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Improving Parametric Mortgage Prepayment Models with Non-parametric Kernel Regression

Author

Listed:
  • Michael LaCour-Little

    (Washington University in St. Louis and Wells Fargo Home Mortgage in Clayton, MO 63105)

  • Michael Marschoun

    (PMI Mortgage Insurance Co., San Francisco, CA 94111)

  • Clark L. Maxam

    (Montana State University, Bozeman, MT 59717)

Abstract

Developing a good prepayment model is a central task in the valuation of mortgages and mortgage-backed securities but conventional parametric models often have bad out-of-sample predictive ability. A likely explanation is the highly non-linear nature of the prepayment function. Non-parametric techniques are much better at detecting non-linearity and multivariate interaction. This article discusses how non-parametric kernel regression may be applied to loan level event histories to produce a better parametric model. By utilizing a parsimonious specification, a model can be produced that practitioners can use in valuation routines based on Monte Carlo interest rate simulation.

Suggested Citation

  • Michael LaCour-Little & Michael Marschoun & Clark L. Maxam, 2002. "Improving Parametric Mortgage Prepayment Models with Non-parametric Kernel Regression," Journal of Real Estate Research, American Real Estate Society, vol. 24(3), pages 299-328.
    • Handle: RePEc:jre:issued:v:24:n:3:2002:p:299-328
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    References listed on IDEAS

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    Cited by:

    1. Fang, Lu & Munneke, Henry J., 2021. "A spatial analysis of borrowers’ mortgage termination decision – A nonparametric approach," Regional Science and Urban Economics, Elsevier, vol. 86(C).
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    3. William Miles, 2011. "Long-Range Dependence in U.S. Home Price Volatility," The Journal of Real Estate Finance and Economics, Springer, vol. 42(3), pages 329-347, April.
    4. Agatha M. Poroshina, 2014. "Credit Risk Modeling Of Residential Mortgage Lending In Russia," HSE Working papers WP BRP 30/FE/2014, National Research University Higher School of Economics.

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