Comparative analysis of credit risk models for loan portfolios

In this paper, we compare credit risk models that are used for loan portfolios, both from a theoretical perspective and via simulation studies. This study is distinct from previous studies in its inclusion of new models, consideration of sector correlation and performance of comprehensive sensitivity analysis. CreditRisk(++), CreditMetrics, the Basel II internal-ratings-based method and the Mercer Oliver Wyman model are considered. Risk factor distribution and the relationship between risk components and risk factors are the key distinguishing characteristics of each model. CreditRisk(++), due to its extra degree of freedom, has the highest flexibility to fit various loss distributions. It turns out that sector covariance is the most important risk component for risk management in terms of risk sensitivity. Risk sensitivities not only differ between models but also depend on the input parameters and the quantile at which risk is measured. This implies that risk models can only be judged in terms of the portfolio under consideration, and banks should evaluate them based on their own portfolios.