Vector-Valued Multivariate Conditional Value-at-Risk
Optimization and Control
2020-06-02 v1 Risk Management
Abstract
In this study, we propose a new definition of multivariate conditional value-at-risk (MCVaR) as a set of vectors for discrete probability spaces. We explore the properties of the vector-valued MCVaR (VMCVaR) and show the advantages of VMCVaR over the existing definitions given for continuous random variables when adapted to the discrete case.
Cite
@article{arxiv.1708.01324,
title = {Vector-Valued Multivariate Conditional Value-at-Risk},
author = {Merve Merakli and Simge Kucukyavuz},
journal= {arXiv preprint arXiv:1708.01324},
year = {2020}
}