Application of the Bayesian approach in loss given default modelling

Aneta Ptak-Chmielewska () and Paweł Kopciuszewski
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Aneta Ptak-Chmielewska: Warsaw School of Economics
Paweł Kopciuszewski: Vistula University of Warsaw, ING Hubs Poland

Bank i Kredyt, 2023, vol. 54, issue 6, 625-650

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.

Keywords: small samples; LGD; Bayesian approach; logistic and linear regression; unresolved cases (search for similar items in EconPapers)
JEL-codes: C1 C11 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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