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Risk Sharing with Deep Neural Networks

Author

Listed:
  • Matteo Burzoni
  • Alessandro Doldi
  • Enea Monzio Compagnoni

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.

Suggested Citation

  • Matteo Burzoni & Alessandro Doldi & Enea Monzio Compagnoni, 2022. "Risk Sharing with Deep Neural Networks," Papers 2212.11752, arXiv.org, revised Jun 2023.
    • Handle: RePEc:arx:papers:2212.11752
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    References listed on IDEAS

    as
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