IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v44y2022ics154461232100146x.html
   My bibliography  Save this article

An Empirical Evaluation of Sensitivity Bounds for Mean-Variance Portfolio Optimisation

Author

Listed:
  • Paskaramoorthy, Andrew
  • Woolway, Matthew

Abstract

It is commonly thought that a poorly conditioned covariance matrix causes the sensitivity of mean-variance optimised portfolios to deviations in expected return forecasts. In this research, we question this explanation and show that it does not necessarily hold when a budget constraint is included in the optimisation problem. Our research is centred on the analytical results derived by Best and Grauer (1991) that describes the maximum amount by which a portfolio and its performance can change due to changes in the mean vector. Our empirical analysis shows that these derived bounds can overstate the actual corresponding maximums by several orders of magnitude. We explain these results with reference to the original derivations. In conclusion, we find that these bounds, and the condition number, in particular, are unable to characterise portfolio sensitivity.

Suggested Citation

  • Paskaramoorthy, Andrew & Woolway, Matthew, 2022. "An Empirical Evaluation of Sensitivity Bounds for Mean-Variance Portfolio Optimisation," Finance Research Letters, Elsevier, vol. 44(C).
    • Handle: RePEc:eee:finlet:v:44:y:2022:i:c:s154461232100146x
    DOI: 10.1016/j.frl.2021.102065
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S154461232100146X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2021.102065?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ledoit, Olivier & Wolf, Michael, 2004. "A well-conditioned estimator for large-dimensional covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 88(2), pages 365-411, February.
    2. Victor DeMiguel & Lorenzo Garlappi & Raman Uppal, 2009. "Optimal Versus Naive Diversification: How Inefficient is the 1-N Portfolio Strategy?," Review of Financial Studies, Society for Financial Studies, vol. 22(5), pages 1915-1953, May.
    3. Hurley, W.J. & Brimberg, Jack, 2015. "A note on the sensitivity of the strategic asset allocation problem," Operations Research Perspectives, Elsevier, vol. 2(C), pages 133-136.
    4. Kan, Raymond & Zhou, Guofu, 2007. "Optimal Portfolio Choice with Parameter Uncertainty," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 42(3), pages 621-656, September.
    5. ., 2021. "Empirical analysis, research design and methodology," Chapters, in: A Guide to Islamic Asset Management, chapter 4, pages 77-165, Edward Elgar Publishing.
    6. Best, Michael J & Grauer, Robert R, 1991. "On the Sensitivity of Mean-Variance-Efficient Portfolios to Changes in Asset Means: Some Analytical and Computational Results," Review of Financial Studies, Society for Financial Studies, vol. 4(2), pages 315-342.
    7. Okhrin, Yarema & Schmid, Wolfgang, 2006. "Distributional properties of portfolio weights," Journal of Econometrics, Elsevier, vol. 134(1), pages 235-256, September.
    8. ., 2021. "The empirical context: the global apparel value chain," Chapters, in: The Contest for Value in Global Value Chains, chapter 3, pages 22-32, Edward Elgar Publishing.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lassance, Nathan & Vanderveken, Rodolphe & Vrins, Frédéric, 2022. "On the optimal combination of naive and mean-variance portfolio strategies," LIDAM Discussion Papers LFIN 2022006, Université catholique de Louvain, Louvain Finance (LFIN).
    2. Kourtis, Apostolos & Dotsis, George & Markellos, Raphael N., 2012. "Parameter uncertainty in portfolio selection: Shrinking the inverse covariance matrix," Journal of Banking & Finance, Elsevier, vol. 36(9), pages 2522-2531.
    3. Ding, Wenliang & Shu, Lianjie & Gu, Xinhua, 2023. "A robust Glasso approach to portfolio selection in high dimensions," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 22-37.
    4. Chavez-Bedoya, Luis & Rosales, Francisco, 2022. "Orthogonal portfolios to assess estimation risk," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 906-937.
    5. Istvan Varga-Haszonits & Fabio Caccioli & Imre Kondor, 2016. "Replica approach to mean-variance portfolio optimization," Papers 1606.08679, arXiv.org.
    6. Thomas Trier Bjerring & Omri Ross & Alex Weissensteiner, 2017. "Feature selection for portfolio optimization," Annals of Operations Research, Springer, vol. 256(1), pages 21-40, September.
    7. Johannes Bock, 2018. "An updated review of (sub-)optimal diversification models," Papers 1811.08255, arXiv.org.
    8. Irina Murtazashvili & Nadia Vozlyublennaia, 2013. "Diversification Strategies: Do Limited Data Constrain Investors?," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 36(2), pages 215-232, June.
    9. Palczewski, Andrzej & Palczewski, Jan, 2014. "Theoretical and empirical estimates of mean–variance portfolio sensitivity," European Journal of Operational Research, Elsevier, vol. 234(2), pages 402-410.
    10. Kircher, Felix & Rösch, Daniel, 2021. "A shrinkage approach for Sharpe ratio optimal portfolios with estimation risks," Journal of Banking & Finance, Elsevier, vol. 133(C).
    11. Varga-Haszonits, Istvan & Caccioli, Fabio & Kondor, Imre, 2016. "Replica approach to mean-variance portfolio optimization," LSE Research Online Documents on Economics 68955, London School of Economics and Political Science, LSE Library.
    12. Ruili Sun & Tiefeng Ma & Shuangzhe Liu, 2018. "A Stein-type shrinkage estimator of the covariance matrix for portfolio selections," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(8), pages 931-952, November.
    13. DeMiguel, Victor & Martin-Utrera, Alberto & Nogales, Francisco J., 2013. "Size matters: Optimal calibration of shrinkage estimators for portfolio selection," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 3018-3034.
    14. Härdle, Wolfgang & Klochkov, Yegor & Petukhina, Alla & Zhivotovskiy, Nikita, 2021. "Robustifying Markowitz," IRTG 1792 Discussion Papers 2021-018, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    15. Eckhard Platen & Renata Rendek, 2017. "Market Efficiency and the Growth Optimal Portfolio," Research Paper Series 386, Quantitative Finance Research Centre, University of Technology, Sydney.
    16. Füss, Roland & Miebs, Felix & Trübenbach, Fabian, 2014. "A jackknife-type estimator for portfolio revision," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 14-28.
    17. Raymond Kan & Xiaolu Wang & Guofu Zhou, 2022. "Optimal Portfolio Choice with Estimation Risk: No Risk-Free Asset Case," Management Science, INFORMS, vol. 68(3), pages 2047-2068, March.
    18. Wolfgang Karl Hardle & Yegor Klochkov & Alla Petukhina & Nikita Zhivotovskiy, 2022. "Robustifying Markowitz," Papers 2212.13996, arXiv.org.
    19. Behr, Patrick & Guettler, Andre & Truebenbach, Fabian, 2012. "Using industry momentum to improve portfolio performance," Journal of Banking & Finance, Elsevier, vol. 36(5), pages 1414-1423.
    20. Candelon, B. & Hurlin, C. & Tokpavi, S., 2012. "Sampling error and double shrinkage estimation of minimum variance portfolios," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 511-527.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:finlet:v:44:y:2022:i:c:s154461232100146x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.