Rating Models and Validation - Oesterreichische Nationalbank

Rating Models and Validation
sample matches that of the input data fields required in the rating model.
Therefore, it is absolutely necessary that financial data such as annual financial
statements are based on uniform legal regulations. In the case of qualitative
data, individual data field definitions also have to match; however,
highly specific wordings are often chosen in the development of qualitative
modules. This makes benchmark comparisons of various rating models
exceptionally difficult. It is possible to map data which are defined or categorized
differently into a common data model, but this process represents
another potential source of errors. This may deserve special attention in
the interpretation of benchmarking results.
— Consistency of target values:
In addition to the consistency of input data fields, the definition of target
values in the models examined must be consistent with the data in the
benchmark sample. In benchmarking for rating models, this means that
all models examined as well as the underlying sample have to use the same
definition of a default. If the benchmark sample uses a narrower (broader)
definition of a credit default than the rating model, this will increase
(decrease) the modelÕs discriminatory power and simultaneously lead to
overestimates (underestimates) of default rates.
— Structural consistency:
The structure of the data set used for benchmarking has to depict the respective
rating modelsÕ area of application with sufficient accuracy. For example,
when testing corporate customer ratings it is necessary to ensure that the
company size classes in the sample match the rating modelsÕ area of application.
The sample may have to be cleansed of unsuitable cases or optimized
for the target area of application by adding suitable cases. Other aspects
which may deserve attention in the assessment of a benchmark sampleÕs representativity
include the regional distribution of cases, the structure of the
industry, or the legal form of business organizations. In this respect, the
requirements imposed on the benchmark sample are largely the same as
the representativity requirements for the data set used in rating model
development.
6.4 Stress Tests
6.4.1 Definition and Necessity of Stress Tests
In general, stress tests can be described as instruments for estimating the potential
effects an extraordinary — but plausible — event may have on an institution.
The term ÒextraordinaryÓ in this definition implies that stress tests evaluate
the consequences of events which have a low probability of occurrence. However,
crisis events must not be so remote from practice that they become
implausible. Otherwise, the stress test would yield unrealistic results from
which no meaningful measures could be derived.
The specific need for stress tests in lending operations can be illustrated by
the following experiences with historical crisis events:
130 Guidelines on Credit Risk Management