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OPTIMAL ASSET ALLOCATION FOR DC PENSION DECUMULATION WITH A VARIABLE SPENDING RULE

Published online by Cambridge University Press:  15 April 2020

Peter A. Forsyth*
Affiliation:
David R. Cheriton School of Computer Science, University of Waterloo, WaterlooON, CanadaN2L 3G1, E-Mail: [email protected]
Kenneth R. Vetzal
Affiliation:
School of Accounting and Finance, University of Waterloo, WaterlooON, CanadaN2L 3G1, E-Mail: [email protected]
Graham Westmacott
Affiliation:
PWL Capital, 20 Erb Street W., Suite 506, Waterloo, ON, CanadaN2L 1T2, E-Mail: [email protected]

Abstract

We determine the optimal asset allocation to bonds and stocks using an annually recalculated virtual annuity (ARVA) spending rule for DC pension plan decumulation. Our objective function minimizes downside withdrawal variability for a given fixed value of total expected withdrawals. The optimal asset allocation is found using optimal stochastic control methods. We formulate the strategy as a solution to a Hamilton–Jacobi–Bellman (HJB) Partial Integro Differential Equation (PIDE). We impose realistic constraints on the controls (no-shorting, no-leverage, discrete rebalancing) and solve the HJB PIDEs numerically. Compared to a fixed-weight strategy which has the same expected total withdrawals, the optimal strategy has a much smaller average allocation to stocks and tends to de-risk rapidly over time. This conclusion holds in the case of a parametric model based on historical data and also in a bootstrapped market based on the historical data.

Type
Research Article
Copyright
© Astin Bulletin 2020

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References

Bengen, W. (1994) Determining withdrawal rates using historical data. Journal of Financial Planning 7, 171180.Google Scholar
Bernhardt, T. and Donnelly, C. (2018) Pension decumulation strategies: A state of the art report. Technical Report, Risk Insight Lab, Heriot Watt University.Google Scholar
Blake, D., Cairns, A. J. G. and Dowd, K. (2003) Pensionmetrics 2: Stochastic pension plan design during the distribution phase. Insurance: Mathematics and Economics 33, 2947.Google Scholar
Blake, D., Wright, D. and Zhang, Y. (2014) Age-dependent investing: Optimal funding and investment strategies in defined contribution pension plans when members are rational life cycle financial planners. Journal of Economic Dynamics and Control 38, 105124.CrossRefGoogle Scholar
Brugiere, P. (1996) Optimal Portfolio and Optimal Trading in a Dynamic Continuous Time Framework. Nurenberg, Germany: AFIR Colloquium.Google Scholar
Cont, R. and Mancini, C. (2011) Nonparametric tests for pathwise properties of semimartingales. Bernoulli 17, 781813.CrossRefGoogle Scholar
Dang, D.-M. and Forsyth, P. A. (2014) Continuous time mean-variance optimal portfolio allocation under jump diffusion: A numerical impulse control approach. Numerical Methods for Partial Differential Equations 30, 664698.CrossRefGoogle Scholar
Dang, D.-M. and 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 250, 827841.CrossRefGoogle Scholar
de Jong, F. and Zhou, Y. (2014). Habit formation: Implications for pension plans. Design Paper 32, Netspar.Google Scholar
Forsyth, P., Kennedy, J., Tse, S. and Windcliff, H. (2012). Optimal trade execution: A mean-quadratic variation approach. Journal of Economic Dynamics and Control 36, 19711991.CrossRefGoogle Scholar
Forsyth, P. and Labahn, G. (2019) ϵ–Monotone Fourier methods for optimal stochastic control in finance. Journal of Computational Finance 22(4), 2571.CrossRefGoogle Scholar
Forsyth, P. and Vetzal, K. (2019) Optimal asset allocation for retirement savings: Deterministic vs. time consistent adaptive strategies. Applied Mathematical Finance 26(1), 137.CrossRefGoogle Scholar
Forsyth, P. A. and Vetzal, K. R. (2017) Robust asset allocation for long-term target-based investing. International Journal of Theoretical and Applied Finance 20(3), 1750017 (electronic).CrossRefGoogle Scholar
Forsyth, P. A., Vetzal, K. R. and Westmacott, G. (2019) Management of portfolio depletion risk through optimal life cycle asset allocation. North American Actuarial Journal 23(3), 447468.CrossRefGoogle Scholar
Freedman, B. (2008) Efficient post-retirement asset allocation. North American Actuarial Journal 12, 228241.CrossRefGoogle Scholar
Gerrard, R., Haberman, S. and Vigna, E. (2004) Optimal investment choices post-retirement in a defined contribution pension scheme. Insurance: Mathematics and Economics 35, 321342.Google Scholar
Gerrard, R., Haberman, S. and Vigna, E. (2006) The management of decumulation risk in a defined contribution pension plan. North American Actuarial Journal 10, 84110.CrossRefGoogle Scholar
Kou, S. G. (2002) A jump-diffusion model for option pricing. Management Science 48, 10861101.CrossRefGoogle Scholar
Liang, X. and Young, V. R. (2018) Annuitization and asset allocation under exponential utility. Insurance: Mathematics and Economics 79, 167183.Google Scholar
MacDonald, B.-J., Jones, B., Morrison, R. J., Brown, R. L. and Hardy, M. (2013) Research and reality: A literature review on drawing down retirement financial savings. North American Actuarial Journal 17, 181215.CrossRefGoogle Scholar
Mancini, C. (2009) Non-parametric threshold estimation models with stochastic diffusion coefficient and jumps. Scandinavian Journal of Statistics 36, 270296.CrossRefGoogle Scholar
Menoncin, F. and Vigna, E. (2017) Mean-variance target based optimisation for defined contribution pension schemes in a stochastic framework. Insurance: Mathematics and Economics 76, 172184.Google Scholar
Milevsky, M. A. and Young, V. R. (2007) Annuitization and asset allocation. Journal of Economic Dynamics and Control 31, 31383177.CrossRefGoogle Scholar
Patton, A., Politis, D. and White, H. (2009) Correction to: automatic block-length selection for the dependent bootstrap. Econometric Reviews 28, 372375.CrossRefGoogle Scholar
Peijnenburg, K., Nijman, T. and Werker, B. J. (2016) The annuity puzzle remains a puzzle. Journal of Economic Dynamics and Control 70, 1835.CrossRefGoogle Scholar
Politis, D. and White, H. (2004) Automatic block-length selection for the dependent bootstrap. Econometric Reviews 23, 5370.CrossRefGoogle Scholar
Smith, G. and Gould, D. P. (2007, Spring) Measuring and controlling shortfall risk in retirement. Journal of Investing 16, 8295.CrossRefGoogle Scholar
van Staden, P. M., Dang, D.-M. and Forsyth, P. A. (2019) Mean-quadratic variation portfolio optimization: A desirable alternative to time-consistent mean-variance optimization? SIAM Journal on Financial Mathematics 10(3), 815856.CrossRefGoogle Scholar
Vettese, F. (2018) Retirement Income for Life: Getting More Without Saving More. Toronto: Milner.Google Scholar
Waring, M. B. and Siegel, L. B. (2015) The only spending rule article you will ever need. Financial Analysts Journal 71(1), 91107.CrossRefGoogle Scholar
Westmacott, G. (2017) The retiree’s dilemma: The Deckards. PWL Capital White Paper, http://www.pwlcapital.com/retirees-dilemmma-deckards/Google Scholar
Westmacott, G. and Daley, S. (2015) The design and depletion of retirement portfolios. PWL Capital White Paper, http://www.pwlcapital.com/design-depletion-of-retirement-portfolios/Google Scholar