(Risk Model Discussion) Distinguish between the Structural and the Reduced-form Approaches

Distinguish between the structural approaches and the reduced-form approaches to predicting default.

The foundation of a structural approach (e.g., the Merton model) is the financial and economic theoretical assumptions that describe the overall path to default. Under this approach, building a model involves estimating the formal relationships that link the relevant variables of the model. In contrast, reduced form models (e.g., statistical and numerical approaches) arrive at a final solution using the set of variables that is most statistically suitable without factoring in the theoretical or conceptual causal relationships among variables.

A reduced form model will not make any ex ante assumptions about causal drivers for default (unlike structural models); specific firm characteristics are linked to default, using statistics to tie them to default data. As such, the default event itself represents a real-life event. The independent variables in these models are combined based on their estimated contribution to the final result and can change in terms of relevance depending on firm size, firm sector, and economic cycle stage.

A significant model risk in reduced form approaches results from a models dependency on the sample used to estimate it. To derive valid results, there must be a strong level of homogeneity between the sample and the population to which the model is applied.

Reduced form models used for credit risk can be classified into statistical and numerical- based categories. Statistical-based models use variables and relations that are selected and calibrated by statistical procedures. Numerical-based approaches use algorithms that connect actual defaults with observed variables. Both approaches can aggregate profiles, such as industry, sector, size, location, capitalization, and form of incorporation, into homogeneous top-down segment classifications. A bottom-up approach may also be used, which would classify variables based on case-by-case impacts. While numerical and statistical methods are primarily considered bottom-up approaches, experts-based approaches tend to be the most bottom up.

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