AI 中文总结
本文提出时间异质多路图神经网络框架，用于评估全球银行业系统性风险、识别传染渠道，其预测性能优于各类基准模型，还可量化银行系统重要性、评估国家溢出效应并揭示传导路径。
AI 中文摘要
本文开发了一种统一框架，用于评估全球银行业的系统性风险并识别传染渠道，采用的是时间异质多路图神经网络（Temporal Heterogeneous Multiplex Graph Neural Network）。我们构建了一个协调的季度面板数据，整合了银行基本面、信用违约互换（CDS）利差以及宏观经济指标，并将这些数据表示为动态多路网络，该网络通过金融相似性和流动性共变关系将银行连接起来，同时加入了国家层面的宏观经济关系作为补充。该模型将图卷积层与循环门控循环单元（GRU）动态机制相结合，并融入了可学习的融合门，以捕捉对不同传染渠道的时变依赖。实证结果表明，该框架在CDS利差的短期变化预测上，表现优于传统计量经济学、机器学习以及基于图的基准模型。除预测功能外，本文还提供了一个可解释的框架，可通过压力测试量化银行层面的系统重要性，评估宏观经济冲击下的国家层面溢出效应，并通过边扰动分析揭示传导路径。稳健性测试证实了预测准确性和系统性风险排名的稳定性。
英文摘要
This paper develops a unified framework for assessing systemic risk and identifying contagion channels in the global banking system using a Temporal Heterogeneous Multiplex Graph Neural Network. We construct a harmonised quarterly panel combining bank fundamentals, CDS spreads, and macroeconomic indicators, and represent these data as dynamic multiplex networks linking banks through financial similarity and liquidity co-movement, augmented with country-level macroeconomic relationships. The model integrates graph convolutional layers with recurrent GRU dynamics and incorporates a learnable fusion gate to capture time-varying reliance on alternative contagion channels. Empirical results show that the framework outperforms conventional econometric, machine learning, and graph-based benchmarks for short-term changes in CDS spreads. Beyond forecasting, we provide an interpretable framework to quantify bank-level systemic importance via stress testing, assess country-level spillovers under macroeconomic shocks, and uncover transmission pathways through edge perturbation analysis. Robustness tests confirm the stability of both predictive accuracy and systemic risk rankings.
