IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v77y2025ics1544612325002041.html
   My bibliography  Save this article

Putting VAR forecasts of the real price of crude oil to the test

Author

Listed:
  • Ellwanger, Reinhard
  • Snudden, Stephen

Abstract

This study reevaluates crude oil price forecasts from state-of-the-art VAR models (Baumeister et al., 2022). Unlike Baumeister et al., who use the average-price no-change forecast, we employ the end-of-period no-change forecast, corresponding to the traditional random walk hypothesis. VAR forecasts do not significantly outperform the random walk for horizons under one year. The average-price benchmark systematically biases the Diebold–Mariano test statistic, affecting inference on forecast improvements up to 18 months. Similar biases are observed for alternative forecast criteria. The fact that naive benchmark choice alters inference even at extended horizons is relevant for all forecasts targeting averaged series.

Suggested Citation

  • Ellwanger, Reinhard & Snudden, Stephen, 2025. "Putting VAR forecasts of the real price of crude oil to the test," Finance Research Letters, Elsevier, vol. 77(C).
    • Handle: RePEc:eee:finlet:v:77:y:2025:i:c:s1544612325002041
    DOI: 10.1016/j.frl.2025.106940
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612325002041
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2025.106940?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 search for a different version of it.

    More about this item

    Keywords

    Forecasting and prediction methods; Oil price forecasts;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

    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:eee:finlet:v:77:y:2025:i:c:s1544612325002041. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    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.