This cumulative thesis contributes to the literature on credit risk modeling and focuses on comovements of risk parameters that intensify losses during recessions. The models provide more precise estimates of credit risk and a better understanding of systematic risk. This can improve risk-based capital reserves and can help to avoid a severe underestimation of risk and capital shortfalls in economic downturn periods. Furthermore, the discussion of regulatory requirements and the supervision of internal risk models can benefit from empirical results.
The first study extends the scope of loss given default (LGD) modeling by proposing the quantile regression to separately regress each quantile of the distribution. This approach enables a new look on covariate and particularly downturn effects that vary over quantiles. The second study analyzes the length of workout processes by a Cox proportional hazards model. Systematic effects are examined by the inclusion of time-varying frailties. The third study presents a copula model for the lifetime expected loss that combines accelerated failure time models for the default time with a beta regression of the LGD. The use of copulas provide continuous-time LGD forecasts and flexible dependence structures between default risk and loss severity. The fourth study combines a Probit model for the probability of default and a fractional response model for the LGD to demonstrate the impact of revised loan loss provisioning on bank capital requirements. In addition, goodness-of-fit measures enable to validate these approaches. Simulation studies and analyses of representative portfolios provide implications and demonstrate the significance of empirical results
