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The purpose of this article is to examine the inter-relationship and direction of causality among three macroeconomic variables such as trade liberalization, financial development and economic growth.

The empirical analysis is based on the principal component analysis as method to construct financial development index (FDI), augmented Dickey–Fuller and Phillips–Perron tests as the unit root test, Johansen’s co-integration test and VECM for direction of causality in the long run among TOP, FDI and economic growth.

The empirical results confirmed that there exists a long-run association among trade openness, financial development and economic growth. This study has also found that there is bidirectional causality between financial development and growth. However, the causality runs from growth to finance is stronger than that from finance to growth. This study also observed unidirectional causality that runs from financial development and economic growth to trade openness.

The policy implications that could be drawn from the present study is that, initiation of financial reforms to improve the size of financial system would lead to higher economic growth. Another key implication from this study is that because trade openness has no effect on both domestic financial sector development and output growth, it would be better to deploy the resources into creating a sustained domestic demand rather than concentrating more on the external front in general and trade openness in particular.

The study constructs a summary IFD for India by taking into account four broad financial development indicators for the period 1971-2012. The present paper also suggests that it would be better to deploy the resources to create a sustained domestic demand rather than concentrating more on the external front in general and trade openness in particular.

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.