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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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    Cited by:

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    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.

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