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Dirichlet Bayesian Model Averaging for Aggregate Mortgage Default Risk

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Listed:
  • Feng Xu
  • Jian Hua
  • Dazhong Wu

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.

Suggested Citation

  • Feng Xu & Jian Hua & Dazhong Wu, 2025. "Dirichlet Bayesian Model Averaging for Aggregate Mortgage Default Risk," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 17(9), pages 1-14, September.
    • Handle: RePEc:ibn:ijefaa:v:17:y:2025:i:9:p:14
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    References listed on IDEAS

    as
    1. Chen, Yuh-Jen & Wu, Chun-Han & Chen, Yuh-Min & Li, Hsin-Ying & Chen, Huei-Kuen, 2017. "Enhancement of fraud detection for narratives in annual reports," International Journal of Accounting Information Systems, Elsevier, vol. 26(C), pages 32-45.
    2. Charles A. Capone, 2003. "Mortgage default and default resolutions: their impact on communities," Proceedings 887, Federal Reserve Bank of Chicago.
    3. James B. Kau & Donald C. Keenan & Taewon Kim, 1993. "Transaction Costs, Suboptimal Termination and Default Probabilities," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 21(3), pages 247-263, September.
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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