Applied Non-Parametric Regression Techniques: Estimating Prepayments on Fixed Rate Mortgage-Backed Securities

Posted: 25 Apr 2002  

Clark L. Maxam

Trailcrest Capital Advisors; Braddock Financial Corporation - Tabor Center

Michael LaCour-Little

California State University at Fullerton

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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 non-linear 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 non-linear relationships and optionality exist.

Keywords: mortgage, prepayment, nonparametric

Suggested Citation

Maxam, Clark L. and LaCour-Little, Michael, Applied Non-Parametric Regression Techniques: Estimating Prepayments on Fixed Rate Mortgage-Backed Securities. The Journal of Real Estate Finance & Economics. Available at SSRN: https://ssrn.com/abstract=302378

Clark L. Maxam (Contact Author)

Trailcrest Capital Advisors ( email )

6781 Nautique Circle
Larkspur, CO 80118
United States

Braddock Financial Corporation - Tabor Center ( email )

1200 17th Street, Suite 880
Denver, CO 80202
United States

Michael LaCour-Little

California State University at Fullerton ( email )

5133 Mihaylo Hall
Fullerton, CA 92834-6848
United States
657-278-4014 (Phone)
657-278-2161 (Fax)

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