Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/312404 
Year of Publication: 
2024
Series/Report no.: 
Technical Paper No. 09/2024
Publisher: 
Deutsche Bundesbank, Frankfurt a. M.
Abstract: 
We study the credit risk of banks in Germany from lending to non-financial firms. We model changes in Expected Credit Loss, which is derived from the guidelines in the IFRS 9 accounting standard. We map the accounting model to a dataset with individual loans as the unit of observation (AnaCredit). We present new approaches to modeling two well-known credit risk parameters: Loss Given Default (LGD), and Probability of Default (PD), which both affect Expected Credit Loss. First, we obtain an approxima tion of the Loss Given Default for each individual loan. This step makes use of the detailed collateral data available in AnaCredit and reveals a heterogeneity in LGD that is typically ignored in top-down stress tests. Second, regarding PD, we encounter a missing data problem since only a subset of banks reports default probabilities in AnaCredit. We employ machine learning algorithms to impute missing default probabilities. With the help of these credit risk parameters, we then apply the stress test model to two ad-hoc scenarios in which the downturn in CRE markets worsens to varying degrees and report how this would affect the capital of German banks.
Subjects: 
Stress test
Credit Risk
Banks
Non-financial Firms
Commercial Real Estate
Germany
JEL: 
G17
G21
C53
Document Type: 
Working Paper

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