Bayesian Inference for Generalized Linear Mixed Models of Portfolio Credit Risk

@inproceedings{McNeil2005BayesianIF,
  title={Bayesian Inference for Generalized Linear Mixed Models of Portfolio Credit Risk},
  author={Alexander J. McNeil and Jonathan Wendin},
  year={2005}
}
The aims of this paper are threefold. First we highlight the usefulness of generalized linear mixed models (GLMMs) in the modelling of portfolio credit default risk. The GLMM-setting allows for a flexible specification of the systematic portfolio risk in terms of observed fixed effects and unobserved random effects, in order to explain the phenomena of default dependence and time-inhomogeneity in empirical default data. Second we show that computational Bayesian techniques such as the Gibbs… CONTINUE READING
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