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On the estimation of the global minimum variance portfolio

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  • Kempf, Alexander
  • Memmel, Christoph

Abstract

Expected returns can hardly be estimated from time series data. Therefore, many recent papers suggest investing in the global minimum variance portfolio. The weights of this portfolio depend only on the return variances and covariances, but not on the expected returns. The weights of the global minimum variance portfolio are usually estimated by replacing the true return covariance matrix by its time series estimator. However, little is known about the distributions of the estimated weights and return parameters of this portfolio. Our contribution is to determine these distributions. The knowledge of these distributions allows us to calculate the extent of the estimation risk an investor faces and to answer important questions in asset management.

Suggested Citation

  • Kempf, Alexander & Memmel, Christoph, 2005. "On the estimation of the global minimum variance portfolio," CFR Working Papers 05-02, University of Cologne, Centre for Financial Research (CFR).
  • Handle: RePEc:zbw:cfrwps:0502
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    References listed on IDEAS

    as
    1. Ravi Jagannathan & Tongshu Ma, 2003. "Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps," Journal of Finance, American Finance Association, vol. 58(4), pages 1651-1683, August.
    2. Larry R. Gorman & Bjorn N. Jorgensen, 2002. "Domestic versus International Portfolio Selection: A Statistical Examination of the Home Bias," Multinational Finance Journal, Multinational Finance Journal, vol. 6(3-4), pages 131-166, September.
    3. Raymond Kan & Guofu Zhou, 2012. "Tests of Mean-Variance Spanning," Annals of Economics and Finance, Society for AEF, vol. 13(1), pages 139-187, May.
    4. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    5. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    6. Ledoit, Olivier & Wolf, Michael, 2003. "Improved estimation of the covariance matrix of stock returns with an application to portfolio selection," Journal of Empirical Finance, Elsevier, vol. 10(5), pages 603-621, December.
    7. repec:bla:jfinan:v:58:y:2003:i:4:p:1651-1684 is not listed on IDEAS
    8. Merton, Robert C., 1980. "On estimating the expected return on the market : An exploratory investigation," Journal of Financial Economics, Elsevier, vol. 8(4), pages 323-361, December.
    9. Dickinson, J. P., 1974. "The Reliability of Estimation Procedures in Portfolio Analysis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 9(3), pages 447-462, June.
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    Citations

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    Cited by:

    1. Valentina Galvani & Stuart Landon, 2013. "Riding the yield curve: a spanning analysis," Review of Quantitative Finance and Accounting, Springer, vol. 40(1), pages 135-154, January.
    2. Ahmad W. Bitar & Nathan de Carvalho & Valentin Gatignol, 2023. "Covariance matrix estimation for robust portfolio allocation," Working Papers hal-04046454, HAL.
    3. Syed Zakir Abbas ZAIDI*, 2017. "Determinants Of Stocks For Optimal Portfolio," Pakistan Journal of Applied Economics, Applied Economics Research Centre, vol. 27(1), pages 1-27.

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    More about this item

    Keywords

    Global Minimum Variance Portfolio; Weight Estimation; Estimation Risk;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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