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arXiv 2212.11752q-fin.RMmath.PR
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基于深度神经网络的风险分担

Risk Sharing with Deep Neural Networks

Matteo Burzoni, Alessandro Doldi, Enea Monzio Compagnoni

AI总结:

针对不同风险度量下的金融头寸最优分担问题,提出基于神经网络的求解框架,证明近似解收敛于理论值,并通过数值实验验证。

摘要:

我们研究在参考风险度量可能不同的智能体之间最优分担金融头寸的问题。该问题等价于计算风险度量的下确界卷积,并找到所谓的最优配置。我们提出了一种基于神经网络的框架来解决该问题,并证明了近似下确界卷积以及近似最优配置收敛于相应的理论值。我们通过若干数值实验验证了研究结果。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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