IDEAS home Printed from https://ideas.repec.org/a/taf/applec/v58y2026i4p756-779.html

Forecasting realized volatility of the oil future prices via machine learning

Author

Listed:
  • Myung Jun Kim
  • Byung-June Kim
  • Taeyoon Kim
  • Bong-Gyu Jang

Abstract

This paper explores the potential use of machine learning models in crude oil realized volatility forecasting through a variety of empirical analyses and robustness checks. Although the conventional Heterogeneous Autoregressive (HAR) model is widely accepted, the machine learning models with the HAR factors can significantly improve its forecasting performance. We also found that macroeconomic variables such as supply factors, implied volatility indices and uncertainty factors can be useful in forecasting oil volatility.

Suggested Citation

  • Myung Jun Kim & Byung-June Kim & Taeyoon Kim & Bong-Gyu Jang, 2026. "Forecasting realized volatility of the oil future prices via machine learning," Applied Economics, Taylor & Francis Journals, vol. 58(4), pages 756-779, January.
    • Handle: RePEc:taf:applec:v:58:y:2026:i:4:p:756-779
    DOI: 10.1080/00036846.2025.2458248
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00036846.2025.2458248
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00036846.2025.2458248?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:applec:v:58:y:2026:i:4:p:756-779. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEC20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.