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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
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(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.
D(SER10(-1)) -3.658002 0.117802 -31.05203 0.0000D(SER10(-1),2) 1.820555 0.101674 17.90575 0.0000D(SER10(-2),2) 1.108887 0.078794 14.07318 0.0000D(SER10(-3),2) 0.562906 0.051964 10.83259 0.0000D(SER10(-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 -9.254145
Log likelihood 7231.986 Durbin-Watson stat 2.004107
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Unit root test for NSE Nifty
ADF Test Statistic -29.68170 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(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.
D(SER05(-1)) -3.357618 0.113121 -29.68170 0.0000D(SER05(-1),2) 1.623331 0.098130 16.54270 0.0000D(SER05(-2),2) 0.922058 0.075729 12.17568 0.0000D(SER05(-3),2) 0.408524 0.049648 8.228373 0.0000D(SER05(-4),2) 0.120220 0.025206 4.769520 0.0000
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