Forecasting the real prices of crude oil using robust regression models with regularization constraints

In this paper, we forecast the real price of crude oil via a robust loss function (Huber), with regularization constraints including LASSO, Ridge, and Elastic Net. These modifications are designed to avoid problems with overfitting and improve out-of-sample predictive performance. The efficient implementation of penalized regression for Huber losses is supported by the accelerated proximal gradient algorithm. Our results indicate that equal-weight mean combinations based on robust parameter design and parameterization penalties can outperform the benchmark no-change model at all horizons (up to two years). We also find that combinations of forecasts from robust penalized models can significantly outperform those based on OLS in horizons of longer than three months. These models have consistent and significantly higher directional accuracy than the no-change model, with success ratios of up to 63.9%.