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Understanding dynamic mean variance asset allocation

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  • Lioui, Abraham
  • Poncet, Patrice

Abstract

We provide a new portfolio decomposition formula that sheds light on the economics of portfolio choice for investors following the mean-variance (MV) criterion. We show that the number of components of a dynamic portfolio strategy can be reduced to two: the first is preference free and hedges the risk of a discount bond maturing at the investor’s horizon while the second hedges the time variation in pseudo relative risk tolerance. Both components entail strong horizon effects in the dynamic asset allocation as a result of time-varying risk tolerance and investment opportunity sets. We also provide closed-form solutions for the optimal portfolio strategy in the presence of market return predictability. The model parameters are estimated over the period 1963 to 2012 for the U.S. market. We show that (i) intertemporal hedging can be very large, (ii) the MV criterion hugely understates the true extent of risk aversion for high values of the risk aversion parameter, and the more so the shorter the investment horizon, and (iii) the efficient frontiers seem problematic for investment horizons shorter than one year but satisfactory for large horizons. Overall, adopting the MV model leads to acceptable results for medium and long term investors endowed with medium or high risk tolerance, but to very problematic ones otherwise.

Suggested Citation

  • Lioui, Abraham & Poncet, Patrice, 2016. "Understanding dynamic mean variance asset allocation," European Journal of Operational Research, Elsevier, vol. 254(1), pages 320-337.
    • Handle: RePEc:eee:ejores:v:254:y:2016:i:1:p:320-337
    DOI: 10.1016/j.ejor.2016.04.003
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    Citations

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    Cited by:

    1. Bodnar, Taras & Parolya, Nestor & Schmid, Wolfgang, 2018. "Estimation of the global minimum variance portfolio in high dimensions," European Journal of Operational Research, Elsevier, vol. 266(1), pages 371-390.
    2. Hong, Yi & Jin, Xing, 2018. "Semi-analytical solutions for dynamic portfolio choice in jump-diffusion models and the optimal bond-stock mix," European Journal of Operational Research, Elsevier, vol. 265(1), pages 389-398.
    3. Bi, Junna & Jin, Hanqing & Meng, Qingbin, 2018. "Behavioral mean-variance portfolio selection," European Journal of Operational Research, Elsevier, vol. 271(2), pages 644-663.
    4. Ruili Sun & Tiefeng Ma & Shuangzhe Liu & Milind Sathye, 2019. "Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review," JRFM, MDPI, vol. 12(1), pages 1-34, March.
    5. Yufeng Lin & Xiaogang Wang & Yuehua Wu, 2023. "An Adaptive Multiple-Asset Portfolio Strategy with User-Specified Risk Tolerance," Mathematics, MDPI, vol. 11(7), pages 1-35, March.
    6. Peter A. Forsyth & Kenneth R. Vetzal, 2019. "Defined Contribution Pension Plans: Who Has Seen the Risk?," JRFM, MDPI, vol. 12(2), pages 1-27, April.
    7. Tan, Ken Seng & Weng, Chengguo & Zhang, Jinggong, 2022. "Optimal dynamic longevity hedge with basis risk," European Journal of Operational Research, Elsevier, vol. 297(1), pages 325-337.

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