Structured factor copulas for modeling the systemic risk of European and United States banks
本文使用信用违约互换数据，通过因子Copula模型分析欧美银行的联合违约概率，发现区域内银行间依赖同时受系统和特质传染渠道影响，而整体银行系统以系统传染为主，并识别出近年压力时期。
In this paper, we employ Credit Default Swaps (CDS) to model the joint and conditional distress probabilities of banks in Europe and the U.S. using factor copulas. We propose multi-factor, structured factor, and factor-vine models where the banks in the sample are clustered according to their geographic location. We find that within each region, the co-dependence between banks is best described using both, systematic and idiosyncratic, financial contagion channels. However, if we consider the banking system as a whole, then the systematic contagion channel prevails, meaning that the distress probabilities are driven by a latent global factor and region-specific factors. In all cases, the co-dependence structure of bank CDS spreads is highly correlated in the tail. The out-of-sample forecasts of several measures of systemic risk allow us to identify the periods of distress in the banking sector over the recent years including the COVID-19 pandemic, the interest rate hikes in 2022, and the banking crisis in 2023. • We identify the systematic and idiosyncratic sources of financial contagion – global and local – through factor copula models. • We employ the Variational Bayes (VB) method for estimating the multi-factor, structured factor, and factor-vine copulas. • Within each region, the co-dependence between banks is best described using both, systematic and idiosyncratic, financial contagion channels. • The systematic contagion channel prevails when considering the banking system as a whole.