Risk Sharing with Deep Neural Networks
Risk Management
2023-06-21 v2 Probability
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.
Keywords
Cite
@article{arxiv.2212.11752,
title = {Risk Sharing with Deep Neural Networks},
author = {Matteo Burzoni and Alessandro Doldi and Enea Monzio Compagnoni},
journal= {arXiv preprint arXiv:2212.11752},
year = {2023}
}