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Applied Nonparametric Regression Techniques: Estimating Prepayments on Fixed-Rate Mortgage-Backed Securities

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  • Maxam, Clark L
  • LaCour-Little, Michael

Abstract

We assess nonparametric kernel-density regression as a technique for estimating mortgage loan prepayments--one of the key components in pricing highly volatile mortgage-backed securities and their derivatives. The highly nonlinear and so-called irrational behavior of the prepayment function lends itself well to an estimator that is free of both functional and distributional assumptions. The technique is shown to exhibit superior out-of-sample predictive ability compared to both proportional-hazards and proprietary-practitioner models. Moreover, the best kernel model provides this improved predictive power utilizing a more parsimonious specification in terms of both data and covariates. We conclude that the technique may prove useful in other financial modeling applications, such as default modeling, and other derivative pricing problems where highly nonlinear relationships and optionality exist. Copyright 2001 by Kluwer Academic Publishers

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  • Maxam, Clark L & LaCour-Little, Michael, 2001. "Applied Nonparametric Regression Techniques: Estimating Prepayments on Fixed-Rate Mortgage-Backed Securities," The Journal of Real Estate Finance and Economics, Springer, vol. 23(2), pages 139-160, September.
    • Handle: RePEc:kap:jrefec:v:23:y:2001:i:2:p:139-60
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    Cited by:

    1. 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.
    2. 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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