Solutions to specification errors in stress testing models

The regulatory and business need to expand the use of macroeconomic-scenario-based forecasting and stress testing in retail lending has led to a rapid expansion in the types and complexity of models being applied. As these models become more sophisticated and include lifecycle, credit quality, and macroeconomic effects, model specification errors become a common, but rarely identified feature of many of these models. This problem was discovered decades ago in demography with Age-Period-Cohort (APC) models, and we bring those insights to the retail lending context with a detailed discussion of the implications here. Although the APC literature proves that no universal, data-driven solution is possible, we propose a domain-specific solution that is appropriate to lending. This solution is demonstrated with an auto loan portfolio.