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Markowitz portfolios under transaction costs

Author

Listed:
  • Olivier Ledoit
  • Michael Wolf

Abstract

Markowitz portfolio selection is a cornerstone in finance, in academia as well as in the industry. Most academic studies either ignore transaction costs or account for them in a way that is both unrealistic and suboptimal by (i) assuming transaction costs to be constant across stocks and (ii) ignoring them at the portfolio-selection state and simply paying them after the fact. Our paper proposes a method to fix both shortcomings. As we show, if transaction costs are accounted for (properly) at the portfolio-selection stage, net performance in terms of the Sharpe ratio often increases, in particular for high-turnover strategies.

Suggested Citation

  • Olivier Ledoit & Michael Wolf, 2022. "Markowitz portfolios under transaction costs," ECON - Working Papers 420, Department of Economics - University of Zurich, revised Sep 2024.
    • Handle: RePEc:zur:econwp:420
    as

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    File URL: https://www.zora.uzh.ch/id/eprint/221804/13/econwp420.pdf
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    References listed on IDEAS

    as
    1. Olivier Ledoit & Michael Wolf, 2022. "The Power of (Non-)Linear Shrinking: A Review and Guide to Covariance Matrix Estimation [Design-Free Estimation of Variance Matrices]," Journal of Financial Econometrics, Oxford University Press, vol. 20(1), pages 187-218.
    2. Victor DeMiguel & Lorenzo Garlappi & Raman Uppal, 2009. "Optimal Versus Naive Diversification: How Inefficient is the 1-N Portfolio Strategy?," The Review of Financial Studies, Society for Financial Studies, vol. 22(5), pages 1915-1953, May.
    3. Guanhao Feng & Stefano Giglio & Dacheng Xiu, 2020. "Taming the Factor Zoo: A Test of New Factors," Journal of Finance, American Finance Association, vol. 75(3), pages 1327-1370, June.
    4. Victor DeMiguel & Lorenzo Garlappi & Francisco J. Nogales & Raman Uppal, 2009. "A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms," Management Science, INFORMS, vol. 55(5), pages 798-812, May.
    5. Frahm, Gabriel & Memmel, Christoph, 2010. "Dominating estimators for minimum-variance portfolios," Journal of Econometrics, Elsevier, vol. 159(2), pages 289-302, December.
    6. Ledoit, Oliver & Wolf, Michael, 2008. "Robust performance hypothesis testing with the Sharpe ratio," Journal of Empirical Finance, Elsevier, vol. 15(5), pages 850-859, December.
    7. Robert F. Engle & Olivier Ledoit & Michael Wolf, 2019. "Large Dynamic Covariance Matrices," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(2), pages 363-375, April.
    8. Gianluca De Nard & Olivier Ledoit & Michael Wolf, 2021. "Factor Models for Portfolio Selection in Large Dimensions: The Good, the Better and the Ugly [Using Principal Component Analysis to Estimate a High Dimensional Factor Model with High-frequency Data," Journal of Financial Econometrics, Oxford University Press, vol. 19(2), pages 236-257.
    9. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    10. repec:hal:journl:peer-00741629 is not listed on IDEAS
    11. Garman, Mark B & Klass, Michael J, 1980. "On the Estimation of Security Price Volatilities from Historical Data," The Journal of Business, University of Chicago Press, vol. 53(1), pages 67-78, January.
    12. Mengmeng Ao & Li Yingying & Xinghua Zheng, 2019. "Approaching Mean-Variance Efficiency for Large Portfolios," The Review of Financial Studies, Society for Financial Studies, vol. 32(7), pages 2890-2919.
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    More about this item

    Keywords

    Covariance matrix estimation; mean-variance efficiency; multivariate GARCH; portfolio selection; transaction costs;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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