ABSTRACT

"PD curve calibration" refers to the transformation of a set of rating grade level probabilities of default (PDs) to another average PD level that is determined by a change of the underlying portfolio-wide PD. This paper presents a framework that allows us to explore a variety of calibration approaches and the conditions under which they are fit for purpose. We test the approaches discussed by applying them to publicly available data sets of agency rating and default statistics that can be considered typical for the scope of application of the approaches. We show that the popular "scaled PDs" approach is theoretically questionable and identify an alternative calibration approach ("scaled likelihood ratio") that is both theoretically sound and performs better on the test data sets.