Bounds for rating override rates

TL;DR

This paper discusses the natural error rate of statistical credit rating models, proposing it as a benchmark to evaluate whether observed override rates are appropriate, based on the model's discriminatory power.

Contribution

It introduces the concept of a natural error rate for rating models, linking it to discriminatory power and providing a method to assess override rate appropriateness.

Findings

Natural error rate can be calculated from the rating model's discriminatory power.

Observed override rates can be evaluated against the natural error rate.

The approach helps detect potential misuse or miscalibration of rating overrides.

Abstract

Overrides of credit ratings are important correctives of ratings that are determined by statistical rating models. Financial institutions and banking regulators agree on this because on the one hand errors with ratings of corporates or banks can have fatal consequences for the lending institutions and on the other hand errors by statistical methods can be minimised but not completely avoided. Nonetheless, rating overrides can be misused in order to conceal the real riskiness of borrowers or even entire portfolios. That is why rating overrides usually are strictly governed and carefully recorded. It is not clear, however, which frequency of overrides is appropriate for a given rating model within a predefined time period. This paper argues that there is a natural error rate associated with a statistical rating model that may be used to inform assessment of whether or not an observed override rate is adequate. The natural error rate is closely related to the rating model's discriminatory power and can readily be calculated.