VaR-implied tail-correlation matrices
AbstractEmpirical 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. --
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Bibliographic InfoPaper provided by Center for Financial Studies (CFS) in its series CFS Working Paper Series with number 2013/05.
Date of creation: 2013
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Downside risk; Estimation efficiency; Portfolio optimization; Positive semidefiniteness; Solvency II; Value-at-Risk;
Find related papers by JEL classification:
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-11-16 (All new papers)
- NEP-ECM-2013-11-16 (Econometrics)
- NEP-RMG-2013-11-16 (Risk Management)
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