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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 Liñán, Laura Lopeman, Madeleine de Armas, Jesica Franco, Guillermo Juan, Angel A. |
Others: | Universitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3) Guy Carpenter & Company, LLC Universitat Pompeu Fabra |
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 |
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. |
Keywords: | catastrophe bonds risk of natural hazards classification techniques earthquakes insurance |
DOI: | 10.2436/20.8080.02.64 |
Document type: | info:eu-repo/semantics/article |
Version: | info:eu-repo/semantics/publishedVersion |
Issue Date: | Jul-2017 |
Publication license: | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
Appears in Collections: | Articles cientÍfics Articles |
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File | Description | Size | Format | |
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41.2.7.calvet-etal.pdf | 419,79 kB | Adobe PDF | View/Open |
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