Abstract
In contemporary world, binary choice models are used in many areas. However, for all such areas, a problem arises when the share of one of the classes in the data sample is small. If this share is significantly small, this class is referred to as low default class. The purpose of this paper is to examine the definitions of such a portfolio and the approaches to building models on its basis. Although various methods exist for obtaining results, this paper shows that distinguishing a low default portfolio class, on the one hand, benefits banks, as does any more detailed segmentation, but, on the other hand, it deteriorates the statistical properties of the models for the probability of default. It is therefore justified that for the internal rating based approach in the framework of Basel II and Basel III the regulator should require that banks build their models based on combined data sets discouraging them from setting excessive low default portfolio classes.
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