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http://hdl.handle.net/10609/78828
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 earthquakes insurance |
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 TRA2015-71883-REDT 2016-1-ES01-KA108-023465 |
Also see: | https://doi.org/10.2436/20.8080.02.64 |
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 |
URI: | http://hdl.handle.net/10609/78828 |
ISSN: | 1696-2281MIAR |
Appears in Collections: | Articles Articles |


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