Predicting loss severities for residential mortgage loans: A three-step selection approach
Hung Xuan Do,
Daniel Rösch and
European Journal of Operational Research, 2018, vol. 270, issue 1, 246-259
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.
Keywords: Analytics; Default; Loss given default; Residential mortgage; Selection model (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:270:y:2018:i:1:p:246-259
Access Statistics for this article
European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati
More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().