Dynamic implied correlation modeling and forecasting in structured finance

Löhr, S; Mursajew, O; Rösch, D; Scheule, H

Dynamic implied correlation modeling and forecasting in structured finance

Löhr, S Mursajew, O Rösch, D Scheule, H

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Publication Type:: Journal Article
Citation:: Journal of Futures Markets, 2013, 33 (11), pp. 994 - 1023
Issue Date:: 2013-11-01

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Field	Value	Language
dc.contributor.author	Löhr, S	en_US
dc.contributor.author	Mursajew, O	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	2013-11-01	en_US
dc.identifier.citation	Journal of Futures Markets, 2013, 33 (11), pp. 994 - 1023	en_US
dc.identifier.issn	0270-7314	en_US
dc.identifier.uri	http://hdl.handle.net/10453/27740
dc.description.abstract	Correlations are the main drivers for credit portfolio risk and constitute a major element in pricing credit derivatives such as synthetic single-tranche collateralized debt obligation swaps. This study suggests a dynamic panel regression approach to model and forecast implied correlations. Random effects are introduced to account for unobservable time-specific effects on implied tranche correlations. The implied-correlation forecasts of tranche spreads are compared to forecasts using historical correlations from asset returns. The empirical findings support our proposed dynamic mixed-effects regression correlation model. © 2013 Wiley Periodicals, Inc.	en_US
dc.relation.ispartof	Journal of Futures Markets	en_US
dc.relation.isbasedon	10.1002/fut.21626	en_US
dc.subject.classification	Finance	en_US
dc.title	Dynamic implied correlation modeling and forecasting in structured finance	en_US
dc.type	Journal Article
utslib.citation.volume	11	en_US
utslib.citation.volume	33	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	11	en_US
pubs.publication-status	Published	en_US
pubs.volume	33	en_US

Abstract:

Correlations are the main drivers for credit portfolio risk and constitute a major element in pricing credit derivatives such as synthetic single-tranche collateralized debt obligation swaps. This study suggests a dynamic panel regression approach to model and forecast implied correlations. Random effects are introduced to account for unobservable time-specific effects on implied tranche correlations. The implied-correlation forecasts of tranche spreads are compared to forecasts using historical correlations from asset returns. The empirical findings support our proposed dynamic mixed-effects regression correlation model. © 2013 Wiley Periodicals, Inc.

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http://hdl.handle.net/10453/27740