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Exploring volatility interconnections between AI tokens, AI stocks, and fossil fuel markets: evidence from time and frequency-based connectedness analysis

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  • Yousaf, Imran
  • Ijaz, Muhammad Shahzad
  • Umar, Muhammad
  • Li, Yanshuang

Abstract

Energy and artificial intelligence (AI) are two of the top fields of the present time. However, investors of conventional energy assets have yet to consider the rapidly emerging AI-based assets for diversification. Owing to the rise of new categories of assets in the last two decades, which has sparked the interest of global investors to attain the most favorable outcomes, we examine volatility connectedness between AI stocks (MSFT-Microsoft, GOOG-Alphabet, AMZN-Amazon), AI tokens (AGIX-SingularityNET, OCEAN-Ocean Protocol, FET-Fetch.ai), and fossil fuel markets (WTI, BRENT, and GAS-natural gas) over the period from May 6, 2019, to July 8, 2023. We apply a novel three-dimensional framework in which we model time-domain and frequency-domain volatility spillovers at the median-, lower- and upper-quantiles in static as well as dynamic settings to achieve the objectives of this paper. We find the variation in static and dynamic connectedness between markets over time-frequencies and quantiles. Results reveal that AGIX, BRENT, FET, MSFT, and WTI are recipients, whereas AMZN, GAS, GOOG, and OCEAN are transmitters of the spillovers at the median quantile. Furthermore, we demonstrated that the short-term and long-term metrics for dynamic total connectedness might not consistently exhibit the same direction. Moreover, our analysis indicates that the short-term fluctuations predominantly influence the network's overall shock transmission, while the longer-term aspect has the potential to alter the role of a net transmitter or receiver of shocks. These findings provide portfolio managers, regulators, and policymakers with valuable information regarding portfolio adjustments, hedging, and financial stability.

Suggested Citation

  • Yousaf, Imran & Ijaz, Muhammad Shahzad & Umar, Muhammad & Li, Yanshuang, 2024. "Exploring volatility interconnections between AI tokens, AI stocks, and fossil fuel markets: evidence from time and frequency-based connectedness analysis," Energy Economics, Elsevier, vol. 133(C).
    • Handle: RePEc:eee:eneeco:v:133:y:2024:i:c:s0140988324001981
    DOI: 10.1016/j.eneco.2024.107490
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    References listed on IDEAS

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    More about this item

    Keywords

    AI tokens; AI stocks; Energy market; Spillover; Quantile analysis;
    All these keywords.

    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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