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

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

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.

Details

OriginalspracheEnglisch
Seiten (von - bis)1422-1446
Seitenumfang25
FachzeitschriftJournal of Forecasting
Jahrgang43
Ausgabenummer5
Frühes Online-Datum2024
PublikationsstatusVeröffentlicht - Aug. 2024
Peer-Review-StatusJa

Externe IDs

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

Schlagworte

Schlagwörter

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