模型不确定性下的最优停时：随机停时方法
Optimal stopping under model uncertainty: randomized stopping times approach
摘要：
在这项工作中，我们考虑最优停时问题的条件凸风险措施称为优化确定性等价物。在不假设任何类型的时间一致性的风险措施的基本家庭，我们得到一个新的表示的解决方案的最优停时问题。特别地，我们将Rogers（2002）的加法对偶表示推广到不确定条件下的最优停时。最后，我们开发了几个蒙特卡罗算法，并说明其权力下的平均风险价值的最优停时。
英文摘要：
In this work we consider optimal stopping problems with conditional convex risk measures called optimised certainty equivalents. Without assuming any kind of time-consistency for the underlying family of risk measures, we derive a novel representation for the solution of the optimal stopping problem. In particular, we generalise the additive dual representation of Rogers (2002) to the case of optimal stopping under uncertainty. Finally, we develop several Monte Carlo algorithms and illustrate their power for optimal stopping under Average Value at Risk.
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