Skip to main content

Main menu

  • Home
  • Current Issue
  • Past Issues
  • Videos
  • Submit an article
  • More
    • About JFI
    • Editorial Board
    • Published Ahead of Print (PAP)
  • IPR Logo
  • About Us
  • Journals
  • Publish
  • Advertise
  • Videos
  • Webinars
  • More
    • Awards
    • Article Licensing
    • Academic Use
  • Follow IIJ on LinkedIn
  • Follow IIJ on Twitter

User menu

  • Sample our Content
  • Request a Demo
  • Log in

Search

  • ADVANCED SEARCH: Discover more content by journal, author or time frame
The Journal of Fixed Income
  • IPR Logo
  • About Us
  • Journals
  • Publish
  • Advertise
  • Videos
  • Webinars
  • More
    • Awards
    • Article Licensing
    • Academic Use
  • Sample our Content
  • Request a Demo
  • Log in
The Journal of Fixed Income

The Journal of Fixed Income

ADVANCED SEARCH: Discover more content by journal, author or time frame

  • Home
  • Current Issue
  • Past Issues
  • Videos
  • Submit an article
  • More
    • About JFI
    • Editorial Board
    • Published Ahead of Print (PAP)
  • Follow IIJ on LinkedIn
  • Follow IIJ on Twitter

Rise of the Machines: Application of Machine Learning to Mortgage Prepayment Modeling

Glenn M. Schultz and Frank J. Fabozzi
The Journal of Fixed Income Winter 2022, jfi.2021.1.123; DOI: https://doi.org/10.3905/jfi.2021.1.123
Glenn M. Schultz
is the director of mortgage prepayment modeling for MUFG Securities, in New York, New York
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Frank J. Fabozzi
is a professor of finance at EDHEC Business School in Nice, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Info & Metrics
  • PDF (Subscribers Only)
Loading

Click to login and read the full article.

Don’t have access? Click here to request a demo 
Alternatively, Call a member of the team to discuss membership options
US and Overseas: +1 646-931-9045
UK: 0207 139 1600

Abstract

Key to the valuation of agency residential mortgage-backed securities (MBS) is the modeling of voluntary prepayment and default behaviors of the underlying borrowers in the mortgage pool. The proliferation of both pool and loan level data coupled with access to advanced machine learning algorithms have opened the door to the application of machine learning to mortgage prepayment modeling. The modular prepayment model, one that relies on defined functions to predict mortgage prepayment, has dominated the MBS market nearly since its inception. However, machine learning models are beginning to make inroads and, in some cases, replacing traditional modular prepayment models. The modular and machine learning model differ in the following ways: In the case of modular prepayment models, either added or multiplicative, the modeler defines both the functional form of each feature as well as the “tuning” of the parameters passed to each. Machine learning or “second generation” mortgage prepayment models differ in the sense that the modeler “tunes” the hyperparameters which determine the bias variance tradeoff while the machine determines the functional form of each feature of the model. In this article, we propose a machine learning mortgage prepayment model using a boosted gradient classifier, trained at the loan level and generalized to the pool level. A gradient boosted classifier is a tree-based model using an ensemble of weak learners to create a strong committee for prediction.

Key Findings

  • ▪ Machine learning mortgage prepayment models are proving competitive, if not superior, to the traditional modular mortgage prepayment model.

  • ▪ Key to training a machine learning prepayment model is managing the bias variance tradeoff through the proper selection of the machine hyperparameters.

  • ▪ One of the most powerful techniques to improve performance of a machine learning prepayment model is “boosting”; an ensemble method which improves the predictive accuracy of the model by combining the output of many “weak leaners” into a “strong committee.”

  • © 2021 Pageant Media Ltd
View Full Text

Don’t have access? Click here to request a demo

Alternatively, Call a member of the team to discuss membership options

US and Overseas: +1 646-931-9045

UK: 0207 139 1600

Log in using your username and password

Forgot your user name or password?
PreviousNext
Back to top

Explore our content to discover more relevant research

  • By topic
  • Across journals
  • From the experts
  • Monthly highlights
  • Special collections

In this issue

The Journal of Fixed Income: 32 (3)
The Journal of Fixed Income
Vol. 32, Issue 3
Winter 2023
  • Table of Contents
  • Index by author
  • Complete Issue (PDF)
Print
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word on The Journal of Fixed Income.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Rise of the Machines: Application of Machine Learning to Mortgage Prepayment Modeling
(Your Name) has sent you a message from The Journal of Fixed Income
(Your Name) thought you would like to see the The Journal of Fixed Income web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Rise of the Machines: Application of Machine Learning to Mortgage Prepayment Modeling
Glenn M. Schultz, Frank J. Fabozzi
The Journal of Fixed Income Nov 2021, jfi.2021.1.123; DOI: 10.3905/jfi.2021.1.123

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Save To My Folders
Share
Rise of the Machines: Application of Machine Learning to Mortgage Prepayment Modeling
Glenn M. Schultz, Frank J. Fabozzi
The Journal of Fixed Income Nov 2021, jfi.2021.1.123; DOI: 10.3905/jfi.2021.1.123
del.icio.us logo Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Tweet Widget Facebook Like LinkedIn logo

Jump to section

  • Article
    • Abstract
    • COHORT MODELING VERSUS LOAN-LEVEL MODELING
    • THE MORTGAGE PREPAYMENT MODEL
    • OVERVIEW OF MACHINE LEARNING
    • MACHINE LEARNING MORTGAGE PREPAYMENT MODEL
    • PREPAYMENT MODEL
    • CONCLUSION
    • ENDNOTES
    • REFERENCES
  • Info & Metrics
  • PDF (Subscribers Only)
  • PDF (Subscribers Only)

Similar Articles

Cited By...

  • No citing articles found.
  • Google Scholar
LONDON
One London Wall, London, EC2Y 5EA
United Kingdom
+44 207 139 1600
 
NEW YORK
41 Madison Avenue, New York, NY 10010
USA
+1 646 931 9045
reply@pm-research.com
 

Stay Connected

  • Follow IIJ on LinkedIn
  • Follow IIJ on Twitter

MORE FROM PMR

  • Home
  • Awards
  • Investment Guides
  • Videos
  • About PMR

INFORMATION FOR

  • Academics
  • Agents
  • Authors
  • Content Usage Terms

GET INVOLVED

  • Advertise
  • Publish
  • Article Licensing
  • Contact Us
  • Subscribe Now
  • Log in
  • Update your Profile
  • Give us your feedback

© 2023 With Intelligence Ltd | All Rights Reserved | ISSN: 1059-8596 | E-ISSN: 2168-8648

  • Site Map
  • Terms & Conditions
  • Privacy Policy
  • Cookies