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On the Use of Mean-Variance and Quadratic Approximations in Implementing Dynamic Investment Strategies: A Comparison of Returns and Investment Policies

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Author Info

  • Robert R. Grauer

    (Department of Economics and Faculty of Business Administration, Simon Fraser University, Burnaby, British Columbia, Canada V5A 1S6)

  • Nils H. Hakansson

    (Haas School of Business, University of California, Berkeley, 350 Barrows Hall, Berkeley, California 94720)

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    Abstract

    This paper compares two approximation schemes for calculating the optimal portfolios in the discrete-time dynamic investment model, specifically, the mean-variance (MV) and the quadratic approximations, to the exact power function method. Future returns are estimated via the empirical probability assessment approach. The results show that (i) with quarterly revision, the MV model approximates the dynamic model very well; (ii) with annual revision, there are often sharp differences between the power function model and the MV approximation; and (iii) these differences become even larger when the quadratic approximation is used.

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    File URL: http://dx.doi.org/10.1287/mnsc.39.7.856
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 39 (1993)
    Issue (Month): 7 (July)
    Pages: 856-871

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    Handle: RePEc:inm:ormnsc:v:39:y:1993:i:7:p:856-871

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    Related research

    Keywords: dynamic investment; mean-variance analysis; asset allocation; investment management;

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    Cited by:
    1. Michael W. Brandt & Amit Goyal & Pedro Santa-Clara & Jonathan Storud, 2004. "A Simulation Approach to Dynamic Portfolio Choice with an Application to Learning About Return Predictability," NBER Working Papers 10934, National Bureau of Economic Research, Inc.
    2. Dokuchaev, Nikolai, 2007. "Discrete time market with serial correlations and optimal myopic strategies," European Journal of Operational Research, Elsevier, vol. 177(2), pages 1090-1104, March.
    3. Elena Vigna, 2009. "Mean-variance inefficiency of CRRA and CARA utility functions for portfolio selection in defined contribution pension schemes," CeRP Working Papers 89, Center for Research on Pensions and Welfare Policies, Turin (Italy).
    4. Stambaugh, Robert F., 1997. "Analyzing investments whose histories differ in length," Journal of Financial Economics, Elsevier, vol. 45(3), pages 285-331, September.
    5. Fletcher, Jonathan & Hillier, Joe, 2002. "On the usefulness of linear factor models in predicting expected returns in mean-variance analysis," International Review of Financial Analysis, Elsevier, vol. 11(4), pages 449-466.
    6. Alexandra Rodkina & Nikolai Dokuchaev, 2014. "On asymptotic optimality of Merton's myopic portfolio strategies for discrete time market," Papers 1403.4329, arXiv.org.
    7. Lecluyse C. & Van Woensel T. & Peremans H., 2007. "Vehicle Routing with Stochastic Time-Dependent Travel Times," Working Papers 2007018, University of Antwerp, Faculty of Applied Economics.
    8. Elena Vigna, 2009. "Mean-variance inefficiency of CRRA and CARA utility functions for portfolio selection in defined contribution pension schemes," Carlo Alberto Notebooks 108, Collegio Carlo Alberto, revised 2009.
    9. Grauer, Robert R. & Hakansson, Nils H., 1995. "Stein and CAPM estimators of the means in asset allocation," International Review of Financial Analysis, Elsevier, vol. 4(1), pages 35-66.
    10. Zhang, Wei-Guo & Liu, Yong-Jun & Xu, Wei-Jun, 2012. "A possibilistic mean-semivariance-entropy model for multi-period portfolio selection with transaction costs," European Journal of Operational Research, Elsevier, vol. 222(2), pages 341-349.
    11. Chamberlain, Trevor W., 1996. "The investment decisions of individuals and firms," International Review of Financial Analysis, Elsevier, vol. 5(2), pages 87-97.
    12. Zhang, Xili & Zhang, Weiguo & Xiao, Weilin, 2013. "Multi-period portfolio optimization under possibility measures," Economic Modelling, Elsevier, vol. 35(C), pages 401-408.

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