Quantile co-movement and dependence between energy-focused sectors and artificial intelligence

Christian Urom, Gideon Ndubuisi, Khaled Guesmi and Ramzi Benkraien

Technological Forecasting and Social Change, 2022, vol. 183, issue C

Abstract: This paper examines the dependence between Artificial Intelligence (AI) and eight energy-focused sectors (including renewable energy and coal) across different market conditions and investment horizons. This paper adopts both linear and non-linear models such as quantile regressions and quantile cross-spectral coherency models. Evidence from the linear model suggests that the performance of energy-focused corporations, especially those in the renewable energy sector depends strongly on the performance of AI. Results from the non-linear model indicate that dependence varies across both energy sectors, market conditions as well as investment horizons. By considering both negative and positive shocks on AI, we demonstrate that the dependence of energy corporations on AI also varies according to the direction of shocks on AI. Interestingly, negative and positive shocks on AI impact differently on the performance of energy corporations across different sectors and market conditions. Besides, we found that the dependence became stronger during the first wave of the COVID-19 pandemic. Our findings hold profound implications for portfolio managers and investors, who may be interested in holding the assets of AI and those of energy corporations.

Keywords: Tail dependence; Artificial intelligence; Energy corporations; Quantile-spectral coherence (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:183:y:2022:i:c:s0040162522003663

DOI: 10.1016/j.techfore.2022.121842

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