Artikel
Combining a matheuristic with simulation for risk management of stochastic assets and liabilities
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.
- Language
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Englisch
- Bibliographic citation
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Journal: Risks ; ISSN: 2227-9091 ; Volume: 8 ; Year: 2020 ; Issue: 4 ; Pages: 1-14 ; Basel: MDPI
- Classification
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Wirtschaft
- Subject
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assets and liabilities management
risk management
uncertainty
matheuristics
simulation
- Event
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Geistige Schöpfung
- (who)
-
Bayliss, Christopher
Serra, Marti
Nieto, Armando
Juan, Angel A.
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
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Basel
- (when)
-
2020
- DOI
-
doi:10.3390/risks8040131
- Handle
- Last update
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10.03.2025, 11:42 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Bayliss, Christopher
- Serra, Marti
- Nieto, Armando
- Juan, Angel A.
- MDPI
Time of origin
- 2020
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