It is well known that there are asymmetric dependence structures between financial returns. In this paper we use a new nonparametric measure of local dependence, the local Gaussian correlation, to improve portfolio allocation. We extend the classical mean-variance framework, and show that the portfolio optimization is straightforward using our new approach, only relying on a tuning parameter (the bandwidth). The new method is shown to outperform the equally weighted (1/N) portfolio and the classical Markowitz portfolio for monthly asset returns data.

翻译：众所周知,金融回报之间有不对称依赖结构。 在本文中,我们使用一种新的非参数性的地方依赖性衡量方法,即当地高斯的关联性,来改善投资组合的分配。 我们扩展了典型的中位变量框架,并表明投资组合的优化使用我们的新办法是直截了当的,只依赖于调频参数(带宽 ) 。 新的方法显示,月度资产回报数据比同等加权(1/N)投资组合和典型的马尔科维茨投资组合效果要好。