VaR-implied tail-correlation matrices

Stefan Mittnik

No 2013/05, CFS Working Paper Series from Center for Financial Studies (CFS)

Abstract: 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 effi cient tail-correlation estimates by use of overidenti cation strategies and how to guarantee positive semidefi niteness, 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.

Keywords: Downside risk; Estimation efficiency; Portfolio optimization; Positive semidefiniteness; Solvency II; Value-at-Risk (search for similar items in EconPapers)
JEL-codes: C1 G11 (search for similar items in EconPapers)
Date: 2013
New Economics Papers: this item is included in nep-ecm and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Journal Article: VaR-implied tail-correlation matrices (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:cfswop:201305

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