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Can Robust Optimization Offer Improved Portfolio Performance? An Empirical Study of Indian market

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Listed:
  • Shashank Oberoi

    (Indian Institute of Technology Guwahati)

  • Mohammed Bilal Girach

    (Indian Institute of Technology Guwahati)

  • Siddhartha P. Chakrabarty

    (Indian Institute of Technology Guwahati)

Abstract

The emergence of robust optimization has been driven primarily by the necessity to address the demerits of the Markowitz model. There has been a noteworthy debate regarding consideration of robust approaches as superior or at par with the Markowitz model, in terms of portfolio performance. In order to address this skepticism, we perform empirical analysis of three robust optimization models, namely the ones based on box, ellipsoidal and separable uncertainty sets. We conclude that robust approaches can be considered as a viable alternative to the Markowitz model, not only in simulated data but also in a real market setup, involving the Indian indices of S&P BSE 30 and S&P BSE 100. Finally, we offer qualitative and quantitative justification regarding the practical usefulness of robust optimization approaches from the point of view of number of stocks, sample size and types of data.

Suggested Citation

  • Shashank Oberoi & Mohammed Bilal Girach & Siddhartha P. Chakrabarty, 2020. "Can Robust Optimization Offer Improved Portfolio Performance? An Empirical Study of Indian market," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 18(3), pages 611-630, September.
    • Handle: RePEc:spr:jqecon:v:18:y:2020:i:3:d:10.1007_s40953-020-00205-z
    DOI: 10.1007/s40953-020-00205-z
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    References listed on IDEAS

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    1. R.H. Tütüncü & M. Koenig, 2004. "Robust Asset Allocation," Annals of Operations Research, Springer, vol. 132(1), pages 157-187, November.
    2. André Alves Portela Santos, 2010. "The Out-of-Sample Performance of Robust Portfolio Optimization," Brazilian Review of Finance, Brazilian Society of Finance, vol. 8(2), pages 141-166.
    3. Sebastián Ceria & Robert A Stubbs, 2006. "Incorporating estimation errors into portfolio selection: Robust portfolio construction," Journal of Asset Management, Palgrave Macmillan, vol. 7(2), pages 109-127, July.
    4. Jang Ho Kim & Woo Chang Kim & Frank J. Fabozzi, 2014. "Recent Developments in Robust Portfolios with a Worst-Case Approach," Journal of Optimization Theory and Applications, Springer, vol. 161(1), pages 103-121, April.
    5. Victor DeMiguel & Francisco J. Nogales, 2009. "Portfolio Selection with Robust Estimation," Operations Research, INFORMS, vol. 57(3), pages 560-577, June.
    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," The Review of Financial Studies, Society for Financial Studies, vol. 4(2), pages 315-342.
    7. B.V. Halldórsson & R.H. Tütüncü, 2003. "An Interior-Point Method for a Class of Saddle-Point Problems," Journal of Optimization Theory and Applications, Springer, vol. 116(3), pages 559-590, March.
    8. Bernd Scherer, 2007. "Can robust portfolio optimisation help to build better portfolios?," Journal of Asset Management, Palgrave Macmillan, vol. 7(6), pages 374-387, March.
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    Cited by:

    1. Ruchika Sehgal & Aparna Mehra, 2023. "Quantile Regression Based Enhanced Indexing with Portfolio Rebalancing," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(3), pages 721-742, September.

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    More about this item

    Keywords

    Robust portfolio optimization; Worst case scenario; Uncertainty sets; S&P BSE 30; S&P BSE 100;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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