Forecasting the prices of crude oil: An iterated combination approach

Abstract

In this paper, we employ an iterated combination approach to examine oil price predictability with a large set of predictors, including 18 macroeconomic variables and 18 technical indicators. The empirical results show that iterated combination approach outperforms the standard combination approach for both in- and out-of-sample. Specifically, the iterated combination forecasts always yield significantly larger out-of-sample R2 values and higher success ratios than the corresponding standard combination forecasts. Furthermore, we document that the results are robust to various settings, including alternative proxies of crude oil prices, three predictor sets, different forecasting windows, and various standard combination approaches. From an asset allocation perspective, we measure the economic value of the iterated combination approaches, where the leverage of oil futures trading is considered. The results suggest that the more accurate forecasts of the iterated combination approaches can generate substantially larger certainty equivalent return (CER) gains for a mean-variance investor in practice.