Deep Neural Network Algorithms for Parabolic PIDEs and Applications in Insurance Mathematics
Numerical Analysis
2021-09-27 v2 Numerical Analysis
Probability
Computational Finance
Machine Learning
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.
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Cite
@article{arxiv.2109.11403,
title = {Deep Neural Network Algorithms for Parabolic PIDEs and Applications in Insurance Mathematics},
author = {Rüdiger Frey and Verena Köck},
journal= {arXiv preprint arXiv:2109.11403},
year = {2021}
}
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24 pages