A deep neural network algorithm for semilinear elliptic PDEs with applications in insurance mathematics

Stefan Kremsner, Alexander Steinicke and Michaela Sz\"olgyenyi

Papers from arXiv.org

Abstract: In insurance mathematics optimal control problems over an infinite time horizon arise when computing risk measures. Their solutions correspond to solutions of deterministic semilinear (degenerate) elliptic partial differential equations. In this paper we propose a deep neural network algorithm for solving such partial differential equations in high dimensions. The algorithm is based on the correspondence of elliptic partial differential equations to backward stochastic differential equations with random terminal time.

Date: 2020-10, Revised 2020-12
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ias and nep-rmg
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Citations: View citations in EconPapers (9)

Published in Risks, 8(4):136, 2020

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2010.15757

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