英文标题：
《Can robust optimization offer improved portfolio performance?: An
  empirical study of Indian market》
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作者：
Shashank Oberoi and Mohammed Bilal Girach and Siddhartha P.
  Chakrabarty
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最新提交年份：
2019
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英文摘要：
  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.
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中文摘要：
稳健优化的出现主要是因为必须解决马科维茨模型的缺点。就投资组合绩效而言，对于是否考虑稳健方法优于或与马科维茨模型相当，存在着值得注意的争论。为了解决这种怀疑，我们对三种稳健优化模型进行了实证分析，即基于长方体、椭球体和可分离不确定性集的模型。我们得出结论，稳健的方法可以被视为马科维茨模型的可行替代方法，不仅在模拟数据中，而且在真实的市场环境中，包括印度标准普尔BSE 30和标准普尔BSE 100指数。最后，我们从股票数量、样本大小和数据类型的角度，对稳健优化方法的实用性提供了定性和定量的证明。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Portfolio Management        项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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