Joint inference on extreme expectiles for multivariate heavy-tailed distributions

Simone A. Padoan (1) , Gilles Stupfler (2, 3)

1 Department of Decision Sciences, Bocconi University
2 ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz]
3 CREST - Centre de Recherche en Économie et Statistique

The notion of expectiles, originally introduced in the context of testing for homoscedasticity and conditional symmetry of the error distribution in linear regression, induces a law-invariant, coherent and elicitable risk measure that has received a significant amount of attention in actuarial and financial risk management contexts. A number of recent papers have focused on the behaviour and estimation of extreme expectile-based risk measures and their potential for risk management. Joint inference of several extreme expectiles has however been left untouched; in fact, even the inference of a marginal extreme expectile turns out to be a difficult problem in finite samples. We investigate the simultaneous estimation of several extreme marginal expectiles of a random vector with heavy-tailed marginal distributions. This is done in a general extremal dependence model where the emphasis is on pairwise dependence between the margins. We use our results to derive accurate confidence regions for extreme expectiles, as well as a test for the equality of several extreme expectiles. Our methods are showcased in a finite-sample simulation study and on real financial data.

Mots clés: Expectiles; Extremal dependence; Heavy tails; Joint convergence; Joint inference; Tail copula; Testing; Asymmetric least squares

Domaines: Statistiques [stat]

Simone A. Padoan, Gilles Stupfler. Joint inference on extreme expectiles for multivariate heavy-tailed distributions. 2020. ⟨hal-02902667v1⟩