Forecasting volatility in the petroleum futures markets: A re-examination and extension

This paper examines the volatility models and their forecasting abilities for four types of petroleum futures contracts traded on the New York Mercantile Exchange. The aim of this paper is twofold. Firstly, it replicates and carries out the robustness checks using the rigorous model confidence set test on the out-of-sample volatility forecast analysis undertaken by Sadorsky (Energy Economics, 2006; 28, 467–488) through the same statistical models but with the extended data on daily prices of petroleum futures. Our test results largely confirm the findings obtained in the replicated paper. Secondly, our paper also explores the relevance of some statistical complexities (e.g., model optimality, regime switches, and alternative distribution functions) in volatility forecasting through a large number of moving windows. Our results, in general, show that accounting for the model optimality, structural breaks, and using the asymmetric heavy-tailed distribution functions in the estimations lead to significant forecasting accuracy gains.