Rating Models and Validation - Oesterreichische Nationalbank

Rating Models and Validation
4 Assessing the ModelsÕ Suitability for
Various Rating Segments
In general, credit assessment procedures have to fulfill a number of requirements
regardless of the rating segments in which they are used. These requirements
are the result of business considerations applied to credit assessment as
well as documents published on the IRB approaches under Basel II. The fundamental
requirements are listed in chart 26 and explained in detail further below.
Chart 26: Fundamental Requirements of Rating Models
4.1 Fulfillment of Essential Requirements
4.1.1 PD as Target Value
The probability of default reflected in the rating forms the basis for risk management
applications such as risk-based loan pricing. Calculating PD as the target
value is therefore a basic prerequisite for a rating model to make sense in the
business context.
The data set used to calculate PD is often missing in heuristic models and
might have to be accumulated by using the rating model in practice. Once this
requirement is fulfilled, it is possible to calibrate results to default probabilities
even in the case of heuristic models (see section 5.3).
Statistical models are developed on the basis of an empirical data set, which
makes it possible to determine the target value PD for individual rating classes
by calibrating results with the empirical development data. Likewise, it is possible
to calibrate the rating model (ex post) in the course of validation using the
data gained from practical deployment.
One essential benefit of logistic regression is the fact that it enables the direct
calculation of default probabilities. However, calibration or rescaling may also
54 Guidelines on Credit Risk Management