IDEAS home Printed from https://ideas.repec.org/a/bba/j00010/v3y2024i2p56-71d389.html
   My bibliography  Save this article

The Effects of Artificial Intelligence on Oil Shocks: Evidence from a Wavelet-Based Quantile-on-Quantile Approach

Author

Listed:
  • Pengchao He

    (School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, China)

  • Nuan Zhao

    (School of Marxism Studies, Nanjing University of Aeronautics and Astronautics, Nanjing, China)

Abstract

This study examines the effects of artificial intelligence on oil shocks (supply, demand, and risk shocks) across different time scales and market conditions, using the wavelet-based quantile-on-quantile approach. The empirical results have discovered that in the short term, artificial intelligence exerts significant negative impacts on supply and risk shocks, with these adverse effects gradually diminishing over time. Notably, artificial intelligence begins to positively influence supply shock in the medium to long term. In contrast, demand shock is initially positively affected, but these benefits diminish over time. The outcomes gained from this study not only give policymakers valuable insights for developing more precise energy policies, but also provide investors with nuanced market perspectives and risk assessments.

Suggested Citation

  • Pengchao He & Nuan Zhao, 2024. "The Effects of Artificial Intelligence on Oil Shocks: Evidence from a Wavelet-Based Quantile-on-Quantile Approach," Review of Economic Assessment, Anser Press, vol. 3(2), pages 56-71, June.
    • Handle: RePEc:bba:j00010:v:3:y:2024:i:2:p:56-71:d:389
    as

    Download full text from publisher

    File URL: https://www.anserpress.org/journal/rea/3/2/32/pdf
    Download Restriction: no
    File URL: https://www.anserpress.org/journal/rea/3/2/32
    Download Restriction: no
    ---><---

    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:bba:j00010:v:3:y:2024:i:2:p:56-71:d:389. 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: Ramona Wang (email available below). General contact details of provider: https://www.anserpress.org .

    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.