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Editorial to the special issue on Copulae of Statistics & Risk Modeling

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  • Okhrin Ostap

    (Humboldt-Universität zu Berlin)

Abstract

Copulae became an extremely popular tool in different areas of research. Since the first applications in risk management in the late 90th, they attracted many other quantitatively oriented sciences like biostatistics, hydrology and finance. The main reason originates in the Sklar (1959) theorem, which allows for separation of the marginal distributions from the dependency structure between the random variables.

Suggested Citation

  • Okhrin Ostap, 2013. "Editorial to the special issue on Copulae of Statistics & Risk Modeling," Statistics & Risk Modeling, De Gruyter, vol. 30(4), pages 281-286, December.
    • Handle: RePEc:bpj:strimo:v:30:y:2013:i:4:p:281-286:n:5
    DOI: 10.1524/strm.2013.9014
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    References listed on IDEAS

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    1. repec:taf:jnlbes:v:30:y:2012:i:2:p:275-287 is not listed on IDEAS
    2. Wanling Huang & Artem Prokhorov, 2014. "A Goodness-of-fit Test for Copulas," Econometric Reviews, Taylor & Francis Journals, vol. 33(7), pages 751-771, October.
    3. Bouezmarni, Taoufik & Rombouts, Jeroen V.K. & Taamouti, Abderrahim, 2010. "Asymptotic properties of the Bernstein density copula estimator for [alpha]-mixing data," Journal of Multivariate Analysis, Elsevier, vol. 101(1), pages 1-10, January.
    4. Hofert, Marius, 2008. "Sampling Archimedean copulas," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5163-5174, August.
    5. Giacomini, Enzo & Härdle, Wolfgang & Spokoiny, Vladimir, 2009. "Inhomogeneous Dependence Modeling with Time-Varying Copulae," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(2), pages 224-234.
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    Keywords

    Copula; multivariate distribution;

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