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Deep learning algorithms for solving high dimensional nonlinear backward stochastic differential equations

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  • Lorenc Kapllani
  • Long Teng

Abstract

In this work, we propose a new deep learning-based scheme for solving high dimensional nonlinear backward stochastic differential equations (BSDEs). The idea is to reformulate the problem as a global optimization, where the local loss functions are included. Essentially, we approximate the unknown solution of a BSDE using a deep neural network and its gradient with automatic differentiation. The approximations are performed by globally minimizing the quadratic local loss function defined at each time step, which always includes the terminal condition. This kind of loss functions are obtained by iterating the Euler discretization of the time integrals with the terminal condition. Our formulation can prompt the stochastic gradient descent algorithm not only to take the accuracy at each time layer into account, but also converge to a good local minima. In order to demonstrate performances of our algorithm, several high-dimensional nonlinear BSDEs including pricing problems in finance are provided.

Suggested Citation

  • Lorenc Kapllani & Long Teng, 2020. "Deep learning algorithms for solving high dimensional nonlinear backward stochastic differential equations," Papers 2010.01319, arXiv.org, revised Jun 2022.
    • Handle: RePEc:arx:papers:2010.01319
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    References listed on IDEAS

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    1. repec:dau:papers:123456789/5524 is not listed on IDEAS
    2. Anne Eyraud-Loisel, 2005. "Backward stochastic differential equations with enlarged filtration: Option hedging of an insider trader in a financial market with jumps," Post-Print hal-01298905, HAL.
    3. Bouchard, Bruno & Touzi, Nizar, 2004. "Discrete-time approximation and Monte-Carlo simulation of backward stochastic differential equations," Stochastic Processes and their Applications, Elsevier, vol. 111(2), pages 175-206, June.
    4. N. El Karoui & S. Peng & M. C. Quenez, 1997. "Backward Stochastic Differential Equations in Finance," Mathematical Finance, Wiley Blackwell, vol. 7(1), pages 1-71, January.
    5. Stefan Ankirchner & Christophette Blanchet-Scalliet & Anne Eyraud-Loisel, 2010. "CREDIT RISK PREMIA AND QUADRATIC BSDEs WITH A SINGLE JUMP," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 13(07), pages 1103-1129.
    6. Bergman, Yaacov Z, 1995. "Option Pricing with Differential Interest Rates," The Review of Financial Studies, Society for Financial Studies, vol. 8(2), pages 475-500.
    7. Céline Labart & Jérôme Lelong, 2011. "A Parallel Algorithm for solving BSDEs - Application to the pricing and hedging of American options," Working Papers hal-00567729, HAL.
    8. Eyraud-Loisel, Anne, 2005. "Backward stochastic differential equations with enlarged filtration: Option hedging of an insider trader in a financial market with jumps," Stochastic Processes and their Applications, Elsevier, vol. 115(11), pages 1745-1763, November.
    9. Stefan Ankirchner & Christophette Blanchet-Scalliet & Anne Eyraud-Loisel, 2009. "Credit risk premia and quadratic BSDEs with a single jump," Papers 0907.1221, arXiv.org, revised Jun 2010.
    10. Du, Kai & Zhang, Qi, 2013. "Semi-linear degenerate backward stochastic partial differential equations and associated forward–backward stochastic differential equations," Stochastic Processes and their Applications, Elsevier, vol. 123(5), pages 1616-1637.
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