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Optimal consumption, investment and housing with means-tested public pension in retirement

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  • Andréasson, Johan G.
  • Shevchenko, Pavel V.
  • Novikov, Alex

Abstract

In this paper, we develop an expected utility model for retirement behaviour in the decumulation phase of Australian retirees with sequential family status subject to consumption, housing, investment, bequest, and government-provided means-tested Age Pension. We account for mortality risk and risky investment assets, and we introduce a “health” proxy to capture the decreasing level of consumption for older retirees. Then, we find the optimal housing at retirement, the optimal consumption and optimal risky asset allocation depending on age and wealth. The model is solved numerically as a stochastic control problem, and it is calibrated using the maximum likelihood method with empirical data of consumption and housing from the Australian Bureau of Statistics 2009–2010 Survey. The model fits the characteristics of the data well to explain the behaviour of Australian retirees. The key findings are as follows. First, the optimal policy is highly sensitive to means-tested Age Pension early in retirement, but this sensitivity fades with age. Second, the allocation to risky assets shows a complex relationship with the means-tested Age Pension. As a general rule, when wealth decreases, the proportion allocated to risky assets increases, because the Age Pension works as a buffer against investment losses. Third, couples can be more aggressive with risky allocations owing to their longer life expectancy compared with singles.

Suggested Citation

  • Andréasson, Johan G. & Shevchenko, Pavel V. & Novikov, Alex, 2017. "Optimal consumption, investment and housing with means-tested public pension in retirement," Insurance: Mathematics and Economics, Elsevier, vol. 75(C), pages 32-47.
  • Handle: RePEc:eee:insuma:v:75:y:2017:i:c:p:32-47
    DOI: 10.1016/j.insmatheco.2017.04.003
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    References listed on IDEAS

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    Cited by:

    1. 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, Open Access Journal, vol. 5(3), pages 1-21, September.
    2. Andreas Lichtenstern & Pavel V. Shevchenko & Rudi Zagst, 2019. "Optimal life-cycle consumption and investment decisions under age-dependent risk preferences," Papers 1908.09976, arXiv.org.
    3. Adam Butt & Gaurav Khemka & Geoffrey J. Warren, 2019. "What Dividend Imputation Means for Retirement Savers," The Economic Record, The Economic Society of Australia, vol. 95(309), pages 181-199, June.
    4. Wen Chen & Nicolas Langren'e, 2020. "Deep neural network for optimal retirement consumption in defined contribution pension system," Papers 2007.09911, arXiv.org, revised Jul 2020.
    5. Bonsoo Koo & Athanasios A. Pantelous & Yunxiao Wang, 2020. "Novel Utility-based Life Cycle Models to Optimise Income in Retirement in the Presence of Heterogeneous Preferences," Monash Econometrics and Business Statistics Working Papers 21/20, Monash University, Department of Econometrics and Business Statistics.
    6. Wen Chen & Nicolas Langrené, 2020. "Deep neural network for optimal retirement consumption in defined contribution pension system [Réseau de neurones profond pour consommation à la retraite optimale en système de retraite à cotisatio," Working Papers hal-02909818, HAL.

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    More about this item

    Keywords

    Dynamic programming; Stochastic control; Optimal policy; Retirement; Means-tested age pension; Defined-contribution pension;
    All these keywords.

    JEL classification:

    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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