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Title: Statistical and machine learning approaches for the minimization of trigger errors in earthquake catastrophe bonds
Author: Calvet Liñan, Laura  
Lopeman, Madeleine
Armas Adrián, Jésica de
Franco, Guillermo
Juan Pérez, Ángel Alejandro
Others: Universitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3)
Guy Carpenter & Company, LLC
Universitat Pompeu Fabra
Keywords: catastrophe bonds
risk of natural hazards
classification techniques
Issue Date: Jul-2017
Publisher: SORT: Statistics and Operations Research Transactions
Citation: Calvet-Liñan, L., Lopeman, M., de Armas Adrián, J., Franco, G. & Juan, A.A. (2017). Statistical and machine learning approaches for the minimization of trigger errors in earthquake catastrophe bonds. SORT: Statistics and Operations Research Transactions, 41(2), 1-20. doi: 10.2436/20.8080.02.64
Project identifier: TRA2013-48180-C3-P
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Abstract: Catastrophe bonds are financial instruments designed to transfer risk of monetary losses arising from earthquakes, hurricanes, or floods to the capital markets. The insurance and reinsurance industry, governments, and private entities employ them frequently to obtain coverage. Parametric catastrophe bonds base their payments on physical features. For instance, given parameters such as magnitude of the earthquake and the location of its epicenter, the bond may pay a fixed amount or not pay at all. This paper reviews statistical and machine learning techniques for designing trigger mechanisms and includes a computational experiment. Several lines of future research are discussed.
Language: English
ISSN: 1696-2281MIAR
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