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Événements et séminaires - LSTA
Laboratoire de Statistique Théorique et Appliquée


17/01/2017 - Jonathan El Methni (MAP5, UPD)

Reportée au 24 janvier à 10h30

Groupe de travail Théorie des valeurs extrêmes
Extreme versions of Wang risk measures and their estimations for heavy-tailed distributions

In this presentation, we build simple extreme analogues of Wang distortion risk measures and we show how this makes it possible to consider many standard measures of extreme risk, including the usual extreme Value-at-Risk or Tail-Value-at-Risk, as well as the recently introduced extreme Conditional Tail Moment, in a unified framework. We then introduce adapted estimators when the random variable of interest has a heavy-tailed distribution and we prove their asymptotic normality. The finite sample performance of our estimators is assessed in a simulation study and we showcase our techniques on a real dataset.