Tid: 12 september 2005 kl 1515-1600
Plats : Seminarierummet 3733, Institutionen för matematik, KTH, Lindstedts väg 25, plan 7. Karta!
Föredragshållare: Jonathan Wendin, ETH Zürich.
Titel: Bayesian Inference for Generalized Linear Mixed Models of Portfolio Credit Risk
Sammanfattning: The aims of this talk are twofold. 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 sampler can be successfully applied to fit models with serially correlated random effects, which are special instances of state space models.
The talk is concluded with some empirical results from a study using Standard & Poor's default data on US firms.
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