Predicting loss severities for residential mortgage loans: A three-step selection approach

Do, HX; Rösch, D; Scheule, H

Predicting loss severities for residential mortgage loans: A three-step selection approach

Do, HX

Rösch, D Scheule, H

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Publication Type:: Journal Article
Citation:: European Journal of Operational Research, 2018, 270 (1), pp. 246 - 259
Issue Date:: 2018-10-01

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Field	Value	Language
dc.contributor.author	Do, HX https://orcid.org/0000-0001-7662-5566	en_US
dc.contributor.author	Rösch, D	en_US
dc.contributor.author	Scheule, H https://orcid.org/0000-0001-7283-5654	en_US
dc.date.issued	2018-10-01	en_US
dc.identifier.citation	European Journal of Operational Research, 2018, 270 (1), pp. 246 - 259	en_US
dc.identifier.issn	0377-2217	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130386
dc.description.abstract	© 2018 Elsevier B.V. This paper develops a novel framework to model the loss given default (LGD) of residential mortgage loans which is the dominant consumer loan category for many commercial banks. LGDs in mortgage lending are subject to two selection processes: default and cure, where the collateral value exceeds the outstanding loan amount. We propose a three-step selection approach with a joint probability framework for default, cure (i.e., zero-LGD) and non-zero loss severity information. The proposed methodology demonstrates improved performance in out-of-time predictions compared to widely used OLS regressions.	en_US
dc.relation.ispartof	European Journal of Operational Research	en_US
dc.relation.isbasedon	10.1016/j.ejor.2018.02.057	en_US
dc.subject.classification	Operations Research	en_US
dc.title	Predicting loss severities for residential mortgage loans: A three-step selection approach	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	270	en_US
utslib.for	1502 Banking, Finance and Investment	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Finance Discipline
pubs.organisational-group	/University of Technology Sydney/Strength - QFRC - Quantitative Finance Research Centre
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	270	en_US

Abstract:

© 2018 Elsevier B.V. This paper develops a novel framework to model the loss given default (LGD) of residential mortgage loans which is the dominant consumer loan category for many commercial banks. LGDs in mortgage lending are subject to two selection processes: default and cure, where the collateral value exceeds the outstanding loan amount. We propose a three-step selection approach with a joint probability framework for default, cure (i.e., zero-LGD) and non-zero loss severity information. The proposed methodology demonstrates improved performance in out-of-time predictions compared to widely used OLS regressions.

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http://hdl.handle.net/10453/130386