Combining a Matheuristic with Simulation for Risk Management of Stochastic Assets and Liabilities

Christopher Bayliss, Marti Serra, Armando Nieto and Angel Juan
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Christopher Bayliss: IN3—Computer Science Department, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
Marti Serra: IN3—Computer Science Department, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
Armando Nieto: IN3—Computer Science Department, Universitat Oberta de Catalunya, 08018 Barcelona, Spain

Risks, 2020, vol. 8, issue 4, 1-14

Abstract: 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.

Keywords: assets and liabilities management; risk management; uncertainty; matheuristics; simulation (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2020
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