Improving Parametric Mortgage Prepayment Models with Non-parametric Kernel Regression     Authors: Michael LaCour-Little, Michael Marschoun and Clark L. Maxam   Start Page: 299 End Page: 328 Volume: 24 Issue Number: 03 Year: 2002 Publication: Journal of Real Estate Research   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.