Research on Risk Contagion Mechanism of Financial Markets Based on Complex Network Theory

Abstract

Against deepening global financial integration, traditional linear risk tools cannot capture time-lagged cascading risk spillovers, while existing epidemic-network studies separately analyze multilayer topology, temporal transmission and node centrality without unified frameworks. This paper develops a temporal multilayer Susceptible-Exposed-Infected-Recovered (SEIR) coupled contagion model integrating time-resolved paths and statistically validated Katz-Bonacich centrality. It sorts three branches of contagion literature and identifies three key defects in prior static, fragmented models. Two progressive dynamic models are constructed, incorporating latent risk states and liquidity-endogenous recovery coefficients. Three core diffusion drivers-network phase-transition traits, time-lagged linkages and asset community heterogeneity—are clarified. These mechanisms jointly explain how local financial shocks can evolve into broader cross-market and cross-sector contagion through interconnected network structures. A multi-tiered governance framework covering micro capital regulation, spectral macro supervision and targeted systemically important financial institution (SIFI) oversight is put forward. The study acknowledges linear vector autoregression (VAR) and exogenous liquidity limitations, and proposes cross-border, fintech-inclusive extensions for follow-up research.