The Interlinkage of Stock Market and Exchange Rates: the Case of Asia

The Interlinkage of Stock Market and Exchange Rates: The Case of Asia

Nikhil Sebastian Department of Management Studies, MBA Finance Management, Christ (Deemed to be University), Bangalore 560029, Karnataka, India.

ABSTRACT: The study has been undertaken to analyze the impact of foreign exchange rates of Asian countries on the respective Asian stock markets. The study has been conducted on 10 Asian Countries based on their top GDP growth rate. The study has selected major stock indices and respective exchange rates. The study collected daily closing prices of stock indices and exchange rates for the period of 10 years from 1st January 2008 to 1st January 2018. The stock indices considered are NIFTY50 (India), SSE 50 (China), IDX Composite (Indonesia), SET 50 (Thailand), KLCI Composite (Malaysia), SET50 (Thailand), NIKKEI 225 (Tokyo), HKEX (Hong Kong), SGX (Singapore), TAIEX (Taiwan) and ADX (UAE). The currencies considered are INR Rupees, Chinese Renminbi, Indonesian Rupiah, Malaysian Ringgit, Thai Baht, Japanese Yen, Hong Kong Dollar, Singapore Dollar, New Taiwan Dollar and UAE Dirham. The study uses Regression Analysis and Granger Causality Test to study the relationship between stock index and exchange rate. The study found no impact of exchange rates on the respective stock indices. Further study found no causality between exchange rates and stock index of the respective country. The study conducted can help the investors in taking decisions relating to their investments in stock markets and can also help them understand that there is no relationship or no price relationship between the exchange rate and stock market.

KEYWORDS: Stock Index, Exchange Rate, Asian Countries, GDP Growth.

INTRODUCTION: Exchange rate is a key financial variable that affects the choices made by foreign exchange investors, exporters, importers, bankers, businesses, financial institutions, policymakers and tourists in the developed as well as developing economy. Exchange rate variations affect the value of international investment portfolios, competitiveness of exports and imports, value of international reserves, currency value of debt payments, and the price to tourists in terms of the value of their currency. Movements in exchange rates thus have important inferences for the economy’s business cycle, trade and funds flows and are therefore vital for understanding financial developments and changes in trade and industry policy. It is therefore important to know the relationship of foreign exchange with the stock market because international reserves accumulation has been the ideal policy recently adopted by developing economies to achieve financial stability. The aim of this policy is to increase liquidity and thus reduce the risk of suffering a speculative attack. The recent variations of the foreign exchange in the Asian Economies has led to the Japan’s TOPIX index down by 0.9%; Nikkei 225 fall by 0.3%; Shanghai Composite up by 0.4%; Taiwan’s TAIEX index has gone up by 0.6% ; India’s S&P BSE Sensex Index rose up by 0.7%; NSE Nifty 50 rose up by 0.7%; Singapore’s Straits Times Index has gone down by 0.5%; Malaysia’s KLCI has gone down by 0.6%; Jakarta Composite went by down 0.6%; Thailand’s SET fell by 0.6%; Therefore, in a region like Asia where the economies are still developing, and capital markets are still in a weak condition, very few studies have been made so far to investigate the relationship between stock prices and exchange rates and have found disagreeing results which encourages to conduct the study to detect the relationship between the exchange rates and stock indices. Previous studies have found no significant relationship between exchange rates and stock indices, such as, Nieh and Lee (2001), Bahmani-Oskooee and Sohrabian (2015). Nieh and Lee (2015) Bahmani-Oskooee and Sohrabian (2015). Previous studies have also

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© 2019 JETIR March 2019, Volume 6, Issue 3 www.jetir.org (ISSN-2349-5162) shown significant relationship between foreign exchange and stock market indices, such as, Roll (2015) and Fauziah, Moeljadi, & Ratnawati (2015). Literature Review: (Farooq, Keung, & Kazmi, 2005) The authors imply if there's any linkage between each the factors then the emergencies will be turned away either by overseeing rate or by receiving autochthonous arrangements to stable the stock exchange. additionally, the money specialists will use this relationship between stock costs and rate to foresee the conduct of those factors. apart from the truth, that the paper doesn't find any real conclusion, strategy system for the rate administration. (Apte, 2011) This paper researches the empiric examination with one in every of the important stock exchange lists bolsters the speculation of such unpredictability linkages whereas for the opposite list there shows up to be associate degree overflow from the remote exchange market to the stock market nonetheless not the other path spherical. (Ray, 2012) States the relationship between remote exchange reserves of Republic of India and bovine spongiform encephalitis market exploitation of the premise of yearly info from the year 1990-91 to 2010-11. The Granger relation test recommends that stock exchange capitalization (SMC) doesn't Granger cause remote exchange reserve (FOREXR) in any respect whereas outside exchange reserve (FOREXR) Granger causes stock market capitalization (SMC). (Suriani, Kumar, Jamil, & Muneer, 2015) during their investigation also showed that there's no relationship exists between rate and stock price and each the factors are autonomous of every alternative. (Yu & Liao, 2017) In their paper stated that there exists two-way instability overflow impact between currency market and currency market, anyway there's simply simplex instability overflow impact from stock exchange to currency market, that is exhibited from currency market to currency market. (Roll 2015) also studied the US stock prices and exchange rates and found a positive relationship between the two markets. (Chow et al. 2015) examined the same markets but found no relationship between stock returns and real exchange rate returns. They repeated the exercise with a longer time horizon and found a positive relationship between the two variables. (BHATTACHARYA, 2015)This paper explores the idea of the causal relationship between stock prices what's more, macroeconomic totals in the outside area in India. The outcomes propose that there is no causal linkage between stock prices and the three factors under thought. (Han & Zhou, 2017)In this research the relationship amongst stock and remote exchange rates for BRICS nations pre-and post-U.S. sub-prime emergency and European sovereign obligation emergency. With a wide arrangement of exchange rates, the blended c-vine copula models are utilized. The outcomes demonstrate the connections are negative for most of the stock/exchange rate sets. (Qayyum & Kemal, 2016)The paper looks at the unpredictability overflow between the stock market and the remote exchange market in Pakistan. The paper states there is solid relationship between the instability of outside exchange market and the unpredictability of returns in stock market. (Kumarasamy & Chellasamy, 2017)The association between the two financial factors stock returns and exchange rates. The study found and firmly trusts that both the Stock and Exchange Rate Returns, were stationary at the level frame itself and connection affirmed that there was opposite relationship between Returns from Indian Stock Market and Forex Rate Returns. Objectives of the Study: 1. To analyze the impact of exchange rates on the performance of the selected Asian Countries as per their GDP growth. 2. To assess the Granger Causality relationship between the FOREX market and the Stock Market performance of the selected Asian Countries. Hypothesis:

1. Null hypothesis (H01); Foreign exchange rate does not Granger cause stock indices.

2. Second null hypothesis (H02); Stock indices do not Granger cause foreign exchange rate.

METHODOLOGY: Data: The average daily nominal exchange rates of US dollar in terms of Indian Rupee, Chinese Yuan, Indonesian Rupiah, Malaysian Ringgit, Japanese Yen, Thai Baht, Hong-Kong Dollar, Singapore Dollar, New Taiwan Dollar and UAE Dirham and daily values of

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NIFTY 50 Index, Shanghai Stock Exchange Index, Malaysia Stock Exchange Index, Indonesia Stock Exchange Index, The Thailand Stock Exchange Index, The Tokyo Stock Exchange Index, Hong-Kong Stock Exchange Index, Singapore Exchange Limited Index, Taiwan Stock Exchange Index and UAE Stock Index. Samples: The samples include NIFTY50, SSE 50, IDX, KLSE, SET50, Nikkei 225, HKEX, SGX, TAIEX and ADX. The currencies considered are US dollar, INR Rupees, Chinese Renminbi, Indonesian Rupiah, Malaysian Ringgit, Japanese Yen, Thai Baht, Hong- Kong Dollar, Singapore Dollar, New Taiwan Dollar and UAE Dirham. Basis of Selection: The selection of the study samples is based on the top GDP growth of the Asian countries.

Analytical tools used for the study: EViews 10 software package would be used to test the variables. The data series we use in this study are time series data. Thus, before analyzing time series data in an empirical study we should make stationarity test which is commonly done by unit root test. There are a variety of unit root tests used in econometric literature principally Augmented Dickey-Fuller (ADF) test. In this study we use both unit root test to investigate whether the time series data used in this study are stationary or not. Augmented Dickey- Fuller test is obtained by the following regression equation:

------Equation 1

where Yt is a vector of non-stationary variables and C is the constant term.

The Granger method seeks to determine how much of a variable, Y which is the stock exchange indices, can be explained by past values of Y which is the stock exchange indices and whether adding lagged values of another variable, X which is the value of foreign exchange, can improve the explanation.

Data Analysis: ------Equation 2 1. Stationarity Unit Root Test: ------Equation 3

1. Stationarity Test: Table 1: Developing Countries (Currency): Level First Order Difference Currency t value Probability Conclusion t value Probability Conclusion Indian -0.894 0.79 Non- -8.667 0 Stationary Rupee stationary Chinese -2.241 0.19 Non- -8.693 0 Stationary Yuan stationary Malaysian -2.580 0.09 Non- -35.66 0 Stationary Ringgit stationary Indonesian -1.36 0.60 Non- -8.851 0 Stationary Rupiah stationary Thai Baht -0.718 0.84 Non- -38.51 0 Stationary stationary

Table 2: Developed Countries (Currency): Level First Order Difference Currency t value Probability Conclusion t value Probability Conclusion Taiwan dollar -1.398 0.58 Non-stationary -14.54 0 Stationary

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Singapore -2.414 0.137 Non-stationary -11.01 0 Stationary Dollar UAE AED -6.015 0 Stationary - - -

Japanese Yen -1.22 0.66 Non-stationary -36.59 0 Stationary

Hong- Kong -2.64 0.08 Non-stationary -20.22 0 Stationary Dollar

Table 3: Developed Countries (Stock Index): Level First Order Difference Stock Index t value Probability Conclusion t value Probability Conclusion

TSE Index -1.06 0.72 Non- -38.74 0 Stationary stationary SEL Index -1.48 0.59 Non- -14.52 0 Stationary stationary ADX Index -1.357 0.60 Non- -40.14 0 Stationary stationary TSE Index -2.20 0.20 Non- -30.18 0 Stationary stationary HKSE -1.78 0.38 Non- -10.99 0 Stationary Index stationary

Table 4: Developing Countries (Stock Index): Level First Order Difference Stock Index t value Probability Conclusion t value Probability Conclusion

BSE Index -2.159 0.22 Non-stationary -29.51 0 Stationary

SSE Index -3.299 0.01 Non-stationary -9.533 0 Stationary

MSE Index -0.88 0.791 Non-stationary -7.58 0 Stationary

ISE Index -1.375 0.59 Non-stationary -20.200 0 Stationary

TSE Index -1.296 0.63 Non-stationary -38.92 0 Stationary

Interpretation: Considering the results from the tables 1,2,3 and 4, null hypothesis of a unit root in the level is accepted in all cases as test statistics are lower than the critical values. So, we can say that exchange rates and stock prices are non-stationary data series and integrated of order one (1). Results also indicate that null hypothesis of a unit root is rejected in all cases when the data series are first differenced. So, the first difference of the data series of the variables is stationary.

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The Cointegration results clearly reveal that both trace test and maximum eigenvalue test accept the null hypothesis of no cointegration to 8 Asian countries-India, China, Japan, Singapore, Malaysia, Thailand, Indonesia and Taiwan. Thus, there is no long-term co-movement between stock prices and exchange rates and none of the variables is predictable based on past values of other variable. But the Cointegration results of the countries Hong Kong and UAE reveal that both trace test and maximum eigenvalue test do rejects the null hypothesis, thus stating that there is long-term co-movement between stock prices and exchange rates.

2. Table 5: Granger Causality Test: Country Null Hypothesis F- Probability Statistic SSE Index does not Granger Cause 0.92539 0.3965 Chinese Yuan China Chinese Yuan does not Granger SSE 0.48725 0.6143 Index HKSE Index does not Granger Cause 8.66443 0.0001 Hong Kong Dollar Hong Kong Hong Kong Dollar does not Granger 15.0554 3.1695 Cause HKSE Index BSE Index does not Granger Cause 1.12854 0.3240 Indian Rupee India Indian Rupee does not Granger Cause 0.31187 0.7322 BSE Index ISE Index does not Granger Cause 2.53448 0.0795 Indonesian Rupiah Indonesia Indonesian Rupiah does not Granger 2.79850 0.0611 Cause ISE Index TSE Index does not Granger Cause 169.608 1.E-69 Japan Japanese Yen Japanese Yen does not Granger Cause 2.52626 0.0802 TSE Index ADX Index does not Granger Cause 0.69870 0.4974 UAE AED UAE UAE AED does not Granger Cause 0.94183 0.3901 ADX Index MSE Index does not Granger Cause 9.13881 0.0001 Malaysian Ringget Malaysia Malaysian Ringget does not Granger 0.25633 0.7739 Cause MSE Index SEL Index does not Granger Cause 3.67822 0.0255 Singapore Dollar Singapore Singapore Dollar does not Granger 19.2715 5.E-09 Cause SEL Index TSE Index does not Granger Cause 0.04709 0.9540 Taiwan Dollar Taiwan Taiwan Dollar does not Granger 18.9545 7.E-09 Cause TSE Index TSE Index does not Granger Cause 3.67822 0.0255 Thai Baht Thailand Thai Baht does not Granger Cause 19.2715 5.E-09 TSE Index

Interpretation: Granger causality results from the above table 5, shows that there is no way causal relationship between stock prices and exchange rates. Therefore, it can be concluded that the stock prices do not influence exchange rates and past values of stock prices cannot be used to improve the forecast of future exchange rates.

Conclusion: In this paper we have explored the association between two important components of an economy named as stock prices and exchange rates. First, we applied unit root test to find the stationarity of data series. The results show that all the data series of the variables are non-stationary and integrated of order one. The Granger causality test was also done to find out any causal relationship

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© 2019 JETIR March 2019, Volume 6, Issue 3 www.jetir.org (ISSN-2349-5162) between stock prices and exchange rates. Results shows that stock prices do not Granger cause exchange rates and exchange rates does not Granger cause stock prices, so there is no way causal relationship between stock prices and exchange rates. The Cointegration results have shown negative impact between the variables for 8 countries and a positive impact between the variables on 2 countries. Since majority of the countries show a negative impact it can concluded that foreign currency does not impact the stock market indices. But our result of no cointegration counters this belief and states that the variables are not predictable based on the past values of other variables. The result of non-stationarity of the data series reveals that there is no chance of profitable speculation in the stock market or foreign exchange market. As there is no way causal relationship between stock prices and exchange rates, market participants cannot use information of one market to improve the forecast of other market. Therefore, the study made above can help the investors in taking decisions relating to their investments in stock markets and can also help them understand that there is no relationship or no price relationship between the exchange rate and stock market. Reference: 1. Apte, P. G. (2011). The Interrelationship Between the Stock Markets and the Foreign Exchange Market.

2. Gulati, D., & Kakhani, M. (2012). Relationship Between Stock Market and Foreign Exchange Market in. Pacific Business Review International, 66-73.

3. Ray, S. (2012). Foreign Exchange Reserve and its Impact on Stock Market Capitalization: Evidence from India. Research on Humanities and Social Sciences, 46-62.

4. Suriani, S., Kumar, M. D., Jamil, F., & Muneer, S. (2015). Impact of Exchange Rate on Stock Market. International Journal of Economics and Financial Issues, 385-388.

5. Yu, Y., & Liao, D. (2017). Empirical Research on Spillover Effect among Stock, Money and Foreign Exchange Market of China. Modern Economy, 655-666.

6. BHATTACHARYA, B. (2015). CAUSAL RELATIONSHIP BETWEEN STOCK MARKET AND EXCHANGE RATE, FOREIGN EXCHANGE RESERVES AND. 1-24.

7. Han, Y., & Zhou, X. (2017). THE RELATIONSHIP BETWEEN STOCK AND EXCHANGE RATES FOR BRICS COUNTRIES PRE- AND POST-CRISIS: A MIXED C-VINE COPULA MODEL. Romanian 38 Journal of Economic Forecasting, 38-59.

8. Qayyum, A., & Kemal, A. R. (2016). Volatility Spillover Between the Stock Market and the Foreign Exchange Market in Pakistan. Pakistan Institute of Development Economics, 1-18.

9. Kumarasamy, U., & Chellasamy, P. (2017). An Empirical Study on Indian Stock Market and Foreign Exchange Rates – A Review on Relationship. Int. Journal of Management and Development Studies, 01-09.

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