Forecasting crude oil prices: A reduced-rank approach

Abstract

We use a reduced-rank approach (RRA) to forecast oil prices. The empirical results show that the RRA model outperforms the competitive models both in-sample and out-of-sample. We also find that the RRA model can generate economic value for investors. In addition, we explore the driving force of RRA's predictive power and show that the RRA model can effectively identify the predictive information of indicators, including magnitude and direction, and apply appropriate loadings to the predictors accordingly. Finally, our results are robust to multiple alternatives.