Robust optimization (RO) models have attracted a lot of interest in the area of portfolio selection. RO extends the framework of traditional portfolio optimization models, incorporating uncertainty through a formal and analytical approach into the modeling process. Although several RO models have been proposed in the literature, comprehensive empirical assessments of their performance are rather lacking. The objective of this study is to fill in this gap in the literature. To this end, we consider different types of RO models based on popular risk measures and conduct an extensive comparative analysis of their performance using data from the US market during the period 2005-2020. For the analysis, two different robust versions of the mean-variance model are considered, together with robust models for conditional value-at-risk and the Omega ratio. The robust versions are compared against the nominal ones through various portfolio performance metrics, focusing on out-of-sample results.

中文翻译：

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稳健的投资组合选择优化方法：比较分析。

稳健优化 (RO) 模型在投资组合选择领域引起了很多兴趣。RO 扩展了传统投资组合优化模型的框架，通过正式和分析方法将不确定性纳入建模过程。尽管文献中已经提出了几种 RO 模型，但对其性能的综合实证评估却相当缺乏。本研究的目的是填补文献中的这一空白。为此，我们考虑了基于流行风险度量的不同类型的 RO 模型，并使用 2005-2020 年期间美国市场的数据对其性能进行了广泛的比较分析。在分析中，考虑了均值方差模型的两种不同稳健版本，以及用于条件风险价值和 Omega 比率的稳健模型。