A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk
提出一个非高斯多变量状态空间模型，将1981-2005年美国不同评级和年龄类公司的季度违约频率分解为系统性和公司特定风险成分，并处理非高斯特征、共同风险因子动态和缺失观测等问题。
We model 1981-2005 quarterly default frequencies for a panel of U.S. firms in different rating and age classes from the Standard and Poor database. The data are decomposed into systematic and firm-specific risk components, where the systematic component reflects the general economic conditions and the default climate. We need to cope with: the shared exposure of each age cohort, industry, and rating class to the same systematic risk factor; strongly non-Gaussian features of the individual time series; possible dynamics of an unobserved common risk factor; changing default probabilities over the age of the rating; and missing observations. We propose a non-Gaussian multivariate state-space model that deals with all of these issues simultaneously. The model is estimated using importance sampling techniques that have been modified to a multivariate setting. We show in a simulation study that such a multivariate approach improves the performance of the importance sampler. In our empirical work, we find that systematic credit risk may differ substantially in terms of magnitude and timing across industries. © 2008 American Statistical Association.