Parameterizing Credit Risk Models

The present paper shows how the parameters of three popular portfolio credit risk models can be empiricallyestimated by banks using a Maximum Likelihood framework. We apply the method to a database of Germanfirms provided by Deutsche Bundesbank and analyze the inclusion of macroeconomic and borrower specificrating factors. Given the uniform ML estimation methodology, we compare the parameter estimates and theforecasted loss distributions for the credit risk models and find that they perform in very similar ways, in contrastto the differences found in some previous studies...