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This study examines asymmetric volatility spillovers between Shanghai and Hong Kong equity markets using a novel regime-dependent spillover index within a Markov-switching VAR framework. High-frequency data analysis reveals: (1) Post-2014 Stock Connect amplifies spillovers with pronounced asymmetry, particularly adverse shock dominance during turbulence, establishing Shanghai as the primary negative volatility transmitter; (2) While regime-switching asymmetric models enhance forecasting accuracy, portfolio strategies under conventional BEKK and aBEKK models are constrained by post-Program integration. Our regime-dependent ( RD ) model significantly improves portfolio efficiency while reducing rebalancing costs. Crucially, by leveraging regimes of realized volatility derived from intraday 5-minute data, the RD approach provides policymakers and investors with superior tools for mitigating cross-market risk transmission during financial liberalization. Findings demonstrate that accounting for regime shifts and asymmetry is essential for improvement of volatility forecast and effective risk management in emerging markets. • We apply a Markov switching VAR ( MS-VAR ) spillover index to study regime-dependent volatility spillover asymmetry between Shanghai and Hong Kong stock markets. • Instability in linear volatility causality motivates this regime-dependent approach to measuring spillovers and their asymmetry. • This asymmetry intensifies during high volatility regimes and strengthens post-Stock Connect Program implementation. • Regime-switching and asymmetry improve volatility forecasts. However portfolio strategies under conventional BEKK and aBEKK models are constrained by post-Program integration. Regime-dependent ( RD ) model boosts portfolio efficiency and lowers rebalancing costs.
Lin et al. (Tue,) studied this question.