Borrower Level Models for Stress Testing Corporate Probability of Default and the Quantification of Model Risk

This paper addresses the building of obligor level hazard rate corporate probability-of-default (“PD”) models for stress testing, departing from the predominant practice in wholesale credit modeling of constructing segment level models for this purpose. We build models based upon varied of financial, credit rating, equity market and macroeconomic factors with an extensive history of large corporate firms sourced from Moody’s. We develop distance-to-default (“DTD”) risk factors and design hybrid structural/Merton-reduced form models as challengers to versions of the models containing only the other variables. We measure the model risk attributed to various modeling assumptions according to the principle of relative entropy and observe that the omitted variable bias with respect to the DTD risk factor, neglect of interaction effects and incorrect link function specification has the greatest, intermediate and least impacts, respectively. Our conclusion is that validation methods chosen in the stress testing context should be capable of testing model assumptions, given the sensitive regulatory uses of these models and concerns raised in the industry about the effect of model misspecification on capital and reserves. Our research is accretive to the literature by offering state of the art techniques as viable options in the arsenal of model validators, developers and supervisors seeking to manage model risk.