Bayliss, Christopher ORCID: 0000-0003-0031-5937, Serra, Marti, Nieto, Armando and Juan, Angel A
(2020)
Combining a Matheuristic with Simulation for Risk Management of Stochastic Assets and Liabilities.
RISKS, 8 (4).
p. 131.
Text
2020_Bayliss___SimMatheuristic_for_AL_Risk_Management (1).pdf - Author Accepted Manuscript Download (355kB) | Preview |
Abstract
<jats:p>Specially in the case of scenarios under uncertainty, the efficient management of risk when matching assets and liabilities is a relevant issue for most insurance companies. This paper considers such a scenario, where different assets can be aggregated to better match a liability (or the other way around), and the goal is to find the asset-liability assignments that maximises the overall benefit over a time horizon. To solve this stochastic optimisation problem, a simulation-optimisation methodology is proposed. We use integer programming to generate efficient asset-to-liability assignments, and Monte-Carlo simulation is employed to estimate the risk of failing to pay due liabilities. The simulation results allow us to set a safety margin parameter for the integer program, which encourage the generation of solutions satisfying a minimum reliability threshold. A series of computational experiments contribute to illustrate the proposed methodology and its utility in practical risk management.</jats:p>
Item Type: | Article |
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Uncontrolled Keywords: | assets and liabilities management, risk management, uncertainty, matheuristics, simulation |
Depositing User: | Symplectic Admin |
Date Deposited: | 14 Dec 2020 16:05 |
Last Modified: | 17 Mar 2024 10:50 |
DOI: | 10.3390/risks8040131 |
Open Access URL: | https://doi.org/10.3390/risks8040131 |
Related URLs: | |
URI: | https://livrepository.liverpool.ac.uk/id/eprint/3110361 |