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

Pengchao He and Nuan Zhao
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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

Review of Economic Assessment, 2024, vol. 3, issue 2, 56-71

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.

Keywords: Artificial intelligence; Oil shocks; Wavelet analysis; Quantile-on-quantile approach (search for similar items in EconPapers)
Date: 2024
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