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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.