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Life-cycle funds: Much Ado about Nothing?

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  • Stefan Graf
Abstract
The core idea of life-cycle funds or target-date funds is to decrease the fund's equity exposure and conversely increase its bond exposure towards the fund's target date. Such funds have been gaining significant market share and were recently set as default choice of asset allocation in numerous defined contribution schemes or related old-age provision products in several countries. Hence, an assessment of life-cycle funds’ risk-return profiles – that is, the probability distribution of returns – is essential for sustainable financial planning of a large group of investors. This paper studies the risk-return profile of life-cycle funds in particular compared to simple balanced or lifestyle funds that apply a constant equity portion throughout the fund's term instead. In a Black–Scholes model, we derive balanced funds that reproduce the risk-return profile of an arbitrary life-cycle fund for single and regular contributions. We then analyze the accuracy of our results under more complex asset models with stochastic interest rates, stochastic equity volatility and jumps. We further show that frequently used ‘rule of thumb approximations’ that only take into account the life-cycle fund's average equity portion are not suitable to approximate a life-cycle fund's risk-return profile. Our results on the one hand facilitate sustainable financial planning and on the other hand challenge the very existence of life-cycle funds since appropriately calibrated balanced funds can offer a similar (often dominating) risk-return profile.

Suggested Citation

  • Stefan Graf, 2017. "Life-cycle funds: Much Ado about Nothing?," The European Journal of Finance, Taylor & Francis Journals, vol. 23(11), pages 974-998, September.
    • Handle: RePEc:taf:eurjfi:v:23:y:2017:i:11:p:974-998
    DOI: 10.1080/1351847X.2016.1151805
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    Cited by:

    1. T. R. B. den Haan & K. W. Chau & M. van der Schans & C. W. Oosterlee, 2020. "Rule-based Strategies for Dynamic Life Cycle Investment," Papers 2011.02596, arXiv.org.
    2. Chendi Ni & Yuying Li & Peter Forsyth & Ray Carroll, 2020. "Optimal Asset Allocation For Outperforming A Stochastic Benchmark Target," Papers 2006.15384, arXiv.org.
    3. Subur Harahap & Armanu Thoyib & Sumiati Sumiati & Atim Djazuli, 2022. "The Impact of Financial Literacy on Retirement Planning with Serial Mediation of Financial Risk Tolerance and Saving Behavior: Evidence of Medium Entrepreneurs in Indonesia," IJFS, MDPI, vol. 10(3), pages 1-22, August.
    4. 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.
    5. 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.

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