Li Zeng, Wee-Yeap Lau
This study investigates the dynamic impact of climate change performance on extreme tail risk transmission across global financial markets. Based on the "Too Extreme to Fail" conceptual framework, we propose a cascading failure network model using QRNN-∆CoVaR and QRNN-∆CoES to quantify the domino effect of tail risk propagation. The model captures tail dependencies and reveals how variations in climate governance performance modulate the intensity and pathways of risk contagion. Our main analysis utilizes daily market data from 1998 to 2024, aligned with the Climate Change Performance Index data from 2007 through a matched time window approach. The findings demonstrate that climate-sensitive factors significantly amplify systemic vulnerabilities, whereas superior climate governance serves as a critical risk buffer during periods of extreme volatility. Empirical results reveal significant spatial and temporal heterogeneity in risk contribution, with certain regions exhibiting higher sensitivity and momentum during major financial crises. Backtesting results confirm that our proposed nonlinear framework provides superior accuracy in quantifying global systemic risks compared to traditional linear methods, offering a robust tool for climate-integrated financial stability monitoring.