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Risk sharing with deep neural networks

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Listed:
  • M. Burzoni
  • A. Doldi
  • E. 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

  • M. Burzoni & A. Doldi & E. Monzio Compagnoni, 2024. "Risk sharing with deep neural networks," Quantitative Finance, Taylor & Francis Journals, vol. 24(2), pages 233-252, February.
    • Handle: RePEc:taf:quantf:v:24:y:2024:i:2:p:233-252
    DOI: 10.1080/14697688.2024.2307493
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