Rating Models and Validation - Oesterreichische Nationalbank

Rating Models and Validation
1. In the first step, the quantitative data items are combined to form indicator
components. These indicator components combine the numerous items into
sums which are meaningful in business terms and thus enable the information
to be structured in a manner appropriate for economic analysis. However,
these absolute indicators are not meaningful on their own.
2. In order to enable comparisons of borrowers, the second step calls for the
definition of relative indicators due to varying sizes. Depending on the type of
definition, a distinction is made between constructional figures, relative figures,
and index figures. 51
For each indicator, it is necessary to postulate a working hypothesis which
describes the significance of the indicator in business terms. For example, the
working hypothesis ÒG > BÓ means that the indicator will show a higher average
value for good companies than for bad companies. Only those indicators for
which a clear and unmistakable working hypothesis can be given are useful in
developing a rating. The univariate analyses serve to verify whether the presumed
hypothesis agrees with the empirical values.
In this context, it is necessary to note that the existence of a monotonic
working hypothesis is a crucial prerequisite for all of the statistical rating models
presented in chapter 3 (with the exception of artificial neural networks). In
order to use indicators which conform to non-monotonic working hypotheses
such as revenue growth (average growth is more favorable than low or excessively
high growth), it is necessary to transform these hypotheses in such a
way that they describe a monotonic connection between the transformed indicatorÕs
value and creditworthiness or the probability of default. The transformation
to default probabilities described below is one possible means of achieving
this end.
Analyzing Indicators for Hypothesis Violations
The process of analyzing indicators for hypothesis violations involves examining
whether the empirically determined relationship confirms the working hypothesis.
Only in cases where an indicatorÕs working hypothesis can be confirmed
empirically is it possible to use the indicator in further analyses. If this is not
the case, the indicator cannot be interpreted in a meaningful way and is thus
unsuitable for the development of a rating system which is comprehensible
and plausible from a business perspective. The working hypotheses formulated
for indicators can be analyzed in two different ways.
The first approach uses a measure of discriminatory power (e.g. the Powerstat
value 52 ) which is already calculated for each indicator in the course of univariate
analysis. In this context, the algorithm used for calculation generally assumes
ÒG > BÓ as the working hypothesis for indicators. 53 If the resulting discriminatory
power value is positive, the indicator also supports the empirical hypothesis
G > B. In the case of negative discriminatory power values, the empirical
hypothesis is G < B. If the sign before the calculated discriminatory power value
does not agree with that of the working hypothesis, this is considered a violation
of the hypothesis and the indicator is excluded from further analyses. Therefore,
51 For more information on defining indicators, see BAETGE, J./HEITMANN, C., Kennzahlen.
52 Powerstat (Gini coefficient, accuracy ratio) and alternative measures of discriminatory power are discussed in section 6.2.1.
53 In cases where the working hypothesis is ÒG < B,Ò the statements made further below are inverted.
76 Guidelines on Credit Risk Management