Abstract:
Volatility spillovers inherently reflect inter-temporal dependence across markets; however, traditional models typically incorporate contemporaneous dependence in their measurements, thereby overestimating the intensity of market volatility spillovers. This paper innovatively constructs a two-factor TVP-VAR-DY model that effectively disentangles systematic factors such as global economic growth and local resonance such as regional policy coordination, to calculate volatility spillovers across ten major global stock markets under seven extreme event shocks. The findings reveal that: (1) Overall, the United States, Germany, and Hong Kong exhibit relatively higher net volatility spillover indices, serving as primary risk transmitters in the global stock market network. (2) Economic and financial events such as the subprime mortgage crisis and the European sovereign debt crisis demonstrate strong immediate and persistent contagion effects, triggering synchronized global market co-movements through panic sentiment and capital reallocation channels. (3) Regional conflicts or geopolitical events, including Brexit, the Russia-Ukraine conflict, and China-US trade frictions, create concentrated risk exposures in deeply affected regions and induce severe global market volatility through policy expectation spillovers and deteriorating market confidence. (4) Natural disasters and public health emergencies, such as the Great East Japan Earthquake, initially exhibit limited impact scope; however, as negative effects (e.g., nuclear leakage) spread, shocks escalate from localized problems to systemic risks, affecting global markets through supply chain disruptions and industrial chain interruptions. This paper provides a more reliable measurement methodology for volatility spillover research and offers insights for China to precisely monitor risk sources and better respond to external financial market shocks.
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