Machine learning in oil market volatility forecasting: the role of feature selection and forecast horizon

Ahmet Göncü, Tolga U. Kuzubaş and Burak Saltoğlu

Journal of Energy Markets

Abstract: In this paper we investigate oil market volatility prediction using a comprehensive data set of 205 variables spanning macroeconomic, financial, energy-related and sentiment indicators. We employ machine learning techniques for variable selection and dimension reduction, combining hard thresholding, soft thresholding and principal component analysis, evaluated through an out-of-sample time-series backtesting framework. The empirical findings reveal that financial variables dominate short-horizon forecasting, while macroeconomic and sentiment factors become progressively more important at longer horizons. A hybrid approach combining principal component analysis with preliminary variable filtering improves forecast accuracy, particularly for medium-term predictions. Support vector regression and random forest methods demonstrate strong performance when paired with appropriate feature selection techniques, suggesting the importance of capturing nonlinear relationships while maintaining robustness to outliers. These results indicate that effective oil volatility forecasting requires careful consideration of both the forecast horizon and the interaction between feature selection and machine learning methods.

References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.risk.net/node/7962657 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rsk:journ2:7962657

Access Statistics for this article

More articles in Journal of Energy Markets from Journal of Energy Markets
Bibliographic data for series maintained by Thomas Paine ().