Asymptotically distribution-free goodness-of-fit testing for tail copulas

S.U. Can, John Einmahl, E.V. Khmaladze, R.J.A. Laeven

Research output: Contribution to journalArticleScientificpeer-review

9 Citations (Scopus)


Let (X1, Y1), … , (Xn, Yn) be an i.i.d. sample from a bivariate distribution function that lies in the max-domain of attraction of an extreme value distribution. The asymptotic joint distribution of the standardized component-wise maxima max( Xi) and max(Yi) is then characterized by the marginal extreme value indices and the tail copula R. We propose a procedure for constructing asymptotically distribution-free goodness-of-fit tests for the tail copula R. The procedure is based on a transformation of a suitable empirical process derived from a semi-parametric estimator of R. The transformed empirical process converges weakly to a standard Wiener process, paving the way for a multitude of asymptotically distribution-free goodness-of-fit tests. We also extend our results to the m-variate (m > 2) case. In a simulation study we show that the limit theorems provide good approximations for finite samples and that tests based on the transformed empirical process have high power.
Original languageEnglish
Pages (from-to)878-902
Number of pages24
JournalAnnals of Statistics
Issue number2
Publication statusPublished - 2015


  • Extreme value theory
  • tail dependence
  • goodness-of-fit testing


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