Risk sharing with deep neural networks

M. Burzoni, A. Doldi and E. Monzio Compagnoni

Quantitative Finance, 2024, vol. 24, issue 2, 233-252

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.

Date: 2024
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DOI: 10.1080/14697688.2024.2307493

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