Document Type : Research Article
Authors
1 Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran
2 Assistant Professor of Insurance Research Center
Abstract
Asset-liability management (ALM) is a critical issue for insurance companies because the premiums received from policyholders should be invested according to regulatory frameworks while providing suitable profitability, and simultaneously, the insurer should fulfill its obligations to policyholders on time. Our focus is on participating (with-profit) life insurance policies, where policyholders not only receive a guaranteed profit but also participate in the return of the insurer's investment-portfolio. Due to the risks of death and surrender, uncertainty in asset returns, the broad range of insurance products and regulations, it is difficult to make optimal decisions. In this paper, we aim to present a new multi-stage stochastic programming ALM model for with-profit life insurance policies. Compared to existing models that involve some simplifications, our model incorporates more details and is closer to reality. Specifically, our model is multi-stage and updates the amount of policies’ investment reserves based on the realized return of the investment-portfolio. Evaluation of the model across a variety of datasets confirms the effectiveness of the proposed model.
Keywords
69 (2021), 1015-1348.
[2] A. Bohnert, N. Gatzert, and P.L. Jørgensen, On the management of life insurance company risk by strategic choice of product mix, investment strategy and surplus appropriation
schemes, Insurance: Mathematics and Economics, 60 (2015), 83-97.
[3] X. Chen, Q. Liu, and D. A. Ralescu, A bi-level optimization model for the asset-liability
management of insurance companies, Journal of Industrial and Management Optimization,
19 (2023), 3003-3019.
[4] S. Dauland, and E. G. Vidal, Replication of insurance liabilities, RiskMetrics Journal, 9
(2009), 76-96.
[5] L. Devineau, and M. Chauvigny, Replicating portfolios: Calibration techniques for the
calculation of the Solvency II economic capital, Bulletin Fran¸cais d’Actuariat, 11 (2011),
59-97.
[6] G. Dutta, H. V. Rao, S. Basu, and M. K. Tiwari, Asset liability management model with
decision support system for life insurance companies: Computational results, Computers &
Industrial Engineering, 128 (2019), 985-998.
[7] J. L. Fernandez, A. M. Ferreiro-Ferreiro, J. A. Garc ´ ´ıa-Rodr´ıguez, and C. Vazquez ´ ,
GPU parallel implementation for asset-liability management in insurance companies, Journal
of Computational Science, 24 (2018), 232-254.
[8] M. D. Francesco, and R. Simonella, A stochastic asset liability management model for
life insurance companies, Financial Markets and Portfolio Management, 37 (2023), 61-94.
[9] T. Gerstner, M. Griebel, M. Holtz, R. Goschnick, and M. Haep, A general assetliability management model for the efficient simulation of portfolios of life insurance policies,
Insurance: Mathematics and Economics, 42 (2008), 704-716.
[10] N. Gulpnar, D. Pachamanova, and E. ¸Canakolu ¨ , A robust assetliability management
framework for investment products with guarantees, OR Spectrum, 38 (2016), 1007-1041.
[11] O. Gurin, Bond Portfolio Optimization at Life Insurance Companies: Duration Spread
Ratio Optimization vs. Mean-Variance Optimization, SSRN, (2024).
[12] H. C. Huang, and Y. T. Lee, Optimal asset allocation for a general portfolio of life insurance policies, Insurance: Mathematics and Economics, 46 (2010), 271-280.
[13] H. Markowitz, Portfolio selection, Journal of Finance, 46 (1952), 71-91.
[14] J. Natolski, and R. Werner, Mathematical analysis of different approaches for replicating
portfolios, European Actuarial Journal, 4 (2014), 411-435.
[15] J. G. Orreborn, Asset-Liability Management with in Life Insurance, Degree Projects in
Financial Mathematics (2017).
[16] H. V. Rao, and G. Dutta, and S. Basu, New asset liability management model with
decision support system for life insurance companies: interface design issues for database
and mathematical models, International Journal of Revenue Management, 10 (2018), 71-91.
)