Computing the Gerber-Shiu function with interest and a constant dividend barrier by physics-informed neural networks

Zan Yu and Lianzeng Zhang

Papers from arXiv.org

Abstract: In this paper, we propose a new efficient method for calculating the Gerber-Shiu discounted penalty function. Generally, the Gerber-Shiu function usually satisfies a class of integro-differential equation. We introduce the physics-informed neural networks (PINN) which embed a differential equation into the loss of the neural network using automatic differentiation. In addition, PINN is more free to set boundary conditions and does not rely on the determination of the initial value. This gives us an idea to calculate more general Gerber-Shiu functions. Numerical examples are provided to illustrate the very good performance of our approximation.

Date: 2024-01
New Economics Papers: this item is included in nep-big, nep-cmp and nep-net
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