Efficient Model Points Selection in Insurance by Parallel Global Optimization Using Multi CPU and Multi GPU
| dc.contributor.author | Ferreiro-Ferreiro, Ana Maria | |
| dc.contributor.author | GarcÌa-RodrÌguez, JosÈ Antonio | |
| dc.contributor.author | Souto, Luis A. | |
| dc.contributor.author | V·°zquez, Carlos | |
| dc.date.accessioned | 2020-02-21T10:07:59Z | |
| dc.date.available | 2020-02-21T10:07:59Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | In the insurance sector, Asset Liability Management refers to the joint management of the assets and liabilities of a company. The liabilities mainly consist of the policies portfolios of the insurance company, which usually contain a large amount of policies. In the article, the authors mainly develop a highly efficient automatic generation of model points portfolios to represent much larger real policies portfolios. The obtained model points portfolio must retain the market risk properties of the initial portfolio. For this purpose, the authors propose a risk measure that incorporates the uncertain evolution of interest rates to the portfolios of life insurance policies, following Ferri (Optimal model points portfolio in life, 2019, arXiv:1808.00866 ). This problem can be formulated as a minimization problem that has to be solved using global numerical optimization algorithms. The cost functional measures an appropriate distance between the original and the model point portfolios. In order to solve this problem in a reasonable computing time, sequential implementations become prohibitive, so that the authors speed up the computations by developing a high performance computing framework that uses hybrid architectures, which consist of multi CPUs together with accelerators (multi GPUs). Thus, in graphic processor units (GPUs) the evaluation of the cost function is parallelized, which requires a Monte Carlo method. For the optimization problem, the authors compare a metaheuristic stochastic differential evolution algorithm with a multi path variant of hybrid global optimization Basin Hopping algorithms, which combines Simulated Annealing with gradient local searchers (Ferreiro et al. in Appl Math Comput 356:282-298, 2019a). Both global optimizers are parallelized in a multi CPU together with a multi GPU setting. | de |
| dc.identifier.doi | 10.1007/s12599-019-00626-y | |
| dc.identifier.pissn | 1867-0202 | |
| dc.identifier.uri | http://dx.doi.org/10.1007/s12599-019-00626-y | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/31858 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | Business & Information Systems Engineering: Vol. 62, No. 1 | |
| dc.relation.ispartofseries | Business & Information Systems Engineering | |
| dc.subject | Basin hopping | |
| dc.subject | Differential evolution | |
| dc.subject | HPC | |
| dc.subject | Hybrid optimization algorithms | |
| dc.subject | Model points portfolio | |
| dc.subject | Monte Carlo simulation | |
| dc.subject | Multi CPU | |
| dc.subject | Multi GPU | |
| dc.subject | Risk functional | |
| dc.title | Efficient Model Points Selection in Insurance by Parallel Global Optimization Using Multi CPU and Multi GPU | de |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 20 | |
| gi.citation.startPage | 5 |