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Deep Neural Network Framework Based on Backward Stochastic Differential Equations for Pricing and Hedging American Options in High Dimensions

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  • Yangang Chen
  • Justin W. L. Wan

Abstract

We propose a deep neural network framework for computing prices and deltas of American options in high dimensions. The architecture of the framework is a sequence of neural networks, where each network learns the difference of the price functions between adjacent timesteps. We introduce the least squares residual of the associated backward stochastic differential equation as the loss function. Our proposed framework yields prices and deltas on the entire spacetime, not only at a given point. The computational cost of the proposed approach is quadratic in dimension, which addresses the curse of dimensionality issue that state-of-the-art approaches suffer. Our numerical simulations demonstrate these contributions, and show that the proposed neural network framework outperforms state-of-the-art approaches in high dimensions.

Suggested Citation

  • Yangang Chen & Justin W. L. Wan, 2019. "Deep Neural Network Framework Based on Backward Stochastic Differential Equations for Pricing and Hedging American Options in High Dimensions," Papers 1909.11532, arXiv.org.
    • Handle: RePEc:arx:papers:1909.11532
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    References listed on IDEAS

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    Cited by:

    1. Sebastian Becker & Patrick Cheridito & Arnulf Jentzen, 2020. "Pricing and Hedging American-Style Options with Deep Learning," JRFM, MDPI, vol. 13(7), pages 1-12, July.
    2. Timothy DeLise, 2021. "Neural Options Pricing," Papers 2105.13320, arXiv.org.
    3. Raj G. Patel & Chia-Wei Hsing & Serkan Sahin & Samuel Palmer & Saeed S. Jahromi & Shivam Sharma & Tomas Dominguez & Kris Tziritas & Christophe Michel & Vincent Porte & Mustafa Abid & Stephane Aubert &, 2022. "Quantum-Inspired Tensor Neural Networks for Option Pricing," Papers 2212.14076, arXiv.org, revised Mar 2024.
    4. Stefan Kremsner & Alexander Steinicke & Michaela Szolgyenyi, 2020. "A deep neural network algorithm for semilinear elliptic PDEs with applications in insurance mathematics," Papers 2010.15757, arXiv.org, revised Dec 2020.

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