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Asset and liability management in insurance firms: A biased-randomised approach combining heuristics with monte-carlo simulation

Asset and liability management in insurance firms: A biased-randomised approach combining heuristics with monte-carlo simulation
Asset and liability management in insurance firms: A biased-randomised approach combining heuristics with monte-carlo simulation

The management of assets and liabilities is of critical importance for insurance companies and banks. Complex decisions need to be made regarding how to assign assets to liabilities such in a way that the overall benefit is maximised over a multi-period horizon. At the same time, the risk of not being able to cover the liabilities at any given period must be kept under a certain threshold level. This optimisation problem is known in the literature as the asset and liability management (ALM) problem. In this work, we propose a biased-randomised algorithm to solve a real-life instance of the ALM problem. Firstly, we outline a greedy heuristic. Secondly, we transform it into a probabilistic algorithm by employing Monte-Carlo simulation and biased-randomisation techniques. According to our computational tests, the probabilistic algorithm is able to generate, in short computing times, solutions that outperform by far the ones currently practised in the sector.

Asset and liability management, Biased randomised algorithm, Heuristics, Monte carlo
405-414
Operational Research Society
Nieto, Armando
8d1369e2-a1cc-4091-9f3b-375269336d6b
Juan, Angel A.
681f726e-e136-4028-816e-927f41c326d3
Bayliss, Christopher
5fb04968-5cbf-40d8-84b0-02e8c7e94a59
Kizys, Renatas
9d3a6c5f-075a-44f9-a1de-32315b821978
Fakhimi, Masoud
Boness, Tom
Robertson, Duncan
Nieto, Armando
8d1369e2-a1cc-4091-9f3b-375269336d6b
Juan, Angel A.
681f726e-e136-4028-816e-927f41c326d3
Bayliss, Christopher
5fb04968-5cbf-40d8-84b0-02e8c7e94a59
Kizys, Renatas
9d3a6c5f-075a-44f9-a1de-32315b821978
Fakhimi, Masoud
Boness, Tom
Robertson, Duncan

Nieto, Armando, Juan, Angel A., Bayliss, Christopher and Kizys, Renatas (2021) Asset and liability management in insurance firms: A biased-randomised approach combining heuristics with monte-carlo simulation. Fakhimi, Masoud, Boness, Tom and Robertson, Duncan (eds.) In Operational Research Society 10th Simulation Workshop, SW 2021 - Proceedings. Operational Research Society. pp. 405-414 . (doi:10.36819/SW21.044).

Record type: Conference or Workshop Item (Paper)

Abstract

The management of assets and liabilities is of critical importance for insurance companies and banks. Complex decisions need to be made regarding how to assign assets to liabilities such in a way that the overall benefit is maximised over a multi-period horizon. At the same time, the risk of not being able to cover the liabilities at any given period must be kept under a certain threshold level. This optimisation problem is known in the literature as the asset and liability management (ALM) problem. In this work, we propose a biased-randomised algorithm to solve a real-life instance of the ALM problem. Firstly, we outline a greedy heuristic. Secondly, we transform it into a probabilistic algorithm by employing Monte-Carlo simulation and biased-randomisation techniques. According to our computational tests, the probabilistic algorithm is able to generate, in short computing times, solutions that outperform by far the ones currently practised in the sector.

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SW20_RevisedPaper_10 (1) - Accepted Manuscript
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More information

Published date: 22 March 2021
Additional Information: Funding Information: This work has been partially funded by the agreement between the Divina Pastora Seguros and the Universitat Oberta de Catalunya. Publisher Copyright: © 2021 SW 2021. All rights reserved. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
Venue - Dates: 10th Operational Research Society Simulation Workshop, SW 2021, , Virtual, Online, 2021-03-22 - 2021-03-26
Keywords: Asset and liability management, Biased randomised algorithm, Heuristics, Monte carlo

Identifiers

Local EPrints ID: 449945
URI: http://eprints.soton.ac.uk/id/eprint/449945
PURE UUID: 3dabae6e-2682-4b01-9d68-f4cf5b164e14
ORCID for Renatas Kizys: ORCID iD orcid.org/0000-0001-9104-1809

Catalogue record

Date deposited: 28 Jun 2021 16:32
Last modified: 28 Apr 2022 02:27

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Contributors

Author: Armando Nieto
Author: Angel A. Juan
Author: Renatas Kizys ORCID iD
Editor: Masoud Fakhimi
Editor: Tom Boness
Editor: Duncan Robertson

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