Oil Price Volatility Prediction Using Out-Of-Sample Analysis – Prediction Efficiency of Individual Models, Combination Methods, And Machine Learning Based Shrinkage Methods

Abstract

In this study, we compare the efficacy of forecast combination methods against machine learning-based shrinkage techniques in predicting oil price volatility. Our analysis is based on heterogeneous autoregressive (HAR) model framework. We employ eight individual HAR models and their variations, alongside five distinct combination methods for aggregating forecasts derived from HAR models and their variants. Additionally, we incorporate two widely recognized machine learning-based shrinkage methods, namely the elastic net and the lasso. Machine learning (ML) techniques, including elastic net and lasso, exhibit promise in estimating individual extended HAR models and combination sampled approaches. Meanwhile, model confidence set (MCS) estimation techniques demonstrate notably superior out-of-sample forecasting performance for the chosen sample. Our empirical findings reveal that both the elastic net and the lasso exhibit superior out-of-sample prediction accuracy in comparison to the individual HAR models and their variants, as well as the five combination techniques. Furthermore, we provide statistical evidence demonstrating the notably higher directional accuracy achieved by the elastic net and lasso methodologies. Importantly, our results remain statistically consistent across a range of robustness analyses. These findings hold significance for investors and policymakers, as they suggest potential economic benefits derived from allocating portfolios in alignment with oil price volatility estimates.