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On the covariance of the asymptotic empirical copula process

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  • Genest, Christian
  • Segers, Johan

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Suggested Citation

  • Genest, Christian & Segers, Johan, 2010. "On the covariance of the asymptotic empirical copula process," LIDAM Reprints ISBA 2010017, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvar:2010017
    Note: In : Journal of Multivariate Analysis, vol. 101, no. 8, p. 1837-1845 (2010)
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    Cited by:

    1. Hofert, Marius & Oldford, Wayne, 2018. "Visualizing dependence in high-dimensional data: An application to S&P 500 constituent data," Econometrics and Statistics, Elsevier, vol. 8(C), pages 161-183.
    2. Gery Geenens & Arthur Charpentier & Davy Paindaveine, 2014. "Probit Transformation for Nonparametric Kernel Estimation of the Copula Density," Working Papers ECARES ECARES 2014-23, ULB -- Universite Libre de Bruxelles.
    3. Alexandra Dias, 2024. "Maximum Pseudo-Likelihood Estimation of Copula Models and Moments of Order Statistics," Risks, MDPI, vol. 12(1), pages 1-26, January.
    4. Bücher Axel, 2014. "A note on nonparametric estimation of bivariate tail dependence," Statistics & Risk Modeling, De Gruyter, vol. 31(2), pages 151-162, June.
    5. Nadja Klein & Thomas Kneib, 2020. "Directional bivariate quantiles: a robust approach based on the cumulative distribution function," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(2), pages 225-260, June.
    6. Mainik, Georg, 2015. "Risk aggregation with empirical margins: Latin hypercubes, empirical copulas, and convergence of sum distributions," Journal of Multivariate Analysis, Elsevier, vol. 141(C), pages 197-216.
    7. Hofert, Marius & Prasad, Avinash & Zhu, Mu, 2022. "Multivariate time-series modeling with generative neural networks," Econometrics and Statistics, Elsevier, vol. 23(C), pages 147-164.
    8. Bücher, Axel & Ruppert, Martin, 2013. "Consistent testing for a constant copula under strong mixing based on the tapered block multiplier technique," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 208-229.
    9. Genest Christian & Mesfioui Mhamed & Nešlehová Johanna G., 2019. "On the asymptotic covariance of the multivariate empirical copula process," Dependence Modeling, De Gruyter, vol. 7(1), pages 279-291, January.
    10. Christian Genest & Johanna Nešlehová & Jean-François Quessy, 2012. "Tests of symmetry for bivariate copulas," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(4), pages 811-834, August.
    11. Berghaus, Betina & Bücher, Axel, 2014. "Nonparametric tests for tail monotonicity," Journal of Econometrics, Elsevier, vol. 180(2), pages 117-126.
    12. Holger Dette & Marc Hallin & Tobias Kley & Stanislav Volgushev, 2011. "Of Copulas, Quantiles, Ranks and Spectra - An L1-Approach to Spectral Analysis," Working Papers ECARES ECARES 2011-038, ULB -- Universite Libre de Bruxelles.
    13. Aleksy Leeuwenkamp & Wentao Hu, 2023. "New general dependence measures: construction, estimation and application to high-frequency stock returns," Papers 2309.00025, arXiv.org.
    14. Georg Mainik, 2015. "Risk aggregation with empirical margins: Latin hypercubes, empirical copulas, and convergence of sum distributions," Papers 1508.02749, arXiv.org.
    15. Bücher, Axel & Kojadinovic, Ivan & Rohmer, Tom & Segers, Johan, 2014. "Detecting changes in cross-sectional dependence in multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 132(C), pages 111-128.

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