Volatility forecasting for crude oil futures

This article studies the forecasting properties of linear GARCH models for closing-day futures prices on crude oil, first position, traded in the New York Mercantile Exchange from January 1995 to November 2005. To account for fat tails in the empirical distribution of the series, we compare models based on the normal, Student’s t and generalized exponential distribution. We focus on out-of-sample predictability by ranking the models according to a large array of statistical loss functions. The results from the tests for predictive ability show that the GARCH-G model fares best for short horizons from 1 to 3 days ahead. For horizons from 1 week ahead, no superior model can be identified. We also consider out-of-sample loss functions based on value-at-risk that mimic portfolio managers and regulators’ preferences. Exponential GARCH models display the best performance in this case.