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Credit line exposure at default modelling using Bayesian mixed effect quantile regression

URN to cite this document:
urn:nbn:de:bvb:355-epub-524377
DOI to cite this document:
10.5283/epub.52437
Betz, Jennifer ; Nagl, Maximilian ; Rösch, Daniel
Date of publication of this fulltext: 21 Jun 2022 16:50

This publication is part of the DEAL contract with Wiley.


Abstract

For banks, credit lines play an important role exposing both liquidity and credit risk. In the advanced internal ratings-based approach, banks are obliged to use their own estimates of exposure at default using credit conversion factors. For volatile segments, additional downturn estimates are required. Using the world's largest database of defaulted credit lines from the US and Europe and ...

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