Machine Learning Group, Computer Science Department, Technical University of Berlin, Berlin, Germany.
PLoS One. 2013 Jul 3;8(7):e67503. doi: 10.1371/journal.pone.0067503. Print 2013.
Robust and reliable covariance estimates play a decisive role in financial and many other applications. An important class of estimators is based on factor models. Here, we show by extensive Monte Carlo simulations that covariance matrices derived from the statistical Factor Analysis model exhibit a systematic error, which is similar to the well-known systematic error of the spectrum of the sample covariance matrix. Moreover, we introduce the Directional Variance Adjustment (DVA) algorithm, which diminishes the systematic error. In a thorough empirical study for the US, European, and Hong Kong stock market we show that our proposed method leads to improved portfolio allocation.
稳健可靠的协方差估计在金融和许多其他应用中起着决定性的作用。一类重要的估计量是基于因子模型的。在这里，我们通过广泛的蒙特卡罗模拟表明，来自统计因子分析模型的协方差矩阵表现出一种系统性误差，类似于样本协方差矩阵谱的众所周知的系统性误差。此外，我们引入了方向方差调整（DVA）算法，该算法可以减少系统性误差。在对美国、欧洲和香港股票市场的一项彻底的实证研究中，我们表明，我们提出的方法可以改进投资组合分配。
