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Deep Neural Network Algorithms for Parabolic PIDEs and Applications in Insurance Mathematics

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  • Rudiger Frey
  • Verena Kock

Abstract

In recent years a large literature on deep learning based methods for the numerical solution partial differential equations has emerged; results for integro-differential equations on the other hand are scarce. In this paper we study deep neural network algorithms for solving linear and semilinear parabolic partial integro-differential equations with boundary conditions in high dimension. To show the viability of our approach we discuss several case studies from insurance and finance.

Suggested Citation

  • Rudiger Frey & Verena Kock, 2021. "Deep Neural Network Algorithms for Parabolic PIDEs and Applications in Insurance Mathematics," Papers 2109.11403, arXiv.org, revised Sep 2021.
    • Handle: RePEc:arx:papers:2109.11403
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    References listed on IDEAS

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    Cited by:

    1. Alessandro Gnoatto & Marco Patacca & Athena Picarelli, 2022. "A deep solver for BSDEs with jumps," Papers 2211.04349, arXiv.org.

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