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

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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
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    References listed on IDEAS

    as
    1. Chatziantoniou, Ioannis & Elsayed, Ahmed H. & Gabauer, David & Gozgor, Giray, 2023. "Oil price shocks and exchange rate dynamics: Evidence from decomposed and partial connectedness measures for oil importing and exporting economies," Energy Economics, Elsevier, vol. 120(C).
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