Abstract
In some credit portfolios the number of observed defaults is always very limited. This is particularly evident in the Loss Given Default (LGD) estimation based on the new definition of default (the new definition of default was introduced in European banks in recent years) where only a small sample of empirical data is observed. The basic proposed LGD model is based on splitting recoveries into two classes of recoveries: value close to 0 or close to 1. This paper addresses also the problem with unresolved cases using the Bayesian approach, which assumes a distribution of further recoveries for unresolved cases. The Bayesian approach is considered with a combination of two binary models. The modelling approach for LGD is illustrated on real data for a long time period for mortgage loans. The proposed methodology takes into account the specificity of LGD data for both bimodal LGD distribution and uncertainty about unresolved cases, which lead to reduce a model bias.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
