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Optimal control of the decumulation of a retirement portfolio with variable spending and dynamic asset allocation

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Listed:
  • Peter A. Forsyth
  • Kenneth R. Vetzal
  • Graham Westmacott

Abstract

We extend the Annually Recalculated Virtual Annuity (ARVA) spending rule for retirement savings decumulation to include a cap and a floor on withdrawals. With a minimum withdrawal constraint, the ARVA strategy runs the risk of depleting the investment portfolio. We determine the dynamic asset allocation strategy which maximizes a weighted combination of expected total withdrawals (EW) and expected shortfall (ES), defined as the average of the worst five per cent of the outcomes of real terminal wealth. We compare the performance of our dynamic strategy to simpler alternatives which maintain constant asset allocation weights over time accompanied by either our same modified ARVA spending rule or withdrawals that are constant over time in real terms. Tests are carried out using both a parametric model of historical asset returns as well as bootstrap resampling of historical data. Consistent with previous literature that has used different measures of reward and risk than EW and ES, we find that allowing some variability in withdrawals leads to large improvements in efficiency. However, unlike the prior literature, we also demonstrate that further significant enhancements are possible through incorporating a dynamic asset allocation strategy rather than simply keeping asset allocation weights constant throughout retirement.

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  • Peter A. Forsyth & Kenneth R. Vetzal & Graham Westmacott, 2021. "Optimal control of the decumulation of a retirement portfolio with variable spending and dynamic asset allocation," Papers 2101.02760, arXiv.org.
  • Handle: RePEc:arx:papers:2101.02760
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    References listed on IDEAS

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    1. Dang, D.M. & Forsyth, P.A., 2016. "Better than pre-commitment mean-variance portfolio allocation strategies: A semi-self-financing Hamilton–Jacobi–Bellman equation approach," European Journal of Operational Research, Elsevier, vol. 250(3), pages 827-841.
    2. Van Staden, Pieter M. & Dang, Duy-Minh & Forsyth, Peter A., 2018. "Time-consistent mean–variance portfolio optimization: A numerical impulse control approach," Insurance: Mathematics and Economics, Elsevier, vol. 83(C), pages 9-28.
    3. Peijnenburg, Kim & Nijman, Theo & Werker, Bas J.M., 2016. "The annuity puzzle remains a puzzle," Journal of Economic Dynamics and Control, Elsevier, vol. 70(C), pages 18-35.
    4. Bonnie-Jeanne MacDonald & Bruce Jones & Richard Morrison & Robert Brown & Mary Hardy, 2013. "Research and Reality: A Literature Review on Drawing Down Retirement Financial Savings," North American Actuarial Journal, Taylor & Francis Journals, vol. 17(3), pages 181-215.
    5. Cecilia Mancini, 2009. "Non‐parametric Threshold Estimation for Models with Stochastic Diffusion Coefficient and Jumps," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(2), pages 270-296, June.
    6. Lin, Yijia & MacMinn, Richard D. & Tian, Ruilin, 2015. "De-risking defined benefit plans," Insurance: Mathematics and Economics, Elsevier, vol. 63(C), pages 52-65.
    7. Peter A. Forsyth & Kenneth R. Vetzal, 2019. "Optimal Asset Allocation for Retirement Saving: Deterministic Vs. Time Consistent Adaptive Strategies," Applied Mathematical Finance, Taylor & Francis Journals, vol. 26(1), pages 1-37, January.
    8. Andrew Patton & Dimitris Politis & Halbert White, 2009. "Correction to “Automatic Block-Length Selection for the Dependent Bootstrap” by D. Politis and H. White," Econometric Reviews, Taylor & Francis Journals, vol. 28(4), pages 372-375.
    9. S. G. Kou & Hui Wang, 2004. "Option Pricing Under a Double Exponential Jump Diffusion Model," Management Science, INFORMS, vol. 50(9), pages 1178-1192, September.
    10. Cui, Xiangyu & Gao, Jianjun & Shi, Yun & Zhu, Shushang, 2019. "Time-consistent and self-coordination strategies for multi-period mean-Conditional Value-at-Risk portfolio selection," European Journal of Operational Research, Elsevier, vol. 276(2), pages 781-789.
    11. Duy-Minh Dang & P. A. Forsyth & K. R. Vetzal, 2017. "The 4% strategy revisited: a pre-commitment mean-variance optimal approach to wealth management," Quantitative Finance, Taylor & Francis Journals, vol. 17(3), pages 335-351, March.
    12. Forsyth, Peter A. & Vetzal, Kenneth R. & Westmacott, Graham, 2020. "Optimal Asset Allocation For Dc Pension Decumulation With A Variable Spending Rule," ASTIN Bulletin, Cambridge University Press, vol. 50(2), pages 419-447, May.
    13. Milevsky, Moshe A. & Young, Virginia R., 2007. "Annuitization and asset allocation," Journal of Economic Dynamics and Control, Elsevier, vol. 31(9), pages 3138-3177, September.
    14. Peter A. Forsyth & Kenneth R. Vetzal, 2017. "Dynamic mean variance asset allocation: Tests for robustness," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 4(02n03), pages 1-37, June.
    15. Dimitris Politis & Halbert White, 2004. "Automatic Block-Length Selection for the Dependent Bootstrap," Econometric Reviews, Taylor & Francis Journals, vol. 23(1), pages 53-70.
    16. Shefrin, Hersh M & Thaler, Richard H, 1988. "The Behavioral Life-Cycle Hypothesis," Economic Inquiry, Western Economic Association International, vol. 26(4), pages 609-643, October.
    17. Forsyth, Peter A., 2020. "Optimal dynamic asset allocation for DC plan accumulation/decumulation: Ambition-CVAR," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 230-245.
    18. Gao, Jianjun & Xiong, Yan & Li, Duan, 2016. "Dynamic mean-risk portfolio selection with multiple risk measures in continuous-time," European Journal of Operational Research, Elsevier, vol. 249(2), pages 647-656.
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