Data Compression Algorithms, Marine Liability Modeling, and Hierarchical Risk Aggregation in Reinsurance

Détails

Ressource 1 Sous embargo jusqu'au 01/02/2020.
Etat: Public
Version: Après imprimatur
Licence: Non spécifiée
ID Serval
serval:BIB_14E97DA0A4D8
Type
Thèse: thèse de doctorat.
Collection
Publications
Titre
Data Compression Algorithms, Marine Liability Modeling, and Hierarchical Risk Aggregation in Reinsurance
Auteur(s)
Guevara Alarcón William Miguel
Directeur(s)
Albrecher Hansjörg
Détails de l'institution
Université de Lausanne, Faculté des hautes études commerciales
Statut éditorial
Acceptée
Date de publication
20/05/2019
Langue
anglais
Résumé
Global reinsurers are companies that operate worldwide providing protection for the most extreme and complicated risks. Therefore, these companies continuously face new challenges. Particularly, the constant increase in computer processing power and available digital information requires mechanisms to efficiently store and transfer the influx of data. Technological advances produce an increasing accumulation of risks, for example, bigger construction projects, taller skyscrapers or longer vessels. The aggregate risk of global reinsurers must be estimated in a precise and reliable manner, considering appropriately the dependencies between different portfolios, to allow for adequate risk management. The present thesis discusses models and methodologies related to these particular challenges. Firstly, algorithms to reduce the amount of information from large data sets, while keeping the shape of their distributions and controlling the approximation error, are developed. Secondly, we focus on two issues related to the marine liability insurance market. The expected number and cost of a set of large claims is analyzed and modeled. Moreover, the optimal participation percentages in reinsurance contracts under restrictions in the total accumulation of risk are studied. Finally, the precision and variability of the simulations of a hierarchical risk aggregation dependency model under different methodologies are examined.
Création de la notice
22/05/2019 20:04
Dernière modification de la notice
20/08/2019 13:43
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