  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.
