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arXiv 0902.2518math.OCmath.PR
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部分信息下最优停时的一种模拟方法

A Simulation Approach to Optimal Stopping Under Partial Information

Mike Ludkovski

摘要:

研究了非线性部分可观测最优停止问题的数值解。将系统状态看作是一个多维扩散,并驱动观测过程的漂移,这是另一个具有相关噪声的多维扩散。这种模型中的控制器是不完全知道她的环境是感兴趣的应用概率和金融数学。提出了一种基于粒子滤波和回归蒙特卡罗方法的近似数值算法。该算法保持了一个连续的状态空间,并产生一个综合的方法来过滤和控制子问题。我们的方法是完全基于模拟的,因此允许一个强大的实现方面的模型规范。我们进行了误差分析,我们的计划,并说明了几个计算实例。离散观测的随机波动率模型的扩展也被认为是。

英文摘要:

We study the numerical solution of nonlinear partially observed optimal stopping problems. The system state is taken to be a multi-dimensional diffusion and drives the drift of the observation process, which is another multi-dimensional diffusion with correlated noise. Such models where the controller is not fully aware of her environment are of interest in applied probability and financial mathematics. We propose a new approximate numerical algorithm based on the particle filtering and regression Monte Carlo methods. The algorithm maintains a continuous state-space and yields an integrated approach to the filtering and control sub-problems. Our approach is entirely simulation-based and therefore allows for a robust implementation with respect to model specification. We carry out the error analysis of our scheme and illustrate with several computational examples. An extension to discretely observed stochastic volatility models is also considered.

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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