An analysis of extreme risk spillover effects and their determinants between AI-related assets and Islamic banking indices

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Purpose This study uses the time-varying parameter vector autoregressive (TVP-VAR) frequency connectedness approach to examine the interconnectedness between artificial intelligence (AI)-related financial assets and Islamic banking indices in financial markets. It reveals linkages across different market segments and their influence on spillovers between segments at different investment horizons. Design/methodology/approach The research methodology involves using the TVP-VAR model. This model allows the authors to analyze return spillovers across different time frames by capturing the dynamic nature of the relationships between variables. The authors also consider various global factors in the regression analysis for rigor (Chatziantoniou et al., 2023). Findings This research shows that short-term changes impact extreme risk interconnectedness more than medium- or long-term changes. Well-established market indices like AI-related stocks (MSFT, GOOG and NVDA) and Islamic banks (Saudi Arabia, UAE) consistently contribute to or transmit returns. In contrast, most AI-related tokens and Asian Islamic banks tend to receive shocks. Two indices related to gold and the uncertainty of the US dollar demonstrate potential for hedging and predictability in interconnectedness. Practical implications The results emphasize the vital role of short-term changes in diversifying a portfolio and managing risks, providing valuable insights for financial analysts and professionals in AI-related finance, Islamic banking and portfolio management. Originality/value The rising importance of AI-related stocks and tokens in investing has raised concerns about their compatibility with traditional financial instruments, especially in Islamic finance (Rabbani et al., 2023; Darehshiri et al., 2022; Yousaf et al., 2022). This paper examines the connections among AI-related stocks, AI-related tokens and Islamic banking indices to shed light on their correlations and potential impacts on the financial landscape.