Modeling Stock Market Risk Contagion via Complex Networks: A Multilayer Framework and Strategic Interventions

With increasing global financial interconnectivity, the risk of contagion within stock markets has become more prominent, particularly during periods of economic turbulence. Traditional models often overlook the impact of indirect contagion and the evolving topology of financial networks, limiting their effectiveness in capturing real-world propagation dynamics. This study develops a comprehensive modeling framework grounded in complex network theory and epidemic dynamics to analyze risk transmission in stock markets. First, a dual-layer contagion model is proposed to differentiate the risk diffusion mechanisms between high- and low-risk entities, with spillover effects quantified via Conditional Value at Risk (CoVaR). Second, a core-periphery SIRS model is introduced, accounting for indirect contagion through network neighbors, guided by mutual information entropy. Third, simulations of various initial infection scenarios and intervention strategies reveal that early containment of central nodes significantly suppresses the scope of contagion. Empirical validation using CSI 300 data confirms the models' practical relevance. The findings offer strategic insights for financial regulators and market participants in mitigating systemic risks and enhancing market resilience.