Applications of generalized structural equation modeling for enhanced credit risk management

The integration of the generalized structural equation modeling (GSEM) framework to widely used statistical packages like Stata offers significant opportunities for credit risk management. GSEM techniques bring to bear a modular and all-inclusive approach to statistical model building. We illustrate the “game changing” potential of the GSEM framework with an application to credit risk stress testing and loss forecasting for a representative portfolio of mortgages originated over the past 20 years. Specifically, we analyze a representative dataset of USA mortgage loans originated over the past 20 years that includes detailed loan-level information on monthly loan performance and other relevant loan and borrower characteristics. Our analysis and discussion illustrates how GSEM techniques can significantly impact every aspect of a model-driven risk management framework, from model development, documentation, and validation to model production, as well as to other, perhaps less obvious, aspects of model building like model risk management, enhanced team collaboration, minimization of proliferation of disparate datasets within projects, and the promotion of a holistic and collaborative approach to model building.