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 CBOE Volatility Index (VIX) and an implementation of a Subset Autoregression with Exogenous Variables (SARX). Coecient and standard error estimates obtained from SARX determined by conditioning on a single "best model" ignore model uncertainty and result in under-estimated standard errors and over-estimated coecients. We find that the MMI forecast outperforms a single model forecast for both in and out of sample data sets over a variety of statistical performance measures, and further and that weighting models according to the BIC generally yields superior results both in and out of sample when compared to the AIC.