Multi‐model Forecasts of the West Texas Intermediate Crude Oil Spot Price

Abstract

We measure the performance of multi‐model inference (MMI) forecasts compared to predictions made from a single model for crude oil prices. We forecast the West Texas Intermediate (WTI) crude oil spot prices using total OECD petroleum inventory levels, surplus production capacity, the Chicago Board Options Exchange Volatility Index and an implementation of a subset autoregression with exogenous variables (SARX). Coefficient and standard error estimates obtained from SARX determined by conditioning on a single ‘best model’ ignore model uncertainty and result in underestimated standard errors and overestimated coefficients. We find that the MMI forecast outperforms a single‐model forecast for both in‐ and out‐of‐sample datasets over a variety of statistical performance measures, and further find that weighting models according to the Bayesian information criterion generally yields superior results both in and out of sample when compared to the Akaike information criterion. Copyright © 2016 John Wiley & Sons, Ltd.