This study examines the forecasting power of the gas price and uncertainty indices for crude oil prices. The complex characteristics of crude oil price such as a non-linear structure, time-varying, and non-stationarity motivate us to use a newly proposed approach of machine learning tools called XGBoost Modelling. This intelligent tool is applied against the SVM and ARIMAX (p,d,q) models to assess the complex relationships between crude oil prices and their forecasters. Empirical evidence shows that machine learning models, such as the SVM and XGBoost models, dominate traditional models, such as ARIMAX, to provide accurate forecasts of crude oil prices. Performance assessment reveals that the XGBoost model displays superior prediction capacity over the SVM model in terms of accuracy and convergence. The superior performance of XGBoost is due to its lower complexity and costs, high accuracy, and rapid processing times. The feature importance analysis conducted by the Shapley additive explanation method (SHAP) highlights that the different uncertainty indexes and the gas price display a significant ability to forecast future WTI crude prices. Additionally, the SHAP values suggest that the oil implied volatility captures valuable forecasting information of gas prices and other uncertainty indices that affect the WTI crude oil price.
Keywords: Complex relationship; Crude oil price; Gas price; Shapley additive explanation method; Uncertainty indexes; eXtreme Gradient Boosting.
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.