具有瓦瑟斯坦距离的分布鲁棒均值-方差投资组合
Distributionally Robust Mean-Variance Portfolio Selection with Wasserstein Distances
摘要：
我们重新Markowitz的均值-方差投资组合选择模型，考虑一个分布稳健的版本，其中分布不确定性的区域是周围的经验措施和概率测度之间的差异是由所谓的瓦瑟斯坦距离。我们把这个问题归结为一个带有额外正则化项的经验方差最小化问题。此外，我们扩展了最近的推理方法，以选择分布不确定性的大小以及相关的强大的目标回报率在数据驱动的方式。
英文摘要：
We revisit Markowitz's mean-variance portfolio selection model by considering a distributionally robust version, where the region of distributional uncertainty is around the empirical measure and the discrepancy between probability measures is dictated by the so-called Wasserstein distance. We reduce this problem into an empirical variance minimization problem with an additional regularization term. Moreover, we extend recent inference methodology in order to select the size of the distributional uncertainty as well as the associated robust target return rate in a data-driven way.
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