This paper proposes a deep learning approach for solving optimal stopping problems and high-dimensional American-style options pricing problems. Through state-space partition, the method does not require recalculation of the structure of networks when the price of the asset changes, which makes tracking valuation more efficient. This paper also offers theoretical proof for the existence of a deep learning network that can determine the optimal stopping time via state-space partition. We present convergence proofs for the estimators and also test the method on Bermuda max-call options as examples.

中文翻译：

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一种通过状态空间划分对高维美式期权进行定价的深度学习方法

本文提出了一种深度学习方法来解决最优停止问题和高维美式期权定价问题。通过状态空间划分，该方法在资产价格变化时不需要重新计算网络结构，这使得跟踪估值更加高效。本文还为深度学习网络的存在提供了理论证明，该网络可以通过状态空间划分来确定最佳停止时间。我们提供了估计器的收敛证明，并以百慕大最大看涨期权为例测试了该方法。