论文标题
基于差异的凸风险措施
Convex Risk Measures based on Divergence
论文作者
论文摘要
风险度量将概率理论或统计数据连接到优化，特别是凸优化。如今，它们是金融申请和涉及风险规避的保险的标准。本文研究了Orlicz空间上的广泛风险度量。表征功能描述了决策者对增加损失的风险评估。我们将风险措施与Rockafellar开发的关键公式将基于凸双重风险的平均价值风险开发的关键公式联系起来，这对于相应的优化问题至关重要。我们表征双重并提供互补表示。
Risk measures connect probability theory or statistics to optimization, particularly to convex optimization. They are nowadays standard in applications of finance and in insurance involving risk aversion. This paper investigates a wide class of risk measures on Orlicz spaces. The characterizing function describes the decision maker's risk assessment towards increasing losses. We link the risk measures to a crucial formula developed by Rockafellar for the Average Value-at-Risk based on convex duality, which is fundamental in corresponding optimization problems. We characterize the dual and provide complementary representations.
