VaR-implied tail-correlation matrices : [Version October 2013]

Empirical evidence suggests that asset returns correlate more strongly in bear markets than conventional correlation estimates imply. We propose a method for determining complete tail correlation matrices based on Value-
Empirical evidence suggests that asset returns correlate more strongly in bear markets than conventional correlation estimates imply. We propose a method for determining complete tail correlation matrices based on Value-at-Risk (VaR) estimates. We demonstrate how to obtain more efficient tail-correlation estimates by use of overidentification strategies and how to guarantee positive semidefiniteness, a property required for valid risk aggregation and Markowitz{type portfolio optimization. An empirical application to a 30-asset universe illustrates the practical applicability and relevance of the approach in portfolio management.
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Metadaten
Author:Stefan Mittnik
URN:urn:nbn:de:hebis:30:3-324823
Parent Title (German):Center for Financial Studies (Frankfurt am Main): CFS working paper series ; No. 2013,05
Series (Serial Number):CFS working paper series (2013, 05)
Publisher:Center for Financial Studies
Place of publication:Frankfurt, M.
Document Type:Working Paper
Language:English
Year of Completion:2013
Year of first Publication:2013
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2013/12/13
Tag:Downside risk; Estimation efficiency; Portfolio optimization; Positive semidefiniteness; Solvency II; Value-at-risk
Issue:Version October 20, 2013
Pagenumber:20
HeBIS PPN:349704708
Institutes:Center for Financial Studies (CFS)
Dewey Decimal Classification:330 Wirtschaft
JEL-Classification:C10 General
G11 Portfolio Choice; Investment Decisions
Sammlungen:Universitätspublikationen
Licence (German):License Logo Veröffentlichungsvertrag für Publikationen

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