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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Veröffentlicht in:Communications in nonlinear science & numerical simulation Jg. 117; S. 106881
Hauptverfasser: Han, Yuecai, Li, Nan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.02.2023
Schlagworte:
Deep learning
Monte Carlo
Multiple optimal stopping
Swing options
Deep learning
Multiple optimal stopping
Swing options
Monte Carlo
ISSN:1007-5704, 1878-7274
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Zusammenfassung: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