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Empirical Comparison of Robust Portfolios’ Investment Effects


  • Bartosz Kaszuba


The purpose of this article is to assess whether correct application of robust estimators in construction of minimum variance portfolios' (MVP) allows to achieve better investment results in comparison with portfolios defined using classical MLE estimators. Theoretical robust portfolios properties and portfolios investment effect are compared. This paper proposes a practical methodology of comparing alternative estimation methods, based on random portfolio selection. This approach enables to analyse investment effects of various portfolios. The empirical analysis shows that for MVP portfolios with nonnegative constraints created using robust methods, there is no significant risk improvement, and that even for most robust methods, there is an observable significant risk increase compared to the risk of classical portfolios. This paper also shows that robust portfolio estimators cause higher transaction cost.

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  • 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.
  • Handle: RePEc:rfb:journl:v:05:y:2013:i:1:p:047-061

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    References listed on IDEAS

    1. Luigi Grossi & Fabrizio Laurini, 2011. "Robust estimation of efficient mean–variance frontiers," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 5(1), pages 3-22, April.
    2. Beatriz Vaz de Melo Mendes & Ricardo Pereira Camara Leal, 2005. "Robust multivariate modeling in finance," International Journal of Managerial Finance, Emerald Group Publishing, vol. 1(2), pages 95-106, April.
    3. 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.
    4. C Papahristodoulou & E Dotzauer, 2004. "Optimal portfolios using linear programming models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(11), pages 1169-1177, November.
    5. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    6. Schäfer Juliane & Strimmer Korbinian, 2005. "A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-32, November.
    7. 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 1651-1684, August.
    8. Khan, Jafar A. & Van Aelst, Stefan & Zamar, Ruben H., 2007. "Robust Linear Model Selection Based on Least Angle Regression," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1289-1299, December.
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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.

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