Pricing and optimization of sidecar and collateralized reinsurance portfolios with stochastic programming

Nick Georgiopoulos

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The paper demonstrates how to use optimization methods in insurance risks and handles practical computational aspects of the optimization.
A novel method of approximating a compound Poisson process is put forward as well as a method to price and optimize portfolios of insurance transfers.
The methods try to solve computational problems and provide efficient calculations. Risk-return trade-offs are analyzed and stability of results.

Abstract

In this paper we study the problems of pricing and optimizing sidecar and collateralized reinsurance portfolios. The academic literature on sidecar portfolio optimization that takes into account the institutional aspects of the creation of a sidecar is somewhat limited. These aspects include the full collateralization of risk and the separation of the sponsor of risks and the sidecar, which requires the creation of a special purpose insurer. We employ a stochastic programming approach to solve these problems, which has advantages over other methods and captures the underlying institutional structure of the sidecar. We detail the method and numerical results for compound Poisson-distributed risks, showing how optimal portfolios are built by maximizing return while controlling for tail risk.

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