Forecasting realized volatility of crude oil futures prices based on machine learning

Jiawen Luo, Tony Klein, Thomas Walther and Qiang Ji

Journal of Forecasting, 2024, vol. 43, issue 5, 1422-1446

Abstract: Extending the popular HAR model with additional information channels to forecast realized volatility of WTI futures prices, we show that machine learning‐generated forecasts provide better forecasting quality and that portfolios that are constructed with these forecasts outperform their competing models resulting in economic gains. Analyzing the selection process, we show that information channels vary across forecasting horizon. Variable selection produces clusters and provides evidence that there are structural changes with regard to the significance of information channels.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1002/for.3077

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:wly:jforec:v:43:y:2024:i:5:p:1422-1446

Access Statistics for this article

Journal of Forecasting is currently edited by Derek W. Bunn

More articles in Journal of Forecasting from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().