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Backward Deep BSDE Methods and Applications to Nonlinear Problems

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  • Yajie Yu
  • Bernhard Hientzsch
  • Narayan Ganesan

Abstract

In this paper, we present a backward deep BSDE method applied to Forward Backward Stochastic Differential Equations (FBSDE) with given terminal condition at maturity that time-steps the BSDE backwards. We present an application of this method to a nonlinear pricing problem - the differential rates problem. To time-step the BSDE backward, one needs to solve a nonlinear problem. For the differential rates problem, we derive an exact solution of this time-step problem and a Taylor-based approximation. Previously backward deep BSDE methods only treated zero or linear generators. While a Taylor approach for nonlinear generators was previously mentioned, it had not been implemented or applied, while we apply our method to nonlinear generators and derive details and present results. Likewise, previously backward deep BSDE methods were presented for fixed initial risk factor values $X_0$ only, while we present a version with random $X_0$ and a version that learns portfolio values at intermediate times as well. The method is able to solve nonlinear FBSDE problems in high dimensions.

Suggested Citation

  • Yajie Yu & Bernhard Hientzsch & Narayan Ganesan, 2020. "Backward Deep BSDE Methods and Applications to Nonlinear Problems," Papers 2006.07635, arXiv.org.
    • Handle: RePEc:arx:papers:2006.07635
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    File URL: http://arxiv.org/pdf/2006.07635
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    References listed on IDEAS

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    1. Bernhard Hientzsch, 2019. "Introduction to Solving Quant Finance Problems with Time-Stepped FBSDE and Deep Learning," Papers 1911.12231, arXiv.org.
    2. Jian Liang & Zhe Xu & Peter Li, 2019. "Deep Learning-Based Least Square Forward-Backward Stochastic Differential Equation Solver for High-Dimensional Derivative Pricing," Papers 1907.10578, arXiv.org, revised Oct 2020.
    3. Narayan Ganesan & Yajie Yu & Bernhard Hientzsch, 2020. "Pricing Barrier Options with DeepBSDEs," Papers 2005.10966, arXiv.org, revised Sep 2024.
    4. Warin Xavier, 2018. "Nesting Monte Carlo for high-dimensional non-linear PDEs," Monte Carlo Methods and Applications, De Gruyter, vol. 24(4), pages 225-247, December.
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    Cited by:

    1. Qi Feng & Man Luo & Zhaoyu Zhang, 2021. "Deep Signature FBSDE Algorithm," Papers 2108.10504, arXiv.org, revised Aug 2022.

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