Combining Simulation with a Biased-Randomized Heuristic to Develop Parametric Bonds for Insurance Coverage against Earthquakes

The social and economic impact of natural catastrophes on communities is a concern for many governments and corporations across the globe. A class of financial instruments, parametric hedges, is emerging in the (re)insurance market as a promising approach to close the protection gap related to natur...

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Bibliographic Details
Published inProceedings - Winter Simulation Conference pp. 1328 - 1329
Main Authors Bayliss, Christopher, Estrada-Moreno, Alejandro, Juan, Angel A., Franco, Guillermo, Guidotti, Roberto
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2019
Subjects
Analytical models
Computational modeling
Earthquakes
Heuristic algorithms
Insurance
Monte Carlo methods
Optimization
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ISSN1558-4305
DOI10.1109/WSC40007.2019.9004955

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Summary:The social and economic impact of natural catastrophes on communities is a concern for many governments and corporations across the globe. A class of financial instruments, parametric hedges, is emerging in the (re)insurance market as a promising approach to close the protection gap related to natural hazards. This paper focuses on the design of such parametric hedges, which have the objective of maximizing the risk transferred subject to a budget constraint. With Greece as a case study, one of the most seismic prone European regions, with limited seismic insurance penetration, this paper proposes a biased-randomized algorithm to solve the optimization problem. The algorithm hybridizes Monte Carlo simulation with a heuristic to generate a variety of solutions. A simulation stage allows for analyzing the payout distribution of each solution. Results show the impact of the problem resolution on the transferred risk and on the distribution of triggered payments.
ISSN:1558-4305
DOI:10.1109/WSC40007.2019.9004955