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A Bayesian approach to modeling mortgage default and prepayment

Author

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  • Bhattacharya, Arnab
  • Wilson, Simon P.
  • Soyer, Refik
Abstract
In this paper we present a Bayesian competing risk proportional hazards model to describe mortgage defaults and prepayments. We develop Bayesian inference for the model using Markov chain Monte Carlo methods. Implementation of the model is illustrated using actual default/prepayment data and additional insights that can be obtained from the Bayesian analysis are discussed.

Suggested Citation

  • Bhattacharya, Arnab & Wilson, Simon P. & Soyer, Refik, 2019. "A Bayesian approach to modeling mortgage default and prepayment," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1112-1124.
    • Handle: RePEc:eee:ejores:v:274:y:2019:i:3:p:1112-1124
    DOI: 10.1016/j.ejor.2018.10.047
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    1. Fitzpatrick, Trevor & Mues, Christophe, 2016. "An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market," European Journal of Operational Research, Elsevier, vol. 249(2), pages 427-439.
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    Cited by:

    1. Vikram Ojha & JeongHoe Lee, 2021. "Default analysis in mortgage risk with conventional and deep machine learning focusing on 2008–2009," Digital Finance, Springer, vol. 3(3), pages 249-271, December.
    2. Medina-Olivares, Victor & Lindgren, Finn & Calabrese, Raffaella & Crook, Jonathan, 2023. "Joint models of multivariate longitudinal outcomes and discrete survival data with INLA: An application to credit repayment behaviour," European Journal of Operational Research, Elsevier, vol. 310(2), pages 860-873.
    3. Luong, Thi Mai & Scheule, Harald, 2022. "Benchmarking forecast approaches for mortgage credit risk for forward periods," European Journal of Operational Research, Elsevier, vol. 299(2), pages 750-767.
    4. Feng Xu & Dazhong Wu & Jian Hua & Tih Koon Tan, 2022. "Preventive Maintenance for Mortgage Loans of Low-Income Borrowers," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 14(3), pages 1-66, February.
    5. Sheikh Rabiul Islam & William Eberle & Sheikh K. Ghafoor & Sid C. Bundy & Douglas A. Talbert & Ambareen Siraj, 2019. "Investigating bankruptcy prediction models in the presence of extreme class imbalance and multiple stages of economy," Papers 1911.09858, arXiv.org.
    6. Jiang, Cuiqing & Wang, Zhao & Zhao, Huimin, 2019. "A prediction-driven mixture cure model and its application in credit scoring," European Journal of Operational Research, Elsevier, vol. 277(1), pages 20-31.
    7. Shi, Yong & Qu, Yi & Chen, Zhensong & Mi, Yunlong & Wang, Yunong, 2024. "Improved credit risk prediction based on an integrated graph representation learning approach with graph transformation," European Journal of Operational Research, Elsevier, vol. 315(2), pages 786-801.
    8. Congjin Zhou & Guojing Wang & Yinghui Dong & Pin Wang, 2024. "The Valuation at Origination of Mortgages with Full Prepayment and Default Risks," Methodology and Computing in Applied Probability, Springer, vol. 26(2), pages 1-26, June.

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