英文标题：
《A Backward Simulation Method for Stochastic Optimal Control Problems》
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作者：
Zhiyi Shen and Chengguo Weng
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最新提交年份：
2019
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英文摘要：
A number of optimal decision problems with uncertainty can be formulated into a stochastic optimal control framework. The Least-Squares Monte Carlo (LSMC) algorithm is a popular numerical method to approach solutions of such stochastic control problems as analytical solutions are not tractable in general. This paper generalizes the LSMC algorithm proposed in Shen and Weng (2017) to solve a wide class of stochastic optimal control models. Our algorithm has three pillars: a construction of auxiliary stochastic control model, an artificial simulation of the post-action value of state process, and a shape-preserving sieve estimation method which equip the algorithm with a number of merits including bypassing forward simulation and control randomization, evading extrapolating the value function, and alleviating computational burden of the tuning parameter selection. The efficacy of the algorithm is corroborated by an application to pricing equity-linked insurance products.
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中文摘要：
许多具有不确定性的最优决策问题可以被表述为一个随机最优控制框架。最小二乘蒙特卡罗（LSMC）算法是一种常用的数值方法，用于逼近解析解通常不可处理的随机控制问题的解。本文推广了Shen和Weng（2017）提出的LSMC算法，以解决一类广泛的随机最优控制模型。我们的算法有三大支柱：辅助随机控制模型的构建、状态过程后作用值的人工模拟和保形筛估计方法，该方法使算法具有绕过前向模拟和控制随机化、避免值函数外推、，以及减轻调谐参数选择的计算负担。该算法的有效性得到了股票挂钩保险产品定价应用的验证。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Computational Finance 计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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