exc rate volatility

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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: [email protected]

** Research Scholar, Department of Commerce, DSB Campus, Kumaun University, Nainital 263002, India.E-mail: [email protected]

*** Lecturer, Department of Economics, Government P G College, Agastyamuni, Rudraprayag, India.E-mail: [email protected]


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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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E h R V l l I I d P f l I d S k M k

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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E h R V l l I I d P f l I d S k M k

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

exc rate volatility

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