  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.
