Value-at-risk estimations of energy commodities via long-memory, asymmetry and fat-tailed GARCH models

In this paper, we evaluate the value-at-risk (VaR) and the expected shortfalls for some major crude oil and gas commodities for both short and long trading positions. Classical VaR estimations as well as RiskMetrics and other extensions to cases considering for long-range memory, asymmetry and fat-tail in energy markets volatility were conducted. We computed the VaR for three ARCH/GARCH-type models including FIGARCH, FIAPARCH and HYGARCH. These models were estimated in the presence of three alternative innovation’s distributions: normal, Student and skewed Student. Our results show that considering for long-range memory, fat-tails and asymmetry performs better in predicting a one-day-ahead VaR for both short and long trading positions. Moreover, the FIAPARCH model outperforms the other models in the VaR’s prediction. These results present several potential implications for energy markets risk quantifications and hedging strategies.