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Value-at-Risk estimation of energy commodities: A long-memory GARCH–EVT approach

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  • Youssef, Manel
  • Belkacem, Lotfi
  • Mokni, Khaled
Abstract
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.

Suggested Citation

  • Youssef, Manel & Belkacem, Lotfi & Mokni, Khaled, 2015. "Value-at-Risk estimation of energy commodities: A long-memory GARCH–EVT approach," Energy Economics, Elsevier, vol. 51(C), pages 99-110.
    • Handle: RePEc:eee:eneeco:v:51:y:2015:i:c:p:99-110
    DOI: 10.1016/j.eneco.2015.06.010
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    More about this item

    Keywords

    Extreme value theory; Long-range-memory; Value-at-Risk; Expected shortfall oil price and energy commodities volatility;
    All these keywords.

    JEL classification:

    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • G1 - Financial Economics - - General Financial Markets
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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