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.

Sprache
Englisch

Erschienen in
Journal: Risks ; ISSN: 2227-9091 ; Volume: 8 ; Year: 2020 ; Issue: 4 ; Pages: 1-14 ; Basel: MDPI

Klassifikation
Wirtschaft
Thema
assets and liabilities management
risk management
uncertainty
matheuristics
simulation

Ereignis
Geistige Schöpfung
(wer)
Bayliss, Christopher
Serra, Marti
Nieto, Armando
Juan, Angel A.
Ereignis
Veröffentlichung
(wer)
MDPI
(wo)
Basel
(wann)
2020

DOI
doi:10.3390/risks8040131
Handle
Letzte Aktualisierung
10.03.2025, 11:42 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Bayliss, Christopher
  • Serra, Marti
  • Nieto, Armando
  • Juan, Angel A.
  • MDPI

Entstanden

  • 2020

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