Forecasting the real price of oil - Time-variation and forecast combination

Abstract

This paper sheds light on the questions whether it is possible to generate an accurate forecast of the real price of oil and how it can be improved using forecast combinations. For this reason, the following paper will investigate the out-of-sample performance of seven individual forecasting models. The results show that it is possible to construct better forecasts compared to a no-change benchmark for horizons up to 24 months with gains in the MSPE ratio as high as 25%. In addition, some of the existing models will be extended, e.g. the U.S. inventories model by introducing more suitable real-time measures for the Brent crude oil price and the VAR model of the global oil market by using different measures for the economic activity. Furthermore, the time performance investigated by constructing recursively estimated MSPE ratios discovers potential weaknesses of the used models. Hence, several different combination approaches are tested with the goal of demonstrating that a combination of individual models is beneficial for the forecasting performance. Thereby, a combination consisting of four models has proven to have a lower MSPE ratio than the best individual models over the medium run and, in addition, to be remarkably stable over time.