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The contagion effect of artificial intelligence across innovative industries: From blockchain and metaverse to cleantech and beyond

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  • Naeem, Muhammad Abubakr
  • Arfaoui, Nadia
  • Yarovaya, Larisa

Abstract

Artificial Intelligence (AI) stands as a transformative force across business, technology, and science, yet its comprehensive impact on innovative industries remains relatively unexplored. This study delves into the interconnectedness between AI and pivotal sectors such as cryptocurrency, blockchain, metaverse, democratized banking, and Cleantech, among others. Employing the conditional autoregressive value-at-risk (CAViaR) and time-varying parameters vector autoregressions (TVP-VAR) methods, we scrutinize daily data spanning from June 1, 2018, to October 11, 2023, encompassing 12 stock indices representing each industry. Our findings unveil a strong contagion effect from AI to other innovative sectors, with the exception of Cleantech, which appears to have decoupled from the AI surge. Notably, democratized banking and the metaverse emerge as key recipients of this contagion. Examination of tail-risk spillovers highlights AI as one of the most influential risk transmitters during market tumult, while cryptocurrency and blockchain consistently function as net risk receivers throughout the sample period. The implications of these findings are multifaceted, offering substantive insights into the risk profiles of these critical innovative sectors. Investors and regulatory bodies stand to benefit significantly from this analysis, as it illuminates potential avenues for portfolio diversification and deepens understanding of contagion mechanisms within these evolving industries.

Suggested Citation

  • Naeem, Muhammad Abubakr & Arfaoui, Nadia & Yarovaya, Larisa, 2025. "The contagion effect of artificial intelligence across innovative industries: From blockchain and metaverse to cleantech and beyond," Technological Forecasting and Social Change, Elsevier, vol. 210(C).
    • Handle: RePEc:eee:tefoso:v:210:y:2025:i:c:s0040162524006206
    DOI: 10.1016/j.techfore.2024.123822
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