Economic policy uncertainty and AI-driven stock spillovers: Implications for portfolio diversification

This study examines how economic policy uncertainty (EPU) shapes return spillovers between major AI-driven stocks. Using daily data for seven leading AI-driven firms, this study estimates a time-varying connectedness via the TVP-VAR framework and links it to macroeconomic uncertainty using a GARCH-MIDAS-EPU model. The pre- and post-COVID analyses capture structural shifts in spillover dynamics. The results show Microsoft and Google are primary transmitters of shocks with Nvidia acting as a critical intermediary, while Tesla and IBM act as net receivers. EPU amplifies short-term connectedness but dampens long-term spillovers, reflecting risk-averse behavior and delayed investment under uncertainty. In post-pandemic periods, long-term volatility persistence strengthens and connectedness becomes more sensitive to policy shocks. Further, the portfolio testing reveals that strategies minimizing correlation and connectedness outperform traditional variance-based approaches during periods of heightened uncertainty. These findings highlight AI-driven equities as a critical channel of macro-policy risk transmission and provide actionable insights for investors, portfolio managers, and policymakers in innovation-driven financial markets.