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Artificial intelligence assets and energy markets: Risk correlation dynamics and determinants

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  • Liu, Min
  • Huang, Jianzhong
  • Liu, Shuai

Abstract

Energy market stability is vital for global economic growth, yet increasing financial, geopolitical, and climate uncertainties have heightened cross-market risks. Although artificial intelligence (AI) has emerged as a transformative driver of technology and energy systems, its financial connections to energy markets remain underexplored. Existing studies primarily focus on traditional assets or clean energy linkages, overlooking AI as a distinct asset class in global risk transmission. This study investigates the dynamic risk correlations between AI assets and energy markets using a mixed-frequency DCC-MIDAS(-X) framework over the period March 2018 to December 2023, encompassing major events such as the COVID-19 pandemic and the 2022 energy crisis. The results reveal four key findings. (1) AI assets act as an effective hedge for natural gas and China's INE crude oil and a moderate diversifier for WTI, Brent, gasoline, and gas oil. (2) A persistently strong correlation exists between AI and clean energy, reflecting deep technological and investment integration. (3) Pronounced regional heterogeneity is identified: Western benchmarks exhibit stronger AI linkages than Asian and Middle Eastern markets. (4) Dollar fluctuation, climate policy uncertainty, and economic policy uncertainty significantly strengthen AI-energy correlations, while trade and geopolitical risks have weak effects. Overall, the findings highlight the growing financial interconnectedness between the technology and energy sectors. The study contributes by incorporating a regional comparative perspective and identifying macro-policy drivers of AI-energy linkages, offering implications for coordinated policy design and strategic portfolio diversification.

Suggested Citation

  • Liu, Min & Huang, Jianzhong & Liu, Shuai, 2026. "Artificial intelligence assets and energy markets: Risk correlation dynamics and determinants," Utilities Policy, Elsevier, vol. 98(C).
    • Handle: RePEc:eee:juipol:v:98:y:2026:i:c:s0957178725002334
    DOI: 10.1016/j.jup.2025.102118
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