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The notion of expectiles, originally introduced in the context of testing forhomoscedasticity and conditional symmetry of the error distribution in linearregression, induces a law-invariant, coherent and elicitable risk measure thathas received a significant amount of attention in actuarial and financial riskmanagement contexts. A number of recent papers have focused on the behaviourand estimation of extreme expectile-based risk measures and their potential forrisk management. Joint inference of several extreme expectiles has however beenleft untouched; in fact, even the inference of a marginal extreme expectileturns out to be a difficult problem in finite samples. We investigate thesimultaneous estimation of several extreme marginal expectiles of a randomvector with heavy-tailed marginal distributions. This is done in a generalextremal dependence model where the emphasis is on pairwise dependence betweenthe margins. We use our results to derive accurate confidence regions forextreme expectiles, as well as a test for the equality of several extremeexpectiles. Our methods are showcased in a finite-sample simulation study andon real financial data.

Joint inference on extreme expectiles for multivariate heavy-tailed distributions