Forecasting crude oil price volatility via a HM-EGARCH model

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.