Asset association and dynamic risk contagion under climate policy uncertainty

In the context of climate policy uncertainty, we introduce a novel discrete-time nonlinear dynamic risk contagion model. This model captures the dynamics of credit risk as it propagates among firms via a multi-path contagion mechanism, spreading risks along diverse pathways between interconnected nodes. Utilizing the Single-Index Model, the LASSO techniques, and the CoVaR method, we map out the industrial chain network and develop systemic risk indicators for firms within this network. Using these indicators, we empirically analyze the impact of climate policy uncertainty on systemic risk. Our theoretical findings underscore the presence of a steady state in networks under climate policy uncertainty. We derive the analytical expressions for the steady state in complete networks. Empirical evidence reveals that climate policy uncertainty significantly amplifies systemic risk in the industrial chain, with upstream firms contributing more to systemic risk and downstream firms experiencing greater risk exposure. • Construct a network-based dynamic nonlinear model for systemic risk contagion under CPU. • Risk contagion mechanism of inter-firm is deeply explored under CPU. • Find the steady state of the systemic risk contagion under CPU. • Empirical analysis of the impact of CPU on systemic risk in industrial chain firms.