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Forecasting crude oil price volatility via a HM-EGARCH model

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  • Lin, Yu
  • Xiao, Yang
  • Li, Fuxing

Abstract

This paper compares uni-regime GARCH-type models, GARCH-type models with Markov and hidden Markov (HM) switching regimes on their forecasting abilities in WTI and Daqing crude oil markets, respectively. Empirical results indicate a HM-EGARCH model outperforms the competitive models, namely the regular GARCH-type models and Markov regime-switching models as well as the other models with hidden Markov regimes through results of six loss functions and the superior predictive ability (SPA) test. More significantly, we find the HM-EGARCH not only performs well in developed crude oil markets, but also in emerging crude oil markets. Therefore, the HM-EGARCH model can be regard as an effective measure of volatility when accounting for different volatility states in the time-changing process.

Suggested Citation

  • Lin, Yu & Xiao, Yang & Li, Fuxing, 2020. "Forecasting crude oil price volatility via a HM-EGARCH model," Energy Economics, Elsevier, vol. 87(C).
    • Handle: RePEc:eee:eneeco:v:87:y:2020:i:c:s0140988320300323
    DOI: 10.1016/j.eneco.2020.104693
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    More about this item

    Keywords

    Crude oil; Forecasting volatility; Hidden Markov EGARCH; SPA test;
    All these keywords.

    JEL classification:

    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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