Revistes Publicacions URV: SORT - Statistics and Operations Research Transactions> 2017

Statistical and machine learning approaches for the minimization of trigger errors in parametric earthquake catastrophe bonds

  • Datos identificativos

    Identificador: RP:2467
    Handle: http://hdl.handle.net/20.500.11797/RP2467
  • Autores:

    Juan, Angel A.
    Franco, Guillermo
    de Armas, Jésica
    Lopeman, Madeleine
    Calvet, Laura
  • Otros:

    Autor/es de la URV: Juan, Angel A. Franco, Guillermo de Armas, Jésica Lopeman, Madeleine Calvet, Laura
    Palabras clave: Catastrophe bonds, risk of natural hazards, classification techniques, earthquakes, insurance
    Resumen: 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 epicentre, 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.
    Año de publicación de la revista: 2017
    Tipo de publicación: info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article
  • Palabras clave:

    Catastrophe bonds, risk of natural hazards, classification techniques, earthquakes, insurance
  • Documentos:

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