Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
In this paper, we evaluate Value-at-Risk (VaR) and expected shortfall (ES) for crude oil and gasoline market. We adopt three long-memory-models including, FIGARCH, HYGARCH and FIAPARCH to forecast energy commodity volatility by capturing some volatility stylized fact such as long-range memory, heteroscedasticity, asymmetry and fat-tails. Then we consider extreme value theory which concentrates on the tail distribution rather than the entire distribution. EVT is considered as a potential framework for the separate treatment of tails of distributions which allows for asymmetry. Our results show that the FIAPARCH model with extreme value theory performs better in predicting the one-day-ahead VaR. Using the fitted long-memory GARCH-model and a simulation approach to estimate VaR for horizons over than one day, backtesting results show that our approach still performs for lower estimation frequencies. Overall, our findings confirm that taking into account long-range memory, asymmetry and fat tails in the behavior of energy commodity prices returns combined with filtering process such as EVT are important in improving risk management assessments and hedging strategies in the high volatile energy market.