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A gradient method for high-dimensional BSDEs

Author

Listed:
  • Gnameho Kossi

    (Essec, Cergy, France; and Department of Quantitative Economics, Maastricht University, Maastricht, Netherlands)

  • Stadje Mitja

    (Faculty of Mathematics and Economics, University of Ulm, Ulm, Germany)

  • Pelsser Antoon

    (Department of Quantitative Economics, Maastricht University, Maastricht, Netherlands)

Abstract

We develop a Monte Carlo method to solve backward stochastic differential equations (BSDEs) in high dimensions. The proposed algorithm is based on the regression-later approach using multivariate Hermite polynomials and their gradients. We propose numerical experiments to illustrate its performance.

Suggested Citation

  • Gnameho Kossi & Stadje Mitja & Pelsser Antoon, 2024. "A gradient method for high-dimensional BSDEs," Monte Carlo Methods and Applications, De Gruyter, vol. 30(2), pages 183-203.
    • Handle: RePEc:bpj:mcmeap:v:30:y:2024:i:2:p:183-203:n:1005
    DOI: 10.1515/mcma-2024-2002
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    References listed on IDEAS

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