凸风险测度下基于变分偏好的最优对冲
Optimal hedging with variational preferences under convex risk measures
AI总结:
该研究构建了凸风险测度下带变分偏好的对冲优化理论框架,推导了风险测度与效用复合的对偶表示、优化问题性质及最优定价条件,并给出具体示例,丰富了对冲优化理论。
摘要:
本文提出了凸风险测度下具备变分偏好的理论对冲优化框架。我们探究了风险测度与效用函数复合形式的一般对偶表示,研究了该优化问题本身作为凸映射与单调映射的性质,推导了最优性与无差异定价条件的相关结果,还探索了该设定下的具体示例。
英文摘要:
We expose a theoretical hedging optimization framework with variational preferences under convex risk measures. We explore a general dual representation for the composition between risk measures and utilities. We study the properties of the optimization problem as a convex and monotone map per se. We also derive results for optimality and indifference pricing conditions. We also explore particular examples inside our setup.
机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。