No abstract

Value at risk (VaR) is used by financial experts to calculate and predict the risk of financial exposure. In the presence of volatility and long memory, it is a model useful for the prediction of loss in the equity index return series. Checking the accuracy of this model is necessary from the practitioners’ point of view. This article initially checks the presence of autoregressive conditional heteroscedastic (ARCH) and long-memory effects in the daily closing price of the Bombay Stock Exchange (BSE)-BANKEX return series. After confirming the ARCH and long-memory presence, it analyses the different methods of VaR calculation such as asymmetric power ARCH (APARCH), fractionally integrated exponential generalised ARCH (FIEGARCH), hyperbolic generalised GARCH (HYGARCH) and risk metrics. Then, it empirically tests the forecasting capacity of these VaR methods through techniques such as the Kupiec likelihood ratio (LR test) and dynamic quantile test. Furthermore, it checks the root-mean-squared error (RMSE) and mean absolute error (MAE) to determine the model with the least error. From the set of VaR models used here, by and large it concludes that the BANKEX return series has both long-memory and asymmetry effects. By comparing these models, it is implied that the HYGARCH model gives a better result, although the other models have their significance in the estimation and forecasting of the BANKEX return series. JEL: C12, D81, C530

The macro stress testing is an integral part of the credit risk management exercise to judge the strength of banks in hypothetical stress situations. The present paper develops the macroeconomic stress testing model and checks the resilience of banks across different groups of banks in India like the public sector, private, and foreign banks in maintaining the minimum required regulatory capital at the time of stress situations. The stress testing exercise is undertaken by the use of panel data models to evaluate the impact of likely changes in macro parameters on the non-performing loans under three stress scenarios like baseline, medium, and severe. The impact of these stress testing is substantial for the public sector banks as compared to private and foreign banks. Except for the few banks, all other public sector banks are not able to sustain the macro stress scenarios and became insolvent. In opposite to this, all the foreign banks are able to sustain all the stress scenarios. The private banks are also able to withstand the assumed crisis, except few banks failed to do so. The study may be useful for studying the preparedness of banks to face the crisis period and also helps them to get themselves equipped with the required capital to meet those circumstances.

This paper investigates the impact of COVID-19 on the cryptocurrency market. It empirically examines the level of volatility and the dynamic conditional correlations among cryptocurrencies pre-COVID-19 and during COVID-19. We find significant dynamic conditional correlations among cryptocurrencies and that the level of volatility is higher during COVID-19 than pre-COVID-19.