Backtesting of Value at Risk Methodology: Analysis of Banking Shares in India

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