Abstract
In this study, we consider the construction of through-the-cycle (“TTC”) PD models designed for credit underwriting uses and point-in-time (“PIT”) PD models suitable for early warning uses, considering which validation elements should be emphasized in each case. We build PD models using a long history of large corporate firms sourced from Moody’s, with a large number of financial, equity market and macroeconomic variables as candidate explanatory variables. We construct a Merton model-style distance-to-default (“DTD”) measure and build hybrid structural reduced-form models to compare with the financial ratio and macroeconomic variable-only models. In the hybrid models, the financial and macroeconomic explanatory variables still enter significantly and improve the predictive accuracy of the TTC models, which generally lag behind the PIT models in that performance measure. We conclude that care must be taken to judiciously choose the manner in which we validate TTC vs. PIT models, as criteria may be rather different and be apart from standards such as discriminatory power. This study contributes to the literature by providing expert guidance to credit risk modeling, model validation and supervisory practitioners in controlling the model risk associated with such modeling efforts.
Highlights
It is expected that financial market participants have accurate measures of a counterparty’s capacity to fulfil future debt obligations, conventionally measured by a credit rating or a score, typically associated with a probability of default (“PD”)
We address the validation of PD models under both rating philosophies, highlighting that the validation of either system exhibits a particular set of challenges
We have developed alternative simple and general econometrically estimated PD models of both TTC and PIT designs
Summary
It is expected that financial market participants have accurate measures of a counterparty’s capacity to fulfil future debt obligations, conventionally measured by a credit rating or a score, typically associated with a probability of default (“PD”). PD rating models, there is the additional question of demonstrating the accuracy of PD estimates at the borrower level, which may not be obvious from observing average PD estimates versus default rates over time Considering both types of model, there is the question of whether the relative contributions of risk factors are conceptually intuitive, as we would expect that certain variables would dominate in either of these constructs. One possibility is for the underlying cycle to be estimated from historical data based upon some theoretical framework, but in this study, we prefer commonly used macroeconomic factors in conjunction with obligorlevel default data, in line with industry practice Related to this point, we do not explicitly address how TTC PD models can be transformed into PIT PD rating models, or vice versa.
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