Dynamic Tail Risk Connectedness between Artificial Intelligence and Fintech Stocks

Shoaib Ali, Nassar Al-Nassar, Ali Awais Khalid and Charbel Salloum
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Shoaib Ali: UIR - Université Internationale de Rabat, LAU - Lebanese American University
Nassar Al-Nassar: Qassim University [Kingdom of Saudi Arabia]
Ali Awais Khalid: LUMS - Lahore University of Management Sciences
Charbel Salloum: Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School

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Abstract: This study investigates the tail risk connectedness between financial technology (FinTech) and artificial intelligence (AI) stocks using the Time-Varying Parameter Vector Autoregressive (TVP-VAR) model. The asymmetric slope Conditional Autoregressive Value-at-Risk (CAViaR) approach was employed to quantify tail risk. Our study period spans from June 13, 2018, to September 15, 2023, inclusive of pre- and post-COVID-19 pandemic periods. The results indicate a significant increase in total tail risk spillovers during the initial wave of the COVID-19 pandemic, with spillovers being more pronounced at the 5% level, followed by the 10% and 2.5% levels. Predominantly, AI stocks emerged as persistent net transmitters of shocks, while FinTech stocks acted as shock receivers. The gold volatility and geopolitical risk (VIX and EPU) decrease (increase) the total system connectedness. The findings of this study advocate that investors and policymakers should consider incorporating FinTech and AI stocks in portfolios for enhanced risk diversification during periods of crisis. These nascent assets exhibit substantial growth potential, offering investors the opportunity for elevated returns, thus promoting household financial inclusion and technology adoption.

Keywords: FinTech; Artificial intelligence; Equities; Connectedness; VaR (search for similar items in EconPapers)
Date: 2024-12-04
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Published in Annals of Operations Research, 2024, ⟨10.1007/s10479-024-06349-y⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04981681

DOI: 10.1007/s10479-024-06349-y

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