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VaR-implied tail-correlation matrices

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  • Mittnik, Stefan

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. --

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Bibliographic Info

Paper provided by Center for Financial Studies (CFS) in its series CFS Working Paper Series with number 2013/05.

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Date of creation: 2013
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Handle: RePEc:zbw:cfswop:201305

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Related research

Keywords: Downside risk; Estimation efficiency; Portfolio optimization; Positive semidefiniteness; Solvency II; Value-at-Risk;

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  1. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2002. "Mixed normal conditional heteroskedasticity," CFS Working Paper Series 2002/10, Center for Financial Studies (CFS).
  2. Cotter, John & Longin, Francois, 2006. "Implied correlation from VaR," MPRA Paper 3506, University Library of Munich, Germany.
  3. Mittnik, Stefan, 1990. "Macroeconomic forecasting experience with balanced state space models," International Journal of Forecasting, Elsevier, vol. 6(3), pages 337-348, October.
  4. Andrew Ang & Geert Bekaert, 2002. "International Asset Allocation With Regime Shifts," Review of Financial Studies, Society for Financial Studies, vol. 15(4), pages 1137-1187.
  5. G. Andrew Karoly & Rene Stulz, . "Why do Markets Move Together? An Investigation of U.S.-Japan Stock Return Comovements," Research in Financial Economics 9603, Ohio State University.
  6. Okimoto, Tatsuyoshi, 2008. "New Evidence of Asymmetric Dependence Structures in International Equity Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 43(03), pages 787-815, September.
  7. Robert Engle & Simone Manganelli, 2000. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Econometric Society World Congress 2000 Contributed Papers 0841, Econometric Society.
  8. Ang, Andrew & Chen, Joseph, 2002. "Asymmetric correlations of equity portfolios," Journal of Financial Economics, Elsevier, vol. 63(3), pages 443-494, March.
  9. Campbell, Rachel A.J. & Forbes, Catherine S. & Koedijk, Kees G. & Kofman, Paul, 2008. "Increasing correlations or just fat tails?," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 287-309, March.
  10. Keith Kuester & Stefan Mittnik & Marc S. Paolella, 2006. "Value-at-Risk Prediction: A Comparison of Alternative Strategies," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(1), pages 53-89.
  11. Cambanis, Stamatis & Huang, Steel & Simons, Gordon, 1981. "On the theory of elliptically contoured distributions," Journal of Multivariate Analysis, Elsevier, vol. 11(3), pages 368-385, September.
  12. Longin, Francois & Solnik, Bruno, 1995. "Is the correlation in international equity returns constant: 1960-1990?," Journal of International Money and Finance, Elsevier, vol. 14(1), pages 3-26, February.
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