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

Shashank Oberoi (), Mohammed Bilal Girach () and Siddhartha P. Chakrabarty ()
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Shashank Oberoi: Indian Institute of Technology Guwahati
Mohammed Bilal Girach: Indian Institute of Technology Guwahati
Siddhartha P. Chakrabarty: Indian Institute of Technology Guwahati

Journal of Quantitative Economics, 2020, vol. 18, issue 3, No 6, 630 pages

Abstract: 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.

Keywords: Robust portfolio optimization; Worst case scenario; Uncertainty sets; S&P BSE 30; S&P BSE 100 (search for similar items in EconPapers)
JEL-codes: G11 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s40953-020-00205-z

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