The Impact of Data Aggregation and Risk Attributes on Stress Testing Models of Mortgage Default

Journal of Credit Risk 16(3), 35–74 DOI: 10.21314/JCR.2020.269

40 Pages Posted: 2 Nov 2020 Last revised: 19 Nov 2020

See all articles by Feng Li

Feng Li

Government of the United States of America - Office of the Comptroller of the Currency (OCC)

Yan Zhang

Government of the United States of America - Office of the Comptroller of the Currency (OCC)

Multiple version iconThere are 2 versions of this paper

Date Written: September 11, 2020

Abstract

Stress testing models have been developed at various levels of data aggregation with or without risk attributes, but there is limited research on the joint impact of these modeling choices. In this paper, we investigate how data aggregation and risk attributes affect the development and performance of stress testing models by studying residential mortgage loan defaults. We develop mortgage default models at various data aggregation levels including loan-level, segment-level, and top-down. We also compare the models with and without risk attributes as control variables. We assess model performance for goodness-of-fit, prediction accuracy, and projection sensitivity for stress testing purposes. We find that the loan-level models do not always win among models with various data aggregation levels, and including risk attributes greatly improves goodness-of-fit and projection accuracy for models of all data aggregation levels. The findings suggest that it is important to consider data aggregation and risk attributes in developing stress testing models.

Keywords: loan-level, segment-level, top-down, back-test, sensitivity, loss forecast

JEL Classification: G21, G28, C18, C52

Suggested Citation

Li, Feng and Zhang, Yan, The Impact of Data Aggregation and Risk Attributes on Stress Testing Models of Mortgage Default (September 11, 2020). Journal of Credit Risk 16(3), 35–74 DOI: 10.21314/JCR.2020.269, Available at SSRN: https://ssrn.com/abstract=3690970 or http://dx.doi.org/10.2139/ssrn.3690970

Feng Li

Government of the United States of America - Office of the Comptroller of the Currency (OCC) ( email )

400 7th Street SW
Washington, DC 20219
United States

Yan Zhang (Contact Author)

Government of the United States of America - Office of the Comptroller of the Currency (OCC) ( email )

400 7th St. SW
Washington, DC 20219
United States
202-6495492 (Phone)

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
34
Abstract Views
218
PlumX Metrics