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Robust Portfolio Selection

Author

Listed:
  • Victoria-Feser, M.-P.

Abstract

In this paper, we discuss one of the reasons leading practitionners to the rejection of the Markowitz model and propose a new stastistical method to avoid this problem. To be more precise, we discuss the problem of statistical robustness of the Markowitz optimizer and show that the latter is not robust, meaning that a few extreme assets prices or returns can lead to irrelevant 'optimal' portfolios. We then propose a robust Markowitz optimizer and show that it is far more stable than the classical version.

Suggested Citation

  • Victoria-Feser, M.-P., 2000. "Robust Portfolio Selection," Papers 2000.14, Ecole des Hautes Etudes Commerciales, Universite de Geneve-.
    • Handle: RePEc:fth:ehecge:2000.14
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    Citations

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    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. La Gubu & Dedi Rosadi & Abdurakhman, 2020. "Robust Mean–Variance Portfolio Selection Using Cluster Analysis: A Comparison between Kamila and Weighted K-Mean Clustering," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 10(10), pages 1169-1186, October.
    3. 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.

    More about this item

    Keywords

    INVESTMENTS ; MODELS ; STATISTICAL ANALYSIS ; PRICES;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory

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