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

Research output: Contribution to journalResearch articleContributedpeer-review



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.


Original languageEnglish
JournalJournal of Forecasting
Publication statusE-pub ahead of print - 2024

External IDs

ORCID /0000-0003-4359-987X/work/154193050
Scopus 85185664082
Mendeley 17e195ad-5ba2-38b0-8fb7-e70bf01dc584



  • exogenous predictors, crude oil, forecasting, realized volatility, machine learning