Abstract
The majority of industry credit portfolio risk models, as well as recent scientific results, are based on isolated modules for default probabilities and recoveries in the event of default. This paper shows that these common methods lead to various econometric drawbacks when the parameters are interpreted and aggregated for risk capital allocation and pricing purposes. This paper provides a top down approach in which individual credit risk parameters are derived analytically from a single model. This model allows for a i) dynamic, ii) consistent, and iii) unbiased modeling of credit portfolio risks. An empirical analysis provides evidence for the inferred relationship between credit quality, recovery and correlation.
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