IDEAS home Printed from https://ideas.repec.org/p/fam/rpseri/rp140.html
   My bibliography  Save this paper

Robust Mean-Variance Portfolio Selection

Author

Listed:
  • Cédric Perret-Gentil

    (Union Bancaire Privée)

  • Maria-Pia Victoria-Feser

    (HEC,University of Geneva)

Abstract

This paper investigates model risk issues in the context of mean-variance portfolio selection. We analytically and numerically show that, under model misspecification, the use of statistically robust estimates instead of the widely used classical sample mean and covariance is highly beneficial for the stability properties of the mean-variance optimal portfolios. Moreover, we perform simulations leading to the conclusion that, under classical estimation, model risk bias dominates estimation risk bias. Finally, we suggest a diagnostic tool to warn the analyst of the presence of extreme returns that have an abnormally large influence on the optimization results.

Suggested Citation

  • Cédric Perret-Gentil & Maria-Pia Victoria-Feser, 2005. "Robust Mean-Variance Portfolio Selection," FAME Research Paper Series rp140, International Center for Financial Asset Management and Engineering.
    • Handle: RePEc:fam:rpseri:rp140
    as

    Download full text from publisher

    File URL: http://www.swissfinanceinstitute.ch/rp140.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Barry, Christopher B, 1974. "Portfolio Analysis under Uncertain Means, Variances, and Covariances," Journal of Finance, American Finance Association, vol. 29(2), pages 515-522, May.
    2. Alexander, Gordon J & Resnick, Bruce G, 1985. "More on Estimation Risk and Simple Rules for Optimal Portfolio Selection," Journal of Finance, American Finance Association, vol. 40(1), pages 125-133, March.
    3. Victoria-Feser, M.-P., 2000. "Robust Portfolio Selection," Papers 2000.14, Ecole des Hautes Etudes Commerciales, Universite de Geneve-.
    4. Mark Britten‐Jones, 1999. "The Sampling Error in Estimates of Mean‐Variance Efficient Portfolio Weights," Journal of Finance, American Finance Association, vol. 54(2), pages 655-671, April.
    5. Ter Horst, J.R. & de Roon, F.A. & Werker, B.J.M., 2000. "Incorporating Estimation Risk in Portfolio Choice," Discussion Paper 2000-65, Tilburg University, Center for Economic Research.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Giuseppe Pandolfo & Carmela Iorio & Roberta Siciliano & Antonio D’Ambrosio, 2020. "Robust mean-variance portfolio through the weighted $$L^{p}$$ L p depth function," Annals of Operations Research, Springer, vol. 292(1), pages 519-531, September.
    2. Hubert, Mia & Dierckx, Goedele & Vanpaemel, Dina, 2013. "Detecting influential data points for the Hill estimator in Pareto-type distributions," Computational Statistics & Data Analysis, Elsevier, vol. 65(C), pages 13-28.
    3. Aida Toma & Samuela Leoni-Aubin, 2015. "Robust Portfolio Optimization Using Pseudodistances," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-26, October.
    4. Bartosz Kaszuba, 2012. "Empirical Comparison of Robust Portfolios’ Investment Effects," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 5(1), pages 047-061, June.
    5. Stanislav Škapa & Tomáš Meluzín & Marek Zinecker, 2013. "A critical evaluation of risk-return characteristics of environmentally focused stock's companies," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 61(2), pages 501-506.

    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. Penaranda, Francisco, 2007. "Portfolio choice beyond the traditional approach," LSE Research Online Documents on Economics 24481, London School of Economics and Political Science, LSE Library.
    2. Richard C. Burgess & Roger P. Bey, 1988. "Optimal Portfolios: Markowitz Full Covariance Versus Simple Selection Rules," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 11(2), pages 153-163, June.
    3. Ter Horst, J.R. & de Roon, F.A. & Werker, B.J.M., 2000. "Incorporating Estimation Risk in Portfolio Choice," Discussion Paper 2000-65, Tilburg University, Center for Economic Research.
    4. Clarence C. Y. Kwan, 2018. "What really happens if the positive definiteness requirement on the covariance matrix of returns is relaxed in efficient portfolio selection?," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 32(1), pages 77-110, February.
    5. Ter Horst, J.R. & de Roon, F.A. & Werker, B.J.M., 2000. "Incorporating Estimation Risk in Portfolio Choice," Other publications TiSEM 30107fbe-2dc9-43d5-a086-e, Tilburg University, School of Economics and Management.
    6. Bodnar, Taras & Mazur, Stepan & Okhrin, Yarema, 2017. "Bayesian estimation of the global minimum variance portfolio," European Journal of Operational Research, Elsevier, vol. 256(1), pages 292-307.
    7. 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.
    8. Raymond Kan & Daniel R. Smith, 2008. "The Distribution of the Sample Minimum-Variance Frontier," Management Science, INFORMS, vol. 54(7), pages 1364-1380, July.
    9. Muhinyuza, Stanislas & Bodnar, Taras & Lindholm, Mathias, 2020. "A test on the location of the tangency portfolio on the set of feasible portfolios," Applied Mathematics and Computation, Elsevier, vol. 386(C).
    10. Giovanni Bonaccolto & Massimiliano Caporin & Sandra Paterlini, 2018. "Asset allocation strategies based on penalized quantile regression," Computational Management Science, Springer, vol. 15(1), pages 1-32, January.
    11. Mishra, Anil V., 2016. "Foreign bias in Australian-domiciled mutual fund holdings," Pacific-Basin Finance Journal, Elsevier, vol. 39(C), pages 101-123.
    12. Andrew F. Siegel & Artemiza Woodgate, 2007. "Performance of Portfolios Optimized with Estimation Error," Management Science, INFORMS, vol. 53(6), pages 1005-1015, June.
    13. Sebastiano Michele Zema & Giorgio Fagiolo & Tiziano Squartini & Diego Garlaschelli, 2021. "Mesoscopic Structure of the Stock Market and Portfolio Optimization," Papers 2112.06544, arXiv.org.
    14. David E. Allen & Michael McAleer & Abhay K. Singh, 2016. "A Multi-Criteria Portfolio Analysis of Hedge Fund Strategies," Documentos de Trabajo del ICAE 2017-03, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    15. Mr. Piti Disyatat & Mr. Gaston Gelos, 2001. "The Asset Allocation of Emerging Market Mutual Funds," IMF Working Papers 2001/111, International Monetary Fund.
    16. Carmine De Franco & Johann Nicolle & Huyên Pham, 2019. "Dealing with Drift Uncertainty: A Bayesian Learning Approach," Risks, MDPI, vol. 7(1), pages 1-18, January.
    17. Juan-Pedro Gómez & Fernando Zapatero, 2001. "Asset pricing implications of benchmarking: A two-factor CAPM," Economics Working Papers 693, Department of Economics and Business, Universitat Pompeu Fabra.
    18. Maillet, Bertrand & Tokpavi, Sessi & Vaucher, Benoit, 2015. "Global minimum variance portfolio optimisation under some model risk: A robust regression-based approach," European Journal of Operational Research, Elsevier, vol. 244(1), pages 289-299.
    19. Viet Anh Nguyen & Fan Zhang & Shanshan Wang & Jose Blanchet & Erick Delage & Yinyu Ye, 2021. "Robustifying Conditional Portfolio Decisions via Optimal Transport," Papers 2103.16451, arXiv.org, revised Apr 2024.
    20. Patrick Bielstein, 2018. "International asset allocation using the market implied cost of capital," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 32(1), pages 17-51, February.

    More about this item

    Keywords

    Mean-variance e .cient frontier; Outliers; Model risk; Robust es-timation;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:fam:rpseri:rp140. 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: Ridima Mittal (email available below). General contact details of provider: https://edirc.repec.org/data/fameech.html .

    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.