  The intention with this paper is to provide all the estimation concepts and
techniques that are needed to implement a two-phases approach to the parametric
estimation of probability of default (PD) curves. In the first phase of this
approach, a raw PD curve is estimated based on parameters that reflect
discriminatory power. In the second phase of the approach, the raw PD curve is
calibrated to fit a target unconditional PD. The concepts and techniques
presented include a discussion of different definitions of area under the curve
(AUC) and accuracy ratio (AR), a simulation study on the performance of
confidence interval estimators for AUC, a discussion of the one-parametric
approach to the estimation of PD curves by van der Burgt (2008) and alternative
approaches, as well as a simulation study on the performance of the presented
PD curve estimators. The topics are treated in depth in order to provide the
full rationale behind them and to produce results that can be implemented
immediately.
