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Multivariate Copula Models at Work: Outperforming the desert island copula?

  • Fischer, Matthias J.
  • Köck, Christian
  • Schlüter, Stephan
  • Weigert, Florian

Since the pioneering work of Embrechts and co-authors in 1999, copula models enjoy steadily increasing popularity in finance. Whereas copulas are well-studied in the bivariate case, the higher-dimensional case still offers several open issues and it is by far not clear how to construct copulas which sufficiently capture the characteristics of financial returns. For this reason, elliptical copulas (i.e. Gaussian and Student-t copula) still dominate both empirical and practical applications. On the other hand, several attractive construction schemes appeared in the recent literature prom sing flexible but still manageable dependence models. The aim of this work is to empirically investigate whether these models are really capable to outperform its benchmark, i.e. the Student-t copula (which is termed by Paul Embrechts as "desert island copula" on account of its excellent fit to financial returns) and, in addition, to compare the fit of these different copula classes among themselves.

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Paper provided by Friedrich-Alexander-University Erlangen-Nuremberg, Chair of Statistics and Econometrics in its series Discussion Papers with number 79/2007.

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Date of creation: 2007
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Handle: RePEc:zbw:faucse:792007
Contact details of provider: Web page: http://www.statistik.wiso.uni-erlangen.de/

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  1. Patricia Mariela Morillas, 2005. "A method to obtain new copulas from a given one," Metrika, Springer, vol. 61(2), pages 169-184, 04.
  2. Koehler, K. J. & Symanowski, J. T., 1995. "Constructing Multivariate Distributions with Specific Marginal Distributions," Journal of Multivariate Analysis, Elsevier, vol. 55(2), pages 261-282, November.
  3. Paola Palmitesta & Corrado Provasi, 2005. "Aggregation of Dependent Risks Using the Koehler–Symanowski Copula Function," Computational Economics, Society for Computational Economics, vol. 25(1), pages 189-205, February.
  4. W. Breymann & A. Dias & P. Embrechts, 2003. "Dependence structures for multivariate high-frequency data in finance," Quantitative Finance, Taylor & Francis Journals, vol. 3(1), pages 1-14.
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