Assessing the pricing of exchange rate risks

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Full Length Article

Dynamics of the impact of currency fluctuations on stock markets in India:

Assessing the pricing of exchange rate risks

Smita Mahapatra * , Saumitra N. Bhaduri

Madras School of Economics, Chennai, Tamil Nadu, India Received 3 March 2017; revised 2 April 2018; accepted 28 April 2018

Available online 21 July 2018

Abstract

This paper studies the dynamics of the impact of currency fluctuation on Indian stock market by assessing the pricing of exchange rate risk during the period 2005e2016, specifically before and after financial crises. Estimating a two-factor arbitrage pricing model, using a random coefficient model, the paper presents evidence that stock returns react significantly to foreign exchange rate fluctuations in the post-crisis period.

Particularly, during the last four years of our sample, 2012e2016, the exchange rate risk factor is becoming a prominent determinant of stock returns, indicating that Indian investors are increasingly expecting a risk premium on their investment for their added exposure to exchange rate risk. This is also further corroborated by the study by highlighting the fact that higher the foreign exchange exposure of industry, measured by trade balance (net inflows), higher is their sensitivity to exchange rate risk (b^S). A plausible reason for such premium could be the inadequate hedging by Indian firms to mitigate the exchange rate risk.

Copyright©2018, BorsaIstanbul Anonim S¸irketi. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-_ ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

JEL classification:G01; G11; G12; G32

Keywords:Exchange rate risk; Arbitrage pricing theory; Stock returns; Risk premium; Hedging; Financial crisis

1. Introduction

As the global economy is gradually regaining its balance after the 2007e08 recession and the European financial crisis, a gradual process of tightening monetary policy by the U.S.

and other large economies has been mounting pressure on the currencies of some major EMEs (Emerging Market Econo- mies). This had led to a sell-off in several currencies in early 2014 indicating a potential crisis in these countries that could destabilize the ongoing global recovery process. Many EMEs have been hit by currency crises over the past two to three

decades of which the most obvious example is the Asian financial crisis in 1997e1998.

The present study is focused on analyzing the impact of such exchange rate fluctuations in recent years on the stock market of one of the important emerging markets, India.

Specifically, the study examines the reaction of investors in terms of changes in premium demands due to the added exposure of risks associated with this kind of vulnerability.

For this analysis, the questions addressed are threefold:

First, whether the exchange rate fluctuations impact stock markets such that it prices the exchange rate risk as a part of the market premium. Secondly, how the shocks in foreign exchange market affect this relationship. And finally, if the currency crisis periods are more vulnerable to such changes in investor sentiments.

The financial theory argues that in a well-developed financial market, foreign exchange risk is a part of the un- systematic risk that can be hedged away. According to the

*Corresponding author. Madras School of Economics, Gandhi Mandapam Road, Behind Government Data Center, Kottur, Chennai, Tamil Nadu, 600025, India.

E-mail addresses: [email protected] (S. Mahapatra), [email protected](S.N. Bhaduri).

Peer review under responsibility of Borsa_Istanbul Anonim S¸irketi.

Borsa _ Istanbul Review

Borsa_Istanbul Review 19-1 (2019) 15e23 http://www.elsevier.com/journals/borsa-istanbul-review/2214-8450

https://doi.org/10.1016/j.bir.2018.04.004

2214-8450/Copyright©2018, BorsaIstanbul Anonim S¸irketi. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND_ license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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modern portfolio theory, only risks that cannot be diversified away (systematic risks) should be priced by the capital market i.e., shareholders must be paid a premium for bearing such risks. The exchange rate fluctuations can influence firm performance in two ways: The direct impact could be on net foreign monetary and real domestic assets of the firm. Indi- rectly, it may affect aggregate demand, the cost of traded inputs, competing imported goods and inflationary expectations.

However, as these foreign exchange risks are perceived as insignificant or diversifiable through active hedging, these sources of risks should not be priced and hence should not impact firms’ cost of capital. However, the more recent Arbitrage Pricing theory proposed by Ross (1976) suggests that if the economy can be represented by certain pervasive factors, then these factors may be priced in the sense that investors will be willing to pay a premium to avoid these sources of risk (Jorion, 1991). To date, a number of studies have investigated the possible sources of exchange rate exposure but not many have addressed the problem of empirically measuring whether this exposure commands a risk premium in the stock markets. The ones that have, are mostly centered around developed economies.

In a pioneering study, Jorion (1991) examines such risk- rewards on the US stock markets from the year 1971e1987 using a multi-factor framework of Arbitrage Pricing models.

The relation between expected returns and the sensitivity to market and exchange rate movements is analyzed using a two- factor model. The empirical results suggest that the unconditional risk premium associated with foreign currency exposure appears to be insignificant and exchange rate risk is not priced in the U.S. stock markets. On similar lines, the study byChen, Roll, and Ross (1986)finds that macroeconomic variables like industrial production, expected and unexpected inflation, the spread between long and short interest rates, and the spread between high and low-grade bonds are sources of risk that are significantly priced. Whereas market portfolio, oil price risks and aggregate consumption are not priced in the stock markets. Sweeney (1986) conducted a comparable study using interest rate changes (yield on long-term U.S. government bonds) as the second factor instead of exchange rates. It was empirically shown that most interest-sensitive stocks were in the utility industries and there was reasonable evidence that the interest factor was priced in the sense of APT (two-factor model). Some studies find significant exchange rate risk using conditional models. For example, De Santis and Gerard (1998), using a parsimonious multivariate GARCH process, have found that a significant fraction of the total risk premium is generally represented by the premium for bearing currency risk for four countries: Germany, Japan, the United Kingdom, and the United States. A similar study by Choi et al., 1998 reports that ‘conditional currency risk’plays a crucial role in explaining the stock returns of firms in Japan.Patro, Wald, and Wu (2002)uses a GARCH framework with a panel approach and estimates a time-varying two-factor asset pricing model for weekly equity index returns of 16 OECD countries. Using the trade-weighted basket of exchange rates and the MSCI world market index as risk factors, they report significant

currency risk exposures in country equity index returns.

Several other studies find significant foreign exchange exposure using unconditional factor models on international data.

For instance,Roll (1992) as one of the three explanatory influences as per his inferences, documents that exchange rate fluctuations explain a major portion of the variation in equity index returns for developed countries. Ferson and Harvey (1994)empirically examine multifactor asset pricing models for the returns on eighteen developed markets. The factors chosen, measure global economic risks and they conclude that foreign exchange risk is one of the most crucial factors in explaining international equity index returns.

Since developing countries have a very different institu- tional framework and economic environment, it is important to study this aspect of these economies, distinctly. For instance, within EMs there are commodity exporters (Brazil, South Africa, Russia) and Commodity Importers (India, China, Turkey). Emerging markets like India, Brazil, Turkey, etc.

have been attracting enormous amounts of capital from international investors over the past decade to give a boost to their rapidly growing economies. However, unlike large economies, EMEs are faced with a threat of ‘capital flight’if there is any instability detected in the economy by investors.

Further, the impact of global changes is not of the same intensity in all emerging markets. It depends on upon factors like the liquidity in the market and the access to international markets. Some investors and analysts are suspicious of a looming financial crisis in emerging markets while others argue that the root cause of vulnerability in exchange rates lie in structural problems in the domestic economies of the EMEs rather than the effect of external shocks.

At an aggregate level, there are various studies showing a relationship between exchange rate changes and stock returns.

For instance, Chun Mun (2008) examines the effect of fluctuations in foreign exchange rates on international stock market volatility and cross-market correlations between the U.S. stock markets and the appreciating/depreciating local currencies of the sample countries considered. These include the Pacific-basin developing countries which depend, to a great extent on international trade and equity flows through FDIs for their economic growth. The Asian financial crisis indicates the period of financial turmoil and fluctuations in exchange rates. The observations made by the empirical analysis suggested that fluctuating exchange rates significantly contribute to a higher local equity market volatility and the post-crisis contribution of exchange rate imbalance on the U.S.-local market correlation was higher than what it was before the crisis. On similar lines,Aquino (2005)investigates the foreign exchange exposure faced by Philippine firms around the Asian financial crisis period (1992e2001) and finds that while stock returns were not impacted by exchange rate fluctuations before the crisis, there was a significant impact of the fluctuations in stock returns after the crisis i.e., after 1997.

The analysis was based on the two-factor APT model which is the basis of the present study as well. The study also finds that during the post-crisis period, investors started expecting a premium on their investments for their added exposure to

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exchange rate risk as perceived by them. Another interesting study byChkili and Nguyen (2014)investigates the dynamic relationship between stock market returns and exchange rates for the BRICS nations between the years 1997 and 2013 using a regime switching model. The results from the Markov Switching model applied on the data suggests that stock markets have more influence on exchange rates during both low and high volatility periods. Bailey and Chung (1995) studies the impact of political and exchange rate risks on the risk premiums reflected in equity returns of individuals from Mexico and finds some evidence of equity market premiums for exposure to these risks. Another interesting study byHan, Xu, and Yin (2017) empirically investigates the impact of investor attention on the movement of exchange rates for 9 countries. Google Search Volume of key terms such as“Dol- lar”,“USD”etc. has been used as a proxy for ‘attention’. The findings highlight that lagged investor attention significantly influences currency returns although the effect is short term.

The results also demonstrate the reverse analogy that changes in exchange returns have an impact on investor attention and that too a long-lasting one.

Overall, these investigations suggest that the risk factors that are essentially ‘priced’by investors may vary across space and time and hence the results and conclusions of these studies cannot be generalized. The present study aims to fill the gap in the literature by extending such analyses to the developing world by focusing on India.

Two major financial crises in the last decadeeThe Global Financial crisis (2007e08) and the Eurozone Debt crisis (2010e11), that shook the world have brought about an increasing interest among researchers to study about financial markets with the backdrop of these crisis years and understand the impact of such structural breaks. Several studies have suggested that during crisis periods, there is an increased co- movement of markets and a rise in exchange rate volatility which in turn has significant implications for the risk manage- ment strategies of international investors. A study along these lines by P. Dua and Tuteja (2016) examines the financial contagion and cross-market correlation between the stock and currency markets of China, Eurozone, India, Japan and the US during the two global crisis periods mentioned above. The results show significant contagion across the asset classes and demonstrate the non-existence of the benefits of portfolio diversification for stock markets as shocks after crises are transmitted internationally. These findings make it important for one to study the impact of any such shock or instability on the perception of investors about their expectation on risk premiums and stock returns during such crisis periods. Our sample, therefore, is justified as it covers these years in particular.

In the light of currency crises and other such events in the EMEs in the recent past, a country like India which is one of the fastest growing economies in the world makes for an appropriate choice for our study. Given the increasing openness of the Indian economy, Indian companies can be substantially affected by movements in the value of the currency. A strong evidence of this relationship and the volatility spillover between the Indian stock markets and foreign exchange markets has been examined

byMishra, Malhotra, and Swain (2007). The results of the study show that volatility in both markets is highly persistent and predictable based on past innovations.

India had seen a near currency crisis situation in the year 2013 which was mainly caused due to two reasons. Firstly, many EMEs including India were caught off guard with their domestic currencies weakening against the US dollar when the US Federal Reserve floated the possibility of a taper in May 2013 and tightening of monetary policy. The second reason being that on the domestic front, the Rupee depreciation situation as a result of stagnant reforms and declining foreign investments, was compounded by the government passing the Food Security Bill in late 2013 as a populist pre-election strategy. Thus, apart from a structural break due to the global financial crisis of 2008, it is expected that the period around 2013 which seems to be a high volatility exchange rate regime also exhibits a risk that could affect investor sentiments in India to some extent. The current study empirically shows that currency risk is increasingly being perceived as a risk that is priced by investors in the Indian stock markets in the recent years post the global financial crisis in 2008.

The paper is arranged into four sections, the first of which introduces the topic, purpose of the study and describes the theoretical framework in which the analysis has been conducted. Section 2 describes the model and hypothesis tested.

The third section on ‘Sample’ explains the construction of variables along with their sources. This is followed by a section on the ‘Methodology and Empirical Results’ of the various statistical tests performed, and the final chapter con- cludes the study with some important inferences.

2. Empirical model

The hypothesis of the study and details of the two-factor APT model employed have been explained in this section.

We attempt to test the hypothesis that the exchange rate risk is not priced as a premium by investors during low exchange rate volatility regimes, however during (near) currency crisis periods foreign exchange rate risk essentially becomes a risk that is priced in the stock markets.

The model for the analysis has been adopted from a study by Jorion (1991)who has followed Ross's approach of Arbi- trage Pricing Theory (APT).

The two-factor version ofRoss (1976)APT implies a linear relationship between expected returns and the sensitivity to market and exchange rate movements:

ER~i

¼ d₀þd₁b^m_i þd_sb^s_i ð1Þ

where.

Returns can be defined as nominal returns in excess of the risk-free rate,Rit¼R^*_itRfi,

b^m_i : ith stock's sensitivity to market movements and b^s_i : ith stock's sensitivity to exchange rate movements.

In this model, the market excess return could be interpreted as a transformation of the original factors which are not

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observable anyway. Since the market's sensitivity to exchange rate movementsb^m_s ¼0 by construction, this implies

ER~_i

¼ d₀þ ER~_m

d₀

b^m_i þd_sb^s_i ð2Þ Assuming stationarity, the rate of return on an asset at time t can be statistically decomposed into an expected component EðR~itÞand an innovation,

R~_it¼ER~_it

þb^m_iR~_mtER~_mt

þb^s_iF~_stþ~εit ð3Þ where, ~Fst : residual of the regression of the exchange rate movement against the rate of return on the stock market:

F~st¼R~st b

g₀þgb₁R~mt

ð4Þ Given that there will be some correlation between the exchange rate and the market, one cannot simply use the exchange rate as the second factor. Without orthogonalization, the second factor could appear to be priced because of a nonzero correlation with a priced market.

Under rational expectations, substituting (2) in (3) yields:

R~_it¼ d₀

1b^m_i þd_sb^s_i

þb^m_iR~_mtþb^s_iF~_stþ~εit ð5Þ This is the restricted model to be tested.

Exchange rate exposure orthogonal (statistically independent)to the market, is priced if the coefficientds is non-zero, which would imply a rejection of the mean-variance efficiency of the market.

The unrestricted equation is given by

R~_it¼a_iþb^m_iR~_mtþb^s_iF~_stþ~εit ð6Þ Given the restricted and unrestricted models, the cross- section restriction is tested by a likelihood ratio test.

3. Data

The study uses secondary data for analysis. The dependent variable in the test equation (6), ‘~Rit’ is a series of returns (excess of risk-free rate) on companies'common stocks. This data was collected using Prowess which is a database of the financial performance of Indian companies maintained by Centre for Monitoring Indian Economy (CMIE). All the companies listed in the S&P BSE 500 index have been included in the analysis. The data on the monthly adjusted closing prices for the period January 2005eJanuary 2016 were collected for each of these 500 companies and fifteen portfolios (number of companies in each portfolio given in paren- thesis) were created based on the pre-defined sets given in Prowess. The average of the company stock prices for each month was taken as the portfolio averages on which monthly returns were calculated. The portfolios are:

1. Food&agro-based products (26) 2. Textiles (16)

3. Chemicals and chemical products (78) 4. Consumer goods (21)

5. Construction material (24) 6. Metals and metal products (24) 7. Machinery (31)

8. Transport equipment (26)

9. Miscellaneous manufacturing (9) 10. Diversified (11)

11. Mining (6) 12. Electricity (15)

13. Services - other than financial (102) 14. Construction and Real Estate (32) 15. Financial Services (79)

For the variable, R~mt which is the market excess returns series, data on the monthly closing prices were collected from the official Bombay Stock Exchange (BSE) website. S&P BSE 500 index represents almost 93% of the total market capitali- zation on BSE. It can be considered as a good proxy for the market index since the underlying 500 companies cover all the major industries of the economy and BSE is the oldest stock exchange in India. The risk-free rate was calculated as the log of ten-year bond yield monthly rate collected from RBI. The exchange rate return series (~R_stÞwas calculated from the USD- INR*¹ monthly average currency rates collated from the RBI website. The exchange rate can be defined as the domestic currency price of a unit foreign currency. The second independent variable used in the test equation isF~st, which is the residual of the regression of the exchange rate movement against the rate of return on the stock market.

‘Returns’, as the rate of change of each of the above variables were calculated as the natural log of the ratio of the prices of one period to its previous period. The benefit of using returns over prices is in ‘normalization’ (measuring all variables in a comparable metric) that it provides. If prices are assumed to be distributed lognormally, then log returns will follow a normal distribution and that is a major advantage since most of the classical statistics presume normality.

The timeline for this study is between January 2005 and January 2016 (Returns data begin from February 2005 since it is calculated as a ratio of current to the previous time period). The entire period of study has been divided into three sub-periods with the purpose of accommodating the possible structural breaks caused due to the financial crisis or near-crisis situations that would help us comprehend whether and if so, then in what way investors reacted to such relative instability (Table 1).

GDP growth rates depicted inFig. 1, highlights the possible structural breaks considered.

4. Methodology and Empirical Results

The market excess return series and exchange rate series have been tested for stationarity using the ADF test. The correlation between the excess market return series and the exchange rate series is calculated to be 0.125 which is a reasonable explanation to justify the use of the residuals of the regression between these two variables in order to ensure orthogonality.Fig. 2shows a basic time series plot of the changes in exchange rate series to

1 It must be noted that using USD-INR as the exchange rate measure reflects 80% of the foreign exchange market in Inda. An alternative measure, INR NEER which takes 36 currencies into account based on their weights, might provide a broder base proxy and can be considered for future research.

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highlight the fluctuations in this variable in the period considered. The substantial volatility of the exchange rates in the speculated currency crisis years of 2008 and 2013 can be inferred on observing the line graph.

The exchange rate exposure represented by the variable,F~st

is calculated first. The parameters, bg₀andg^₁in equation (4) are estimated using Ordinary Least Squares and the F~st series is constructed which is used as the second independent variable in our final test equation(5).

The Hausman test between random and fixed effects suggested that a random effects model would fit the data better.

However, according to Hsiao and Pesaran (2004), “Conven- tional models do not allow the interaction of the individual specific and/or time-varying differences in the included explanatory variables.” This was the rationale behind using Random Coefficient models for estimation with the kind of panel data we have.Aquino (2005)uses the Generalized Least

Squares Seemingly Unrelated Regression estimation technique to simultaneously estimate the betas and pricing parameters.

Due to the advantages that Random coefficient models provide, especially in terms of degrees of freedom and simplified computation, the present study chooses to use this estimation technique. These models are called as random coefficient models because they assume that the individual specific regression parameters are random, i.e. each represents a draw from a population. All the variants of these type of models are a particular case of the more general class of linear mixed effects models. Linear mixed effects models can be thought of as extensions of linear regression models for the cases where data needs to be summarized in groups. In Swamy's Random Coefficient linear regression model, rather than only the inter- cept varying across groups, all the coefficients are allowed to vary. The ‘xtrc’ function in the software Stata, that uses the Swamy Random Coefficient model (Swamy, 1970) has been used to estimate the beta coefficients (b^mandb^s) in the unrestricted equation(6)which gives us the market and exchange rate exposure coefficients for the fifteen industry portfolios. The possibility of a non-linear relationship between stock returns and foreign exchange exposure has been explored by including the quadratic term of exchange rate variable in the regression.

Our main findings remain invariant post such robustness test.

Most of the industry portfolios except for few industries such as

‘Electricity’, ‘Construction and Real Estate’ and ‘Financial Services’, we do not observe non-linearity.

The coefficients across industries seem to be very different in all the time periods. Most of the industries have recorded a significant negative exposure which might imply that these industries tend to import a considerable portion of their factor inputs and are vulnerable to currency depreciation. On the other hand, an industry like “Financial Services” shows a significant positive exposure for the entire time period of analysis and the second sub-period chosen. This indicates export-oriented foreign operations and that the industry tends to benefit from currency depreciation. A theoretical intuition along these lines could be that higher sectoral import intensity can influence the impact of exchange rate changes on national stock returns. To validate this hypothesis, the exchange rate exposure (b^s) for each industry portfolio was plotted against its trade balance (Refer Fig. 3 in Annexure). Except for two sectors (Financial services and Misc. manufacturing), it could be observed that industries with higher imports had a ‘significant’ negative exposure. Future research is needed to elaborate on this by using alternate measures of international exposure.

Tables 2e5report the systematic risk and the exchange rate exposure of the industry portfolios. The exposure coefficients reported, after projection on the market risk factor and the GDP Growth (Annual %)

12 10 8 6 4 2 0

2005 2006 2007 2008 2009 2010 Year

2011 2012 2013 2014 2015

% GDP growth rate

Fig. 1. Annual Growth Rate of GDP at Market Prices based on Constant Rupee. Note: Based on official government statistics, the new base year is 2011/12. The report was made using the data from the official website of the Reserve Bank of India.

Exchange rate ﬂuctuaons

0.12 0.1 0.08 0.06 0.04 0.02 0 -0.02 -0.04 -0.06

Month

Returnonexchangerate Feb-05 Aug-05 Feb-06 Aug-06 Feb-07 Aug-07 Feb-08 Aug-08 Feb-09 Aug-09 Feb-10 Aug-10 Feb-11 Aug-11 Feb-12 Aug-12 Feb-13 Aug-13 Feb-14 Aug-14 Feb-15

Fig. 2. Exchange Rate Fluctuations (February 2005eJanuary 2016). Note:

Time series line plot of the exchange rate returns series to highlight the fluctuations in the period considered. Monthly data on the USD-INR exchange rates were obtained from the official website of Reserve Bank of India.

Table 1

Explanation of subsample periods.

Sub-period Feb 2005eDec 2007 Jan 2008eDec 2011 Jan 2012eJan 2016

Rationale Pre-crisis period of presumed overall economic stability and high GDP growth rates.

The global financial crisis period with the initial plunge

in growth rate followed by gradual recovery.

A near-crisis situation in FY 2012e13 with another plunge in GDP growth rate and a volatile rupee value, followed by gradual recovery.

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exchange rate factor are denoted asb^mandb^srespectively. A test of the joint significance was conducted using the Wald test with the null hypothesis that coefficients are jointly equal to zero, results of which have been reported below each table.

The null is rejected for the market return but not rejected for exchange rate exposure when tested for the entire study period (Feb 2005 to Jan 2016). For the first sub-period (Feb 2005 to Dec 2007), the null is rejected for both, the market return series and for exchange rate exposure. The results for the

second sub-period (Jan 2008 to Dec 2011) and the third sub- period (Jan 2012 to Dec 2016) show that the null hypothesis is rejected for the market return but not rejected for exchange rate exposure (at 5% level of significance).

To statistically test the difference in exposure coefficients across the industry portfolios, the t-statistics for equality of the coefficients had been performed. The null hypothesis of equal exchange rate betas is rejected for all the periods except the first sub-period (Feb 2005 to Dec 2007). This implies that

Fig. 3. Exchange rate exposure of Industry portfolios with respect to their Trade Balance. Note: X axiseTrade Balance (Net Inflows) of each Industry portfolio (in US billion $) for FY 2015e16. Y axiseEstimated Exchange rate exposure coefficient (b^s_i) from the equation:R~it¼aiþb^m_iR~mtþb^s_iF~stþ~εit. *The highlighted industries (shaded red) represent the portfolios that are significant at 5% los. Data Source: Trade data retrieved from ‘India's Foreign Trade 2015e16: An Appraisal’ report prepared by the Economic Division of Credit Analysis and Research (CARE).

Table 2

Market and exchange rate exposure of industry portfolios. February 2005eJanuary 2016 (monthly data). Model:R~it¼aiþb^mi R~mtþb^siF~stþ~εit.

S.No. Industry b^mi b^si

Coeff z-stat p-value Coeff z-stat p-value

1 Mining 1.0934 17.66 0.000 0.2889^a 2.41 0.016

2 Food and Processing 0.8000 20.89 0.000 0.0709 0.67 0.506

3 Textiles 0.9743 18.12 0.000 0.1217 1.05 0.292

4 Chemical 0.8178 25.73 0.000 0.1503 1.50 0.135

5 Consumer 0.7433 16.01 0.000 0.1838 1.65 0.100

6 Construction material 0.9856 19.21 0.000 0.1765 1.54 0.122

7 Metal and metal products 1.0599 27.04 0.000 0.3879^a 3.62 0.000

8 Machinery 1.0554 24.62 0.000 0.3633^a 3.31 0.001

9 Transport Equipment 0.9676 21.12 0.000 0.1070 0.96 0.336

10 Misc. manufacturing 0.9097 18.04 0.000 0.1384 1.22 0.224

11 Diversified 1.0677 24.04 0.000 0.3469^a 3.14 0.002

12 Electricity 1.3849 16.73 0.000 0.6579^a 4.89 0.000

13 Construction and real estate 1.3747 24.95 0.000 0.6739^a 5.80 0.000

14 Financial Services 0.6571 24.57 0.000 0.2588^a 2.77 0.006

15 Services (other than financial) 0.9594 26.34 0.000 0.0348 0.33 0.741

Test for joint significance: chi2(1)¼292.56 chi2(1)¼3.18.

Prob>chi2¼0.0000 Prob>chi2¼0.0747.

a Significance at 5% level. All coefficients are significant for the market coefficient,bim and are hence not marked for significance specifically.

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relative stability of exchange rates is a feature of the first sub- period while the others show variability in sensitivity to exchange rate fluctuations. The instability reported in the post- crisis periods and the entire period indicates the importance of the present study. It is evident that market exposure is significant as the null of equal market betas is strongly rejected in all the time periods considered. We thus focus on the pricing of the exchange rate factor since a difference has been observed in the time periods under consideration. Overall, it can be said that sorting by industries generates a substantial dispersion in exposure coefficients, which is an important pre- requisite for pricing tests.

The final test equation(5)has been estimated by regression after plugging in the beta coefficient values obtained

from the previous estimation results of equation (6). The restricted equation to be tested is a linear form equation with four parameters d₀, d_s, b^m_i and b^s_i. The beta coefficients within the restriction, represented in square brackets in equation (5) are treated as dependent variables. The values for d₀and d_s along with their statistics and p-values are reported in Table 6.

The null hypothesis that the constant risk premium coefficient, representing the pricing of exchange rate exposure, d₀¼0 is rejected for all the periods considered. This implies that the model in use accounts for the total differential in excess returns across the different time periods.

The null hypothesis that d_s ¼0 is rejected (at 5% significance level) for the whole period (2005e2016) and for the

Table 3

Market and exchange rate exposure of industry portfolios. February 2005eDecember 2007 (monthly data). Model:R~it¼aiþ b^m_iR~mtþb^s_iF~stþ~εit.

Coefficient z-stat p-value Coefficient z-stat p-value

1 Mining 1.1758 8.16 0.000 0.4366^a 2.34 0.020

3 Textiles 1.0924 11.01 0.000 0.3262 1.64 0.101

4 Chemical 0.8810 12.06 0.000 0.1248 0.61 0.544

5 Consumer 0.6929 6.36 0.000 0.1314 0.67 0.503

8 Machinery 0.9204 9.04 0.000 0.3192 1.61 0.107

9 Transport Equipment 1.0684 11.12 0.000 0.4122^a 2.06 0.039

11 Diversified 1.1567 13.71 0.000 0.4386^a 2.16 0.031

12 Electricity 1.3447 8.54 0.000 0.5096^a 2.79 0.005

14 Financial Services 0.5672 11.61 0.000 0.1184 0.63 0.531

Table 4

Market and exchange rate exposure of industry portfolios. January 2008eJanuary 2012 (monthly data). Model:R~it¼aiþ b^m_i ~Rmtþb^s_iF~stþ~εit.

1 Mining 1.0811 16.87 0.000 0.3519 1.81 0.071

3 Textiles 0.9914 0.69 0.000 0.2591 1.3 0.194

4 Chemical 0.8494 23.68 0.000 0.0906 0.60 0.549

5 Consumer 0.8009 15.04 0.000 0.2729 1.49 0.135

8 Machinery 1.0673 22.12 0.000 0.4232^a 2.41 0.016

11 Diversified 1.0776 20.67 0.000 0.6169^a 3.40 0.001

12 Electricity 1.3022 15.70 0.000 0.7721^a 3.62 0.000

14 Financial Services 0.6937 16.60 0.000 0.4586^a 2.79 0.005

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latest sub-period (2012e2016). It is rejected (at 10% loss) for the second sub-period i.e. between 2008 and 2012. The null is not rejected for the first sub-period before any crisis which shows that exchange rate risk was not priced in this period of stability.

The chi-square goodness of fit statistic is computed using the restricted and unrestricted models and reported in the last column ofTable 6. The likelihood ratio statistics for the entire period and the most recent subperiod (2012e2016) suggest that the model is reasonably well specified and seems to fit the data in the periods in which the pricing coefficients are significant.

The observations indicate that the results for the pricing coefficients are significant for the periods considered post- 2008 which suggest that after the onset of the global financial crisis, there was a slight change in the perception of investors. In the last four years (2012e2016) however, the exchange rate risk factor is becoming prominent as being

‘priced’in the stock market. This essentially means that investors are progressively expecting a risk premium on

investments for their perceived added exposure to exchange rate risk and it has become more prominent in the recent years.

5. Conclusion

The impact of foreign exchange fluctuations on stock returns is increasingly becoming a prominent issue to investors, financiers and policymakers. The paper examines the exposure of fifteen Indian industry groups (comprising the 500 firms listed in the S&P BSE 500 index) to the monthly USD- INR exchange rate fluctuations for different time periods with the overall period being eleven years from February 2005 to January 2016. The analysis is done in the two-factor APT framework.

Our choice of sample period also allows examining the effect of exchange rates on stock markets and the overall investor sentiment in India in the last decade which has not been explored so far, in best of our knowledge. This is an important aspect of the currency crisis that is seen imminent in the forthcoming years and is affecting the investor

Table 5

Market and exchange rate exposure of industry portfolios. January 2012eJanuary 2016 (monthly data). Model:R~it¼aiþb^m_iR~mtþb^s_iF~stþ~εit.

1 Mining 0.9119 10.77 0.000 0.1476 1.17 0.241

3 Textiles 0.8117 7.88 0.000 0.0168 0.13 0.896

4 Chemical 0.7086 9.99 0.000 0.0948 0.76 0.445

5 Consumer 0.7244 8.19 0.000 0.0292 0.23 0.817

6 Construction material 1.1812 12.34 0.000 0.2922^a ¡2.29 0.022

7 Metal and metal products 0.8285 9.58 0.000 0.1451 1.15 0.250

8 Machinery 1.1805 13.75 0.000 0.2891^a ¡2.29 0.022

11 Diversified 0.9366 9.25 0.000 0.0702 0.55 0.584

12 Electricity 1.3149 12.21 0.000 0.3651^a ¡2.82 0.005

13 Construction and real estate 1.3437 17.40 0.000 0.4177^a ¡3.34 0.001

14 Financial Services 0.7337 16.94 0.000 0.0529 0.49 0.627

Table 6

Tests of Pricing of Exchange Rate Exposure. (Monthly data). Two Factor Model:R~it¼ ½d0ð1b^m_iÞ þdsb^s_i þ b^m_iR~mtþb^s_iF~stþ~εit. Period

Factor Prices (t-statistics) [P>jtj] Test of Fit [p-value]

d0 ds c2

Feb 2005eJan 2016 0.0258696^a 0.0144305^a 0.95

(2.49) [0.013] (2.29) [0.022] [0.3302]

Feb 2005eDec 2007 0.0568258^a 0.0025197 14.12

(-3.54) [0.000] (0.27) [0.787] [0.0002]

Jan 2008eDec 2012 0.0860055^a 0.0143851 24.38

(4.12) [0.000] (1.67) [0.096]^b [0.0000]

Jan 2012eJan 2016 0.0239179^a 0.0213261^a 7.23

(2.58) [0.010] (-2.05) [0.040] [1.0000]

Note: t-statistics between parentheses, marginal significance levels between brackets.d0anddsare the pricing coefficients in the test equation. The chi-square statistic tests the cross-sectional restrictions in the two-factor model.

a Significant at the 5% level.

b Significant at the 10% level.

(9)

communities in EMEs gradually. Thus, it is expected that the more recent the period under study, the greater the chances are of capturing the change in investor sentiments. For a country like India, that is going through a transitional phase in its economy with its present government pushing for greater foreign investments for boosting the country's industrial and infrastructural growth, it is necessary to pay attention to how the investors and the stock markets will react to these changes.

Empirical evidence suggests that investors are expectant of a risk premium on their investment owing to the increased risk exposure caused by exchange rate fluctuations, especially since the last few years beginning from the crisis period. This implies inadequate hedging by firms for the exchange rate risk and in the larger macroeconomic sense, it displays market inefficiencies in the stock market and/or foreign exchange market.

The study has qualitatively inferred from the results obtained, that Indian investors now require compensation for bearing exchange rate risk. However, the quantitative aspects such as the difference between the risk premiums demanded by investors in the various time periods are beyond the scope of the present paper and are left for future research.

Conflict of interest

None.

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