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

Laura Calvet ^[1] ; Madeleine Lopeman ; Jésica de Armas ^[2] ; Guillermo Franco ; Angel A. Juan ^[1]
1. [1] Universitat Oberta de Catalunya
  
  Universitat Oberta de Catalunya
  
  Barcelona, España
2. [2] Universitat Pompeu Fabra
  
  Universitat Pompeu Fabra
  
  Barcelona, España
Localización: Sort: Statistics and Operations Research Transactions, ISSN 1696-2281, Vol. 41, Nº. 2, 2017, págs. 373-392
Idioma: inglés
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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.