Multivariate Geometric Expectiles

Klaus Herrmann, Marius Hofert and Melina Mailhot

Papers from arXiv.org

Abstract: A generalization of expectiles for d-dimensional multivariate distribution functions is introduced. The resulting geometric expectiles are unique solutions to a convex risk minimization problem and are given by d-dimensional vectors. They are well behaved under common data transformations and the corresponding sample version is shown to be a consistent estimator. We exemplify their usage as risk measures in a number of multivariate settings, highlighting the influence of varying margins and dependence structures.

Date: 2017-04, Revised 2018-01
New Economics Papers: this item is included in nep-ecm
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