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.

中文翻译：

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稳健的优化能否改善投资组合的绩效？印度市场的实证研究

鲁棒性优化的出现主要是由解决Markowitz模型缺点的必要性驱动的。关于在投资组合绩效方面考虑将稳健方法视为优于或与Markowitz模型相提并论的争论一直存在。为了解决这种怀疑，我们对三个稳健的优化模型进行了实证分析，即基于Box，椭圆体和可分离不确定性集的模型。我们得出的结论是，不仅在模拟数据中，而且在涉及S＆P BSE 30和S＆P BSE 100印度指数的真实市场环境中，健壮的方法都可以视为Markowitz模型的可行替代方案。最后，