论文标题
风险与深层神经网络共享
Risk Sharing with Deep Neural Networks
论文作者
论文摘要
我们考虑到具有可能不同参考风险措施的代理商之间最佳共享财务状况的问题。问题等同于计算风险指标的虚拟卷积并找到所谓的最佳分配。我们提出了一个基于神经网络的框架来解决该问题，并证明了近似INF卷积的收敛以及近似的最佳分配，以对相应的理论值。我们通过几个数值实验来支持我们的发现。
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.
