Risk sharing with deep neural networks
研究了不同风险偏好的代理人之间最优分担金融头寸的问题，提出基于神经网络的求解框架，并证明近似解收敛到理论值。
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.