The performance of composite forecast models of value-at-risk in the energy market

This paper examines a comparative evaluation of the predictive performance of various Value-at-Risk (VaR) models in the energy market. This study extends the conventional research in literature, by proposing composite forecast models for applying to Brent and WTI crude oil prices. Forecasting techniques considered here include the EWMA, stable density, Kernel density, Hull and White, GARCH-GPD, plus composite forecasts from linearly combining two or more of the competing models above. Findings show Hull and White to be the most powerful approach for capturing downside risk in the energy market. Reasonable results are also available from carefully combining VaR forecasts.