Controlling higher level of non-performing loans (NPLs) has become one of the key objectives of the Reserve Bank of India (RBI), as it may impact banking and macroeconomic stability adversely. In this respect, the present study tries to determine risk factors that diminish asset quality of Indian commercial banks in and around the asset quality review period. Pooled and panel logit model has been employed to examine the determinants of NPLs. We find that banks with lower level of capital, reduced profitability, less diversified portfolio, poor operating and managerial efficiency are at greater risk of having diminished asset quality, whereas the size of the bank is positively linked with the higher level of NPAs. In general, empirical analysis proposes that to identify the bank whose asset quality is likely to deteriorate well in advance, the Regulatory and Supervisory Department of the Central Bank may consider lower level of capital, deteriorating profitability and poor operational efficiency as a leading indicator.

Banks are required to maintain an appropriate level of capital which must commensurate with the riskiness of their portfolio. Recently, the Reserve Bank of India (RBI) issued a circular on Prudential Guidelines on Capital Adequacy-Implementation of Internal Models Approach (IMA) for Market Risk to select a suitable method for the banks to determine the regulatory capital requirement under the market risk exposure. Banks which adopt this approach are required to quantify market risk through their own Value-at-Risk (VaR) model. Therefore, it is a challenging task for risk managers of the bank to select an appropriate risk model which reasonably covers the risk of the bank's portfolio. Use of wrongly calibrated risk models may lead to undercapitalised banking system. This article aims at exploring the suitable risk model for measuring foreign exchange risk in banks' portfolio. The objective of present study is to empirically test the appropriate VaR model for foreign exchange rate risk. Value-at-Risk has been estimated for foreign exchange rate risk by using parametric variance-covariance method and non-parametric historical simulation (HS) method. Under parametric method, VaR is estimated by assuming that returns follow normal and Student's t-distribution. Backtesting results for various VaR models have been done based on Kupiec's proportion of failures (KPOF) test and regulatory 'traffic light' test. This article concludes that when returns are non-normal, VaR model based on the assumption of normality significantly underestimates the risk. Our empirical results based on backtesting show that most accurate VaR estimates are obtained from Student's t VaR model.

In this article, we try to identify the determinants of adoption of social media, in particular Facebook, among the Indian scheduled commercial banks. We have employed the survival analysis technique that studies the conditional probability of adoption of social media over time. The Kaplan–Meier nonparametric survival technique is used to study the nature of social media adoption by the Indian banks. Employing Cox’s proportional hazard (CPH) regression model, we have tried to assess the effect of explanatory variables on the adoption hazard rate, that is, joining Facebook. It appears that private sector banks are keen to join Facebook than public sector banks. The empirical analysis suggests that banks with higher non-interest income, intermediation cost and return on assets are inclined to adopt the technological innovations rapidly. The bank with greater ATM coverage is more likely to join Facebook. Medium/small-sized banks tend to be faster in joining Facebook. Banks with higher non-performing assets (NPAs) are yet to join Facebook. However, the number of branches is not associated with joining Facebook.