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

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...

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Bibliographic Details
Published in:	Communications in nonlinear science & numerical simulation Vol. 117; p. 106881
Main Authors:	Han, Yuecai, Li, Nan
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.02.2023
Subjects:	Deep learning Monte Carlo Multiple optimal stopping Swing options Deep learning Multiple optimal stopping Swing options Monte Carlo
ISSN:	1007-5704, 1878-7274
Online Access:	Get full text
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Summary:	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. •A deep learning-based algorithm for optimal multiple stopping problems is introduced.•Lower bounds and upper bounds for the optimal value are constructed.•Applications to high-dimensional swing options are considered.
ISSN:	1007-5704 1878-7274
DOI:	10.1016/j.cnsns.2022.106881