| Autor: |
Christian Urom, Gideon Ndubuisi, Hela Mzoughi, Khaled Guesmi |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Financial Innovation, Vol 10, Iss 1, Pp 1-31 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2199-4730 |
| DOI: |
10.1186/s40854-024-00609-3 |
| Popis: |
Abstract This paper employs wavelet coherence, Cross-Quantilogram (CQ), and Time-Varying Parameter Vector-Autoregression (TVP-VAR) estimation strategies to investigate the dependence structure and connectedness between investments in artificial intelligence (AI) and eight different energy-focused sectors. We find significant evidence of dependence and connectedness between the stock returns of AI and those of the energy-focused sectors, especially during intermediate and long-term investment horizons. The relationship has become stronger since the COVID-19 pandemic. More specifically, results from the wavelet coherence approach show a stronger association between the stock returns of energy-focused sectors and AI, while results from the CQ analysis show that directional predictability from AI to energy-focused sectors varies across sectors, investment horizons, and market conditions. TVP-VAR results show that since the COVID-19 outbreak, AI has become more of a net shock receiver from the energy market. Our study offers crucial implications for investors and policymakers. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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