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This study investigates the impact of real exchange rates on the trade balances between Thailand and its major trading partners. Previous empirical evidence gave mixed results of the impact of real exchange rates on trade balances. In this study, Augmented Dicky‐Fuller and Phillips‐Perron tests for stationarity followed by the cointegration tests are implemented. All variables in the model are nonstationary but cointegrated. In cointegrating regressions, biases are introduced by simultaneity and serial correlation in the error. The specification that deals with these problems is the non‐linear specification of Stock and Watson (1989). By using this non‐linear model as modified by Reinhart (1995), the results show that the impact of real exchange rates (Thai baht/foreign currency) on trade balances is significant in most cases. Therefore, the generalized Marshall‐Lerner condition seems to hold. Furthermore, the results show that the real exchange rates play a more important role in the determination of the bilateral trade balances than other factors. Since the real exchange rate variable plays a major role in this study, the policy recommendation is to prevent exchange rate misalignment. A policy that can neutralize the changes in nominal exchange rates and relative prices should be introduced to prevent further deterioration of the trade balance.

The purpose of the present study is to directly examine the relationship between bilateral exchange rate and stock market index in a bivariate framework during the period of the floating exchange rate regime in Thailand.

The monthly data used in this study are the stock market index or stock prices from the Stock Exchange of Thailand, and the nominal bilateral exchange rate in terms of baht per US dollar from the Bank of Thailand. The period covers July 1997 to June 2010 with 156 observations. This is the period that the country switched from fixed to floating exchange rate regime. The stock market return is calculated by the percentage change of stock market index (or stock prices) while the exchange rate return is the percentage change of the nominal bilateral exchange rate. Three estimation methods are used to capture the interaction between stock and foreign exchange markets: bounds testing for cointegration, non‐causality test, and the two‐step approach with a bivariate GARCH model and Granger causality test.

The results of the present study show that bounds testing for cointegration does not detect the long‐run relationship between stock prices and exchange rate. In addition, the non‐causality test fails the diagnostic test for multivariate normality in the residuals of the estimated VAR model. However, the two‐step approach adequately detects the linkages between the stock and foreign exchange markets. It is found that there exists positive unidirectional causality running from stock market return to exchange rate return. The exchange rate risk causes stock return to fall as expected. Moreover, there are bidirectional causal relations between stock market risk and exchange rate risk, but in different directions.

Since a rising trend in the risk in the foreign exchange market causes stock return to fall, both domestic and foreign investors should be aware of the risk or uncertainty in the foreign exchange market because it can cause their portfolio return to fall. For policymakers, reducing exchange rate risk cannot be done without the associated costs from a rising risk in the stock market.

This study provides an evidence of volatility (or risk) spillovers in stock and foreign exchange markets. In addition, the risk in foreign exchange market that adversely affects return in the stock market is an expected phenomenon under the floating exchange rate regime.

The purpose of this paper is to estimate the relationship between stock prices and exchange rates of eight Asian countries. The analysis is based on methodologies that possess the ability to provide a complete representation of data series from both time and frequency perspectives simultaneously. In addition, instead of limiting the analysis to focus on the conditional mean of the response variable y in the regression equation, the authors investigate the extremes of distribution to reveal a range of hidden relationships between these variables.

Given the limitations of classical methodology of Pearson correlation and least-squares regression, this study estimates the relationship between stock prices and exchange rates through wavelet correlation and cross-correlation to serve as a protocol for different traders who view the market with different time resolutions. In addition, quantile regression technique robust to heteroscedasticity, skewness and leptokurtosis is used to understand the relationship between stock prices and a specified quantile of the exchange rates.

In accordance with the portfolio balance effect, it is observed that stock prices and exchange rates are negatively correlated at all frequencies. In particular, the negative correlation grows with higher time scales (lower frequency intervals). The findings from quantile regression also suggest that the coefficients are more inclined to be negative when exchange rates are extremely high.

The paper contributes to the literature by focussing on the multi-scale relationship between stock prices and exchange rates. In addition, it also analyzes the relationship between stock prices and a specified quantile of the exchange rates.