Amid mounting geopolitical tensions and rapid transformations in global energy dynamics, the transmission of risk between energy and financial systems has become a pressing concern in safeguarding financial stability. This study introduces a unified modelling framework that fuses artificial intelligence with wavelet decomposition, volatility modelling and copula theory to uncover evolving tail dependencies and contagion pathways across crude oil, renewable energy, and financial markets. We enhance the conventional three stage methodology, which consists of the Maximum Overlap Discrete Wavelet Transform, ARMA EGARCH filtering, and Vine Copula modelling, by integrating a Long Short Term Memory neural network. Our empirical investigation, leveraging daily observations from global oil benchmarks, clean energy indices, and financial sector metrics, uncovers pronounced shifts in tail dependencies and contagion intensities during turbulent periods. The prospective volatility signal generated by the LSTM strengthens the model’s ability to capture time varying tail behavior and nonlinear contagion. Using daily data from 2015 to 2025, the framework reveals strong short run asymmetry, with downside contagion dominating, while medium term dynamics show gradual structural adjustments. Out sample tests indicate that the enhanced model outperforms DCC, rolling copulas, GRU and attention-based networks in forecasting tail dependence. Event driven spillover analysis further shows that major shocks reshape transmission routes and shift contagion hubs across markets. By combining deep learning signals with interpretable dependence and network analysis, the framework offers a concise and effective tool for monitoring systemic risks and supporting stress testing and macroprudential supervision.

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.

As the global economy accelerates toward low-carbon transformation, climate financial risks are emerging as a key challenge to monetary policy design and financial stability oversight. This study examines the contagion effects and dynamic interdependencies among domestic climate-sensitive industries, financial climate risk indices, and international climate markets. Using daily data from April 2020 to April 2025, we apply a multiscale tail risk modeling framework that integrates wavelet decomposition, conditional volatility modeling, and vine-copula techniques to capture time-varying and asymmetric dependence structures across markets. The results show that the three markets display volatility clustering, fat tails, and nonlinear dependence. The international climate market shows weaker and more volatile connections with the two domestic markets, suggesting that external climate expectations operate mainly through indirect dependence across market states. The risk spillover results further show that climate financial risk contagion differs between upside and downside states and varies across short and medium horizons. These findings have important implications for integrating climate risk into macroprudential surveillance. Central banks and regulators should strengthen early warning mechanisms, climate stress testing, and scenario analysis by considering market-specific, nonlinear, and multiscale risk spillovers. The main contribution of this study is to integrate multiscale decomposition, conditional volatility modelling, and vine-copula dependence analysis into a unified empirical framework for identifying climate financial risk contagion across markets. The findings offer useful evidence for climate stress testing, early warning systems, and financial stability monitoring in emerging markets.