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In order to obtain a reasonable and reliable forecast method for crude oilprice volatility, this paper evaluates the forecast performance ofsingle-regime GARCH models (including the standard linear GARCH model and thenonlinear GJR-GARCH and EGARCH models) and the two-regime Markov RegimeSwitching GARCH (MRS-GARCH) model for crude oil price volatility at differentdata frequencies and time horizons. The results indicate that, first, thetwo-regime MRS-GARCH model beats other three single-regime GARCH type models inin-sample data estimation under most evaluation criteria, although it appearsinferior under a few of other evaluation criteria. Second, the two-regimeMRS-GARCH model overall provides more accurate volatility forecast for dailydata but this superiority dies way for weekly and monthly data. Third, amongthe three single-regime GARCH type models, the volatility forecast of thenonlinear GARCH models exhibit greater accuracy than the linear GARCH model fordaily data at longer time horizons. Finally, the linear single-regime GARCHmodel overall performs better than other three nonlinear GARCH type models inValue-at-Risk (VaR) forecast.

Forecasting crude oil market volatility: can the Regime Switching GARCH model beat the single-regime GARCH models?