Journal of Credit Risk
ISSN:
1744-6619 (print)
1755-9723 (online)
Editor-in-chief: Linda Allen and Jens Hilscher
Need to know
- We investigate how data aggregation and risk attributes affect stress testing models of mortgage default.
- Loan-level models are not always the winning model.
- Risk attributes contribute significantly to model performance.
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 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 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. These findings suggest that it is important to consider data aggregation and risk attributes when developing stress testing models.
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Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. Copying this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/
If you would like to purchase additional rights please email info@risk.net