A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk
by Siem Jan Koopman of Vrije Universiteit Amsterdam,
May 17, 2005
Abstract: We model 1981-2002 annual default frequencies for a panel of US firms in different rating and age classes from the Standard and Poor's database. The data is decomposed into a systematic and firm-specific risk component, where the systematic component reflects the general economic conditions and default climate. We have to cope with (i) the shared exposure of each age cohort and rating class to the same systematic risk factor; (ii) strongly non-Gaussian features of the individual time series; (iii) possible dynamics of an unobserved common risk factor; (iv) changing default probabilities over the age of the rating, and (v) 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.
Keywords: credit risk, multivariate unobserved component models, importance sampling, non-Gaussian state space models.
Published in: Journal of Business and Economic Statistics, Vol. 26, No. 4, (October 2008), pp. 510-525.