Global Optimization for Automatic Model Points Selection in Life Insurance Portfolios
Use este enlace para citar
http://hdl.handle.net/2183/27738
A non ser que se indique outra cousa, a licenza do ítem descríbese como Atribución 4.0 Internacional (CC BY 4.0)
Coleccións
- GI-M2NICA - Artigos [56]
- OpenAIRE [266]
Metadatos
Mostrar o rexistro completo do ítemTítulo
Global Optimization for Automatic Model Points Selection in Life Insurance PortfoliosData
2021-02-25Cita bibliográfica
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
Resumo
[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.
Palabras chave
Model points portfolio
Risk management
Risk functional
Hybrid optimization algorithms
LIBOR market model
Monte Carlo simulation
Risk management
Risk functional
Hybrid optimization algorithms
LIBOR market model
Monte Carlo simulation
Versión do editor
Dereitos
Atribución 4.0 Internacional (CC BY 4.0)
ISSN
2227-7390