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Complete Prepayment Models for Mortgage-Backed Securities

Author

Listed:
  • Pan Kang

    (HERMES Lab for Financial Modeling and Simulation, Decision Sciences Department, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Stavros A. Zenios

    (HERMES Lab for Financial Modeling and Simulation, Decision Sciences Department, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

Abstract

The estimation of prepayment rates for pools of mortgages is a critical component in determining the value of mortgage-backed securities---MBS for short---and derivative products. This paper discusses the development of prepayment models for pools of fixed-rate mortgages. The models are complete: calibrated functional forms are given for all of the factors that determine prepayment rates. Hence, the models can be used as benchmarks against the simple models of the Public Securities Association, the Federal Housing Administration experience, or the variety of projected prepayment rates generated by proprietary industry models. The key factors that determine prepayment rates are: (1) refinancing incentive, (2) seasonal variations, (3) seasoning of the mortgage pool, and (4) burnout effect. Each factor is modeled separately and is calibrated using historical data. A multiplicative relationship determines the prepayment rate of the mortgage pool. A novel feature of our model is the use of basis functions that capture the complex interactions between the control variables, i.e., interest rate differentials and time, and the response parameter, i.e., prepayment rates.

Suggested Citation

  • Pan Kang & Stavros A. Zenios, 1992. "Complete Prepayment Models for Mortgage-Backed Securities," Management Science, INFORMS, vol. 38(11), pages 1665-1685, November.
    • Handle: RePEc:inm:ormnsc:v:38:y:1992:i:11:p:1665-1685
    DOI: 10.1287/mnsc.38.11.1665
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    Citations

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    Cited by:

    1. Justin Sirignano & Kay Giesecke, 2019. "Risk Analysis for Large Pools of Loans," Management Science, INFORMS, vol. 65(1), pages 107-121, January.
    2. Erwin Charlier & Arjan Van Bussel, 2003. "Prepayment Behavior of Dutch Mortgagors: An Empirical Analysis," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 31(2), pages 165-204, June.
    3. Consiglio, Andrea & Zenios, Stavros A., 1997. "A model for designing callable bonds and its solution using tabu search," Journal of Economic Dynamics and Control, Elsevier, vol. 21(8-9), pages 1445-1470, June.
    4. Zenios, Stavros A. & Holmer, Martin R. & McKendall, Raymond & Vassiadou-Zeniou, Christiana, 1998. "Dynamic models for fixed-income portfolio management under uncertainty," Journal of Economic Dynamics and Control, Elsevier, vol. 22(10), pages 1517-1541, August.
    5. Yusuf VARLI & Gökhan ÖVENÇ, 2019. "Dynamic Analysis of Defaults and Prepayments in the Turkish Mortgage Market," Sosyoekonomi Journal, Sosyoekonomi Society, issue 27(39).
    6. S. Nielsen, Soren & Poulsen, Rolf, 2004. "A two-factor, stochastic programming model of Danish mortgage-backed securities," Journal of Economic Dynamics and Control, Elsevier, vol. 28(7), pages 1267-1289, April.
    7. Michael LaCour-Little & Gregory H. Chun, 1999. "Third Party Originators and Mortgage Prepayment Risk: An Agency Problem?," Journal of Real Estate Research, American Real Estate Society, vol. 17(1), pages 55-70.
    8. Zhixin Liu & Ping He & Bo Chen, 2019. "A Markov decision model for consumer term-loan collections," Review of Quantitative Finance and Accounting, Springer, vol. 52(4), pages 1043-1064, May.
    9. Li, Zongyuan & Lai, Rose Neng, 2024. "Are “too big to fail” banks just different in size? – A study on systemic risk and stand-alone risk," International Review of Financial Analysis, Elsevier, vol. 93(C).
    10. Escudero Bueno, Laureano F. & Garín Martín, María Araceli & Merino Maestre, María & Pérez Sainz de Rozas, Gloria, 2005. "A two-stage stochastic integer programming approach," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    11. L. Smith & Baiqiang Jin, 2007. "Modeling exposure to losses on automobile leases," Review of Quantitative Finance and Accounting, Springer, vol. 29(3), pages 241-266, October.
    12. L. Escudero & A. Garín & M. Merino & G. Pérez, 2007. "A two-stage stochastic integer programming approach as a mixture of Branch-and-Fix Coordination and Benders Decomposition schemes," Annals of Operations Research, Springer, vol. 152(1), pages 395-420, July.
    13. L. Douglas Smith & Canser Bilir & Vega W. Huang & Kuo-yao Hung & Mark Kaplan, 2005. "Citibank Models Credit Risk on Hybrid Mortgage Loans in Taiwan," Interfaces, INFORMS, vol. 35(3), pages 215-229, June.
    14. Golub, Bennett & Holmer, Martin & McKendall, Raymond & Pohlman, Lawrence & Zenios, Stavros A., 1995. "A stochastic programming model for money management," European Journal of Operational Research, Elsevier, vol. 85(2), pages 282-296, September.
    15. Joseph L. Breeden, 2024. "An Age–Period–Cohort Framework for Profit and Profit Volatility Modeling," Mathematics, MDPI, vol. 12(10), pages 1-23, May.

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