Risk Sharing with Deep Neural Networks

Matteo Burzoni; Alessandro Doldi; Enea Monzio Compagnoni

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arXiv · 2212.11752

Risk Sharing with Deep Neural Networks

Matteo Burzoni·Alessandro Doldi·Enea Monzio Compagnoni

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Abstract

We consider the problem of optimally sharing a financial position among agents with potentially different reference risk measures. The problem is equivalent to computing the infimal convolution of the risk metrics and finding the so-called optimal allocations. We propose a neural network-based framework to solve the problem and we prove the convergence of the approximated inf-convolution, as well as the approximated optimal allocations, to the corresponding theoretical values. We support our findings with several numerical experiments.

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Matteo Burzoni, Alessandro Doldi, Enea Monzio Compagnoni. 2022-12-22. Risk Sharing with Deep Neural Networks. https://arxiv.org/abs/2212.11752

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