Convexity of linear joint chance constrained optimization with elliptically distributed dependent rows

In this paper, we study the convexity of the linear joint chance constraints. We assume that the constraint row vectors are elliptically distributed. Further, the dependence of the rows is modeled by a family of Archimedean copulas, namely, the Gumbel-Hougaard copulas. Under mild assumptions, we prove the eventual convexity of the feasibility set.