Post on 14-Apr-2018
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E h R V l l I I d P f l I d S k M k 2009 Th I f U P All R h R d
Exchange Rate Volatility:
Impact on Industry Portfolios in Indian Stock Market
This study examines the interaction between changes in the exchange rate of Indian Rupee and returns on different
BSE-based indices representing the firms of different sizes and industries. In absolute sense, the returns on all the
stock portfolios are found to be positively correlated with the external value of Indian Rupee. However, the analysis
with an extended market model of asset pricing shows that the indices of export-oriented industries are negatively
associated with change in exchange rate, after making the adjustment for market trend. Among them,
IT, technology and knowledge-based sectors show high sensitivity towards exchange rate fluctuations. On the other
hand, the indices of financial sector and import-intensive industries show a positive association with the exchange rate
of rupee. The Vector Autoregression (VAR) model shows one-way causality running from stock prices to exchange
rate. This suggests that the portfolio rebalancing activities of Foreign Institutional Investors (FIIs) have a moreimportant role in the dynamic interaction between stock prices and exchange rate.
K N Badhani*, Rajani Chhimwal** and Janki Suyal***
Introduction
The implementation of flexible exchange rate regime, full convertibility of rupee in current
account, and a gradual move towards full capital account convertibility have raised the
volatility of exchange rate, and the issue of exchange rate exposure has become quite important
for the corporate world. The volatility of the exchange rate of Indian Rupee in respect to US
Dollar during recent periods has caused anxiety in many quarters of the economy, particularly
export-oriented sectors such as IT and Business Process Outsourcing (BPO). Since, any
impact on competitiveness and profitability of a firm affects the future value of its expected
cash flow which, in turn, gets reflected in the market price of the its stock, this study makes
an attempt to evaluate the impact of exchange rate fluctuations in the stock prices of different
industry-specific portfolios. Economic theories suggest that under a floating exchange rate
regime, exchange rate appreciation reduces the competitiveness of local industries in
international market. It is likely to have a negative effect on the domestic stock market.
Conversely, in an import-oriented economy, exchange rate appreciation may have a positiveeffect on the stock market as it helps to lower the input costs.
The objective of the study is to examine the sensitivity of different industry-specific and
size-sorted stock portfolios towards changes in exchange rate. For this purpose, the study
uses daily data of exchange rate and different Bombay Stock Exchange (BSE) indices
* Associate Professor, Institute of Rural Management Anand (IRMA), Anand 388001, India. He is the correspondingauthor. E-mail: badhanikn@yahoo.co.in
** Research Scholar, Department of Commerce, DSB Campus, Kumaun University, Nainital 263002, India.E-mail: renu_3feb@yahoo.co.in
*** Lecturer, Department of Economics, Government P G College, Agastyamuni, Rudraprayag, India.E-mail: janki_suyal@yahoo.co.in
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representing different firm-size and industries. The results indicate that in absolute sense, an
appreciation in exchange rate of rupee has a positive impact on stock prices in general.
However, in relative sense, there is a negative impact of appreciation in the external value of
Indian Rupee on the stock prices of export-oriented industries such as Information
Technology (IT), technology and knowledge-based industries.
Review of Literature
After the end of Bretton Woods agreement in 1970, more and more countries adopted flexible
exchange rate regime. Increasing globalization led to the gradual abolition of foreign exchange
controls in the emerging economies together with tremendous increase in cross-border flow
of goods and capital. Adoption of flexible exchange rate regime has increased the volatility of
foreign exchange markets and the risk associated with foreign investments. Therefore, the
academicians as well as the investment managers have started taking great interest in studying
the interaction between stock and foreign exchange markets, as the stock market serves as a
composite indicator of the value of investments in an economy. This interaction can beexamined at different levelsat firm-level, at industry-level and at aggregate market level.
The flow-oriented model of Dornbusch and Fischer (1980) postulates that a change in
exchange rate affects a firms operational exposure, its competitiveness in the international
market and, consequently, its share prices. At macro level, the impact of exchange rate
fluctuations on stock market depends on the relative importance of international trade in
the economy and the nature of trade imbalances of the country. Ma and Kao (1990) find that
the currency appreciation negatively affects the domestic stock market for an export-dominant
country and positively affects the domestic stock market for an import-dominant country.
The portfolio balancing model (Branson, 1983; Frankel, 1983; and Smith, 1992), on the
other hand, suggests that the excessive foreign investment flow induced by booming capital
market increases the demand for local currency, which leads to appreciation of the currency.
Since the pay-off of foreign investors depends on changes in exchange rate as well as changes
in stock prices, they are likely to revise their portfolios according to their expectation about
future changes in exchange rate and stock prices. These expectations, in most of the cases, are
based to extrapolations of the past trends and the feedback trading behavior exhibited by
investors. When, on the basis of the past trends, the foreign investors expect an appreciation
in local currency, they increase their investment in the local market; consequently, the stockprices go up due to the increase in demand. Similarly, when stock price movements show an
upward trend, the foreign investors may increase their investment flows in the country
which pushes up the exchange rate. Therefore, past changes in exchange rate are likely to
cause changes in stock prices and past changes in stock prices are likely to affect the changes
in exchange rate.
While the portfolio balancing hypothesis postulates a short run bidirectional (feedback)
causality arising out of temporary excessive liquidity or illiquidity in the stock and forex
markets, a unidirectional causality running from exchange rate to stock prices is implied in
the flow-oriented model. In flow-oriented model, the correlation between exchange rate and
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stock prices may be positive or negative depending on the nature of trade imbalances of the
country, while the portfolio balancing model suggests a positive correlation between them.
The empirical evidences are rather mixed. Some studies report a positive correlation between
exchange rate and stock prices (e.g., Aggarwal, 1981; Roll, 1992; and Chiang et al., 2000),
while some report a negative relationship (Soenen and Hennigar, 1988; Friberg and Nydahl,
1999; and Gao, 2000). There are also some studies which report no relationship between them
(e.g., Chow et al., 1997).While some studies find longrun cointegration between exchange rate
and stock prices (e.g., Bahmani-Oskooee and Sohrabian, 1992; and Smyth and Nandha, 2003),
others find no cointegration between them (Rapp et al., 1999; and Morley and Pentecost, 2000).
The results of the studies also differ regarding the direction of causality between the variables.
For example, Bahmani-Oskooee and Sohrabian (1992) report bidirectional causal relationship
between stock-prices and exchange rate in the US, while Abdalla and Murinde (1997) report
unidirectional causality running from exchange rate to stock prices for India, Korea and Pakistan.
Ma and Kao (1990) attribute the differences in results to the nature of the trade imbalances in
the country, whereas Morley and Pentecost (2000) argue that the exchange rate control andcentral banks intervention in foreign exchange market may be responsible for a theoretically
inconsistent relationship between stock market and foreign exchange market.
At micro level, the conceptual relationship between stock prices of a firm (or firms in an
industry) and exchange rate is also based on the argument of the competitiveness. The
sensitivity of a firms economic value or its share prices towards changes in exchange rate is
referred to the firms exchange rate exposure (Hekman, 1983). The changes in exchange rate
affect a firms value because future cash flows of the firm will change with exchange rate
fluctuations. Shapiro (1975) argues that the firms exposure should be related to the proportion
of export sales, the level of foreign competition and the degree of substitutability betweenlocal and imported factors of production. Adler and Dumas (1984) show that even firms
whose entire operations are domestic may be affected by exchange rates, if their input and
output prices are influenced by exchange rate movements. Marston (2001) demonstrates
that the net foreign revenues of a firm are the main determinant of a firms exchange rate
exposure. He also argues that the exposure is a function of the firms own elasticity of demand
and the cross elasticity of demand with its competitors. Bonder et al. (2002) show that the
firms with high elasticity of demand have higher exchange rate exposure, while the firms
with inelastic demand can successfully pass on the price changes to consumers.
Since a firms export sales is understood to be the most important determinant of its
foreign exchange exposure, most of the studies have focused on this factor. However, results
of these studies again portray a mixed picture. Jorion (1990) shows that the level of foreign
sales is the main determinant of exchange rate exposure of the US multinational firms.
However, Amihud (1994) in the US and Dominguez and Tesar (2001) in eight non-US
countries find no relationship between foreign sales and exposure in the sample firms.
Another characteristic of a firm which is likely to have a significant implication for its
exchange rate exposure is its size. Size is likely to be associated with exposure in several ways.
First, large firms are likely to have more foreign activities relative to small firms; therefore,
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size serves as a proxy of a firms foreign activities. Big firms are likely to have more exchange
rate exposure than the small firms. Second, firm size is also often used as a proxy for the
amount of information available to the market regarding firms operations. Large firms are
more closely monitored by analysts, therefore, their stock prices are likely to adjust to new
information rapidly when compared to stocks of small firms. The market inefficiency argument
predicts that large firms have higher contemporaneous exposure, while the stocks of small
firms show a lagged effect for exchange rate exposure (Griffin et al., 2002). However, as
Allayannis and Ofek (2001) show, the use of foreign currency derivatives reduces the exposure
and large firms are more likely to use derivatives for hedging. Therefore, these firms may be
successful in reducing their exposure to some extent. Studies analyzing the relationship
between size and exposure show mixed results. He and Ng (1998) and Bonder and Wong
(2003) show that large firms have more exposure than small firms in the US and Japan.
Conversely, Dominguez and Tesar (2001) argue that the exposure varies little with firm size
(Muller and Verschoor, 2006).
In India, most of the studies on the interaction between stock market and foreign exchangemarket have taken up the issue at macro level (Bhattacharya and Mukherjee, 2003 and 2006;
Muhammad and Rasheed, 2003; and Badhani, 2005 and 2006). Most of these studies concluded
that in India, causality runs from exchange rate to stock prices and the flow of foreign portfolio
investment serves as an important intervening variable. These findings can be explained
with the help of portfolio balancing model. The present study aims to extend this analysis
further. Since in an industry, the firms have more homogeneous mix of inputs and outputs, it
is more likely that the firms of the same industry will have more similar exposures than the
firms from different industries (for country arguments, Williamson, 2001). Therefore, this
study examines exposure at the industry level following Dogan and Yalacin (2007). For thispurpose, the industry-specific indices were used. An attempt has also been made to examine
the size-effect on exposure using the indices representing the firms with varying market
capitalization size.
Data and Methodology
This study uses 16 BSE-based stock indices. Out of these, six represent different combinations
of the size of the firms market capitalization, while the remaining ten indices represent different
industries. The indices at BSE were constructed using value weighting system and free-float
methodology. The study covers a period of more than seven years, i.e., from January 2000 toMarch 2007. However, in case of a few indices, the actual sample period may differ due to
nonavailability of the data. Table 1 provides the details of the indices included in this study and
their sample periods. The dollar-rupee exchange rate is used to represent the external value of
the rupee. The exchange rate has been obtained from the Reserve Bank of Indias database and
converted into rupee denomination from the dollar denomination.
The daily closing values of all the indices are log transformed and differenced to obtain
the return on index (Rit). The change in exchange rate (Ex
t) is also obtained using the same
method. The stationarity of the data at level as well as at the differenced form is evaluated
using the Augmented Dickey-Fuller (ADF) and the Phillips-Parron (PP) unit root tests.
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Since these tests are sensitive to lag-length selection, we use Akaike InformationCriterion (AIC) for choice of the lag length. The results (Table 2) show that the indices
possess unit root at level but can be removed while differenced. Therefore, the returns on
indices and change in exchange rate are stationary and suitable for econometric modeling.
When data series are integrated of the same order, there is a possibility that the series may
be cointegrated. Cointegrated time series are associated with each other with a long run
equilibrium relationship. Engle and Granger (1987) show that if two or more variables are
cointegrated, the relationship between them must be modeled in the form of an
error correction model, at level rather without differencing. Valuable information is lost if
these are modeled simply in differenced form without accounting for their equilibriumrelationship. On the other hand, if variables are not cointegrated, they must be included in
an econometric model only after making them stationary through differencing. Therefore,
testing the cointegration among variables is an important step of time series modeling.
As discussed earlier, previous studies do not provide conclusive evidence on the issue whether
stock prices and exchange rates are cointegrated or not. Therefore, we examine the pair-wise
cointegration between stock price indices and exchange rate, using Johansens test (Johansen,
1988; and Johansen and Juselius, 1990). The Johansens test is sensitive towards specification
of intercepts and trends in the Vector Autoregression (VAR) equation. Following Pantula
principle, we used the test with five possible combinations of the specifications of these
Index Sample Period
Sensex 3/1/2000 to 31/3/2007
BSE 100 3/1/2000 to 31/3/2007
BSE 200 3/1/2000 to 31/3/2007BSE 500 3/1/2000 to 31/3/2007
Mid-Cap 11/4/2005 to 31/3/2007
Small-Cap 11/4/2005 to 31/3/2007
Auto 3/1/2000 to 31/3/2007
Metal 3/1/2000 to 31/3/2007
Consumer Durables 3/1/2000 to 31/3/2007
FMCG 3/1/2000 to 31/3/2007Bankex 1/1/2002 to 31/3/2007
Oil and Gas 3/1/2000 to 31/3/2007
IT 3/1/2000 to 31/3/2007
Capital Goods 3/1/2000 to 31/3/2007
Healthcare 3/1/2000 to 31/3/2007
TECK 31/1/2002 to 31/3/2007
Table 1: List of the Indices Included in the Study
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Table
2:UnitRootTests
Variable
ADFTest
PPTest
AtL
evel
AtFirstDifference
AtLevel
AtFirs
tDifference
WithConstant
WithConstantWithConstantWithConstantWithConstan
tWithConstantWithConstantWithConstant
WithoutTrend
andTrend
WithoutTrend
andTrend
WithoutTrend
andTrend
WithoutTren
d
andTrend
Sensex
1.1
6
1.5
6
7.9
4**
8.2
5**
1.5
9
3.7
0
1701.2
8**
1625.3
3**
B
SE100
0.8
0
1.8
1
8.0
6**
8.3
5**
1.0
6
4.9
0
1743.6
9**
1681.3
4**
B
SE200
0.4
7
2.1
2
7.9
9**
8.2
5**
1.0
1
5.9
1
1737.2
6**
1679.7
5**
B
SE500
0.5
1
2.1
0
7.9
4**
8.2
1**
1.0
3
5.8
5
1736.2
1**
1677.0
5**
M
id-Cap
1.6
4
2.0
8
5.3
1**
5.4
1**
4.3
0
11.8
7
424.9
7**
417.2
3**
Small-Cap
2.1
6
2.3
0
4.9
2**
5.0
3**
7.7
9
12.6
5
392.8
9**
384.8
7**
A
uto
0.3
8
2.5
9
8.4
0**
8.5
4**
0.4
2
7.7
5
1814.9
7**
1761.5
2**
M
etal
0.3
2
3.1
6
7.3
8**
7.4
1**
0.6
0
15.7
2
1829.0
5**
1814.8
6**
C
onsumer
0.2
4
2.2
6
6.9
3**
7.3
2**
0.8
8
5.8
3
1976.2
1**
1894.2
7**
D
urables
F
MCG
0.6
5
1.8
2
7.9
7**
8.0
9**
1.5
5
5.7
0
1673.1
2**
1649.6
4**
B
ankex
0.0
9
2.7
6
7.1
0**
7.1
0**
0.1
7
15.5
1
1048.4
7**
1046.8
9**
O
ilandGas
0.9
9
2.2
8
7.8
0**
7.9
1**
1.6
1
7.6
0
1665.0
3**
1632.1
8**
IT
3.3
0*
4.7
6**
7.6
8**
7.9
6**
6.5
4
8.2
3
1722.0
5**
1679.6
6**
C
apitalGoods
1.5
3
1.2
3
8.5
2**
8.7
7**
1.8
0
3.3
8
1887.9
9**
1804.9
0**
H
ealthcare
0.5
1
3.5
7*
7.0
3**
7.2
0**
0.3
0
14.9
8
1612.2
5**
1569.8
3**
T
ECK
1.4
7
3.7
3*
7.3
3**
7.8
6**
3.2
5
5.7
0
1577.6
8**
1491.2
4**
E
xchangeRate
1.3
1
2.2
5
7.3
3**
7.4
9**
4.5
3
7.2
5
2032.3
2**
1981.9
4**
N
ote:**p