Solving extended mean-variance models using tensor analysis
展示了张量分析如何用于确定扩展的均值-方差模型中的最优权重，该模型考虑了多元偏斜椭圆概率分布的风险，为投资组合选择问题提供了更全面的分析框架。
We demonstrate how tensor analysis serves as a powerful tool for determining the optimal weights in extended versions of Markowitz's Mean-Variance model, particularly when addressing risks characterized by multivariate skew-elliptical probability distributions. Our approach preserves the familiar structure of the original mean-variance model while incorporating a risk aversion parameter that reflects the distribution of portfolio returns. This extension offers a more comprehensive representation of optimal portfolio selection problems. Consequently, the proposed model paves the way for advanced analytical solutions of extended versions of the mean-variance model.