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arXiv 1109.5348math.OCcs.SYeess.SYmath.AP
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随机系数随机微分方程的 Dynkin 博弈及相关的倒向随机偏微分变分不等式

Dynkin Game of Stochastic Differential Equations with Random Coefficients, and Associated Backward Stochastic Partial Differential Variational Inequality

South China Normal University(华南师范大学);Fudan University(复旦大学);Ajou University(阿尤大学)

Shanjian Tang, Zhou Yang

AI总结:

研究随机系数随机微分方程的 Dynkin 博弈,通过推广 Krylov 估计和 Itô-Kunita-Wentzell 公式,证明 Nash 均衡点和博弈值由带两个障碍的倒向随机偏微分变分不等式(BSPDVI)的强解刻画,并得到强解的存在唯一性、比较定理及自由边界,最后将最优停止问题作为特例。

摘要:

考虑随机系数随机微分方程的 Dynkin 博弈。首先应用 Qiu 和 Tang 关于倒向随机偏微分方程的最大值原理,将 Markov 过程分布的 Krylov 估计推广到非 Markov 过程,并建立广义的 Itô-Kunita-Wentzell 公式,允许测试函数为取值于适当 Sobolev 空间的 Itô 型随机场。然后证明验证定理:Dynkin 博弈的 Nash 均衡点和博弈值由关联的 Hamilton-Jacobi-Bellman-Isaacs 方程的强解刻画,该方程目前是一个带两个障碍的倒向随机偏微分变分不等式(简称 BSPDVI)。得到了 BSPDVI 强解的存在唯一性结果和比较定理。此外,通过 BSPDVI 的比较定理研究 BSPDVI 强解的单调性,并定义自由边界。最后,将最优停止问题的对应部分作为特殊的 Dynkin 博弈进行识别。

英文摘要:

A Dynkin game is considered for stochastic differential equations with random coefficients. We first apply Qiu and Tang's maximum principle for backward stochastic partial differential equations to generalize Krylov estimate for the distribution of a Markov process to that of a non-Markov process, and establish a generalized Itô-Kunita-Wentzell's formula allowing the test function to be a random field of Itô's type which takes values in a suitable Sobolev space. We then prove the verification theorem that the Nash equilibrium point and the value of the Dynkin game are characterized by the strong solution of the associated Hamilton-Jacobi-Bellman-Isaacs equation, which is currently a backward stochastic partial differential variational inequality (BSPDVI, for short) with two obstacles. We obtain the existence and uniqueness result and a comparison theorem for strong solution of the BSPDVI. Moreover, we study the monotonicity on the strong solution of the BSPDVI by the comparison theorem for BSPDVI and define the free boundaries. Finally, we identify the counterparts for an optimal stopping time problem as a special Dynkin game.

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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