Print Email Facebook Twitter Quantile co-movement and dependence between energy-focused sectors and artificial intelligence Title Quantile co-movement and dependence between energy-focused sectors and artificial intelligence Author Urom, Christian (Paris School of Business) Ndubuisi, G.O. (TU Delft Economics of Technology and Innovation; German Institute of Development and Sustainability; UNU-MERIT) Guesmi, Khaled (Paris School of Business) Benkraien, Ramzi (Audensia Business School) Date 2022 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. Subject Artificial intelligenceEnergy corporationsQuantile-spectral coherenceTail dependence To reference this document use: http://resolver.tudelft.nl/uuid:c979f4f5-5973-4c20-a98e-02c4836519c1 DOI https://doi.org/10.1016/j.techfore.2022.121842 Embargo date 2024-07-28 ISSN 0040-1625 Source Technological Forecasting and Social Change, 183 Part of collection Institutional Repository Document type journal article Rights © 2022 Christian Urom, G.O. Ndubuisi, Khaled Guesmi, Ramzi Benkraien Files file embargo until 2024-07-28