Reinsurance Networks and Their Impact on Reinsurance Decisions: Theory and Empirical Evidence

TLDR

The study examines how reinsurance networks influence insurers’ decisions to purchase reinsurance. Using network theory, the authors build a framework linking reinsurer connectivity to loadings, contagion, and search costs, thereby defining insurers’ optimal network structure. Empirical analysis confirms an inverted‑U relationship between the proportion of reinsurance ceded and the number of reinsurers, shows that a linked network can be optimal ex ante despite contagion risks, and suggests similar dynamics in loan‑sale and OTC dealer networks.

Abstract

Abstract This article investigates the role of reinsurance networks in an insurer's reinsurance purchase decision. Drawing on network theory, we develop a framework that delineates how the pattern of linkages among reinsurers determines three reinsurance costs (loadings, contagion costs, and search and monitoring costs) and characterizes an insurer's optimal network structure. Consistent with empirical evidence based on longitudinal data from the U.S. property and casualty insurance industry, our model predicts an inverted U‐shaped relationship between the insurer's optimal percentage of reinsurance ceded and the number of its reinsurers. Moreover, we find that a linked network may be optimal ex ante even though linkages among reinsurers may spread financial contagion, supporting the model's prediction regarding social capital benefits associated with network cohesion. Our theoretical model and empirical results have implications for other networks such as loan sale market networks and over‐the‐counter dealer networks.