Multivariate Geometric Expectiles
Risk Management
2018-01-19 v2
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.
Cite
@article{arxiv.1704.01503,
title = {Multivariate Geometric Expectiles},
author = {Klaus Herrmann and Marius Hofert and Melina Mailhot},
journal= {arXiv preprint arXiv:1704.01503},
year = {2018}
}