EconPapers    
Economics at your fingertips  
 

Quantile connectedness of artificial intelligence tokens with the energy sector

Farooq Malik and Zaghum Umar

Review of Financial Economics, 2025, vol. 43, issue 2, 135-146

Abstract: Artificial intelligence (AI) tokens are digital assets that integrate AI capabilities by operating on decentralized networks using AI algorithms in order to automate tasks, make intelligent decisions, and swiftly adapt based on data. Given that AI tokens are energy intensive assets, in this paper, we explore how major AI tokens are connected to oil, natural gas, and biofuel under extreme market movements using daily data from June 2019 to March 2024. We find that AI tokens are net transmitters of shocks while the entire energy sector is the net receiver of shocks at the return level. However, both AI tokens and oil are net transmitters of shocks at the volatility level. We also show that total dynamic connectedness significantly increased during the start of COVID‐19 pandemic and the Russian‐Ukraine war. Our quantile‐based connectedness analysis further shows that return and volatility connectedness is considerably higher at low and high quantiles, indicating that shocks to AI tokens spread more intensely during extreme market movements. These results indicate that AI tokens are subject to contagion and thus offer inadequate portfolio diversification under major market movements.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/rfe.1224

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:revfec:v:43:y:2025:i:2:p:135-146

Access Statistics for this article

More articles in Review of Financial Economics from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().

 
Page updated 2025-04-07
Handle: RePEc:wly:revfec:v:43:y:2025:i:2:p:135-146