文章基本信息

标题：Covariate Selection for Mortgage Default Analysis Using Survival Models
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作者：Dongfang Zhang ; Basu Bhandari ; Dennis Black 等
期刊名称：Journal of Mathematical Finance
印刷版ISSN：2162-2434
电子版ISSN：2162-2442
出版年度：2021
卷号：11
期号：2
页码：218-233
DOI：10.4236/jmf.2021.112012
语种：English
出版社：Scientific Research Publishing
摘要：The mortgage sector plays a pivotal role in the financial services industry, and the U.S. economy in general, with the Federal Reserve, St. Louis, reporting Households and Nonprofit Organizations for One-to-Four-Family Residential Mortgages Liability Level at $10.8T in Q3 2020. It has been in the interest of banks to know which factors are the most influential predicting mortgage default, and the implementation of survival models can utilize data from defaulted obligors as well as non-default obligors who are still making payments as of the sampling period cutoff date. Besides the Cox proportional hazard model and the accelerated failure time model, this paper investigates two machine learning-based models, a random survival forest model, and a Cox proportional hazard neural network model DeepSurv. We compare the accuracy of covariate selection for the Cox model, AFT model, random survival forest model, and DeepSurv model, and this investigation is the first research using machine learning based survival models for mortgage default prediction. The result shows that Random survival forest can achieve the most accurate, and stable, covariate selection, while DeepSurv can achieve the highest accuracy of default prediction, and finally, the covariates selected by the models can be meaningful for mortgage programs throughout the banking industry.
关键词：Survival Analysis;Mortgage Default;Cox Model;Random Survival Forest;Accelerated Failure Time Model;DeepSurv