Cascading Losses in Reinsurance Networks

Ariah Klages-Mundt () and Andreea Minca ()
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Ariah Klages-Mundt: Center for Applied Mathematics, Cornell University, Ithaca, New York 14850
Andreea Minca: School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14850

Management Science, 2020, vol. 66, issue 9, 4246-4268

Abstract: We develop a model for contagion in reinsurance networks by which primary insurers’ losses are spread through the network. Our model handles general reinsurance contracts, such as typical excess of loss contracts. We show that simpler models existing in the literature—namely proportional reinsurance—greatly underestimate contagion risk. We characterize the fixed points of our model and develop efficient algorithms to compute contagion with guarantees on convergence and speed under conditions on network structure. We characterize exotic cases of problematic graph structure and nonlinearities, which cause network effects to dominate the overall payments in the system. Last, we apply our model to data on real-world reinsurance networks. Our simulations demonstrate the following. (1) Reinsurance networks face extreme sensitivity to parameters. A firm can be wildly uncertain about its losses even under small network uncertainty. (2) Our sensitivity results reveal a new incentive for firms to cooperate to prevent fraud, because even small cases of fraud can have outsized effect on the losses across the network. (3) Nonlinearities from excess of loss contracts obfuscate risks and can cause excess costs in a real-world system.

Keywords: simulation: applications; networks-graphs; financial institutions: insurance (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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