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ISSN 2222-1697 (Paper) ISSN 2222-2847 (Online)
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A Causal Relationship between Stock Indices and Exchange
Rates-Empirical Evidence from India
Amalendu Bhunia
Fakir Chand College, Diamond Harbour
South 24-Parganas-743331
West Bengal, India
* E-mail of the corresponding author: bhunia.amalendu@gmail.com
Abstract
This paper examines the causal relationship between stock prices and exchange rates, using data from 2
April 2001 to 31 March 2011 about India. Macroeconomic variables are of crucial importance for
determining the effects on stock prices and investment decisions. There are many empirical studies to
disclose the relationship between macroeconomic variables such as interest rate, inflation, exchange
rates, money supply etc. and stock indexes. However, the direction of causality still remains unresolved
in both theory and empirics. In the present study, national, services, financials, industrials, and
technology indices are taken as stock price indices. The results of empirical study indicate that there is
bidirectional causal relationship between exchange rate and all stock market indices. While the
negative causality exists from national, services, financials and industrials indices to exchange rate,
there is a positive causal relationship from technology indices to exchange rate. On the other hand,
negative causal relationship from exchange rate to all stock market indices is determined.
Keywords: Stock Prices, Exchange Rate, Toda-Yamamoto, India
1. Introduction
Many factors, such as enterprise performance, dividends, stock prices of other countries, gross
domestic product, exchange rates, interest rates, current account, money supply, employment, their
information etc. have an impact on daily stock prices (Kurihara, 2006). Especially, the continuing
increases in the world trade and capital movements have made the exchange rates as one of the main
determinants of business profitability and equity prices (Kim, 2003).
The relationship between stock prices and exchange rates has preoccupied the minds of economists
since they both play important roles in influencing the development of a country’s economy. In the
recent years, because of increasing international diversification, cross-market return correlations,
gradual abolishment of capital inflow barriers and foreign exchange restrictions or the adoption of
more flexible exchange rate arrangements in emerging and transition countries, these two markets have
become interdependent. These changes have increased the variety of investment opportunities as well
as the volatility of exchange rates and risk of investment decisions and portfolio diversification process.
Thus, understanding this relationship will help domestic as well as international investors for hedging
and diversifying their portfolio. Also, fundamentalist investors have taken into account these
relationships to predict the future trends for each other (Phylaktis and Ravazzolo, 2005; Mishra et al.,
2007; Nieh and Lee, 2001; Stavárek, 2005). Although, economic theory suggests that foreign exchange
changes can have an important impact on the stock price by affecting cash flow, investment and
profitability of firms, there is no consensus about these relationship and the empirical studies of the
relationship are inconclusive (Joseph, 2002; Vygodina, 2006). However, the linkage between these
financial variables can be established through the instruments of wealth, demand for money, interest
rates etc. (Mishra, 2004).
According to traditional approach, exchange rates lead stock prices. On the other hand, portfolio
balance approach states that exchange rates are determined by market mechanism. In other words,
changes in stock prices might have impact on exchange rate movements. This approach states that
stock price is expected to lead exchange rate with a negative correlation since a decrease in stock prices
reduces domestic wealth, which leads to lower domestic money demand and interest rates. Also, the
decrease in domestic stock prices leads foreign investors to lower demand for domestic assets and
domestic currency. These shifts in demand and supply of currencies cause capital outflows and the
depreciation of domestic currency. On the other hand, when stock prices rise, foreign investors become
willing to invest in a country’s equity securities. Thus, they will get benefit from international
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2. Research Journal of Finance and Accounting www.iiste.org
ISSN 2222-1697 (Paper) ISSN 2222-2847 (Online)
Vol 3, No 1, 2012
diversification. This situation will lead to capital inflows and a currency appreciation. (Granger et al.,
2000; Caporale et al., 2002; Stavárek, 2005; Pan et al, 2007).
Exchange rate changes affect the competitiveness of firms through their impact on input and output
price (Joseph, 2002). When the Exchange rate appreciates, since exporters will lose their
competitiveness in international market, the sales and profits of exporters will shrink and the stock
prices will decline. On the other hand, importers will increase their competitiveness in domestic
markets. Therefore, their profit and stock prices will increase. The depreciation of exchange rate will
make adverse effects on exporters and importers. Exporters will have advantage against other
countries’ exporters and increase their sales and their stock prices will be higher (Yau and Nieh, 2006).
That is, currency appreciation has both a negative and a positive effect on the domestic stock market
for an export-dominant and an import-dominated country, respectively (Ma and Kao, 1990).
Exchange rates can affect stock prices not only for multinational and export oriented firms but also for
domestic firms. For a multinational company, changes in exchange rates will result in both an
immediate change in value of its foreign operations and a continuing change in the profitability of its
foreign operations reflected in successive income statements. Therefore, the changes in economic value
of firm’s foreign operations may influence stock prices. Domestic firms can also be influenced by
changes in exchange rates since they may import a part of their inputs and export their outputs. For
example, a devaluation of its currency makes imported inputs more expensive and exported outputs
cheaper for a firm. Thus, devaluation will make positive effect for export firms (Aggarwal, 1981) and
increase the income of these firms, consequently, boosting the average level of stock prices (Wu, 2000).
Nieh and Lee (2001) state that in an open economy, since the expectations of relative currency values
affect the domestic and foreign interest rate and these changes affect the present value of a firm’s
assets, exchange rates play a crucial role on stock prices, especially for internationally held financial
assets. Wu (2000) explains the positive and negative relationship between exchange rate and stock
prices by a real interest rate and an inflationary disturbance. According to real interest rate disturbance,
when the real interest rate rises, capital inflow increases and the exchange rate fall. However, since
higher real interest rate reduces the present value of future cash flows, stock prices will decline. An
inflationary disturbance may explain negative relationship between exchange rate and stock price. That
is, when inflation increases, the exchange rate rises and because of high inflation expectations,
investors will demand a higher risk premium and high rate of return. As a result, stock prices will
decrease (Wu, 2000). On the other hand, the asset market approach to exchange rate determination
states a weak or no association between exchange rates and stock prices and treats exchange rate to be
the price of an asset (price of one unit of foreign currency). That is, expected future exchange rates
determine the exchange rates and factors affecting exchange rates and stock price may be different
(Muhammad and Rasheed, 2002).
This paper attempts to examine how changes in exchange rates and stock prices are related to each
other for India over the period 2001-2011. The paper is organized as follows: Section 2 contains a brief
literature review. Methodology and empirical results are presented in Section 3 and 4. Concluding
remarks take place in Section 5.
2. Review of literatures
Aggarwal (1981) examines the influence of exchange rate changes on U.S. stock prices using monthly
data for the floating rate period from 1974 to 1978. He finds that stock prices and exchange rates are
positively correlated. Nieh and Lee (2001) examine the relationship between stock prices and exchange
rates for G-7 countries and take the daily closing stock market indices and foreign exchange rates for
the period from October 1, 1993 to February 15, 1996. They find that there is no long-run equilibrium
relationship between stock prices and exchange rates for each G-7 countries. While one day’s short-run
significant relationship has been found in certain G-7 countries, there is no significant correlation in the
United States. These results might be explained by each country’s differences in economic stage,
government policy, expectation pattern, etc.
Kim (2003) uses monthly data for the 1974:01-1998:12 periods in the U.S.A. and the empirical results
of the study reveal that S&P’s common stock price is negatively related to the exchange rate. Ozair
(2006) examines the causal relationship between stock prices and exchange rates in the USA using
quarterly data from 1960 to 2004. The results show no causal linkage and no co-integration between
these two financial variables. Vygodina (2006) empirically searches the exchange rates and stock prices
nexus for large-cap and small-cap stocks for the period 1987-2005 in the USA and used Granger
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3. Research Journal of Finance and Accounting www.iiste.org
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Vol 3, No 1, 2012
causality methodology. The result of study reveals that there is Granger causality from large-cap stocks
to the exchange rate. However there is no causality for small-cap stocks. Stock prices and exchange
rates are affected by the same macroeconomic variables and changes in federal monetary policy in the
USA have an important effect on the nature of these relationship. In other words, the nature of the
relationship between stock prices and exchange rate is changing over time.
Hatemi-J and Irandoust (2002) study a possible causal relation between exchange rates and stock prices
in Sweden. They use monthly nominal effective exchange rates and stock prices over the period 1993-
98. They find that Granger causality is unidirectional from stock prices to effective exchange rates.
Tsoukalas (2003) examines the relationship between stock prices and macroeconomic factors in
Cyprus. The result of study shows strong relationship between stock prices and exchange rates. The
reason of this is that Cypriot economy depends on services (import sector) such as tourism, off shore
banking etc.
Pan et al. (2007) take the data of seven East Asian countries over the period 1988 to 1998 to examine
dynamic linkages between exchange rates and stock prices. The result of study reveals that there is a
bidirectional causal relation for Hong Kong before the 1997 Asian crises. Also, there is a unidirectional
causal relation from exchange rates and stock prices for Japan, Malaysia, and Thailand and from stock
prices to exchange rate for Korea and Singapore. During the Asian crises, there is only a causal relation
from exchange rates to stock prices for all countries except Malaysia. Ibrahim and Aziz (2003) analyze
dynamic linkages between stock prices and four macroeconomic variables for Malaysia and use
monthly data over the period 1977-1998. The empirical results show that the exchange rate is
negatively associated with the stock prices.
Kurihara (2006) chooses the period March 2001-September 2005 to investigate the relationship
between macroeconomic variables and daily stock prices in Japan. He takes Japanese stock prices, U.S.
stock prices, exchange rate (yen/U.S. dollar), the Japanese interest rate etc. The empirical results show
that domestic interest rate does not influence Japanese stock prices. However, the exchange rate and
U.S. stock prices affect Japanese stock prices. Consequently, the quantitative easing policy
implemented in 2001 has influenced Japanese stock prices.
Doong et al. (2005) investigate the dynamic relationship between stocks and exchange rates for six
Asian countries (Indonesia, Malaysia, Philippines, South Korea, Thailand, and Taiwan) over the period
1989-2003. According to the study, these financial variables are not co-integrated. The result of
Granger causality test shows that bidirectional causality can be detected in Indonesia, Korea, Malaysia,
and Thailand. Also, there is a significantly negative relation between the stock returns and the
contemporaneous change in the exchange rates for all countries except Thailand.
Abdalla and Murinde (1997) investigate stock prices-exchange rate relationships in the emerging
financial markets of India, Korea, Pakistan and the Philippines using monthly data from 1985 to 1994.
The empirical results show unidirectional causality from exchange rates to stock prices in India, Korea
and Pakistan. On the contrary, the reverse causation was found for the Philippines. Muhammad and
Rasheed (2002) examine the exchange rates and stock price relationships for Pakistan, India,
Bangladesh and Sri Lanka using monthly data from 1994 to 2000. The empirical results show that there
is a bi-directional long-run causality between these variables for only Bangladesh and Sri Lanka. No
associations between exchange rates and stock prices are found for Pakistan and India. Smyth and
Nandha (2003) investigate the relationship between exchange rates and stock prices for the same
countries over the period 1995-2001. They find that there is no long run relationship between variables.
Also, the empirical results reveal unidirectional causality running from exchange rates to stock prices
for only India and Sri Lanka. That is, changes in exchange rates affect stock prices through influencing
firms’ exports in India and Sri Lanka.
Ajayi and Mougoue (1996) search the relationship between exchange rates and stock indices for eight
advanced economies using daily data from 1985 to 1991. According to results of study, there are
significant short-run and long-run feedback relations between these two financial markets. An increase
in stock price has a negative short-run effect as well as a positive long-run effect on domestic currency
value. Also, currency depreciation has a negative both short-run and long-run effect on the stock
market. Ajayi et al. (1998) take daily market indexes and exchange rates to investigate causal relations
between stock returns and changes in exchange rates for seven advanced markets from 1985 to 1991
and eight Asian emerging markets from 1987 to 1991. The empirical results show that there is a
unidirectional causality between the stock and currency markets in all the advanced economies while
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4. Research Journal of Finance and Accounting www.iiste.org
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Vol 3, No 1, 2012
no consistent causal relations exist in the emerging economies. They explained the different results
between advanced and emerging economies with the differences in the structure and characteristics of
financial markets between these groups.
Erbaykal and Okuyan (2007) examined exchange rates-stock price relations for 13 developing
economies using different time period for each country. The findings provide evidence to indicate
causality relations for eight economies. While there is a unidirectional causality from stock price to
exchange rates in the five of them, bidirectional causality exist for remaining three economies. They
also found no causality for these financial variables in Turkey and this finding is not consistent with our
results. The reason of difference may be the time period used. On the other hand, Sevuktekin and
Nargelecekenler (2007) found positive and bidirectional causality between these two financial variables
in Turkey using monthly data from 1986 to 2006.
3. Methodology
The stationary status of series should be detected when investigating the relationship between exchange
rate and stock market price. In this context, we perform Augmented Dickey-Fuller (ADF) (1979),
Phillips- Perron (PP) (1988) and KPSS (1992) tests in order to determine the integrated level of each
series. ADF test are performed as shown below:
∆yt = α + ϕT + (1- β)yt -1 + Σλ∆yt-j + εt
Where yt is the variable tested for unit root; ∆ is the first difference operator; α is the constant term; T is
a time trend; and p is the lag number. The null hypothesis is H0: (1-β) =0, β=1, implying the non-
stationary of yt. Rejecting the null hypothesis points that yt has no unit root. Lag length is selected by
minimizing AIC. Also, whether residuals are white noise is taken into consideration in selecting proper
lag length. Rejecting null hypothesis requires that the calculated test value is greater than critical values
calculated from MacKinnon (1991). Performing PP test we use ADF equation without non-augmented
form (∆yt-j, j=1,2… are not included, in the DF equation).
In the event that series are integrated of order one, Johansen’s procedure should be used to determine
whether any co-integrating vector among variables exists or not. In this procedure, trace (λtrace) and
maximum eigen value (λmax) statistics are computed, proposed by Johansen (1988) and Johansen and
Juselius (1990). When performing λtrace and λmax test, the null hypothesis that there is r or fewer co-
integrating vectors are tested against at least r + 1 co-integration vectors and r + 1 co-integrating
vectors, respectively. After applying co-integration test, if it is concluded that two series are co-
integrated, error correction models are appropriate to investigate causality relationship. In case series
are integrated different orders, for example I(0) and I(1) or are not co-integrated, it is not possible to
investigate the causality via error correction model. In this situation the one way to determine causality
relationship between series is use of Toda-Yomamoto (TY) (1995) method. The integrated properties of
series are not important in TY method, providing that the risk of misspecification of the order of
integration of the series is minimized. Thus, the causality relationship between series which are
integrated different orders can be investigated. In order to apply TY method, firstly, the VAR order, k,
and the maximum order of integration of the variables, dmax, should be determined in the VAR model.
The sum of k and dmax, is taken into consideration as the total order of VAR, that is (k+dmax)th order
of VAR is estimated. Then, in order to employ causality test, modified Wald test (MWALD), proposed
by Toda and Yamamoto (1995), is applied to the first k VAR coefficients to investigate causality. This
test has an asymptotic χ2 distribution when a VAR (k + dmax) is estimated. A Monte Carlo experiment,
presented in Zapata and Rambaldi (1997), provides evidence that the MWALD test has a comparable
performance in size and power to the likelihood ratio and WALD tests if (i) the correct number of lags
for estimating k + dmax is identified and (ii) no important variables are omitted, provided a sample of
50 or more observations is available. According to Zapata and Rambaldi (1997), the advantage of this
procedure is that it does not require the knowledge of co-integration properties of the system.
To analyze Granger causality between exchange rate and stock market price by using TY procedure, the
following VAR system should be estimated.
lext = α0 + Σα1lext-i + Σα2lext-j + Σλ1jlstpt-i + Σλ2jlstpt-j + +µ1t
lstpt = β0 + Σβ 1lstpt-i + Σβ 2lstpt -j + Σφ 1j lext -i + Σφ 2j lextt-j + +µ2t
where lext is log of nominal exchange rate and lstpt is log of stock market price indices including
infrastructures (lnfr), services (lser), financials (lfin), industrials (lind), and technology (ltec) indices.
All data obtained from various stock exchanges of India, websites of Ministry of Finance, India and
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Vol 3, No 1, 2012
MCX, India. The data are daily and were collected daily from 2 April 2001 to 31 March 2011. The
reason of selecting this period is that exchange rate regime is determined as floating in 2001.
4. Empirical results and analysis
In order to apply the MWALD test, it is necessary to determine the maximum order of integration of
each of the series used in the study. Therefore, ADF, PP, and KPSS tests are employed to determine
integrated status of series. The test results shown in table-1 indicate that the stock market indices have
integrated of order one for all unit root tests. On the other hand, there are different results about the
integrated levels of lex. Although ADF and PP tests are indicate that lex is integrated of order zero,
KPPS test shows that this result is not valid, that is, lex is integrated of order one.
Unit root test results indicate that the maximum order of integration is one, I(1). To apply TY
procedure, we selected the optimal lag length of the VAR determined by AIC and SBC. Thus, the
optimum order of the VAR (k) is 7 in order to detect the causal relationship between lex and lnat, lfin,
lind, and ltec. On the other hand, optimum order of the VAR (k) is taken as 3 when the causal
relationship is investigated between lex and lser. Also, as seen from unit root tests, maximum order of
integration (dmax) is equivalent to one. After specifying VAR (k) and dmax, VAR (k+ dmax) model can
be estimated by using MWALD test statistic. The results of causality tests are shown in table-2.
The results showed in the table-2 indicate that there is bi-directional causal relationship between lex
and all stock market indices. As regards the sign of causality, while the negative causality exists from
lnfr, lser, lfin, lind to lex, there is a positive causal relations from ltec to lex. In addition, negative
causality is found from lex to all stock market indices.
5. Conclusion
Economists have tried to explain exchange rates-stock price nexus for a long time. There have been
many empirical and theoretical studies to define the direction of causality between these two financial
variables. However, the direction of causality still remains unresolved in both theory and empirics.
While some empirical studies find some relations and causality, other studies show no causality
between these two variables. Moreover, direction of causality changes from one economy to another.
Also, the empirical studies for a specific economy may show different results for this relation. The
reason for these differences can be explained by time period used for data, econometric models used
and economic policies of countries.
In this study, we investigated the relationship between mentioned variables in India using daily data
from 2 April 2001 to 31 March 2011. Infrastructures, services, financials, industrial, and technology
indices are taken as stock price indices. The results of empirical study indicate that there is bi-
directional causal relationship between exchange rate and all stock market indices. While the negative
causality exists from infrastructures, services, financials and industrials indices to exchange rate
(supporting portfolio balance approach), there is a positive causal relationship from technology indices
to exchange rate. On the other hand, negative causal relationship from exchange rate to all stock market
indices is determined.
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Table-1: Unit Root Tests
Series ADF PP KPSS Series ADF PP KPSS
lex -3,14 (20)** -3,57(9)*** 0,40* (25) ∆ lex 0,33 (12)
lnat -0,48 (1) -0,54(2) 4,14*** (25) ∆ lnat -31,22 (0)*** -31,22 (0)*** 0,05 (2)
lser -0,20 (0) -0,19(4) 4,13 (25)*** ∆ lser -33,64(0)*** -33,65 (4)*** 0,07 (4)
lfin -0,50 (1) -0,56 (2) 4,04 (25)*** ∆ lfin -31,20 (0)*** -31,20 (0)*** 0,07 (4)
lind -0,75 (1) -0,79 (5) 4,29 (25)*** ∆ lind -31,10 (0)*** -31,09 (4)*** 0,03 (5)
ltec -1,95 (2) -2,11 (10) 2,44 (25)*** ∆ ltec -22,20 (1)*** -32,51 (9)*** 0,04 (9)
PP is the Phillips-Perron, ADF is the Augmented Dickey-Fuller, and KPSS is Kwiatkowski, Phillips,
Schmidt, and Shin test.
***, **, and * indicate rejection of the null hypothesis of non-stationary at the 1%, 5%, and 10%
levels, respectively.
The proper lag order for ADF test is chosen by considering Akaike Information Criteria (AIC) and
white noise of residuals, representing in parenthesis.
For KPSS and PP tests, the bandwidth is chosen using Newey–West method and spectral estimation
uses Bartlett kernel, representing in parenthesis.
The 1% and 5% critical value for the ADF and PP tests is -3,43 and -2,86, respectively.
The 1%, 5%, and 10% critical value for the KPSS test is 0, 74, 0,46, and 0,35 respectively.
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Table-2: Causality Results from Toda-Yamamoto Procedure
The sing of the
dmax and VAR P values for Direction of
sum lagged
(k) MWALD statistics causality
coefficients
From lnat to lex (1,7) 60.89*** (-)
lex ↔ lnat
From lex to lnfr (1,7) 38.42*** (-)
From lser to lex (1,3) 54.08*** (-)
lex ↔ lser
From lex to lser (1,3) 8.11** (-)
From lfin to lex (1,7) 57.69*** (-)
lex ↔ lfin
From lex to lfin (1,7) 36.10*** (-)
From lind to lex (1,7) 56,20*** (-)
lex ↔ lind
From lex to lind (1,7) 41.98*** (-)
From ltec to lex (1,7) 53.11*** (+)
lex ↔ ltec
From lex to ltec (1,7) 44.06*** (-)
lnfr, lex, lser, lfin, lind, and ltec are the logs of infrastructures, nominal exchange rate, services,
financials, industrials and technology, respectively.
k is the lag length used in the system and dmax is the maximum order of integration (1).
↔ denotes the bi-directional causality.
*** and ** indicate significance at the 1% and 5% level, respectively.
54

9. International Journals Call for Paper
The IISTE, a U.S. publisher, is currently hosting the academic journals listed below. The peer review process of the following journals
usually takes LESS THAN 14 business days and IISTE usually publishes a qualified article within 30 days. Authors should
send their full paper to the following email address. More information can be found in the IISTE website : www.iiste.org
Business, Economics, Finance and Management PAPER SUBMISSION EMAIL
European Journal of Business and Management EJBM@iiste.org
Research Journal of Finance and Accounting RJFA@iiste.org
Journal of Economics and Sustainable Development JESD@iiste.org
Information and Knowledge Management IKM@iiste.org
Developing Country Studies DCS@iiste.org
Industrial Engineering Letters IEL@iiste.org
Physical Sciences, Mathematics and Chemistry PAPER SUBMISSION EMAIL
Journal of Natural Sciences Research JNSR@iiste.org
Chemistry and Materials Research CMR@iiste.org
Mathematical Theory and Modeling MTM@iiste.org
Advances in Physics Theories and Applications APTA@iiste.org
Chemical and Process Engineering Research CPER@iiste.org
Engineering, Technology and Systems PAPER SUBMISSION EMAIL
Computer Engineering and Intelligent Systems CEIS@iiste.org
Innovative Systems Design and Engineering ISDE@iiste.org
Journal of Energy Technologies and Policy JETP@iiste.org
Information and Knowledge Management IKM@iiste.org
Control Theory and Informatics CTI@iiste.org
Journal of Information Engineering and Applications JIEA@iiste.org
Industrial Engineering Letters IEL@iiste.org
Network and Complex Systems NCS@iiste.org
Environment, Civil, Materials Sciences PAPER SUBMISSION EMAIL
Journal of Environment and Earth Science JEES@iiste.org
Civil and Environmental Research CER@iiste.org
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Civil and Environmental Research CER@iiste.org
Life Science, Food and Medical Sciences PAPER SUBMISSION EMAIL
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Journal of Biology, Agriculture and Healthcare JBAH@iiste.org
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Journal of Education and Practice JEP@iiste.org
Journal of Law, Policy and Globalization JLPG@iiste.org Global knowledge sharing:
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Journal of Energy Technologies and Policy JETP@iiste.org Periodicals Directory, JournalTOCS, PKP
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Academic Search Engine, Elektronische
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International Affairs and Global Strategy IAGS@iiste.org OCLC WorldCat, Universe Digtial Library ,
Research on Humanities and Social Sciences RHSS@iiste.org NewJour, Google Scholar.
Developing Country Studies DCS@iiste.org IISTE is member of CrossRef. All journals
Arts and Design Studies ADS@iiste.org have high IC Impact Factor Values (ICV).