Razvijemo in predlagamo novo metodologijo za modeliranje kreditnega tveganja, kjer je namesto binarne spremenljivke položaja neplačila, modelirano število dni zamud pri odplačevanju posojila. Rezultati imajo pomembne implikacije za banke, ki lahko z modeliranjem števila dni zamud precej izboljšajo identifikacijo kreditnega tveganja in zmanjšajo procikličnost IRB kapitalskih zahtev. Poleg tega so napovedi števila dni zamud lahko uporabljene tudi v novi metodologiji stres testov, ki mora slediti IFRS 9 računovodskim standardom.

This paper compares parametric, semi-parametric and non-parametric methods in prediction of bankruptcy. Special care is devoted to the effect of choice-based sampling. The choice of the sampling and estimation method lead to a similar trade off. Using choice-based sampling and logit model leads to minimization of risk exposure. Samples unbalanced across groups and Klein and Spady (1993) semi-parametric method allow for better overall prediction accuracy and thus profit maximization. Both the choice of sampling method and the choice of estimation method should be thus made conditional on an explicit objective function of the financial institution in assesing credit risk.