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Multivariate conditional versions of Spearman's rho and related measures of tail dependence

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  • Schmid, Friedrich
  • Schmidt, Rafael
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    Abstract

    A new family of conditional-dependence measures based on Spearman's rho is introduced. The corresponding multidimensional versions are established. Asymptotic distributional results are derived for related estimators which are based on the empirical copula. Particular emphasis is placed on a new type of multidimensional tail-dependence measure and its relationship to other measures of tail dependence is shown. Multivariate tail dependence describes the limiting amount of dependence in the vertices of the copula's domain.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 98 (2007)
    Issue (Month): 6 (July)
    Pages: 1123-1140

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    Handle: RePEc:eee:jmvana:v:98:y:2007:i:6:p:1123-1140

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    Related research

    Keywords: Measure of dependence Copula Spearman's rho Tail dependence Empirical copula Weak convergence;

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    Cited by:
    1. Gaißer, Sandra & Ruppert, Martin & Schmid, Friedrich, 2010. "A multivariate version of Hoeffding's Phi-Square," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2571-2586, November.
    2. Joe, Harry & Li, Haijun & Nikoloulopoulos, Aristidis K., 2010. "Tail dependence functions and vine copulas," Journal of Multivariate Analysis, Elsevier, vol. 101(1), pages 252-270, January.
    3. Klein, Ingo & Tinkl, Fabian, 2011. "Some critical remarks on Zhang's gamma test for independence," Discussion Papers 87/2010, Friedrich-Alexander-University Erlangen-Nuremberg, Chair of Statistics and Econometrics.
    4. Ferreira, H., 2011. "Dependence between two multivariate extremes," Statistics & Probability Letters, Elsevier, vol. 81(5), pages 586-591, May.
    5. Ming Liu & Sumner La Croix, 2014. "A Cross-Country Index of Intellectual Property Rights in Pharmaceutical Innovations," Working Papers 201408, University of Hawaii at Manoa, Department of Economics.
    6. Gaißer, Sandra & Schmid, Friedrich, 2010. "On testing equality of pairwise rank correlations in a multivariate random vector," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2598-2615, November.
    7. Decancq K, 2009. "Copula-based Measurement of Dependence Between Dimensions of Well-being," Health, Econometrics and Data Group (HEDG) Working Papers 09/32, HEDG, c/o Department of Economics, University of York.
    8. Melanie Frick, 2012. "Measures of multivariate asymptotic dependence and their relation to spectral expansions," Metrika, Springer, vol. 75(6), pages 819-831, August.
    9. Ferreira, Helena & Ferreira, Marta, 2012. "Tail dependence between order statistics," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 176-192.
    10. Fung, Thomas & Seneta, Eugene, 2011. "The bivariate normal copula function is regularly varying," Statistics & Probability Letters, Elsevier, vol. 81(11), pages 1670-1676, November.

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