According to the New Capital Adequacy Directive, banks applying the internal-rating-based approach have to estimate their own probabilities of default (PD) for their obligors. However, in practice a substantial part of bank assets often consists of low default portfolios. This impedes not only the development of a statistical scoring model, but also the estimation of PDs and other credit risk parameters, as well as the validation process.

The key concern for regulators is that credit risk might be underestimated because of data scarcity. Supervisory provide no excuse or relief for low default portfolios (LDP). To avoid excluding LDPs from the internal ratings based approach, it is recommended to use some data-enhancing tools. Banks should put more emphasis on alternative data sources, apply alternative methods with more emphasis on qualitative tools.

This paper presents several approaches to estimating the probabilities of default for low default portfolios, their advantages and disadvantages, and provides exemplary calculations using data of one exter¬nal credit register of Lithuania.

The results show that three approaches seem to be most appropriate: those of Pluto and Tasche (2005) without correlation, and those of Kiefer (2006) and Forrest (2005) without correlation. The authors also demonstrates the spheres of LDP problem, approaches to PD estimation for LDPs, and presents exemplary calculations with data of one external credit register of Lithuania, defining LDP as a rating having no more than 20 actual defaults.

The author recommends applying LDP approaches on the rating level, using a concrete number of defaults in order to define LDP without accounting for the total size of rating or portfolio. For ratings not complying with LDP definition (having more than 20 defaults), PDs should be calculated in an ordinary way. If a concrete rating in one year is treated as an LDP and in another doesn’t comply with the LDP definition, LDP approaches should be applied only for the first year.

As the rating system used in this article was developed using a large sample of Lithuanian companies’ data, the conclusions are most actual to banks of Lithuania and their ratings systems to Lithuanian companies. Besides, it is recommended to prepare a common methodology applicable in their entire jurisdiction, or to prepare look-up tables of PDs for banks.