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Optimal savings management for individuals with defined contribution pension plans

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  • Konicz, Agnieszka Karolina
  • Mulvey, John M.

Abstract

The paper provides some guidelines to individuals with defined contribution (DC) pension plans on how to manage pension savings both before and after retirement. We argue that decisions regarding investment, annuity payments, and the size of death sum should not only depend on the individual’s age (or time left to retirement), nor should they solely depend on the risk preferences, but should also capture: (1) economical characteristics—such as current value on the pension savings account, expected pension contributions (mandatory and voluntary), and expected income after retirement (e.g. retirement state pension), and (2) personal characteristics—such as risk aversion, lifetime expectancy, preferable payout profile, bequest motive, and preferences on portfolio composition. Specifically, the decisions are optimal under the expected CRRA utility function and are subject to the constraints characterizing the individual. The problem is solved via a model that combines two optimization approaches: stochastic optimal control and multi-stage stochastic programming. The first method is common in financial and actuarial literature, but produces theoretical results. However, the latter, which is characteristic for operations research, has practical applications. We present the operations research methods which have potential to stimulate new thinking and add to actuarial practice.

Suggested Citation

  • Konicz, Agnieszka Karolina & Mulvey, John M., 2015. "Optimal savings management for individuals with defined contribution pension plans," European Journal of Operational Research, Elsevier, vol. 243(1), pages 233-247.
    • Handle: RePEc:eee:ejores:v:243:y:2015:i:1:p:233-247
    DOI: 10.1016/j.ejor.2014.11.016
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    Cited by:

    1. Yao, Haixiang & Chen, Ping & Li, Xun, 2016. "Multi-period defined contribution pension funds investment management with regime-switching and mortality risk," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 103-113.
    2. Agnieszka Konicz & David Pisinger & Alex Weissensteiner, 2015. "Optimal annuity portfolio under inflation risk," Computational Management Science, Springer, vol. 12(3), pages 461-488, July.
    3. Owadally, Iqbal & Jang, Chul & Clare, Andrew, 2021. "Optimal investment for a retirement plan with deferred annuities allowing for inflation and labour income risk," European Journal of Operational Research, Elsevier, vol. 295(3), pages 1132-1146.
    4. Chen, Zheng & Li, Zhongfei & Zeng, Yan & Sun, Jingyun, 2017. "Asset allocation under loss aversion and minimum performance constraint in a DC pension plan with inflation risk," Insurance: Mathematics and Economics, Elsevier, vol. 75(C), pages 137-150.
    5. Audrius Kabašinskas & Francesca Maggioni & Kristina Šutienė & Eimutis Valakevičius, 2019. "A multistage risk-averse stochastic programming model for personal savings accrual: the evidence from Lithuania," Annals of Operations Research, Springer, vol. 279(1), pages 43-70, August.
    6. Leung, Melvern & Li, Youwei & Pantelous, Athanasios A. & Vigne, Samuel A., 2021. "Bayesian Value-at-Risk backtesting: The case of annuity pricing," European Journal of Operational Research, Elsevier, vol. 293(2), pages 786-801.
    7. Agnieszka Karolina Konicz & David Pisinger & Alex Weissensteiner, 2016. "Optimal retirement planning with a focus on single and joint life annuities," Quantitative Finance, Taylor & Francis Journals, vol. 16(2), pages 275-295, February.
    8. Barucci, Emilio & Biffis, Enrico & Marazzina, Daniele, 2023. "Health insurance, portfolio choice, and retirement incentives," European Journal of Operational Research, Elsevier, vol. 307(2), pages 910-921.
    9. Das, Sanjiv R. & Ostrov, Daniel & Radhakrishnan, Anand & Srivastav, Deep, 2022. "Dynamic optimization for multi-goals wealth management," Journal of Banking & Finance, Elsevier, vol. 140(C).
    10. Koo, Bonsoo & Pantelous, Athanasios A. & Wang, Yunxiao, 2022. "Novel utility-based life cycle models to optimise income in retirement," European Journal of Operational Research, Elsevier, vol. 299(1), pages 346-361.

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