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Authors:
Omar Andres Carmona Cortes
Andrew Rau-Chaplin
Keywords: Risk Analytics; Differential Evolution; Multi-objective; Parallel Computing.
Abstract:
In the reinsurance marketplace, the risk of financial loss in the event of natural catastrophes (such as earthquakes, hurricanes and floods) is exchanged between market participants for a premium. Here, prudent risk management takes the form of a hedge against the risk of a contingent uncertain loss in exchange for a payment. Reinsurance contracts that define the terms of the transfer are elaborated multi-layered financial treaties that represent complex trade-offs between expected return and risk. Formulating an effective risk transfer strategy depends on a careful multi-objective optimization process. In this paper, we study from the perspective of an insurance company the Reinsurance Contract Optimization problem in which, given the structure of a multi-layered reinsurance contract, we are required to discover specific contractual terms that capture the best trade-offs between expected return and risk for the insurer. Our approach is based on an adaptation of Multi-Objective Differential Evolution. In searching for the best mutation operators, we performed an experimental analysis on large-scale real problem instances using industrial datasets and evaluated five different mutation operators. Our experimental results indicate that those mutation operators based on selecting non-dominated individuals from the archive tend to produce better outcomes. Since speed is critical in this application, we also developed a parallel version achieving a speedup up to 9.3 on a 16 core machine.
Pages: 51 to 58
Copyright: Copyright (c) IARIA, 2015
Publication date: July 19, 2015
Published in: conference
ISSN: 2308-4464
ISBN: 978-1-61208-423-7
Location: Nice, France
Dates: from July 19, 2015 to July 24, 2015