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Global Optimization for Automatic Model Points Selection in Life Insurance Portfolios
dc.contributor.author | Ferreiro, Ana M. | |
dc.contributor.author | Ferri, Enrico | |
dc.contributor.author | García Rodríguez, José Antonio | |
dc.contributor.author | Vázquez, Carlos | |
dc.date.accessioned | 2021-04-13T15:30:02Z | |
dc.date.available | 2021-04-13T15:30:02Z | |
dc.date.issued | 2021-02-25 | |
dc.identifier.citation | Ferreiro, A.M.; Ferri, E.; García, J.A.; Vázquez, C. Global Optimization for Automatic Model Points Selection in Life Insurance Portfolios. Mathematics 2021, 9, 472. https://doi.org/10.3390/math9050472 | es_ES |
dc.identifier.issn | 2227-7390 | |
dc.identifier.uri | http://hdl.handle.net/2183/27738 | |
dc.description.abstract | [Abstract] Starting from an original portfolio of life insurance policies, in this article we propose a methodology to select model points portfolios that reproduce the original one, preserving its market risk under a certain measure. In order to achieve this goal, we first define an appropriate risk functional that measures the market risk associated to the interest rates evolution. Although other alternative interest rate models could be considered, we have chosen the LIBOR (London Interbank Offered Rate) market model. Once we have selected the proper risk functional, the problem of finding the model points of the replicating portfolio is formulated as a problem of minimizing the distance between the original and the target model points portfolios, under the measure given by the proposed risk functional. In this way, a high-dimensional global optimization problem arises and a suitable hybrid global optimization algorithm is proposed for the efficient solution of this problem. Some examples illustrate the performance of a parallel multi-CPU implementation for the evaluation of the risk functional, as well as the efficiency of the hybrid Basin Hopping optimization algorithm to obtain the model points portfolio. | es_ES |
dc.description.sponsorship | This research has been partially funded by EU H2020 MSCA-ITN-EID-2014 (WAKEUPCALL Grant Agreement 643045), Spanish MINECO (Grant MTM2016-76497-R) and by Galician Government with the grant ED431C2018/033, both including FEDER financial support. A.F., J.G. and C.V. also acknowledge the support received from the Centro de Investigación de Galicia “CITIC”, funded by Xunta de Galicia and the European Union (European Regional Development Fund- Galicia 2014-2020 Program), by grant ED431G 2019/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C2018/03 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/643045 | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2016-76497-R/ES/METODOS MATEMATICOS Y SIMULACION NUMERICA PARA RETOS EN FINANZAS CUANTITATIVAS, MEDIOAMBIENTE, BIOTECNOLOGIA Y EFICIENCIA INDUSTRIAL | |
dc.relation.uri | https://doi.org/10.3390/math9050472 | es_ES |
dc.rights | Atribución 4.0 Internacional (CC BY 4.0) | es_ES |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Model points portfolio | es_ES |
dc.subject | Risk management | es_ES |
dc.subject | Risk functional | es_ES |
dc.subject | Hybrid optimization algorithms | es_ES |
dc.subject | LIBOR market model | es_ES |
dc.subject | Monte Carlo simulation | es_ES |
dc.title | Global Optimization for Automatic Model Points Selection in Life Insurance Portfolios | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Mathematics | es_ES |
UDC.volume | 9 | es_ES |
UDC.issue | 5 | es_ES |
UDC.startPage | 472 | es_ES |
dc.identifier.doi | 10.3390/math9050472 |
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