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Implementing Individual Savings Decisions For Retirement With Bounds On Wealth

Author

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  • Donnelly, Catherine
  • Guillen, Montserrat
  • Nielsen, Jens Perch
  • Pérez-Marín, Ana Maria

Abstract

We present a savings plan for retirement that removes risk by fixing a constraint on a life-long pension so that it has an upper and a lower bound. This corresponds to the ideas of Nobel laureate R.C. Merton whose implementation has never been published. We show with an illustration that our proposed practical algorithm reproduces the theoretical results after a savings period of around 30 years by using daily, monthly, weekly or yearly updates of the investment positions. We calculate the percentiles of the final accumulated wealth distribution for the adjusted implementation. In the simulated illustration, we observe that the adjusted values converge to the theoretical values of the percentiles when the frequency of update increases. We conclude that monthly adjustments result in a practical way to implement theoretical results that were obtained under the hypothesis of a continuous process by Donnelly et al. (2015). This method is easy to use in practice by pension savers and fund managers.

Suggested Citation

  • Donnelly, Catherine & Guillen, Montserrat & Nielsen, Jens Perch & Pérez-Marín, Ana Maria, 2018. "Implementing Individual Savings Decisions For Retirement With Bounds On Wealth," ASTIN Bulletin, Cambridge University Press, vol. 48(1), pages 111-137, January.
    • Handle: RePEc:cup:astinb:v:48:y:2018:i:01:p:111-137_00
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    Citations

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    Cited by:

    1. Wei Zheng & Zining Liu & Ruo Jia, 2019. "How private sector participation improves retirement preparation: A case from China," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 44(1), pages 123-147, January.
    2. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2018. "Choice of Benchmark When Forecasting Long-term Stock Returns," Graz Economics Papers 2018-08, University of Graz, Department of Economics.
    3. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2021. "Forecasting benchmarks of long-term stock returns via machine learning," Annals of Operations Research, Springer, vol. 297(1), pages 221-240, February.
    4. Gerrard, Russell & Hiabu, Munir & Kyriakou, Ioannis & Nielsen, Jens Perch, 2019. "Communication and personal selection of pension saver’s financial risk," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1102-1111.
    5. Gerrard, Russell & Hiabu, Munir & Nielsen, Jens Perch & Vodička, Peter, 2020. "Long-term real dynamic investment planning," Insurance: Mathematics and Economics, Elsevier, vol. 92(C), pages 90-103.

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