Stochastic orderings for set-valued risk measures
研究了集值风险测度框架下的投资组合聚合问题，提出了随机向量的新随机序概念，并证明了最大相关风险测度在该设定下的一致性性质，对金融风险管理有理论意义。
This paper explores the portfolio aggregation problem within the framework of set-valued risk measures, with a specific emphasis on maximal correlation risk measures, as introduced in this work. We propose a novel stochastic ordering concept for random vectors and establish consistency properties for maximal correlation risk measures in this setting. Furthermore, we demonstrate convex-type consistency for a specific subclass of law-invariant convex set-valued risk measures, highlighting both their theoretical foundations and practical significance.