Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/31011
Authors: 
Czado, Claudia
Pflüger, Carolin
Year of Publication: 
2006
Series/Report no.: 
Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 511
Abstract: 
The internal-ratings based Basel II approach increases the need for the development of more realistic default probability models. In this paper we follow the approach taken in McNeil and Wendin (2006) by constructing generalized linear mixed models for estimating default probabilities from annual data on companies with different credit ratings. The models considered, in contrast to McNeil and Wendin (2006), allow parsimonious parametric models to capture simultaneously dependencies of the default probabilities on time and credit ratings. Macro-economic variables can also be included. Estimation of all model parameters are facilitated with a Bayesian approach using Markov Chain Monte Carlo methods. Special em- phasis is given to the investigation of predictive capabilities of the models considered. In particular predictable model specifications are used. The empirical study using default data from Standard and Poor gives evidence that the correlation between credit ratings further apart decreases and is higher than the one induced by the autoregressive time dynamics.
Subjects: 
credit risk
default probability
asset correlation
generalized linear mixed models
Markov chain Monte Carlo
prediction
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:
File
Size
308.71 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.