Accuracy of mortgage portfolio risk forecasts during financial crises

Lee, Y; Rösch, D; Scheule, H

Accuracy of mortgage portfolio risk forecasts during financial crises

Lee, Y Rösch, D Scheule, H

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Publication Type:: Journal Article
Citation:: European Journal of Operational Research, 2016, 249 (2), pp. 440 - 456
Issue Date:: 2016-03-01

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Field	Value	Language
dc.contributor.author	Lee, Y	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	2016-03-01	en_US
dc.identifier.citation	European Journal of Operational Research, 2016, 249 (2), pp. 440 - 456	en_US
dc.identifier.issn	0377-2217	en_US
dc.identifier.uri	http://hdl.handle.net/10453/39531
dc.description.abstract	© 2015 Elsevier B.V. This paper explores whether factor based credit portfolio risk models are able to predict losses in severe economic downturns such as the recent Global Financial Crisis (GFC) within standard confidence levels. The paper analyzes (i) the accuracy of default rate forecasts, and (ii) whether forecast downturn percentiles (Value-at-Risk, VaR) are sufficient to cover default rate outcomes over a quarterly and an annual forecast horizon. Uninformative maximum likelihood and informative Bayesian techniques are compared as they imply different degrees of uncertainty. We find that quarterly VaR estimates are generally sufficient but annual VaR estimates may be insufficient during economic downturns. In addition, the paper develops and analyzes models based on auto-regressive adjustments of scores, which provide a higher forecast accuracy. The consideration of parameter uncertainty and auto-regressive error terms mitigates the shortfall.	en_US
dc.relation.ispartof	European Journal of Operational Research	en_US
dc.relation.isbasedon	10.1016/j.ejor.2015.09.007	en_US
dc.subject.classification	Operations Research	en_US
dc.title	Accuracy of mortgage portfolio risk forecasts during financial crises	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	249	en_US
utslib.for	1502 Banking, Finance and Investment	en_US
utslib.for	0102 Applied Mathematics	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	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	249	en_US

Abstract:

© 2015 Elsevier B.V. This paper explores whether factor based credit portfolio risk models are able to predict losses in severe economic downturns such as the recent Global Financial Crisis (GFC) within standard confidence levels. The paper analyzes (i) the accuracy of default rate forecasts, and (ii) whether forecast downturn percentiles (Value-at-Risk, VaR) are sufficient to cover default rate outcomes over a quarterly and an annual forecast horizon. Uninformative maximum likelihood and informative Bayesian techniques are compared as they imply different degrees of uncertainty. We find that quarterly VaR estimates are generally sufficient but annual VaR estimates may be insufficient during economic downturns. In addition, the paper develops and analyzes models based on auto-regressive adjustments of scores, which provide a higher forecast accuracy. The consideration of parameter uncertainty and auto-regressive error terms mitigates the shortfall.

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http://hdl.handle.net/10453/39531