Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1151168 | Statistical Methodology | 2007 | 26 Pages |
Statistical modeling of credit risk for retail clients is considered. Due to the lack of detailed updated information about the counterparty, traditional approaches such as Merton’s firm-value model, are not applicable. Moreover, the credit default data for retail clients typically exhibit a very small percentage of default rates. This motivates a statistical model based on survival analysis under extreme censoring for the time-to-default variable. The model incorporates the stochastic nature of default and is based on incomplete information. Consistency and asymptotic normality of maximum likelihood estimates of the parameters characterizing the time-to-default distribution are derived. A criterion for constructing confidence ellipsoids for the parameters is obtained from the asymptotic results. An extended model with explanatory variables is also discussed. The results are illustrated by a data example with 670 mortgages.