Artificial intelligence and clean/dirty energy markets: tail-based pairwise connectedness and portfolio implications

Bechir Raggad () and Elie Bouri ()
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Bechir Raggad: Majmaah University
Elie Bouri: Lebanese American University

Future Business Journal, 2025, vol. 11, issue 1, 1-24

Abstract: Abstract This study investigates the return and volatility connectedness between artificial intelligence (AI) stock ETF and each segment of the energy markets, namely clean energy, dirty energy, and WTI oil. Using a quantile-on-quantile connectedness approach on daily data from 14 September 2016 to 29 January 2024, the results reveal the following. Firstly, the degree of connectedness for the Clean-AI pair is more pronounced than that of the other pairs (AI-Dirty and AI-WTI), and Clean is mainly a receiver of return connectedness from AI stock ETF. Clean, Dirty, and WTI shift in roles to be primary transmitters of volatility shocks. Secondly, return and volatility shocks propagate more strongly at the tails of the conditional distribution than the middle of the distribution, and a dynamic analysis indicates that the average quantile-based total connectedness changes with time and strengthens during the COVID-19 outbreak. Thirdly, a portfolio and risk analysis with tail risk measures confirms the importance of considering a dynamic approach to tail-risk minimization.

Keywords: Artificial intelligence stock index ETF; Clean energy; Dirty energy; WTI oil; Return and volatility connectedness; Quantile on quantile (search for similar items in EconPapers)
JEL-codes: C53 G32 Q29 Q42 Q49 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1186/s43093-025-00451-8

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