Credit Line Exposure at Default Modelling Using Bayesian Mixed Effect Quantile Regression. (12th June 2022)

• Record Type: Journal Article
• Authors: Betz, Jennifer; Nagl, Maximilian; Rösch, Daniel
• Is Part Of: Journal of the Royal Statistical Society. Volume 185:Number 4(2022)
• Page Start: 2035
• Page End: 2072
• Publication Date: 2022-06-12
• Subjects: credit conversion factor -- credit risk -- exposure at default -- global credit data -- quantile regression -- random effects; Social sciences -- Statistical methods -- Periodicals; Statistics -- Periodicals; 300.15195
• DOI: 10.1111/rssa.12855
• Languages: English
• ISSNs: 0964-1998

Abstract

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.