IDEAS home Printed from https://ideas.repec.org/a/eee/reveco/v88y2023icp698-711.html
   My bibliography  Save this article

Forecasting crude oil prices: A reduced-rank approach

Author

Listed:
  • Song, Yixuan
  • He, Mengxi
  • Wang, Yudong
  • Zhang, Yaojie

Abstract

We use a reduced-rank approach (RRA) to forecast oil prices. The empirical results show that the RRA model outperforms the competitive models both in-sample and out-of-sample. We also find that the RRA model can generate economic value for investors. In addition, we explore the driving force of RRA's predictive power and show that the RRA model can effectively identify the predictive information of indicators, including magnitude and direction, and apply appropriate loadings to the predictors accordingly. Finally, our results are robust to multiple alternatives.

Suggested Citation

  • Song, Yixuan & He, Mengxi & Wang, Yudong & Zhang, Yaojie, 2023. "Forecasting crude oil prices: A reduced-rank approach," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 698-711.
    • Handle: RePEc:eee:reveco:v:88:y:2023:i:c:p:698-711
    DOI: 10.1016/j.iref.2023.07.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1059056023002113
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.iref.2023.07.001?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; Crude oil; Reduced-rank approach; Technical indicators; Loadings;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • 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
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    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:reveco:v:88:y:2023:i:c:p:698-711. 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/inca/620165 .

    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.