Exploring the coherency and predictability between the stocks of artificial intelligence and energy corporations  

作　　者：Christian Urom Gideon Ndubuisi Hela Mzoughi Khaled Guesmi 

机构地区：[1]Center of Research for Energy and Climate Change(CRECC),Paris School of Business,Paris,France [2]Delft University of Technology(TU Delft),Delft,The Netherlands [3]Faculty of Economics and Management,University of Tunis El Manar,Tunis,Tunisia

出　　处：《Financial Innovation》2024年第1期391-421,共31页金融创新(英文)

摘　　要：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.

关 键 词：Artificial intelligence Energy-firms Quantile-dependence SPILLOVER 

分 类 号：TN9[电子电信—信息与通信工程]