Improving Parametric Mortgage Prepayment Models with Non-parametric Kernel Regression

Michael LaCour-Little (), Michael Marschoun () and Clark L. Maxam ()
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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

Journal of Real Estate Research, 2002, vol. 24, issue 3, 299-328

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.

JEL-codes: L85 (search for similar items in EconPapers)
Date: 2002
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Citations: View citations in EconPapers (4)

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