Applied Nonparametric Regression Techniques: Estimating Prepayments on Fixed-Rate Mortgage-Backed Securities |
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Authors: | Maxam Clark L LaCour-Little Michael |
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Institution: | (1) New York Life Investment Management, Montana State University College of Business, 51 Madison Avenue, Room 201, New York, NY, 10010;(2) Wells Fargo Home Mortgage, 7911 Forsyth Blvd, Suite 600, Clayton, MO, 63105 |
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Abstract: | We assess nonparametric kernel-density regression as a technique for estimating mortgage loan prepayments—one of the key components in pricing highly volatile mortgage-backed securities and their derivatives. The highly nonlinear and so-called irrational behavior of the prepayment function lends itself well to an estimator that is free of both functional and distributional assumptions. The technique is shown to exhibit superior out-of-sample predictive ability compared to both proportional-hazards and proprietary-practitioner models. Moreover, the best kernel model provides this improved predictive power utilizing a more parsimonious specification in terms of both data and covariates. We conclude that the technique may prove useful in other financial modeling applications, such as default modeling, and other derivative pricing problems where highly nonlinear relationships and optionality exist. |
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Keywords: | mortgage prepayment nonparametric kernel regression |
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