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)
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.
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