Multivariate Hill Estimators

Yves Dominicy, Pauliina Ilmonen, David Veredas

Research output: Contribution to journalArticleScientificpeer-review

12 Citations (Scopus)


We propose two classes of semi-parametric estimators for the tail index of a regular varying elliptical random vector. The first one is based on the distance between a tail probability contour and the observations outside this contour. We denote it as the class of separating estimators. The second one is based on the norm of an arbitrary order. We denote it as the class of angular estimators. We show the asymptotic properties and the finite sample performances of both classes. We also illustrate the separating estimators with an empirical application to 21 worldwide financial market indexes.
Original languageEnglish
Pages (from-to)108–142
Issue number1
Publication statusPublished - Apr 2017
MoE publication typeA1 Journal article-refereed


  • Elliptical distribution
  • Hill estimator
  • L norm
  • Minimum covariance determinant
  • Tail index


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