Estimating and Predicting Value-at-Risk in the presence of structural breaks: A Study based on unbiased extreme value volatility estimator

Abstract

We provide a framework based on the unbiased extreme value volatility estimator to predict long and short position value-at-risk (VaR). The given framework incorporates the impact of asymmetry, structural breaks and fat tails in volatility. We generate forecasts of both long and short position VaR and evaluate the VaR forecasting performance of the proposed framework using various backtesting approaches for both long and short positions and compare the results with that of various alternative models. Our findings indicate that the proposed framework outperforms the alternative models in predicting the long and the short position VaR. Our findings also indicate that the VaR forecasts based on the proposed framework provides the least total loss score for various long and short positions VaR and this supports the superior properties of the proposed framework in forecasting VaR more accurately. The study contributes by providing a framework to predict more accurate VaR measure based on the unbiased extreme value volatility estimator.