Rating Models and Validation - Oesterreichische Nationalbank

Rating Models and Validation
pretation of results deserves special attention. In the course of development, the
bank can use a data pool in order to provide a broader data set (cf. section
5.1.2). In the short term, estimated values can be adjusted conservatively to
compensate for a high degree of variance. In the medium and long term, however,
it is advisable to generate a comprehensive and quality-assured historical
data set. These data provide an important basis for future validation and
back-testing activities, as well as enabling future changes in estimation methodology.
Moreover, Basel II and the draft EU directive require the creation of loss
histories, even for the IRB Foundation Approach. 121
When selecting methods, the bank can take the materiality of each loss component
into account with regard to the effort and precision involved in each
method. Based on a bankÕs individual requirements, it may be appropriate to
implement specific LGD estimation tools for certain customer and transaction
segments. For this purpose, individual combinations of loss parameters and
information carriers can be aggregated to create a business segment-specific
LGD tool using various estimation methods. This tool should reflect the significance
of individual loss components. Throughout the development stage, it is
also important to bear validation requirements in mind as an ancillary condition.
In the sections that follow, we present an example of how to implement estimation
methods for each of the loss parameters: book value loss, interest loss,
and workout costs.
Estimating Book Value Loss
(Example: Recovery Rates for Physical Collateral)
In the course of initial practical implementations at various institutions, segmentation
has emerged as the best-practice approach with regard to implementability,
especially for the recovery rates of physical collateral. In this section, we
briefly present a segmentation approach based on Chart 91 below.
It is first necessary to gather recovery rates for all realized collateral over as
long a time series as possible. These percentages are placed on one axis ranging
from 0% to the highest observed recovery rate. In order to differentiate recovery
rates more precisely, it is then possible to segment them according to various
criteria. These criteria can be selected either by statistical means using discriminatory
power tests or on the basis of expert estimates and conjectured
relationships.
121 Cf. EUROPEAN COMMISSION, draft directive on regulatory capital requirements, Annex D-5, No. 33.
158 Guidelines on Credit Risk Management