论文标题
深度组合优化，以实现最佳停止时间问题：应用秋千选项定价
Deep combinatorial optimisation for optimal stopping time problems : application to swing options pricing
论文作者
论文摘要
提出了一种基于神经网络的随机控制的新方法，并提出了使用离散随机变量的随机化，并应用于最佳停止时间问题。该方法直接模拟策略，不需要动态编程原理或后退随机微分方程的推导。与连续优化直接使用自动分化的连续优化不同，我们提出了一种梯度计算的似然比方法。关于美国和秋千选项的定价的数值测试。所提出的算法成功地在合理的计算时间内定价了高维的美国和摇摆选项，这是经典算法不可能的。
A new method for stochastic control based on neural networks and using randomisation of discrete random variables is proposed and applied to optimal stopping time problems. The method models directly the policy and does not need the derivation of a dynamic programming principle nor a backward stochastic differential equation. Unlike continuous optimization where automatic differentiation is used directly, we propose a likelihood ratio method for gradient computation. Numerical tests are done on the pricing of American and swing options. The proposed algorithm succeeds in pricing high dimensional American and swing options in a reasonable computation time, which is not possible with classical algorithms.
