Time-Varying Systemic Risk: Evidence From a Dynamic Copula Model of CDS Spreads
提出一类新的基于Copula的动态模型来估计高维条件分布，从而衡量系统性风险；用2006-2012年美国100家公司的CDS利差数据发现，个体公司困境概率下降，但联合困境概率（系统性风险）比危机前更高。
This article proposes a new class of copula-based dynamic models for high-dimensional conditional distributions, facilitating the estimation of a wide variety of measures of systemic risk. Our proposed models draw on successful ideas from the literature on modeling high-dimensional covariance matrices and on recent work on models for general time-varying distributions. Our use of copula-based models enables the estimation of the joint model in stages, greatly reducing the computational burden. We use the proposed new models to study a collection of daily credit default swap (CDS) spreads on 100 U.S. firms over the period 2006 to 2012. We find that while the probability of distress for individual firms has greatly reduced since the financial crisis of 2008–2009, the joint probability of distress (a measure of systemic risk) is substantially higher now than in the precrisis period. Supplementary materials for this article are available online.