A new deep neural network algorithm for multiple stopping with applications in options pricing

被引：0
|
作者：
Han, Yuecai [1 ,2 ]
Li, Nan [1 ]
机构：
[1] Jilin Univ, Math Sch, Changchun 130012, Peoples R China
[2] Jilin Univ, Key Lab Symbol Computat Knowledge Engn, Minist Educ, Changchun, Peoples R China
来源：
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION | 2023年 / 117卷
关键词：
Multiple optimal stopping; Deep learning; Monte Carlo; Swing options; VALUATION;
D O I：
10.1016/j.cnsns.2022.106881
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
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.(c) 2022 Elsevier B.V. All rights reserved.
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页数：9
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