Dirichlet Bayesian Model Averaging for Aggregate Mortgage Default Risk

Feng Xu, Jian Hua and Dazhong Wu

International Journal of Economics and Finance, 2025, vol. 17, issue 9, 14

Abstract: Based on Dirichlet process, a Bayesian Model Averaging (BMA) method is proposed in this paper to predict residential mortgage default risk at aggregate level. This ensemble learning algorithm integrates estimates of the default risk provided by two machine learning and one classical forecasting models. Empirical analysis using regional mortgage default data shows that the Dirichlet BMA model performs better than other methods not only in fitting the data given, but also in providing out-of-sample predictions.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ccsenet.org/journal/index.php/ijef/article/download/0/0/52034/56646 (application/pdf)
https://ccsenet.org/journal/index.php/ijef/article/view/0/52034 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ibn:ijefaa:v:17:y:2025:i:9:p:14

Access Statistics for this article

More articles in International Journal of Economics and Finance from Canadian Center of Science and Education Contact information at EDIRC.
Bibliographic data for series maintained by Canadian Center of Science and Education ().