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Forecasting the VaR of the crude oil market: A combination of mixed data sampling and extreme value theory

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  • Lyu, Yongjian
  • Qin, Fanshu
  • Ke, Rui
  • Yang, Mo
  • Chang, Jianing
Abstract
When forecasting the value-at-risk (VaR) of the crude oil market, traditional models often fail to capture the information embedded in low-frequency macro-variables and tend to underestimate the high quantiles caused by adopting commonly used distributions. To address these problems, this paper proposes a new approach, which combines the generalized autoregressive condition heteroskedasticity (GARCH)-mixed data sampling (MIDAS) models with extreme value theory (EVT). Our empirical results show that first, the GARCH-MIDAS models outperform the benchmark models when they incorporate suitable low-frequency macroeconomic variables. Second, the VaR forecasting accuracy of some GARCH-MIDAS models can be further improved when combined with EVT. Third, the EVT-based GARCH-MIDAS model that contains the demand-side information of the oil market achieves the best performance among all the models. Fourth, the historical simulation (HS) method that is widely used by financial institutions is extremely inaccurate.

Suggested Citation

  • Lyu, Yongjian & Qin, Fanshu & Ke, Rui & Yang, Mo & Chang, Jianing, 2024. "Forecasting the VaR of the crude oil market: A combination of mixed data sampling and extreme value theory," Energy Economics, Elsevier, vol. 133(C).
    • Handle: RePEc:eee:eneeco:v:133:y:2024:i:c:s0140988324002081
    DOI: 10.1016/j.eneco.2024.107500
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    References listed on IDEAS

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