Improving Parametric Mortgage Prepayment Models with Non-Parametric Kernel Regression
California State University at Fullerton
Michael A. Marschoun
affiliation not provided to SSRN
Clark L. Maxam
Trailcrest Capital Advisors; Braddock Financial Corporation - Tabor Center
Journal of Real Estate Research, Vol. 24, No. 3, 2002
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.
Number of Pages in PDF File: 30
Keywords: prepayment, valuation, mortgages, non-parametric
JEL Classification: R51Accepted Paper Series
Date posted: May 16, 2007
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