In this paper, we propose a deep learning method to solve high-dimensional optimal multiple stopping problems. We represent the policies of multiple stopping problems by the composition of functions. Using the new representation, we approximate the optimal stopping policy recursively with simulation samples. We also derive lower and upper bounds and confidence intervals for the values. Finally, we apply the algorithm to the pricing of swing options, and it produces accurate results in high-dimensional problems.

中文翻译：

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一种新的多重停止深度神经网络算法在期权定价中的应用

在本文中，我们提出了一种深度学习方法来解决高维最优多重停止问题。我们通过函数的组合来表示多个停止问题的策略。使用新的表示，我们用模拟样本递归地逼近最优停止策略。我们还推导出这些值的下限和上限以及置信区间。最后，我们将该算法应用于波段期权的定价，它在高维问题上产生了准确的结果。