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

Clark L Maxam and Michael LaCour-Little

The Journal of Real Estate Finance and Economics, 2001, vol. 23, issue 2, 139-60

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. Copyright 2001 by Kluwer Academic Publishers

Date: 2001
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://journals.kluweronline.com/issn/0895-5638/contents link to full text (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:jrefec:v:23:y:2001:i:2:p:139-60

Ordering information: This journal article can be ordered from
http://www.springer. ... ce/journal/11146/PS2

Access Statistics for this article

The Journal of Real Estate Finance and Economics is currently edited by Steven R. Grenadier, James B. Kau and C.F. Sirmans

More articles in The Journal of Real Estate Finance and Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().