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Ruin Probabilities for Risk Processes in Stochastic Networks

Author

Listed:
  • Hamed Amini

    (UF - University of Florida [Gainesville])

  • Zhongyuan Cao

    (MATHRISK - Mathematical Risk Handling - UPEM - Université Paris-Est Marne-la-Vallée - ENPC - École des Ponts ParisTech - Inria de Paris - Inria - Institut National de Recherche en Informatique et en Automatique)

  • Andreea Minca

    (ORIE - School of Operations Research and Information Engineering - Cornell University [New York])

  • Agnes Sulem

    (MATHRISK - Mathematical Risk Handling - UPEM - Université Paris-Est Marne-la-Vallée - ENPC - École des Ponts ParisTech - Inria de Paris - Inria - Institut National de Recherche en Informatique et en Automatique)

Abstract

We study multidimensional Cram\'er-Lundberg risk processes where agents, located on a large sparse network, receive losses form their neighbors. To reduce the dimensionality of the problem, we introduce classification of agents according to an arbitrary countable set of types. The ruin of any agent triggers losses for all of its neighbours. We consider the case when the loss arrival process induced by the ensemble of ruined agents follows a Poisson process with general intensity function that scales with the network size. When the size of the network goes to infinity, we provide explicit ruin probabilities at the end of the loss propagation process for agents of any type. These limiting probabilities depend, in addition to the agents' types and the network structure, on the loss distribution and the loss arrival process. For a more complex risk processes on open networks, when in addition to the internal networked risk processes the agents receive losses from external users, we provide bounds on ruin probabilities.

Suggested Citation

  • Hamed Amini & Zhongyuan Cao & Andreea Minca & Agnes Sulem, 2024. "Ruin Probabilities for Risk Processes in Stochastic Networks," Working Papers hal-04393897, HAL.
  • Handle: RePEc:hal:wpaper:hal-04393897
    DOI: 10.48550/arXiv.2302.06668
    as

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