Forecasting Realized Volatility of Crude Oil Futures Prices based on Machine Learning

Jiawen Luo, Tony Klein, Thomas Walther and Qiang Ji

No 2021/04, QBS Working Paper Series from Queen's University Belfast, Queen's Business School

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 which are constructed with these forecasts outperform their competing models and 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.

Keywords: Forecasting; Crude oil; Realized volatility; Exogenous predictors; Machine learning (search for similar items in EconPapers)
JEL-codes: C22 C45 E37 Q47 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/271249/1/qms-rp2021-04.pdf (application/pdf)

Related works:
Journal Article: Forecasting realized volatility of crude oil futures prices based on machine learning (2024)
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:zbw:qmsrps:202104

DOI: 10.2139/ssrn.3701000

Access Statistics for this paper

More papers in QBS Working Paper Series from Queen's University Belfast, Queen's Business School Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().