基于协方差矩阵估计的资产配置策略

Palla László
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引用次数: 2

摘要

协方差矩阵是许多资产配置策略的重要组成部分。目前广泛使用的样本协方差矩阵估计在时间观测次数少而资产数量多或计算涉及高维数据时存在不稳定性问题。在本研究中,我们将重点关注应用于一组markowitz型策略的最重要的估计量,以及最近引入的基于分层树聚类的方法。使用不同协方差矩阵估计的投资组合策略的性能测试依赖于合成和真实股票数据的样本外特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Asset Allocation Strategies Using Covariance Matrix Estimators
Abstract The covariance matrix is an important element of many asset allocation strategies. The widely used sample covariance matrix estimator is unstable especially when the number of time observations is small and the number of assets is large or when high-dimensional data is involved in the computation. In this study, we focus on the most important estimators that are applied on a group of Markowitz-type strategies and also on a recently introduced method based on hierarchical tree clustering. The performance tests of the portfolio strategies using different covariance matrix estimators rely on the out-of-sample characteristics of synthetic and real stock data.
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