Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/287782 
Year of Publication: 
2022
Citation: 
[Journal:] Journal of the Royal Statistical Society: Series A (Statistics in Society) [ISSN:] 1467-985X [Volume:] 185 [Issue:] 4 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2022 [Pages:] 2035-2072
Publisher: 
Wiley, Hoboken, NJ
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 macroeconomic variables, we apply a Bayesian mixed effect quantile regression and find strongly varying covariate effects over the whole conditional distribution of credit conversion factors and especially between United States and Europe. If macroeconomic variables do not provide adequate downturn estimates, the model is enhanced by random effects. Results from European credit lines suggest that high conversion factors are driven by random effects rather than observable covariates. We further show that the impact of the economic surrounding highly depends on the level of utilization one year prior default, suggesting that credit lines with high drawdown potential are most affected by economic downturns and hence bear the highest risk in crisis periods.
Subjects: 
credit conversion factor
credit risk
exposure at default
global credit data
quantile regression
random effects
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article
Document Version: 
Published Version

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.