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arXiv 2405.16636math.PRmath.OCq-fin.MF
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一种关于最优停止边界连续可微性的概率方法

A probabilistic approach to continuous differentiability of optimal stopping boundaries

University of Torino(都灵大学);Collegio Carlo Alberto(卡罗阿尔贝托学院);Univ. Gustave Eiffel(古斯塔夫埃菲尔大学);Univ. Paris Est Creteil(巴黎东克雷泰伊大学);CNRS(法国国家科学研究中心);Projet Mathrisk INRIA(INRIA数学风险项目)

Tiziano De Angelis, Damien Lamberton

AI总结:

研究最优停止问题中时变最优边界连续可微性,基于一维时变扩散等,用概率方法证明,条件更一般,还得到最优停止值函数与Stefan问题解联系的概率证明。

摘要:

我们获得了最优停止问题中与时间相关的最优边界连续可微性的首个概率证明。潜在的随机动力学是一维、时间非齐次扩散。收益函数也是时间非齐次的且不一定光滑。此外,我们纳入了依赖时间和状态的贴现率,时间范围可以是有限或无限的。我们的证明论据具有局部性质,这使我们能够在比偏微分方程文献中使用的条件更一般的条件下获得结果。作为我们主要结果的一个副产品,我们还获得了最优停止问题的值函数与Stefan问题的解之间联系的首个概率证明。

英文摘要:

We obtain the first probabilistic proof of continuous differentiability of time-dependent optimal boundaries in optimal stopping problems. The underlying stochastic dynamics is a one-dimensional, time-inhomogeneous diffusion. The gain function is also time-inhomogeneous and not necessarily smooth. Moreover, we include time-and-state-dependent discount rate and the time-horizon can be either finite or infinite. Our arguments of proof are of a local nature that allows us to obtain the result under more general conditions than those used in the PDE literature. As a byproduct of our main result we also obtain the first probabilistic proof of the link between the value function of an optimal stopping problem and the solution of the Stefan's problem.

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。

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