Forecasting crude oil market volatility: Further evidence using GARCH-class models

Abstract

This paper extends the work of Kang et al. (2009). We use a greater number of linear and nonlinear generalized autoregressive conditional heteroskedasticity (GARCH) class models to capture the volatility features of two crude oil markets — Brent and West Texas Intermediate (WTI). The one-, five- and twenty-day out-of-sample volatility forecasts of the GARCH-class models are evaluated using the superior predictive ability test and with more loss functions. Unlike Kang et al. (2009), we find that no model can outperform all of the other models for either the Brent or the WTI market across different loss functions. However, in general, the nonlinear GARCH-class models, which are capable of capturing long-memory and/or asymmetric volatility, exhibit greater forecasting accuracy than the linear ones, especially in volatility forecasting over longer time horizons, such as five or twenty days.