It is well known that there are asymmetric dependence structures between financial returns. This paper describes a portfolio selection method rooted in the classical mean–variance framework that incorporates such asymmetric dependency structures using a nonparametric measure of local dependence, the local Gaussian correlation (LGC). It is shown that the portfolio optimization process for financial returns with asymmetric dependence structures is straightforward using local covariance matrices. The new method is shown to outperform the equally weighted (“1/N”) portfolio and the classical Markowitz portfolio when applied to historical data on six assets.

众所周知，财务收益之间存在不对称的依赖结构。本文描述了一种根植于经典均值-方差框架的投资组合选择方法，该方法使用局部依赖的非参数度量、局部高斯相关 (LGC) 结合了这种非对称依赖结构。结果表明，使用局部协方差矩阵，具有非对称依赖结构的金融回报的投资组合优化过程很简单。当应用于六种资产的历史数据时，新方法的表现优于等权重（“1/N”）投资组合和经典 Markowitz 投资组合。