A copula sample selection model for predicting multi-year LGDs and Lifetime Expected Losses

Krüger, S; Oehme, T; Rösch, D; Scheule, H

A copula sample selection model for predicting multi-year LGDs and Lifetime Expected Losses

Krüger, S Oehme, T Rösch, D Scheule, H

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Publication Type:: Journal Article
Citation:: Journal of Empirical Finance, 2018, 47 pp. 246 - 262
Issue Date:: 2018-06-01

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Field	Value	Language
dc.contributor.author	Krüger, S	en_US
dc.contributor.author	Oehme, T	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.available	2020-06-01T19:08:26Z
dc.date.issued	2018-06-01	en_US
dc.identifier.citation	Journal of Empirical Finance, 2018, 47 pp. 246 - 262	en_US
dc.identifier.issn	0927-5398	en_US
dc.identifier.uri	http://hdl.handle.net/10453/128076
dc.description.abstract	© 2018 Elsevier B.V. Recent credit risk literature has proposed (i) sample selection models for dependencies between the one-year Probability of Default (PD) and Loss Given Default (LGD), and (ii) multi-year approaches which are limited to default risk. This paper provides a model for the simultaneous prediction of continuous default times and multi-year LGDs. These measures are paramount to predict term structures of LGDs and Lifetime Expected Losses for the revised loan loss provisioning framework of IFRS 9 and US GAAP (current expected credit loss, CECL). The model includes a variation of copulas and corrects for sample selection bias of LGDs, which are only observed given a default event. We find empirical evidence that bonds which default closer to origination tend to generate higher LGDs. The model enables more precise estimates of Lifetime Expected Losses and prevents a severe underestimation in contrast to more restricted credit risk models.	en_US
dc.relation.ispartof	Journal of Empirical Finance	en_US
dc.relation.isbasedon	10.1016/j.jempfin.2018.04.001	en_US
dc.subject.classification	Finance	en_US
dc.title	A copula sample selection model for predicting multi-year LGDs and Lifetime Expected Losses	en_US
dc.type	Journal Article
utslib.citation.volume	47	en_US
utslib.for	1502 Banking, Finance and Investment	en_US
utslib.for	1403 Econometrics	en_US
utslib.for	1402 Applied Economics	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.publication-status	Published	en_US
pubs.volume	47	en_US

Abstract:

© 2018 Elsevier B.V. Recent credit risk literature has proposed (i) sample selection models for dependencies between the one-year Probability of Default (PD) and Loss Given Default (LGD), and (ii) multi-year approaches which are limited to default risk. This paper provides a model for the simultaneous prediction of continuous default times and multi-year LGDs. These measures are paramount to predict term structures of LGDs and Lifetime Expected Losses for the revised loan loss provisioning framework of IFRS 9 and US GAAP (current expected credit loss, CECL). The model includes a variation of copulas and corrects for sample selection bias of LGDs, which are only observed given a default event. We find empirical evidence that bonds which default closer to origination tend to generate higher LGDs. The model enables more precise estimates of Lifetime Expected Losses and prevents a severe underestimation in contrast to more restricted credit risk models.

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http://hdl.handle.net/10453/128076