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A stochastic programming model for asset liability management of a Finnish pension company

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  • Petri Hilli
  • Matti Koivu
  • Teemu Pennanen
  • Antero Ranne

Abstract

This paper describes a stochastic programming model that was developed for asset liability management of a Finnish pension insurance company. In many respects the model resembles those presented in the literature, but it has some unique features stemming from the statutory restrictions for Finnish pension insurance companies. Particular attention is paid to modeling the stochastic factors, numerical solution of the resulting optimization problem and evaluation of the solution. Out-of-sample tests clearly favor the strategies suggested by our model over static fixed-mix and dynamic portfolio insurance strategies. Copyright Springer Science+Business Media, LLC 2007

Suggested Citation

  • Petri Hilli & Matti Koivu & Teemu Pennanen & Antero Ranne, 2007. "A stochastic programming model for asset liability management of a Finnish pension company," Annals of Operations Research, Springer, vol. 152(1), pages 115-139, July.
    • Handle: RePEc:spr:annopr:v:152:y:2007:i:1:p:115-139:10.1007/s10479-006-0135-3
    DOI: 10.1007/s10479-006-0135-3
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    References listed on IDEAS

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    1. Kouwenberg, Roy, 2001. "Scenario generation and stochastic programming models for asset liability management," European Journal of Operational Research, Elsevier, vol. 134(2), pages 279-292, October.
    2. David R. Cariño & David H. Myers & William T. Ziemba, 1998. "Concepts, Technical Issues, and Uses of the Russell-Yasuda Kasai Financial Planning Model," Operations Research, INFORMS, vol. 46(4), pages 450-462, August.
    3. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521634809.
    4. Campbell, John Y & Shiller, Robert J, 1987. "Cointegration and Tests of Present Value Models," Journal of Political Economy, University of Chicago Press, vol. 95(5), pages 1062-1088, October.
    5. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    6. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    7. Fleten, Stein-Erik & Hoyland, Kjetil & Wallace, Stein W., 2002. "The performance of stochastic dynamic and fixed mix portfolio models," European Journal of Operational Research, Elsevier, vol. 140(1), pages 37-49, July.
    8. G. Consigli & M. Dempster, 1998. "Dynamic stochastic programmingfor asset-liability management," Annals of Operations Research, Springer, vol. 81(0), pages 131-162, June.
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    Cited by:

    1. Libo Yin & Liyan Han, 2013. "Options strategies for international portfolios with overall risk management via multi-stage stochastic programming," Annals of Operations Research, Springer, vol. 206(1), pages 557-576, July.
    2. Zinan Hu & Ruicheng Yang & Sumuya Borjigin, 2024. "A multistage forecasting model for green bond cost optimization with dynamic corporate risk constraints," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2607-2634, November.
    3. Johan G. Andréasson & Pavel V. Shevchenko, 2017. "Assessment of Policy Changes to Means-Tested Age Pension Using the Expected Utility Model: Implication for Decisions in Retirement," Risks, MDPI, vol. 5(3), pages 1-21, September.
    4. Duarte, Thiago B. & Valladão, Davi M. & Veiga, Álvaro, 2017. "Asset liability management for open pension schemes using multistage stochastic programming under Solvency-II-based regulatory constraints," Insurance: Mathematics and Economics, Elsevier, vol. 77(C), pages 177-188.
    5. Davi Michel Valladão & Álvaro Veiga & Alexandre Street, 2018. "A Linear Stochastic Programming Model for Optimal Leveraged Portfolio Selection," Computational Economics, Springer;Society for Computational Economics, vol. 51(4), pages 1021-1032, April.
    6. Valladão, Davi M. & Veiga, Álvaro & Veiga, Geraldo, 2014. "A multistage linear stochastic programming model for optimal corporate debt management," European Journal of Operational Research, Elsevier, vol. 237(1), pages 303-311.
    7. Woong Bee Choi & Dongyeol Lee & Woo Chang Kim, 2021. "Extending the Scope of ALM to Social Investment: Investing in Population Growth to Enhance Sustainability of the Korean National Pension Service," Sustainability, MDPI, vol. 13(1), pages 1-14, January.
    8. Grzegorz Hałaj, 2016. "Dynamic Balance Sheet Model With Liquidity Risk," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(07), pages 1-37, November.
    9. Sebastiano Vitali & Vittorio Moriggia & Miloš Kopa, 2017. "Optimal pension fund composition for an Italian private pension plan sponsor," Computational Management Science, Springer, vol. 14(1), pages 135-160, January.
    10. John M Mulvey & Woo Chang Kim & Yi Ma, 2010. "Duration-enhancing overlay strategies for defined benefit pension plans," Journal of Asset Management, Palgrave Macmillan, vol. 11(2), pages 136-162, June.

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