed (2002) by applying the techniques of unit root tests, cointegration and

long run Granger non-causality test, tested the causal relationships between stock market

index and exchange rate for India. The results show no long or short run association

between stock prices and exchange rates for India.


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Richard A Ajayi, Joseph Friedman and Seyed M Mehdian (1998) employed

monthly and quarterly data on a set of advanced and emerging economies from 1973-

1983 to examine the relationship between real stock return differentials and changes in

real exchange rates.

Findings provided evidence to indicate unidirectional causality, in the Granger

sense; between the stock and currency markets in all the advanced economies but no

consistent causal relations are observed in emerging economies.

One existing study which focuses exclusively on South Asian markets is Smyth

and Nandha (2003), who employ the Engle and Granger (1987) and Johansen (1988)

methods of cointegration to examine the relationship between exchange rates and stock

prices in Bangladesh, India, Pakistan and Sri Lanka within a cointegration and causality

framework using daily data for 1995 to 2001. Their main finding was that there is no

long-run equilibrium relationship between exchange rates and stock prices in these four

markets.

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.

Alok Kumar Mishra in his article Stock Market and Foreign Exchange Market in

India: Are they Related? attempts to examine whether stock market and foreign


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exchange markets are related to each other or not. The study uses Grangers Causality

test and Vector Auto Regression technique on monthly stock return, exchange rate,

interest rate and demand for money for the period April 1992 to March 2002. The major

findings of the study are:

There exists a unidirectional causality between the exchange rate and interest rate

and between the exchange rate return and demand for money;

There is no Grangers causality between the exchange rate return and stock return.

Through Vector Auto Regression modeling, the study confirms that though stock

return, exchange rate return, the demand for money and interest rate are related to each

other but any consistent relationship doesnt exist between them. The forecast error

variance decomposition further evidences that:

The exchange rate return affects the demand for money,

The interest rate causes exchange rate return change,

The exchange rate return affects the stock return,

The demand for money affects stock return,

The interest rate affects the stock return, and

The demand for money affects the interest rate.

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.


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CHAPTER III

METHODOLOGY


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Methodology of Research:

Problem statement

There are various studies have been done to study the relationship

between exchange rates and stock prices by taking various indices. This study explores

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

relationship between exchange rates and stock prices.

Objectives of the study

To analyze the relationship between stock market and exchange market

To find out whether the relationship changes with different indices

To find out which variable is leading and which variable is lagging.

Hypothesis to be tested

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

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

Data Set

Data are available on the share price index (SPt) and exchange rate (ERt) for NSE

(National Stock Exchange) and BSE Sensex of India. We use daily data (excluding

weekends and holidays) for the period January 2, 2001 to march 31, 2007, which gives a

total of 1600 observations. We use daily data, which is appropriate for this type of study

given that a sampling frequency less than one day may introduce spurious statistical

significance into the tests.


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Study Design

a) Study Type:

The study type is analytical, quantitative and historical. Analytical because facts

and existing information is used for the analysis, Quantitative as relationship is examined

by expressing variables in measurable terms and also Historical as the historical

information is used for analysis and interpretation.

b) Study population:

population is the entire stock market and all indices and exchange rates of rupee

versus currencies of all the countries.

c) Sampling frame:

Sampling Frame would be Indian stock market and rupee versus US Dollar.

d) Sample:

Sample chosen is daily closing values of BSE Sensex, CNX Nifty and exchange

rates of Rupee/Dollar from 1-1-2001 to 31-3-2007.

e) Sampling technique:Deliberate sampling is used because only particular units are selected from the

sampling frame. Such a selection is undertaken as these units represent the population in

a better way and reflect better relationship with the other variable.


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Data gathering procedures and instruments:

Data:

Historical daily share prices and information about their forex exposure.

Historical daily closing values of BSE Sensex, CNX Nifty, CNX IT, BSE Bankex, import

index and export index. Direct and indirect quotes of rupee per dollar.

Data Source:

Historical share prices of the sample companies and the index points for the

period has been taken from the database of Capital Market Publishers (India) Ltd.,

Capitaline 2007 and exchange rates information has been taken from

www.exchangerate.com.

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

National Firms and indirect effect through domestic firms.

Even though exchange rate has effect on stock prices of companies, the study has

been conducted by considering different indices because index values are nothing but the

weighted average of different companys share prices and indices are the proxies of stockmarket.

BSE Sensex is considered as it is a barometer of the state of the economy. It

follows the free float methodology. The companies in the Sensex are domestic

Companies, so it has been taken to see the indirect effect of exchange rates.


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Table NO.1 Constituents of BSE sensex-

1 Reliance Industries 16 Satyam computers

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

11 SBI 26 Gujarat ambuja

12 Hindalco 27 Cipla

13 HLL 28 Siemens

14 L&T 29 Ranbaxy

15 HDFC 30 NTPC


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Constituents of nifty-

1 Reliance Industries 26 Gujarat ambuja

2 ONGC 27 Cipla

3 Bharti Airtel 28 Siemens

4 Tata Consultancy 29 Ranbaxy

5 Infosys tech 30 NTPC

6 Reliance Communication 31 ITC

7 Wipro 32 VSNL

8 ICICI Bank 33 Zee Entertainment

9 ACC 34 MTNL

10 BHEL 35 HPCL

11 SBI 36 Dabur

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


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Tests and Results

Test for Stationarity-

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

time and the value of the covariance between the two time periods depends only on the

distance or gap or lag between the two time periods and not the actual time at which the

covariance is computed. Tests for stationarity are routinely applied to highly persistent

time series. Following Kwiatkowski, Phillips, Schmidt and Shin (1992), standard

stationarity employs a rescaling by an estimator of the long-run variance of the

(potentially) stationary series.

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

nonstationary time series will have time varying mean or a time-varying variance or both.

Hence the results cannot be extrapolated for the entire population.

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

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

shown that the time series is stationary.

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

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

estimate the needed parameters. Therefore, the mean, the variance and the covariance of

the series are not functions of time and depend rather on the lag between the observations

(the difference between the times at which two observations were recorded). To

summarize, if Xt is a discrete time series, its distribution is described by its first two

ments, which under stationarity must depend only on the lag:

E[Xt] = t = ,

V ar(Xt) = 2t = 2,

Cov(Xt, Xts) = E[(Xt t)(Xts ts)] = t,ts = |s|,

Corr(Xt, Xts) = t,ts 2 = t,ts.


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Since all time series data sets contain either deterministic or stochastic trends

(or both), unit root tests and stationarity tests are a way of determining which kind of

trends are present in the data. If only deterministic trends are present, then the series can

be seen as being generated by some non-random, pre-determined function of time with

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

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

innovations with zero mean and constant variance, which form 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 rejection of the hypothesis that there is a

unit root at some level of confidence.

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

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

Monte Carlo simulation. This test is conducted by augmenting the preceding three

equations by adding the lagged values of the dependent variable Yt.

So the required regression is:

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


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

autoregressive process. Imposing the contraints = 0 and = 0 corresponds to modelling

a random walk and using the constraint = 0 corresponds to modelling a random walk

with a drift.

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

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

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

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

such as the Akaike information criterion, Bayesian information criterion or the Hannon

Quinn criterion.

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

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

computed it can be compared to the relvant critical value for the Dickey-Fuller Test. If

the test statistic is less than the critical value then the null hypothesis of = 1 is rejected

and no unit root is present

Where t is a pure white noise error term and the number of lagged differenceterms 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.


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

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

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

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

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

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

accuracy for the unit root tests to be effective.

Then the test statistic T*(bOLS

-1) has a known, documented distribution. Its value

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

the original sample came from a unit root autoregressive process; that is, one in which

b=1.

Properties and Characteristics of Unit Root Processes

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

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

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= y

ty


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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-

Lags

ADF T

Statistic

1% Significance

value

5% Significance

value

10%

Significance

value

1 -48.90367 -2.5671 -1.9396 -1.6157

2 -42.01398 -2.5671 -1.9396 -1.6157

3 -35.34338 -2.5671 -1.9396 -1.6157

4 -29.68170 -2.5671 -1.9396 -1.6157

5 -26.74565 -2.5671 -1.9396 -1.6157

6 -24.85180 -2.5671 -1.9396 -1.6157

7 -24.41056 -2.5671 -1.9396 -1.6157

0 -59.47844 -2.5671 -1.9396 -1.6157

-1 -29.68170 -2.5671 -1.9396 -1.6157

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.


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Exchange Rate-

Lags ADF T

Statistic

1% significance

Value

5% significance

Value

10%

significance

value

1 -48.14002 -2.5671 -1.9396 -1.6157

2 -37.63418 -2.5671 -1.9396 -1.6157

3 -38.42542 -2.5671 -1.9396 -1.6157

4 -31.05203 -2.5671 -1.9396 -1.6157

5-27.54348 -2.5671 -1.9396 -1.6157

6 -24.18888 -2.5671 -1.9396 -1.6157

0 -66.07068 -2.5671 -1.9396 -1.6157

-1 -31.05203 -2.5671 -1.9396 -1.6157

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% criticalvalue.


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BSE Sensex

Lags ADF T statistic 1% significance 5% significance 10%

significance

1 -48.22602 -2.5671 -1.9396 -1.6157

2 -42.03703 -2.5671 -1.9396 -1.6157

3 -34.77614 -2.5671 -1.9396 -1.6157

4 -29.15550 -2.5671 -1.9396 -1.6157

5 -26.75253 -2.5671 -1.9396 -1.6157

6-25.14448 -2.5671 -1.9396 -1.6157

0 -61.03772 -2.5671 -1.9396 -1.6157

-1 -29.16495 -2.5671 -1.9396 -1.6157

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


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Testing for the distribution

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

It is usually a list, ordered by quantity, showing the number of times each value appears.frequency distribution is said to be skewed when its mean and median are different. The

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

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

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

less peaked it is said to be platykurtic.

Normal distribution

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

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

psychological measurements and physical phenomena (like photon counts and noise) can

be approximated well by the normal distribution. While the mechanisms underlying these

phenomena are 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.


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A normal distribution in a variate with mean and variance is a statisticdistribution with probability function

on the domain . While statisticians and mathematicians uniformly use the

term "normal distribution" for this distribution, physicists sometimes call it a Gaussian

distribution and, because of its curved flaring shape, social scientists refer to it as the

"bell curve." Feller (1968) uses the symbol for in the above equation, but then

switches to in Feller (1971).

De Moivre developed the normal distribution as an approximation to the binomialdistribution, 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 NormalDistribution[mu,

sigma]. The so-called "standard normal distribution" is given by taking and in

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

standard normal distribution by changing variables to , so ,

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 theoremstates 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.


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

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

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

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

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

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

distribution and normal difference distribution obtained by respectively adding and

subtracting variates and from two independent normal distributions with arbitrary

means and variances are also normal!


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

Testing data for distribution- bse sensex

Statistics

Stock exchange bse

Valid 1552NMissing 13

Mean -.000759972Std. Error of Mean .0003616586Median -.001513471Mode -.0793110(a)Std. Deviation .0142476978Variance .000Skewness .677Std. Error of Skewness .062Kurtosis 5.494Std. Error of Kurtosis .124Range .1974027Minimum -.0793110Maximum .1180918Sum -1.1794769

a Multiple modes exist. The smallest value is shown

0.15000000.10000000.05000000.0000000-0.0500000-0.1000000

stockexchangebse

400

300

200

100

0

Frequency

Mean = -7.599722586E-

4

Std. Dev. =

0.0142476978

N = 1,552

Histogram


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Testing data for distribution

Exchange rate- Statistics

exchangerate

Valid 1565NMissing 0

Mean -.000044543Std. Error of Mean .0000558450Median .000000000Mode .0000000Std. Deviation .0022092326Variance .000Skewness -.623Std. Error of Skewness .062Kurtosis 10.505

Std. Error of Kurtosis .124Range .0293786Minimum -.0167958Maximum .0125828Sum -.0697099

0.01000000.0000000-0.0100000-0.0200000

exchangerate

500

400

300

200

100

0

Frequency

Mean = -4.454304733E-

5

Std. Dev. =

0.0022092326

N = 1,565

Histogram

Test for Distribution


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Nifty-

Statistics

Valid 1565N

Missing 0Mean .000711871Std. Error of Mean .0003648690Median .001643323Mode .0000000Std. Deviation .0144342454Variance .000Skewness -.885Std. Error of Skewness .062Kurtosis 6.975Std. Error of Kurtosis .124

Range .2102295Minimum -.1305386Maximum .0796909Sum 1.1140785

0.10000000.05000000.0000000-0.0500000-0.1000000-0.1500000

nifty50

300

200

100

0

Frequency

Mean = 7.118712216E-4

Std. Dev. =0.0144342454

N = 1,565

Histogram


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Analysis-

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 variance of the data sets are 00 which confirms the data as to its stationarity

Test for Co-integration

Cointegration 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 cointegrated. 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 cointegrating 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 cointegrated of an order below one).

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

concerning the relationship between two variables having unit roots (i.e. integrated of

order one). series is said to be "integrated of order d" if one can obtain a stationary series

by "differencing" the term d times. such that:

C= Y dX(1)is stationary, where the parameterdis the cointegrating 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 cointegrated. In contrast, lack of

cointegration implies that the aforementioned variables have no link in the long-run.

If two, or more series, are cointegrated, then there exist common factors that affect

both and their permanent or secular trends, and so the series will eventually adjust


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to equilibrium. The implications for diversification are that even if, in the shortterm

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 cointegration between them is of a lower degree. That is if both X and Y are

stationary I(1) the cointegration vector must be stationary I(0). However, if X and Y

are integrated to different degrees, there will not be any parameterdthat satisfies

Equation (1). Thus a long-run relationship implies the requirement that the two

variables should be (i) integrated to the same degree and (ii) a linear combination of

the two variables should exist which is integrated to a lower degree than the

individual variables.

Testing for cointegration involves two steps.

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

2. Estimate the cointegration regression and test for integration.

Assuming that each series has the same number of unit roots, the cointegrationtest can commence. Engle and Granger (1987) proposed seven tests for examining the

hypothesis that two time series are not cointegrated. In cointegration tests, the null

hypothesis is non-cointegration. 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 cointegration is based on the

Cointegrating Regression Durbin-Watson (CRDW) statistic. As a simple rule of

thumb for a quick evaluation of the cointegration hypothesis Banerjee et al (1986)

proposed that: if the CRDW statistic is smaller than the coefficient of determination

(R2) the cointegration hypothesis is likely to be false; otherwise, when CRDW> R2,


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cointegration 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-cointegration is rejected.

Suggesting that the series are not cointegrated.

The test for cointegration involves the significance of the estimated l1 coefficient.

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

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

on the l1 coefficient exceeds the critical value, the m residuals from the cointegration

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

values for this tstatistic are given in Mackinnon (1991).


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Test for co-integration:

Test : Johnsons Co-integration test

Series : NSE Nifty and Exchange Rate

Bse sensex and Exchange Rate

Sample: 1 1567Included observations: 1547Test :Johnson co integration testSeries: 1. Exchange rate and NSE

2. Exchange rate and BSELags interval: 1 to 4

Likelihood 5 Percent 1 Percent HypothesizedEigenvalue Ratio Critical Value Critical Value No. of CE(s)

0.169822854329 540.15012665

19.96 24.60** None

0.150445830265 252.22840788

9.24 12.97** At most 1

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

Results-

As per Johnsons Cointegration 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 short or long term relationship

between exchange rate and stock indices.


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CHAPTER IV

DISCUSSIONS

AND

CONCLUSIONS


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Discussions-

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.

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.

After analyzing the data by using correlation coefficient, we found that in the

short run or 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.

From the results obtained by carrying out various tests, we can see that there is

very less relation exists between exchange rates and stock prices. The possible reasons

for such behavior could be as follows:

One interpretation could be that investors do not use all freely available

information, like past changes in the rupee and the past relation and firm performance,assets and liabilities of company, etc, to predict changes in firm value. More specifically,

at the end of fiscal quarter investors observe the change in the value of rupee over the

period and see what impact the rupee changes has on firm performance, assets and

liabilities. Based upon this information, investors should be able to form an unbiased

expectation about the economic impact of the recent change in rupee on the firm and

incorporate this effect into firms value and share prices. However, at the end of fiscal

quarter, investors systematically underestimate or perhaps overlook this impact.

This underestimation may be corrected only when additional information that directly

relates to the impact of past change in rupee on the firm performance, asset and liabilities

is disclosed during the following quarter.


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On the other hand, 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.

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 few

qualitative factors that influence stock prices like speculation and investor confidencelevel.

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.


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Conclusions:

In this paper, we have examined the long-run and short-run dynamics between

stock prices and exchange rates in India. Our main concerns were to examine whetherthese links were affected by the existence of foreign exchange controls, floating rates and

raising value of Rupee and raising indices in India.

We have examined these issues by applying stationarity tests and cointegration

methodology, which tests for a long-run relationship between the stock market and

exchange rate of the country ie., its real exchange rate.

The following conclusions have been derived from our analysis:

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.

There is no significant relationship between any of the company

share prices with exchange rates individually.

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.

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

take sufficient care while implementing exchange rate policies. Furthermore, indicationsare 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.


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CHAPTER V

BIBLIOGRAPHY


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Bibliography:

Text Books

Multinational Business Finance,

David K. Eieteman, Arthur I. Stonehill and Michel H. Moffett, (Tenth Edition)

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.emeclai.com

www.icicidirect.com www.iciciresearch.com www.easy-forex.com

www.indiainfoline.com

Database of Capital Market Publishers (India) Ltd., Capitaline 2000

Jstor Database


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Reference:

Articles ofICFAI

(The Institute of Chartered Financial Analysts of India)

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

Dynamic Relationship between Exchange rates and stock Prices: an empiricalstudy in the Indian context by Meera Pratap Thakker and Vijay R Chary, the ICFAI

Journal of Applied Finance vol.10 No.8 Pg. 54 to 68

Causality between stock prices and exchange rates: some evidence for India by

M Venkateshwaralu and Rishab Tiwari , the ICFAI Journal of Applied Finance vol.11

No.3, Pg. 5 to 15

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


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CHAPTER VI

ANNEXURES


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Regression- BSE Sensex and Exchange Rate

Descriptive Statistics

MeanStd.

Deviation N

stockexchangebse -.000759972 .0142476978 1552exchange rate -.000034925 .0021643662 1552

Correlations

Stockexchange

bse

Exchange

ratePearsonCorrelation

stockexchangebse1.000 .019

exchangerate .019 1.000Sig. (1-tailed) stockexchangebse . .229

exchangerate .229 .N stockexchangebse 1552 1552

exchangerate 1552 1552

ANOVA(b)

Model Sum ofSquares df Mean Square F Sig.

1 Regression.000 1 .000 .552 .458(a)

Residual .315 1550 .000

Total .315 1551

a Predictors: (Constant), exchangerate

b Dependent Variable: stockexchangebse


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Regression- NSE Nifty and Exchange Rate

Descriptive Statistics

Mean Std. Deviation N

nse .00071187 .014434245 1565

exchrate -.00004454

.002209233 1565

Correlations

nse exchrate

Pearson Correlation nse 1.000 -.032

exchrate -.032 1.000

Sig. (1-tailed) nse . .103

exchrate .103 .N nse 1565 1565

exchrate 1565 1565

Model Summary(b)

Model R R SquareAdjusted R

SquareStd. Error ofthe Estimate Durbin-Watson

1 .032(a) .001 .000 .014431444 1.808

a Predictors: (Constant), exchrate

b Dependent Variable: nse

ANOVA(b)

ModelSum of Squares df Mean Square F Sig.

1 Regression .000 1 .000 1.607 .205(a)

Residual .326 1563 .000

Total.326 1564

a Predictors: (Constant), exchrate

b Dependent Variable: nse


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Coefficientsa

.001 .000 1.925 .054

-.209 .165 -.032 -1.268 .205

(Constant)

exchrate

Model

1

B Std. Error

Unstandardized

Coefficients

Beta

Standardized

Coefficients

t Sig.

Dependent Variable: nsea.

Residuals Statisticsa

-.001932 .00421963 .00071187 .000462621 1565

******** ******** ******** .014426830 1565

-5.716 7.582 .000 1.000 1565

-9.024 5.472 .000 1.000 1565

Predicted Value

Residual

Std. Predicted Value

Std. Residual

Minimum Maximum Mean Std. Deviation N

Dependent Variable: nsea.

Unit Root test for BSE Sensex

ADF Test Statistic -31.05203 1% Critical Value* -2.56715% Critical Value -1.939610% Critical Value -1.6157

*MacKinnon critical values for rejection of hypothesis of a unit root.

Augmented Dickey-Fuller Test EquationDependent Variable: D(SER07,2)Method: Least SquaresDate: 05/15/07 Time: 16:46Sample(adjusted): 7 1565Included observations: 1559 after adjusting endpoints

Variable Coefficient Std. Error t -Statistic Prob.

D(SER07(-1)) -3.658002 0.117802 -31.05203 0.0000D(SER07(-1),2) 1.820555 0.101674 17.90575 0.0000D(SER07(-2),2) 1.108887 0.078794 14.07318 0.0000D(SER07(-3),2) 0.562906 0.051964 10.83259 0.0000D(SER07(-4),2) 0.124101 0.025584 4.850666 0.0000

R-squared 0.805946 Mean dependent var 9.87E-06Adjusted R-squared 0.805447 S.D. dependent var 0.005313S.E. of regression 0.002343 Akaike info criterion -9.271309Sum squared resid 0.008533 Schwarz criterion -9.254145Log likelihood 7231.986 Durbin-Watson stat 2.004107


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Unit Root test for Exchange Rate



Augmented Dickey-Fuller Test EquationDependent Variable: D(SER10,2)

Method: Least SquaresDate: 05/15/07 Time: 16:50

Sample(adjusted): 7 1565Included observations: 1559 after adjusting endpoints

Variable Coefficient Std. Error t-Statistic Prob.


R-squared 0.805946 Mean dependent var 9.87E-06Adjusted R-squared 0.805447 S.D. dependent var 0.005313S.E. of regression 0.002343 Akaike info criterion -9.271309Sum squared resid 0.008533 Schwarz criterion -9.254145

Log likelihood 7231.986 Durbin-Watson stat 2.004107


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Unit root test for NSE Nifty



Augmented Dickey-Fuller Test EquationDependent Variable: D(SER05,2)Method: Least SquaresDate: 05/15/07 Time: 16:41Sample(adjusted): 7 1565Included observations: 1559 after adjusting endpoints

Variable Coefficient

Std. Error t-Statistic Prob.


R-squared 0.774702 Mean dependent var -1.23E-05

Adjusted R-squared 0.774122 S.D. dependent var 0.032278S.E. of regression 0.015341 Akaike info criterion -5.513389Sum squared resid 0.365722 Schwarz criterion -5.496225Log likelihood 4302.687 Durbin-Watson stat 2.026764

Relationship Between Exchange Rate and Stock Indices

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