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A note on a non-parametric tail dependence estimator

Author

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  • Fischer, Matthias J.
  • Dörflinger, Marco

Abstract

We present a non-parametric tail dependence estimator which arises naturally from a specific regression model. Above that, this tail dependence estimator also results from a specific copula mixture.

Suggested Citation

  • Fischer, Matthias J. & Dörflinger, Marco, 2006. "A note on a non-parametric tail dependence estimator," Discussion Papers 76/2006, Friedrich-Alexander University Erlangen-Nuremberg, Chair of Statistics and Econometrics.
    • Handle: RePEc:zbw:faucse:762006
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    References listed on IDEAS

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    1. Rafael Schmidt & Ulrich Stadtmüller, 2006. "Non‐parametric Estimation of Tail Dependence," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(2), pages 307-335, June.
    2. Frahm, Gabriel & Junker, Markus & Schmidt, Rafael, 2005. "Estimating the tail-dependence coefficient: Properties and pitfalls," Insurance: Mathematics and Economics, Elsevier, vol. 37(1), pages 80-100, August.
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    Cited by:

    1. Víctor Adame-García & Fernando Fernández-Rodríguez & Simón Sosvilla-Rivero, 2017. "“Resolution of optimization problems and construction of efficient portfolios: An application to the Euro Stoxx 50 index"," IREA Working Papers 201702, University of Barcelona, Research Institute of Applied Economics, revised Feb 2017.
    2. Fischer, Matthias J. & Hinzmann, Gerd, 2006. "A new class of copulas with tail dependence and a generalized tail dependence estimator," Discussion Papers 77/2006, Friedrich-Alexander University Erlangen-Nuremberg, Chair of Statistics and Econometrics.

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