The present article explores the factors that influence the profitability of Indian banking sector in the post-reform period using the data from 1995 to 2012. The results indicate that operating profits, wage bills, non-performing assets and net interest margin affect the profitability of Indian banks, while the priority sector lending does not have any impact on bank profitability in India. Further, the net interest margin is found to be significantly reducing profitability. Hence, it is suggested that banking sector in India should focus on reducing its operational expenses.

Indian economy witnessed significant transformations in the postreform period both in terms of change in policy paradigm adopting greater market orientation and overall macroeconomic performance with development of a broad-based financial market and increasing global integration. Guided by the fact that domestic savings play a critical role in augmenting capital accumulation and contributing to achieve and sustain high economic growth, an attempt to review and reassess the role of various factors influencing domestic savings under the changed environment is quite relevant for sustaining the growth momentum. In this backdrop, the present study empirically analyzed the role of various determinants of household savings in India with the latest available data. It employed ARDL approach for this purpose due to its suitability for estimating an equation with a mix of stationary and nonstationary variables of order I(1) and address potential endogeneity problems. The estimated results revealed that GDP, dependency ratio, interest rate, and inflation have statistically significant influence on household savings in India, both in the long run and short run. As regards policy implications, we suggest that ensuring price stability and avoiding any disruption to the growth process will be useful for augmenting savings and sustaining the savings-growth spiral in India.

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Purpose – 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. Design/methodology/approach – 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. Findings – 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. Originality value – 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.

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