Yijia Lin is in the Department of Finance, College of Business Administration, University of Nebraska–Lincoln, P.O. Box 880488, Lincoln, NE 68588. Jifeng Yu is in the Department of Management, College of Business Administration, University of Nebraska–Lincoln. Manferd O. Peterson is in the Department of Finance, College of Business Administration, University of Nebraska–Lincoln. Lin can be contacted via e-mail: firstname.lastname@example.org. This article was presented at the American Risk and Insurance Association Annual Meeting in Washington, DC in August 2013. We appreciate helpful comments from Charles Nyce and the participants at the meeting. The authors also thank the two anonymous referees for their very helpful suggestions and comments during the revision process.
Reinsurance Networks and Their Impact on Reinsurance Decisions: Theory and Empirical Evidence
Article first published online: 18 MAR 2014
© The Journal of Risk and Insurance
Journal of Risk and Insurance
How to Cite
Lin, Y., Yu, J. and Peterson, M. O. (2014), Reinsurance Networks and Their Impact on Reinsurance Decisions: Theory and Empirical Evidence. Journal of Risk and Insurance. doi: 10.1111/jori.12032
- Article first published online: 18 MAR 2014
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.