stock prices and exchange rates a review article

Journal of Economic Studies On the relation between stock prices and exchange rates: a review article Mohsen Bahmani-Oskooee, Sujata Saha, Downloaded by UNIVERSITY OF ECONOMICS HO CHI MINH At 18:48 14 January 2018 (PT) Article information: To cite this document: Mohsen Bahmani-Oskooee, Sujata Saha, (2015) "On the relation between stock prices and exchange rates: a review article", Journal of Economic Studies, Vol 42 Issue: 4, pp.707-732, https:// doi.org/10.1108/JES-03-2015-0043 Permanent link to this document: https://doi.org/10.1108/JES-03-2015-0043 Downloaded on: 14 January 2018, At: 18:48 (PT) References: this document contains references to 59 other documents To copy this document: permissions@emeraldinsight.com The fulltext of this document has been downloaded 3115 times since 2015* Users who downloaded this article also downloaded: (2013),"Returns and volatility spillover between stock prices and exchange rates: Empirical evidence from IBSA countries", International Journal of 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exchange rates, exchange rate changes are also said to affect stock prices The purpose of this paper is threefold First, the authors review all empirical literature by dividing them into two groups of univariate and multivariate studies Second, a table which summarizes the main features of each study is provided to help future researchers to have easy access to summary of each study Finally, a new direction for future research is proposed This new direction relies upon non-linear ARDL approach and shows how to investigate symmetric vs asymmetric effects of exchange rate changes on stock prices Design/methodology/approach – The paper reviews existing published work and provides suggestions for future research Findings – The paper reviews existing published work and provides suggestions for future research An application reveals that exchange rate changes have asymmetric effect on stock prices Originality/value – This is the first review paper on the relation between exchange rates and stock prices Keywords Stock prices, Exchange rates, Review article, Asymmetry Paper type Research paper I Introduction One of the areas in financial economics that has received the largest attention is the link between foreign exchange market and the stock market The easiest way to infer the link is by a reference to portfolio approach to exchange rate determination Under this approach wealth is one of the main determinants of the exchange rate An increase in stock prices usually increases public wealth This in turn increases the demand for money and therefore, interest rates By attracting international investment, domestic currency appreciates On the other hand, depreciation of domestic currency can boost exports and eventually profit of exporting firms High profits once announced, can cause share prices to rise Furthermore, depreciation raises cost of imported inputs This can increase production cost even to firms that are not export oriented If higher costs result in lower profits or expectation of lower profits, share prices could be affected For this reason stock prices could move in either direction Clearly if one needs to test the link between stock prices and exchange rates, one has to concentrate on the current floating exchange rate system that began in 1973 For this reason, almost all studies have engaged in empirical analysis using data from post-1973 period While some have concentrated on the link between stock prices and exchange rates at bilateral level, some have included additional determinants of stock prices For this reason, we review the empirical literature between the two variables at bilateral JEL Classification — F31, G15 Journal of Economic Studies Vol 42 No 4, 2015 pp 707-732 © Emerald Group Publishing Limited 0144-3585 DOI 10.1108/JES-03-2015-0043 JES 42,4 Downloaded by UNIVERSITY OF ECONOMICS HO CHI MINH At 18:48 14 January 2018 (PT) 708 level in Section In Section we review studies that have included other variables in their models In Section we propose a direction for future research and finally, we concluded in Section A table is provided in which all features of the reviewed studies are summarized[1] II Review of bivariate studies Perhaps the first study that concentrated on the relation between stock prices and exchange rates is that of Aggarwal (1981) who used monthly data during the period 1974-1978 from the USA By using an aggregate index of stock prices and effective exchange rate of the dollar he showed that there is a positive relation between the two variables, i.e., dollar depreciation or a decline in the effective exchange rate of the dollar caused stock price to decline The implication is that more firms were hurt by depreciation than helped Exactly opposite was concluded when Soenen and Hennigar (1988) looked into the response of stock prices of seven industrial sectors in the USA to changes in the value of the dollar The seven sectors were selected on the belief that they were affected heavily by international trade The seven sectors were automobile, computer, machinery, paper, textile, steel and chemical The finding that the relation between stock price of each sector and the value of the dollar was negative implied that as dollar depreciates, every sector exports more and rips profit from trade None of the studies mentioned above accounted for integrating properties of the two variables nor for cointegration between them Thus, their findings could suffer from spurious regression problem To resolve the issue, Bahmani-Oskooee and Sohrabian (1992) used monthly data from the period of 1973-1988 to show that index of S & P 500 and the effective exchange rate of the dollar are non-stationary variables Application of Engle and Granger (1987) cointegration analysis revealed that there is no long-run relationship between the two variables However, application of the Granger causality test revealed that the two variables Granger cause each other in the short run The Asian financial crisis of 1997 triggered a renewed interest in studying the interaction between exchange rates and stock prices, mostly in developing countries Granger et al (2000) concentrated on East-Asian countries of Hong Kong, Indonesia, Japan, South Korea, Malaysia, the Philippines, Singapore, Thailand and Taiwan and used Granger causality test and Gregory-Hansen cointegration test to analyze the relationship between stock prices and exchange rates They used daily data for the period 1986-1997 and showed that exchange rate changes affect stock prices in Japan and Thailand For Taiwan, the relationship was reversed, that is, stock prices affected exchange rates They found bi-directional relationship between the two variables in Indonesia, South Korea, Malaysia and the Philippines, a finding similar to that of Bahmani-Oskooee and Sohrabian (1992) for the USA However, Singapore failed to show any pattern of relationship Through Granger causality test it was inferred that exchange rates affected stock prices in eight of the nine countries Following the same path, Nieh and Lee (2001) used daily data from the period 1993-1996 and explored the dynamic relationship between stock prices and exchange rates in the G-7 countries (Canada, France, Germany, Italy, Japan, UK and USA) Engle-Granger and Johansen maximum likelihood methods of cointegration were applied Their results, again, supported the findings of Bahmani-Oskooee and Sohrabian (1992) and reported that there was no long-run relationship between stock prices and exchange rates in all of the G-7 countries The results of VECM estimation suggests that the two financial variables not have predictive capabilities for more than two consecutive days and thus there is a short-run significant relationship which lasts only for one day for certain G-7 countries Downloaded by UNIVERSITY OF ECONOMICS HO CHI MINH At 18:48 14 January 2018 (PT) Continuing the same line of research, Smyth and Nandha (2003) examined the relationship between stock prices and exchange rates in four South-Asian countries of Bangladesh, India, Pakistan and Sri Lanka using daily data from 1995 to 2001 Like previous studies, using Engle-Granger as well as Johansen’s cointegration techniques, they were unable to find any long-run equilibrium relationship between the two variables in any of the four countries Using Granger causality test they also concluded that exchange rates Granger cause stock prices in India and Sri Lanka but for Bangladesh and Pakistan they found no evidence of causality running in either direction During a crisis period the fluctuations in stock prices and exchange rates are high The Asian financial crisis of 1997 led to series of financial downfall To consider experiences of other countries in the crisis region, Lean et al (2005) used weekly data from 1991 to 2002 for Hong Kong, Indonesia, Singapore, Malaysia, South Korea, the Philippines and Thailand to study the pre- and post-crisis scenario and the effect of 9-11 terrorist attack Japan was used as a control They applied both cointegration and bivariate causality technique For all of the countries except for the Philippines and Malaysia, they found no evidence of Granger causality between stock prices and exchange rates in the period before Asian financial crisis During the crisis period, they found evidence of causality between the two variables Results show no cointegration between the variables before or during the Asian crisis of 1997 but after the 9-11 terrorist attack, weaker cointegration relationships between the variables were found Phylaktis and Ravazzolo (2005) used monthly data from 1980 to 1998 for Hong Kong, Indonesia, Malaysia, the Philippines, Singapore and Thailand They analyzed the short- and long-run relationships between exchange rates and stock prices and the avenues through which exogenous shocks affect these two variables They found that exchange rates and stock prices are positively related using the method of cointegration and Granger causality tests US stock price is the causing variable which acts as a channel that links the exchange rates of the five countries to their stock market indices Shifting to Europe, Obben and Shakur (2006) analyzed the relationship between the performance of the stock market and exchange rates in New Zealand using a cointegrating VAR approach using weekly data from 1999 to 2005 The five exchange rates were those currencies that are used in the construction of New Zealand’s tradeweighted index series They concluded that both in the short run and long run there is bidirectional causality between the five exchange rates and a couple of share price indices With regards to non-dollar exchange rates, Yau and Nieh (2006) used monthly data of Japan and Taiwan from 1991 to 2005 to study the relationship among stock prices of Taiwan and Japan and NTD/Yen exchange rate They applied Granger causality test and found that there is bi-directional causality between the stock prices of Taiwan and Japan but there is no significant causal relationship between the NTD/Yen exchange rate and the stock prices of Japan and Taiwan From the Johansen method of cointegration it was concluded that there was no long-run relationship among the three variables However, Yau and Nieh (2009) revisited the issue by testing for cointegration with threshold effect between the stock prices and the exchange rates in Japan and Taiwan and the effect of US exchange rate on the financial market of Taiwan Using monthly data from 1991 to 2008, they found evidence of a long-run equilibrium relationship between NTD/JPY and the stock prices of Japan and Taiwan There was no short-run causal relationship between the two countries financial assets which means that exchange rate and stock price movements not affect each other significantly in the short run The results supported the traditional approach that a long-run positive relationship runs from exchange rates of either Japan or USA to stock prices of Taiwan Stock prices and exchange rates 709 JES 42,4 Downloaded by UNIVERSITY OF ECONOMICS HO CHI MINH At 18:48 14 January 2018 (PT) 710 For seven Asian countries (Hong Kong, Japan, Korea, Malaysia, Singapore, Taiwan and Thailand), Pan et al (2007) applied the methods of Granger causality and Johansen cointegration test to examine the linkages between stock prices and exchange rates using daily data from 1988 to 1998 They concluded that during the Asian financial crisis period there is no long-run equilibrium relationship between exchange rates and stock prices For Hong Kong, Japan, Malaysia and Thailand there existed a significant causal relationship from exchange rates to stock prices before the 1997 Asian financial crisis and during the financial crisis period they found causal relationship from exchange rates to stock prices for all countries except for Malaysia All studies reviewed above have assumed that the relation between a stock price and an exchange rate is linear Ismail and Isa (2009) uses Markov Switching VAR model and assume the two variables to be regime dependent They then studied the non-linear relationship between exchange rates and stock prices in Malaysia using monthly data from 1990 to 2005 The Johansen cointegration test suggested evidence of no cointegration between the variables Their analysis showed that a non-linear model is more appropriate to model all the series than the linear model They also found evidence of common regime switching behavior between the variables Whether linear or non-linear relationship, it appears that no study finds evidence of long-run relationship This is also true of Rahman and Uddin (2009) who used monthly data from 2003 to 2008 for Bangladesh, India and Pakistan and the method of Johansen cointegration and Granger causality test Not only they found evidence of no long-run relationship between stock prices and exchange rates, they also found no causal relationship in either direction between the variables The implication is that market participants cannot use information of one market to help to forecast the other market Considering the experience of Australia, using daily data from 2003 to 2006, Richards et al (2009) studied the relationship between the two variables Using Johansen cointegration test they showed that that both stock prices and exchange rates are cointegrated in the long run The method of Granger causality test supported the portfolio balance model which says that changes in stock prices affect changes in exchange rates However, using weekly data from 1989 to 2006, Kutty (2010) was unable to support cointegration in Mexico, though some evidence of short run Granger causality was reported Considering the Chinese experience, the dynamic relationship between exchange rates and stock prices was studied by Zhao (2010) using monthly data from 1991 to 2009 Applying Johansen method of cointegration, the results showed no stable long-run equilibrium relationship between the real effective exchange rate and the stock price The source and the magnitude of the spillovers were identified through vector auto-regression and multivariate generalized autoregressive conditional heteroskedasticity models From the foreign exchange market to the stock market there was no mean spillover effect but there was bi-directional volatility spillover effects Further attempt was made by some studies to consider the link between the two variables by using updated data To that end, Alagidebe et al (2011) used monthly data from 1992 to 2005 for Australia, Canada, Japan, Switzerland and UK Again, they found no long-run relationship between the variables Through Granger causality test it was found that in Canada, Switzerland and UK, there is causal linkage from exchange rates to stock prices and in Japan there is causality running from stock prices to exchange rates In the same line, Harjito and McGowan (2011) used weekly data from 1993 to 2002 for Indonesia, the Philippines, Singapore and Thailand and reported evidence of bi-directional causality in Thailand and Singapore They also found cointegration between exchange rates and stock prices and cointegration among the stock markets of all four countries Downloaded by UNIVERSITY OF ECONOMICS HO CHI MINH At 18:48 14 January 2018 (PT) By using weekly data from 1999 to 2010 for the countries of Australia, New Zealand, Japan, Switzerland, USA, UK and Euro Zone, Katechos (2011) examined the relationship between stock markets and exchange rates in the light of the global equity market returns The method of maximum likelihood regression with GARCH was applied and results showed that there is a link between the exchange rates and the global stock market returns but the characteristics of the currencies determine the sign of the relationship The value of currencies with higher rates of interest is positively related to global equity returns and the value of currencies with lower rates of interest is negatively related to global equity returns Larger is the interest rate differential more is the explanatory power of the model Sticking to weekly data, Lean et al (2011) used weekly data from 1990 to 2005 for Hong Kong, Indonesia, Japan, Korea, Malaysia, the Philippines, Singapore and Thailand and examined the interactions of exchange rates and stock prices by allowing for structural breaks They applied the methods of panel Lagrange Multiplier (LM) cointegration, Gregory-Hansen test for cointegration and Granger causality test to find little evidence of long-run equilibrium relationship between exchange rates and stock prices Only in Korea, exchange rates and stock prices were cointegrated The predictive power of the two variables is limited only to short run, though not for all countries Again, using weekly data during 2000-2008 period, Lee et al (2011) considered the experience of Indonesia, Korea, Malaysia, the Philippines, Taiwan and Thailand to examine the relationship between the two variables and the effect on their correlation due to stock market volatility They used the method of Smooth Transition Conditional Correlation GARCH model to show that in Indonesia, Korea, Malaysia, Thailand and Taiwan there are significant price spillovers from stock market to foreign exchange market Stock market volatility does affect the correlation between the stock and foreign exchange markets For all the countries except for the Philippines, the correlation becomes higher when the stock market becomes more volatile Using rolling regression analysis, Kollias et al (2012) studied the link between the two variables The advantage of using rolling regression is, with the sample size remaining same, at a time, the sample period moves forward by one observation Hence it takes into account of the new information available They used daily data from 2002 to 2008 for European countries and showed that there is no long-run relationship between the two variables The direction of causality depends on the condition of the market There is causality running from exchange rates to stock prices under normal situation whereas causality might run from stock prices to exchange rates during crisis situations Deviating from standard approaches, Tsai (2012) employed quantile regression approach on monthly data from 1992 to 2009 for Singapore, Thailand, Malaysia, the Philippines, South Korea and Taiwan The method of quantile regression helps to study the relationship under different market conditions (“different quantiles of exchange rates”) Exchange rates and stock prices are negatively related when the exchange rates are extremely high or low So depending on the conditions of the market, the relationship can change Considering exchange rates other than against the US dollar, Wickremasinghe (2012) examined the relationship between stock prices and the Sri Lankan exchange rates against the Indian rupee, the Japanese yen, the British pound and the US dollar The results produced no evidence of any long-run relationship between any of the four exchange rates and stock prices in Sri Lanka There was only evidence of unidirectional causality running from stock prices to Sri Lankan exchange rate against US dollar Through variance Stock prices and exchange rates 711 JES 42,4 Downloaded by UNIVERSITY OF ECONOMICS HO CHI MINH At 18:48 14 January 2018 (PT) 712 decomposition analysis it was inferred that most of the variance of the stock price is explained by Indian rupee[2] Most of the papers reviewed so far concentrated either on developed or on developing countries However, Buberkoku (2013) considered both developed and developing countries and used monthly data from 1998 to 2008 to study the relationship between stock prices and exchange rates for countries like Australia, Canada, England, Germany, Japan, Singapore, South Korea, Switzerland and Turkey The methods used were Engle-Granger and Johansen cointegration test and Granger causality test The results showed that in the long run there is no relationship between the variables in the considered countries, except for Singapore In the short run, stock prices affect exchange rates in Canada, Switzerland and Turkey Causality runs from exchange rates to stock prices for Singapore and South Korea But for Australia, England, Germany and Japan there was no causal relationship in either directions To test for sensitivity of the results to data frequency, Tsagkanos and Siriopoulos (2013) used both daily and monthly data from 2008 to 2012 for European Union and USA to study the relationship between the two variables during the financial crisis of 2008 to 2012 They applied methods of structural non-parametric cointegrating regression, Johansen cointegration test and Granger causality test They found that movements in stock prices affect movements in exchange rates in EU in the long run and in USA in the short run Paying special attention to the crisis period, Caporale et al (2014) focussed on the banking crisis period of 2007-2010 to analyze the connections between stock prices and exchange rates For Canada, Euro area, Japan, Switzerland, UK and USA, they used weekly data which were sub-divided into time periods: the precrisis period (2003-2007) and the crisis period (2007-2011) Using Bivariate UEDCCGARCH models they found that in the short run there is unidirectional Granger causality from stock returns to exchange rate changes in the USA and the UK; in the opposite direction in Canada, and for the Euro area and Switzerland there is bi-directional causality Causality-in-variance from stock returns to exchange rate changes is found in the USA and for the Euro area and Japan it is in opposite direction, while there is evidence of bi-directional feedback in Switzerland and Canada During the recent financial crisis, dependence between the two variables has increased Finally, in this bivariate models Yang et al (2014) used daily data from 1997 to 2010 for India, Indonesia, Japan, Korea, Malaysia, the Philippines, Singapore, Taiwan and Thailand to study the relationship between stock returns and exchange rates They applied Granger causality test in quantiles and they found that during the Asian financial crisis, all the countries except for Thailand there are feedback relations between exchange rates and stock prices and in Thailand, stock returns lead exchange rates The causal effects are heterogeneous across different quantiles and different periods and most of the stock and foreign exchange markets are negatively correlated The findings by bivariate studies reviewed above could be biased due to other omitted variables from the models The next group of studies try to address this issue by including other macro variables in their model III Multivariate models Over the past recent years due to the trend of the globalization, capital flows among different international markets has increased which has also led to the increase in close relationship between the stock markets and the foreign exchange markets Empirical studies have also focussed on examining the effect of different macroeconomic Downloaded by UNIVERSITY OF ECONOMICS HO CHI MINH At 18:48 14 January 2018 (PT) variables on stock market The analysis of the effects of different macroeconomic variables like inflation, GDP, industrial production index, money supply, oil prices, interest rates, foreign capital, exchange rates, etc are important since they can help policy makers of an economy to better formulate policies Investors find important and interesting to see how and which variables cause the stock prices to fluctuate Using an APM model, Chen et al (1986) examined the effect of different macroeconomic variables (industrial production, inflation, risk premia, etc.) on the stock returns of the USA and found that macroeconomic variables have significant effect on expected stock returns Fama and French (1993) examined four to five factors that affect stock returns and stock prices Tian and Ma (2010) studied the relationships among stock prices and macroeconomic variables like exchange rates, money supply, industrial production and consumer price index using monthly data from 1995 to 2009 for China They employed the ARDL method of cointegration Their results show that prior to financial liberalization of 2005, no cointegration exists between the major foreign exchange rates and the Shanghai stock price index After the liberalization, cointegration exists Money supply and exchange rates affect stock prices with positive correlation in China and also previous month CPI Granger causes stock prices Using Johansen method of cointegration, Chortareas et al (2011), for countries like Egypt, Kuwait, Oman and Saudi Arabia examined the role of oil prices as a link between the stock markets and exchange rates They used monthly data from 1994 to 2006 and their results show that when oil price is not considered, there is no long run cointegration between exchange rates and stock prices Inclusion of oil prices show no cointegration between exchange rates and stock prices when full sample period is considered Before the oil price shock of 1999, no cointegration among the variables was found After the shock, exchange rates, stock prices and oil prices are cointegrated in Egypt, Oman and Saudi Arabia But for Kuwait, there is long-run relationship only between stock prices and oil prices Real exchange rates are positively related to stock prices in Egypt and Oman and in Saudi Arabia they are negatively related Oil prices have long-run positive effect on stock prices The model employed by Liu and Tu (2011) included exchange rate and foreign capital as determinants of stock prices They used daily data from 2001 to 2007 from Taiwan to study the relationships among the variables and to analyze whether in these markets the properties of asymmetric volatility switching and mean-reverting exists or not They found that the movements of the exchange rate and the stock price index are affected by overbuying and overselling rates of foreign capital All of the three conditional means exhibit asymmetric mean-reverting behavior (negative returns reverting quicker than positive returns) The volatility of the three markets exhibited GARCH effects The model employed by Parsva and Lean (2011) included like interest rates, inflation rates and oil prices as main determinants of stock prices in Egypt, Iran, Jordan, Kuwait, Oman and Saudi Arabia Using monthly data from 2004 to 2010 they estimated their model using Johansen method of cointegration and Granger causality test They found that in the long run, all variables are cointegrated Both in short run and long run there is bi-directional causality between stock prices and exchange rates for Egypt, Iran and Oman before the crisis In Kuwait causality runs from exchange rates to stock prices in the short run Comparing the pre- and post-crisis periods, there was not much distinction in the behavior of exchange rates and stock returns Oil price was also included in a model by Basher et al (2012) who used monthly global data from 1988 to 2008 to examine the relationship among stock prices in emerging markets Additionally, they included Stock prices and exchange rates 713 JES 42,4 Downloaded by UNIVERSITY OF ECONOMICS HO CHI MINH At 18:48 14 January 2018 (PT) 714 global real economic activity as one of the variable which affects oil prices Using a structural VAR model and through the analysis of impulse response function they found that positive shock to oil prices decreases emerging markets stock prices and US dollar exchange rates in the short run Exchange rates respond to changes in oil prices in the short run, a positive shock to oil prices leads to decrease in trade-weighted exchange rates Oil price decreases with increase in oil production but a positive shock to real economic activity increases the price of oil Along similar lines, Eita (2012) employed Johansen’s method and quarterly data from 1998 to 2009 for Namibia to examine the determinants of stock prices The results showed that stock prices are affected by economic activity, exchange rates, inflation, interest rates and money supply Stock prices increase with increase in economic activity and money supply and stock prices decrease with increase in inflation and interest rates Exchange rates, GDP, money supply and inflation move stock market away from equilibrium Similarly, Inegbedion (2012) considers the experience of Nigeria by using data from 2001 to 2009 By applying Cochran-Orcutt Autoregressive Model, the results show that exchange rates and stock prices are negatively related The relationship of stock prices with interest rates and inflation, respectively are not significant But the joint effect of all the variables on stock prices is significant[3] Foreign reserves and interest rates were added as additional variables into the relation between stock price and exchange rate to explore the effects of portfolio adjustment by Lin (2012) Using monthly data during 1986-2010 and the ARDL approach, the model was estimated for Asian emerging countries of India, Indonesia, Korea, Philippines, Taiwan and Thailand During crises periods, in terms of long run cointegration and short-run causality, the co-movement between exchange rates and stock prices became stronger Spillover effect was mostly from stock price shocks to exchange rates Further analysis showed that the co-movement is generally driven by capital account balance than the trade balance Separately, Pakistan was the country of concern by Aslam and Ramzan (2013) who studies the effects of the real effective exchange rate index, CPI, per capita income and discount rate on the stock prices Applying NLS and ARMA techniques revealed that while discount rates and inflation negatively affected Karachi stock price index, per capita income and real effective exchange rate index affected positively Discount rate impacted stock index the most This study helps to understand how effectively a country can control its macroeconomic variables for better performance of the stock market In the same vein, commodity prices are introduced into the relation between the exchange rate and stock market by Groenewold and Paterson (2013) who employed monthly data during 1979-2010 from Australia Their results showed that when commodity prices are not considered, there is no cointegration between exchange rates and stock prices With the inclusion of commodity prices, all the three variables are cointegrated in the long run When only exchange rates and stock prices are considered, there is no causality between them in either direction In the short run, exchange rates affect commodity prices and commodity prices in turn affect stock prices Different macro variables in Pakistan were also considered by Khan et al (2013) who used monthly data from 1998 to 2008 The macroeconomic variables considered were market returns, CPI, risk-free rate of return, industrial production and M2 The results showed that both stock prices and exchange rates affect each other in the short run but there is no long run association between the variables In the long run, market return and risk-free return are not related to stock prices but there is some association of industrial production and stock prices There exists both short run and long-run Downloaded by UNIVERSITY OF ECONOMICS HO CHI MINH At 18:48 14 January 2018 (PT) relationship between stock prices and inflation and money supply Boonyanam (2014) explored the relationship between different monetary variables with stock prices The monetary variables included were nominal bilateral exchange rate in terms of Baht per US dollar, CPI, narrow money and 14 days repurchase rate and the methods used were multivariate cointegration, VECM and variance decomposition analysis Monthly data from 1999 to 2012 was used for Thailand and the results show evidence of long-run relationship between stock prices and monetary variables In the short run, narrow money and interest rate affect stock prices There is one way causality from exchange rates to stock prices and from interest rates to stock prices There was also positive relationship between CPI and stock price Rather than using stock prices, Moore and Wang (2014) examines the source of the relationship between stock return differentials and real exchange rates using monthly data for Australia, Canada, Indonesia, Japan, the Philippines, Malaysia, Singapore, South Korea, Thailand and the UK At the first stage, the dynamic conditional correlation (DCC) is derived between the two variables and then the derived DCC is used to regress on the interest rate differentials and the trade balance With the help of bivariate GARCH model with DCC they found that there is a negative relationship between the relative stock prices and real exchange rates There exists time-varying correlation between stock return differentials and real exchange rate changes The US stock market influences the foreign exchange market and local stock market Trade balance is the major determinant of the dynamic correlation for Asian market and interest rate differential is the key factor for developed countries For the countries where capital mobility is low, economic integration acts as the cause of the linkage and thus it supports the flow-orientated model But where capital mobility is more, financial integration acts as the cause of the linkage which in turn favors the stock-oriented model Finally, the case of Turkey is considered by Tuncer and Turaboglu (2014) who used quarterly data from 1990 to 2008 to examine the short run and long-run relationships between stock prices and GDP, treasury bills rates and exchange rates They employed the method of Johansen test for cointegration to study the long-run relationship and found evidence of long-run relationship between stock prices and the other variables In the short run, stock prices and real effective exchange rate affect GDP but there is no causality relationship from treasury bills to GDP There is causality from real effective exchange rates to stock prices All the variables not affect exchange rates in the short run hence exchange rate is comparatively an exogenous variable In sum, the literature on the relation between stock prices and exchange rate is vast From the review of more recent studies it is clear that the link between the two variables is dependent on the data frequency and period chosen, the countries studied, and other macro variables, etc But in general most of the papers concluded that in the short run, stock prices and exchange rates are related but there is no relationship between them in the long run Other macroeconomic variables like, CPI (inflation rate), interest rates, discount rates, oil prices, money supply, industrial production, GDP and foreign capital also are found to affect stock prices Table I provides the main features of each study reviewed IV A new direction for future research The models reviewed in the previous section and all studies listed in Table I have one common feature They have all assumed that the effects of exchange rate changes on stock prices are symmetric, i.e., if depreciation has positive effects on stock prices, Stock prices and exchange rates 715 VAR ANST GARCHM Model Liu and Tu (2011) Exchange rates and foreign capital Taiwan Findings Daily; January 3, 2001-December 31, 2007 (continued ) There are significant price spillovers from stock market to foreign exchange market in Indonesia, Korea, Malaysia, Thailand and Taiwan Stock market volatility affects the correlation between stock market and foreign exchange market The movements of the exchange rate and the stock price index are affected by overbuy and oversell rates of foreign capital All of the three conditional means exhibit asymmetric mean-reverting behavior (negative returns reverting quicker than positive returns) The volatility of the three markets exhibits GARCH effects prices and oil prices Real exchange rates are positively related to stock prices in Egypt and Oman and in Saudi Arabia they are negatively related Oil prices have longrun positive effect on stock prices Weekly; January Bi-directional causality is present in Thailand and Singapore Cointegration exists between stock prices and 1993-December exchange rates Cointegration exists among the stock 2002 markets in all the four countries Weekly; January There is a link between the exchange rates and the global 1999-August 2010 stock market returns, the sign depends on the nature of the currencies Value of higher yielding currencies is positively related to global stock market returns Value of lower yielding currencies is negatively related to global stock market returns Weekly; January 1, Little evidence of a long-run equilibrium relationship 1990-June 30, 2005 between exchange rates and stock prices The predictive power of the two variables is restricted to the short run, even then it does not hold for all countries Data period Weekly; January Indonesia, Korea, 2000-April 2008 Malaysia, Philippines, Taiwan, Thailand Exchange rates Panel Lagrange Multiplier (LM) cointegration test, Gregory-Hansen test for cointegration, Granger causality test Bivariate STCCExchange rates EGARCH model Lean et al (2011) Lee et al (2011) Hong Kong, Indonesia, Japan, Korea, Malaysia, Philippines, Singapore, Thailand Maximum likelihood regression with GARCH Katechos (2011) Exchange rates Indonesia, Philippines, Singapore and Thailand Australia, New Zealand, Japan, Switzerland, USA, UK, Euro Zone Granger causality test, Exchange rates Johansen cointegration test Countries Harjito and McGowan (2011) Independent variables Methodology References Downloaded by UNIVERSITY OF ECONOMICS HO CHI MINH At 18:48 14 January 2018 (PT) Stock prices and exchange rates 719 Table I Table I Methodology Independent variables Structural VAR Johansen test for cointegration Cochran-Orcutt Autoregressive Model Rolling Granger causality test, rolling cointegration test Basher et al (2012) Eita (2012) Inegbedion (2012) Kollias et al (2012) Exchange rates Exchange rates, inflation rate, interest rate Exchange rates, economic activity (income-GDP), interest rates, inflation, money supply Europe Nigeria Namibia Exchange rates and oil Global analysis prices (and global real economic activity) Egypt, Iran, Jordan, Kuwait, Oman, Saudi Arabia Countries Findings (continued ) In the long run, all the variables are cointegrated Both in short run and long run there is bi-directional causality between stock prices and exchange rates in Egypt, Iran and Oman before the crisis In Kuwait causality runs from exchange rates to stock prices in the short run Between the pre- and post-crisis periods, there was not much distinction in the behavior of exchange rates and stock returns Through the analysis of impulse response function, it is Monthly; January 1988-December found that positive shock to oil prices decreases emerging 2008 markets stock prices and US dollar exchange rates in the short run Exchange rates respond to changes in oil prices in the short run, a positive shock to oil prices leads to decrease in trade-weighted exchange rates Oil price decreases with increase in oil production but a positive shock to real economic activity increases the price of oil Quarterly; 1998:Q1- Stock prices are affected by economic activity, exchange 2009:Q4 rates, inflation, interest rates and money supply Stock prices increase with increase in economic activity and money supply and stock prices decrease with increase in inflation and interest rates Exchange rates, GDP, money supply and inflation move stock market away from equilibrium 2001-2009 Exchange rates and stock prices are negatively related The relationship of stock prices with interest rates and inflation are not significant But the joint effect of all the variables on stock prices is significant Daily; January No long-run relationship between the variables Exchange 2002-December rate affects stock returns during normal times During 2008 crisis, stock returns might affect exchange rates Monthly; January 2004-September 2010 Data period 720 Parsva and Lean (2011) Johansen cointegration Exchange rates, test, Granger causality interest rate, oil price test and inflation rate References Downloaded by UNIVERSITY OF ECONOMICS HO CHI MINH At 18:48 14 January 2018 (PT) JES 42,4 Quantile regression approach Tsai (2012) Engle-Granger cointegration test NLS and ARMA techniques Abidin (2013) Aslam and Ramzan (2013) (continued ) Discount rates and inflation negatively affect Karachi stock price index Per capita income and real effective exchange rate index positively affect Karachi stock price index Discount rate impacts stock index the most Annual; 1991-2012 Daily; January 2006-December 2008 No long-run relationship between exchange rates and stock prices Uni-directional causality runs from stock prices to US dollar exchange rates only No causality is found when exchange rates for Indian rupee, Japanese yen and UK pound are considered From variance decomposition analysis (to examine out-of-sample causal relation), it is inferred that most of the variance of ASPI (All Share Price Index) is explained by Indian rupee No significant long-run relationship between stock markets and exchange rates Monthly; January 1986-December 2004 Monthly; January 1992-December 2009 During crises periods, in terms of long-run cointegration and short-run causality, the co-movement between exchange rates and stock prices became stronger Spillover effect is mostly from stock price shocks to exchange rates Analysis of industry causality showed that the co-movement is generally driven by capital account balance than that of trade Volatilities of changes in foreign reserves and interest rates are more during the crisis and market liberalization period Exchange rates and stock prices are negatively related when the exchange rates are extremely high or low The relationship changes depending on the market conditions Monthly; January 1986-December 2010 India, Indonesia, Korea, Philippines, Taiwan, Thailand Singapore, Thailand, Malaysia, Philippines, South Korea, Taiwan Sri Lanka Findings Data period Countries Australia, Hong Kong, Indonesia, Japan, New Zealand, South Korea, Thailand Real effective exchange Pakistan rate index, CPI, per capita income and discount rate Exchange rates Exchange rates Wickremasinghe (2012) Johansen’s cointegration test, Granger causality test, variance decomposition analysis Exchange rates Granger causality test, Exchange rates, interest rates and ARDL method of foreign reserves cointegration Lin (2012) Independent variables Methodology References Downloaded by UNIVERSITY OF ECONOMICS HO CHI MINH At 18:48 14 January 2018 (PT) Stock prices and exchange rates 721 Table I Johansen test for Exchange rates, market Pakistan cointegration, Englereturn, risk-free rate of Granger causality test return, CPI, industrial production, M2 Exchange rates Structural nonparametric cointegrating regression (SNCR), Johansen test for cointegration, Granger causality test Khan et al (2013) Tsagkanos and Siriopoulos (2013) (continued ) When commodity prices were not considered, no cointegration between exchange rates and stock prices With the inclusion of commodity prices, all the three variables are cointegrated in the long run When only exchange rates and stock prices are considered, there is no causality between them in either direction In the short run, exchange rates affect commodity prices and commodity prices in turn affect stock prices Monthly; July 1998- Both stock prices and exchange rates affect each other in June 2008 the short run but there is no long run association between the variables In the long run, market return and risk-free return are not related to stock prices but there is some association of industrial production and stock prices There exists both short- and long-run relationship between stock prices and inflation and money supply Daily, Monthly; Movements in stock prices affect movements in exchange January 2, 2008rates in EU in the long run and in USA in the short run April 30, 2012 No long-run relationship between the variables in the considered countries except for Singapore In the short run, stock prices affect exchange rates in Canada, Switzerland and Turkey In the short run, exchange rates affect stock prices in Singapore, South Korea No causal relationship exists in Japan, Germany, England and Australia Findings 722 European Union (EU), USA Exchange rates and Johansen test for cointegration, Granger commodity prices causality test Groenewold and Paterson (2013) Monthly; April Japan, Canada, 1998-April 2008 England, Switzerland, Germany, Australia, Singapore, South Korea, Turkey Australia Monthly; December 1979-December 2010 Exchange rates Johansen and EngleGranger cointegration tests, Granger causality test Buberkoku (2013) Data period Countries Independent variables Methodology Table I References Downloaded by UNIVERSITY OF ECONOMICS HO CHI MINH At 18:48 14 January 2018 (PT) JES 42,4 Multivariate cointegration, VECM, Granger causality (Wald test/block exogeneity test), variance decomposition Bivariate VARGARCH model, EngleGranger and Johansen trace test for cointegration, Granger causality test Moore and Wang (2014) Bivariate GARCH model with dynamic conditional correlation (DCC), linear regression Caporale et al (2014) Boonyanam (2014) Exchange rates, trade balance, real interest rate differential, measures of financial development There is long-run relationship among stock prices, exchange rates and oil prices In the long run, exchange rates and oil prices Granger cause stock prices but oil prices and stock prices not affect exchange rates In the short run, there is bi-directional causality between oil prices and stock prices Long-run relationship exists between stock prices and monetary variables In the short run, narrow money and interest rate affect stock prices There is one way causality from exchange rates to stock prices and from interest rates to stock prices Positive relation between CPI and stock price Findings (continued ) Using Bivariate UEDCC-GARCH models they found that in the short run there is uni-directional Granger causality from stock returns to exchange rate changes in USA and UK; in the opposite direction in Canada, and for the Euro area and Switzerland there is bi-directional causality Causality-in-variance from stock returns to exchange rate changes is found in the USA and in the Euro area and in Japan it is in opposite direction, while there is evidence of bi-directional feedback in Switzerland and Canada During the recent financial crisis, dependence between the two variables has increased There is a negative dynamic relationship between the Monthly; for Australia, Canada, developed country- relative stock prices and real exchange rates There exists Japan, UK, Indonesia, Malaysia, 1973-2006 and for time-varying correlation between stock return emerging markets differentials and real exchange rate changes US stock South Korea, market influences the economies foreign exchange and depends on data Philippines, local stock markets Trade balance is the major Singapore, Thailand availability determinant of the dynamic correlation for Asian market (focussing on the Weekly; August 6, 2003-December 28, 2011 Exchange rates USA, UK, Canada, Japan, the Euro Area, Switzerland Monthly; January 1999-December 2012 Thailand Exchange rates, CPI, narrow money, 14 days re-purchase rates Data period Panel cointegration Exchange rates and oil Indonesia, Malaysia, Monthly; January (Engel-Granger), prices the Philippines, 2006-December Granger causality test Singapore, Thailand 2012 Countries Unlu (2013) Independent variables Methodology References Downloaded by UNIVERSITY OF ECONOMICS HO CHI MINH At 18:48 14 January 2018 (PT) Stock prices and exchange rates 723 Table I Table I Methodology Yang et al (2014) India, Indonesia, Japan, Korea, Malaysia, Philippines, Singapore, Taiwan, Thailand Turkey Exchange rates, GDP and treasury bills rate Granger causality test Exchange rates in quantiles Countries Independent variables Daily; January 1, 1997-August 16, 2010 and interest rate differential is the key factor for developed countries floating/managed floating regime period) Quarterly; 1990:Q12008:Q2 There is long-run relationship between stock prices and the other variables In the short run, stock prices and real effective exchange rates affect GDP but there is no causality relationship from treasury bills to GDP There is causality from real effective exchange rates to stock prices All the variables not affect exchange rates in the short run During the Asian financial crisis, all the countries except for Thailand there are feedback relations between exchange rates and stock prices and in Thailand, stock returns lead exchange rates The causal effects are heterogeneous across different quantiles and different periods Most stock and foreign exchange markets are negatively correlated Findings Data period 724 Tuncer and Turaboglu Johansen test for (2014) cointegration, multivariate Vector Error Correction models (VEC) References Downloaded by UNIVERSITY OF ECONOMICS HO CHI MINH At 18:48 14 January 2018 (PT) JES 42,4 Downloaded by UNIVERSITY OF ECONOMICS HO CHI MINH At 18:48 14 January 2018 (PT) appreciation has the opposite effect This need not be the case When domestic currency appreciates (or foreign currency depreciates), cost of imported inputs decline resulting in more profit and hence, a positive effect on stock prices However, when domestic currency depreciates which results in an increased cost of imported input, in order to maintain their market share, some domestic producers may absorb the increased cost by giving up their profit margin In this case, stock prices may not react to depreciation, implying that the effects of exchange rate changes on stock prices could be asymmetric In what follows, we pick a standard model from the literature and demonstrate how asymmetry could be introduced and tested within existing framework and with the help of recent advances in time-series modeling Let us consider the following long-run specification used by previous research (e.g Boonyanam 2014; Moore and Wang, 2014): LnSP t ¼ a þ bLnEX t þ cLnI PI t þ dLnCPI t þ eLnM 2t þ et (1) In Equation (1) in addition to nominal effective exchange rate (EX ), a measure of output proxied by Index of Industrial Production (IPI ), the price level measured by the Consumer Price Index (CPI ), and the money supply measured by nominal M2 are identified to be the determinants of stock prices, SP Estimate of Equation (1) by any method will result in the long-run coefficient estimates In order to infer the short-run effects, the common practice is to specify Equation (1) in an error-correction format as in Equation (2): DLnSP t ¼ a þ þ n1 X k¼1 n4 X k¼0 bk DLnSP tk ỵ n2 X yk DLnCPI tk ỵ dk DLnEX tk ỵ kẳ0 n5 X n3 X Fk DLnI PI tk kẳ0 pk DLnM 2tk ỵ l1 LnSP t1 kẳ0 þ l2 LnEX tÀ1 þ l3 LnI PI tÀ1 þ l4 LnCPI t1 ỵ l5 LnM 2t1 ỵ mt (2) The error-correction model outlined by Equation (2) follows Pesaran et al (2001) ARDL approach to cointegration Variables are said to be cointegrated if linear combination of lagged-level variables as a proxy for lagged error term from Equation (1) are jointly significant Pesaran et al (2001) propose applying the F-test with new critical values that they tabulate If lagged-level variables are jointly significant, estimates of λ2-λ5 normalized on λ1 will yield the long-run effects of exogenous variables on stock prices The short-run effects are then inferred by the coefficient estimates attached to firstdifferenced variables[4] Specification Equation (1) or (2) that are now widely used in this area or other areas in economics assume effects of any of the exogenous variables are symmetric Concentrating on the exchange rate, we try to test this by separating depreciations from appreciations to determine whether these new variables have the same effects To this end, following recent studies in other areas, e.g Apergis and Miller (2006), Delatte and Lopez-Villavicencio (2012), Verheyen (2013), Bahmani-Oskooee and Fariditavana (2014) and Bahmani-Oskooee and Bahmani (2015) we decompose the movement of the LnEX variable into its negative (depreciation) and positive ỵ (appreciation) partial sums as LnEX ẳ LnEX ỵ LnEX tỵ ỵ LnEX t where LnEX t and LnEX t are the partial sum process of positive and negative changes in LnEX Stock prices and exchange rates 725 JES 42,4 More precisely: POS ẳ LnEX tỵ ẳ t t X X DLnEX jỵ ẳ max DLnEX j ; ; j¼1 Downloaded by UNIVERSITY OF ECONOMICS HO CHI MINH At 18:48 14 January 2018 (PT) 726 j¼1 t t X X À Á DLnEX À DLnEX j ; N EG ¼ LnEX À t ¼ j ¼ j¼1 (3) j¼1 The next step is to go back to error-correction Model (2) and replace LnEX by POS and NEG variables as in Equation (4): DLnSP t ẳ a ỵ n1 X bk DLnSP tk ỵ kẳ1 ỵ n4 X kẳ0 Fk DLnI PI tk ỵ n2 X d1;k DPOS tk ỵ kẳ0 n5 X n3 X d2;k DN EGtk kẳ0 yk DLnCPI tk ỵ kẳ0 n6 X pk DLnM 2tk kẳ0 ỵ l1 LnSP t1 þ l2 POS tÀ1 þ l3 N EGtÀ1 þ l4 LnI PI t1 ỵ l5 LnCPI t1 ỵ l6 LnM 2t1 ỵ mt (4) Specification Equation (4) is usually called non-linear ARDL model proposed by Shin et al (2014) who have demonstrated that Pesaran et al.’s (2001) bounds testing approach is equally applicable to Equation (4) Non-linearity is introduced through partial sum or cumulative sum concept included in generating the new variables POS and NEG Once Equation (4) is estimated, estimates of δ1,k and δ2,k will be used to judge short run symmetry or asymmetry effects of exchange rate changes and estimates of normalized λ2 and λ3 will be used for judging the long-run symmetry or asymmetry For demonstrative purpose, we estimate both the linear and non-linear ARDL models (i.e Equations (2) and (4)) using monthly US data over the period 1973M1-2014M3[5] A maximum of lags is imposed on each first-differenced variable and Akainke’s Information Criterion is used to select the optimum model The results are reported in Table II For both models, there are three panels While Panel A reports the short-run estimates, Panel B reports the long-run estimates Finally diagnostic statistics are reported in Panel C From the estimates of the linear model and panel A we gather that for each first-differenced variable there is at least one coefficient that is significant at the 10 percent level, except the money supply Concentrating on our variable of concern, dollar depreciation seems to result in an increase in stock prices in the short run However, this short-run effect is not translated into the long run since normalized long-run coefficient from Panel B is not significant Indeed, none of the variables are significant in the long run This is also reflected by the low and insignificant value of the F-test[6] A few additional diagnostics are also reported in Panel C Using normalized long-run estimates from Panel B and Equation (1) we calculate the error term and call it error correction term, ECM After replacing the linear combination of lagged-level variables by ECMt-1, we re-estimate the model after imposing the same optimum lags A significantly negative coefficient will support convergence and speed with which variables adjust Clearly, percent of the adjustment takes place within Stock prices and exchange rates Section I: estimates of linear Model (2) Downloaded by UNIVERSITY OF ECONOMICS HO CHI MINH At 18:48 14 January 2018 (PT) Panel A: short run Variables ΔlnSPt ΔlnEXt −0.50 ΔlnIPIt −0.26 ΔlnCPIt −1.47 ΔlnM2t −0.02 Lags (3.80) (0.84) (2.29) (1.15) Panel B: long run lnEX lnIPI −1.05 (0.99) 1.10 (0.71) 727 0.65 (2.21) lnCPI 2.71 (1.67) Panel C: diagnostics F ECMt-1 LM 2.24 −0.02 (3.36) 12.13 Section II: estimates of non-linear Model (4) Panel A: short Variables ΔlnSPt ΔPOSt ΔNEGt ΔlnIPIt ΔlnCPIt ΔlnM2t lnM2 −1.05 (1.08) Constant 25.95 (0.98) RESET 17.64 Adjusted R2 0.06 CUSUM (CUSUM2) S (S) lnCPI 2.78 (2.77) lnM2 −2.39 (2.25) Constant 58.42 (2.03) RESET 10.58 Adjusted R2 0.08 CUSUM (CUSUM2) S (S) run −1.07 −0.02 −0.21 −1.43 −0.09 (4.71) (1.15) (0.69) (2.23) (2.17) 0.62 (2.12) Panel B: long run POS NEG 0.64 (0.77) −0.63 (1.08) lnIPI 0.84 (0.82) Panel C: diagnostics F ECMt-1 2.69 −0.04 (4.03) LM 11.49 one month The LM statistic is also reported to test for autocorrelation It has a χ2 distribution with 12 degrees of freedom since data are monthly Given its critical value of 21.03 at the percent level, the LM statistic is insignificant, implying absence of serial correlation in the optimum model We have also reported Ramsey’s RESET statistic to judge misspecification This statistic is also distributed as χ2 but with only one degree of freedom Clearly, it is significant indicating a misspecified model[7] Finally, to establish stability of short- and long-run coefficient estimates we apply the well-known CUSUM and CUSUMSQ tests to the residuals of the optimum model All coefficients are stable and this is indicated by “S”[8] Next we move to estimate of non-linear ARDL model outlined by Equation (4) These results are reported in Section of Table II From the short-run results in Panel A, we gather that while ΔPOS variable carries a negative and significant coefficient, ΔNEG variable does not This supports the fact that exchange rate changes have short-run asymmetric effects on stock prices in the USA More precisely, when dollar appreciates Table II Full-information estimates of Models (2) and (4) for the USA JES 42,4 Downloaded by UNIVERSITY OF ECONOMICS HO CHI MINH At 18:48 14 January 2018 (PT) 728 (or foreign currencies depreciate in terms of dollar), the balance sheet of US multinational firms deteriorate in terms of the dollar and this exerts adverse negative effect on stock prices of multinationals and eventually on overall stock price index in the USA All other variables also seem to have short-run effects on the stock prices in the USA Thus, the non-linear model seems to yield more significant short-run results than the linear model This is also the case in the long run From the long-run results in Panel B, the CPI and M2 carry significant coefficient The variables are not cointegrated by the F-test but they are by the ECMt-1 V Summary and conclusion It is not too difficult to link macro variables to each other and try to understand the feedback effects that exist among them and the link between stock market and foreign exchange market in every country is no exception An increase in stock prices over time is said to increase the wealth leading to an increase in the demand for money, hence interest rates High interest rates in turn can attract foreign capital, causing currency appreciation When domestic currency appreciates or foreign currency depreciates, the balance sheet of domestic multinational firms deteriorates in terms of domestic currency which could be bad news for their shareholders and their share prices On the other hand appreciation of domestic currency or depreciation of foreign currency could be good news for domestic produces due to cheap imported inputs This will have a favorable impact on these firms share prices Overall, exchange rate changes can move stock prices in either direction The main purpose of this paper is to review the literature pertaining to the relation between stock prices and exchange rates The literature was divided into two groups In the first group we reviewed studies that investigate the link between the two variables without including other variables in their model The second group includes studies that they consider multivariate models by including other variables No matter which group we consider, the overall conclusion is that the findings are sensitive to frequency of data used, study period chosen, the country considered, and other macro variables included such as inflation, money supply, domestic production, capital flows, etc However, a general conclusion is that any relation that exists between the two markets is short run In most studies, no long-run relationship was found between stock prices and exchange rates A table which summarizes the main features of each study is also provided In addition to reviewing the literature, we also identified one shortcoming of all studies and proposed a direction for future research All studies in the literature have assumed that the effects of exchange rate changes on stock prices are symmetric Using the US data and non-linear ARDL approach we demonstrated that this may not be the case Notes Franck and Young (1972) and Ang and Ghallab (1976) are two studies that have employed information from pre-1973 period and investigated the effects of devaluation on stock prices No uniform results were found by these studies Again using updated daily data from 2006 to 2008 for Australia, Hong Kong, Indonesia, Japan, New Zealand, South Korea and Thailand Abidin (2013) employed Engle-Granger cointegration test and showed no evidence of long run cointegration relationship between stock markets and exchange rates Downloaded by UNIVERSITY OF ECONOMICS HO CHI MINH At 18:48 14 January 2018 (PT) Unlu (2013) is another study that considers oil prices in studying the link between exchange rates and stock prices using monthly data from 2006 to 2012 for countries like Indonesia, Malaysia, the Philippines, Singapore and Thailand Using panel cointegration and Granger causality tests, Unlu finds evidence of long-run relationship among stock prices, exchange rates and oil prices In the long run, exchange rates and oil prices Granger cause stock prices but oil prices and stock prices not affect exchange rates In the short run, there is bi-directional causality between oil prices and stock prices Note that the advantage of this method is that the critical values that are tabulated by Pesaran et al (2001) account for integrating properties of all variables Indeed, they demonstrate that under this method variables could be I(0), I(1) or combination of the two For more on this and normalization procedure see Bahmani-Oskooee and Tanku (2008) All data come from the International Financial Statistics of the IMF except the S & P 500 index and the nominal effective exchange rate The former comes from Yahoo Finance and the latter from BIS The upper bound critical value of the F-statistic when there are four exogenous variables is 4.02 at the percent significance level and 3.52 at the 10 percent significance level These figures come from Pesaran et al (2001, Table CI (iii) Case III on page 300) Critical value is 3.84 at the usual percent significance level For a graphical presentation of the CUSUM and CUSUMSQ tests see Bahmani-Oskooee et al (2005) References Abidin, S (2013), “Cointegration between stock prices and exchange rates in Asia-Pacific countries”, Investment Management & Financial 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cointegration and dynamic multipliers in a nonlinear ARDL framework”, in Sickels, R and Horrace, W (Eds), Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications, Springer, New York, pp 281-314 Corresponding author Professor Mohsen Bahmani-Oskooee can be contacted at: bahmani@uwm.edu For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: permissions@emeraldinsight.com Downloaded by UNIVERSITY OF ECONOMICS HO CHI MINH At 18:48 14 January 2018 (PT) This article has been cited by: Mohsen Bahmani-Oskooee, Sujata Saha 2018 On the relation between exchange rates and stock prices: a non-linear ARDL approach and asymmetry analysis Journal of Economics and Finance 42:1, 112-137 [Crossref] Mohsen Bahmani-Oskooee, Seyed Hesam Ghodsi, Ferda Halicioglu 2017 UK trade balance with its trading partners: An asymmetry analysis Economic Analysis and Policy 56, 188-199 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Japan and Taiwan Exchange rates Bangladesh, India and Pakistan Malaysia Hong Kong, Japan, Korea, Malaysia, Singapore, Taiwan and Thailand Taiwan and Japan New Zealand Countries Exchange rates Exchange