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The emergence of robust optimization has been driven primarily by thenecessity to address the demerits of the Markowitz model. There has been anoteworthy debate regarding consideration of robust approaches as superior orat par with the Markowitz model, in terms of portfolio performance. In order toaddress this skepticism, we perform empirical analysis of three robustoptimization models, namely the ones based on box, ellipsoidal and separableuncertainty sets. We conclude that robust approaches can be considered as aviable alternative to the Markowitz model, not only in simulated data but alsoin a real market setup, involving the Indian indices of S&P BSE 30 and S&P BSE100. Finally, we offer qualitative and quantitative justification regarding thepractical usefulness of robust optimization approaches from the point of viewof number of stocks, sample size and types of data.

Can robust optimization offer improved portfolio performance?: An empirical study of Indian market