Credit Line Exposure at Default Modelling Using Bayesian Mixed Effect Quantile Regression
利用欧美违约信用额度大数据库和宏观经济变量，通过贝叶斯混合效应分位数回归，发现信用转换因子的协变量效应在条件分布上差异显著，尤其欧美之间；高转换因子多由随机效应驱动，经济衰退对高提取潜力额度影响最大。
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.