Dynamic Tail Risk Connectedness between Artificial Intelligence and Fintech Stocks

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.