Improving Parametric Mortgage Prepayment Models with Non-parametric Kernel Regression
AbstractDeveloping 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.
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Bibliographic InfoArticle provided by American Real Estate Society in its journal Journal of Real Estate Research.
Volume (Year): 24 (2002)
Issue (Month): 3 ()
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Postal: American Real Estate Society Clemson University School of Business & Behavioral Science Department of Finance 401 Sirrine Hall Clemson, SC 29634-1323
Web page: http://www.aresnet.org/
Postal: Diane Quarles American Real Estate Society Manager of Member Services Clemson University Box 341323 Clemson, SC 29634-1323
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