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Tail connectedness between artificial intelligence tokens, artificial intelligence ETFs, and traditional asset classes

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  • Yousaf, Imran
  • Youssef, Manel
  • Goodell, John W.

Abstract

We examine extreme connectedness between the artificial intelligence (AI) tokens, artificial intelligence ETFs, and other asset classes, using the quantile VAR approach of Ando et al., (2022). We find moderate connectedness levels at mean and median quantiles, with AI ETFs (AI tokens) acting as strong (weak) net emitters (receivers) of return spillovers. Findings, confirmed by alternative testing, suggest that, during normal market conditions, AI tokens may offer utility as diversifiers for portfolios of traditional assets. However, at both lower and upper quantiles, connectedness levels increase, consistent with AI tokens and ETFs being sensitive to extreme shocks. Results suggest that AI tokens and ETFs do not diversify the risk of other assets during extreme market conditions. Finally, AI tokens, especially, may offer effective hedging at low cost for traditional assets (gold, equity, real estate, bonds, and currency), except for the oil and cryptocurrency market. Investors including AI assets in portfolios need to diligently monitor for changing market conditions.

Suggested Citation

  • Yousaf, Imran & Youssef, Manel & Goodell, John W., 2024. "Tail connectedness between artificial intelligence tokens, artificial intelligence ETFs, and traditional asset classes," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
    • Handle: RePEc:eee:intfin:v:91:y:2024:i:c:s104244312300197x
    DOI: 10.1016/j.intfin.2023.101929
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    Cited by:

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    2. Abdullah, Mohammad & Sarker, Provash Kumer & Abakah, Emmanuel Joel Aikins & Tiwari, Aviral Kumar & Rehman, Mohd Ziaur, 2024. "Tail risk intersection between tech-tokens and tech-stocks," Global Finance Journal, Elsevier, vol. 61(C).
    3. Mbarek, Marouene & Msolli, Badreddine, 2025. "Assessing linkages between supply chain tokens and other assets: Evidence from a time-frequency quantile connectedness approach," Journal of Behavioral and Experimental Finance, Elsevier, vol. 46(C).
    4. Tao, Miaomiao & Poletti, Stephen & Roubaud, David & Tiwari, Aviral Kumar, 2025. "The Global “Carbon-Energy-Intelligence” Framework: Decoding Cross-Market Interlinkages," Applied Energy, Elsevier, vol. 401(PA).
    5. Čeryová, Barbara & Árendáš, Peter & Kotlebová, Jana, 2026. "Connectedness and risk transmission across artificial intelligence industries," Research in International Business and Finance, Elsevier, vol. 84(C).
    6. Su, Xianfang & Zhao, Yachao, 2025. "Asymmetric time-frequency risk spillovers between the Fourth Industrial Revolution assets and commodity futures: Is economic policy uncertainty a driving factor?," Global Finance Journal, Elsevier, vol. 64(C).
    7. Spyros Papathanasiou & Anastasios Magoutas & Drosos Koutsokostas, 2026. "The systemic footprint: revisiting risk mitigation in long/short and 60/40 portfolios through network connectedness," Review of Derivatives Research, Springer, vol. 29(1), pages 1-31, December.
    8. Naveed Khan & Anam Tariq & Syed Zulfiqar Ali Shah & Hassan Javed, 2026. "Quantile time–frequency connectedness and spillover between artificial intelligence, clean energy, and traditional asset classes: insights and portfolio implications," Future Business Journal, Springer, vol. 12(1), pages 1-39, December.
    9. Yakup Ari & Hakan Kurt & Harun Uçak, 2025. "Volatility Spillovers Among EAGLE Economies: Insights from Frequency-Based TVP-VAR Connectedness," Mathematics, MDPI, vol. 13(8), pages 1-32, April.
    10. Nasir, Rana Muhammad & He, Feng & Yousaf, Imran, 2025. "Relationship of green cryptocurrencies, energy tokens, centralized and decentralized exchange tokens with crypto policy uncertainty," Research in International Business and Finance, Elsevier, vol. 75(C).
    11. Gubareva, Mariya & Shafiullah, Muhammad & Teplova, Tamara, 2025. "Corrigendum to “Cross-quantile risk assessment: The interplay of crude oil, artificial intelligence, clean tech, and other markets” [Energy Economics Volume 141, January 2025, 108085]," Energy Economics, Elsevier, vol. 141(C).
    12. Mbarek, Marouene & Msolli, Badreddine, 2025. "Tokens and cryptocurrencies: Evidence from asymmetric frequency connectedness approach," Research in International Business and Finance, Elsevier, vol. 77(PA).
    13. Wu, Ruirui & Li, Bin & Qin, Zhongfeng, 2024. "Spillovers and dependency between green finance and traditional energy markets under different market conditions," Energy Policy, Elsevier, vol. 192(C).
    14. Ustaoglu, Erkan, 2025. "Static and dynamic return and volatility connectedness between transportation tokens and transportation indices: Evidence from quantile connectedness approach," The North American Journal of Economics and Finance, Elsevier, vol. 75(PA).
    15. Su, Xianfang & Zhao, Yachao, 2025. "Can fourth industrial revolution assets provide diversification benefits for traditional sectoral stocks? Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 90(C).
    16. Aharon, David Y. & Ali, Shoaib & Naveed, Muhammad, 2025. "Who A(m) I? exploring quantile frequency connectedness in emerging AI and IoT token markets," The North American Journal of Economics and Finance, Elsevier, vol. 80(C).
    17. Farooq Malik & Zaghum Umar, 2025. "Quantile connectedness of artificial intelligence tokens with the energy sector," Review of Financial Economics, John Wiley & Sons, vol. 43(2), pages 135-146, April.
    18. Ying-Hui Shao & Yan-Hong Yang & Han-Xian Zhou & Wei-Xing Zhou, 2025. "Dynamic spillovers and investment strategies across artificial intelligence ETFs, artificial intelligence tokens, and green markets," Papers 2503.01148, arXiv.org, revised Jun 2025.
    19. Halilibrahim Gokgoz, 2026. "Artificial Intelligence and Volatility Connectedness in Energy Markets," World Journal of Applied Economics, WERI-World Economic Research Institute, vol. 12(1), pages 1-19, June.

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