Who A(m) I? exploring quantile frequency connectedness in emerging AI and IoT token markets

David Y. Aharon, Shoaib Ali and Muhammad Naveed

The North American Journal of Economics and Finance, 2025, vol. 80, issue C

Abstract: This paper investigates the return spillover and connectedness between Artificial Intelligence (AI) and Internet of Things (IoT) tokens using the Quantile Vector Autoregression (QVAR) and quantile frequency connectedness approach. Using daily data from February 2021 to March 2024 for ten leading AI and IoT tokens, we find that connectedness is both time-varying and asymmetric across quantiles. In the short term, the Total Connectedness Index (TCI) peaks at 69.58 % under extreme market conditions (τ = 0.05), compared to 64.16 % in bull markets (τ = 0.95) and 61.43 % under normal conditions (τ = 0.50). Connectedness is weaker in the medium and long terms, but asymmetry persists as the TCI reaches 10.98 % vs. 5.32 % (medium term) and 10.52 % vs. 2.64 % (long term) for extreme vs. normal quantiles. These findings confirm that return transmission intensifies during periods of elevated market uncertainty, particularly in the left tail of the distribution. Moreover, AI and IOT tokens offer both diversification and hedging benefits against each other. Our analysis provides insights for investors, portfolio managers, and policymakers in understanding systemic risk and optimizing digital asset portfolios.

Keywords: Connectedness; AI; IoT; QVAR; Portfolio diversification (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:80:y:2025:i:c:s1062940825001378

DOI: 10.1016/j.najef.2025.102497

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