A Dynamic Structure for High-Dimensional Covariance Matrices and Its Application in Portfolio Allocation
提出高维协方差矩阵的动态结构及估计方法，理论证明渐近性质，模拟验证有限样本表现，实证显示基于该方法的投资组合在1995-2014年显著跑赢市场，且优于样本协方差、因子模型和收缩估计方法。
Estimation of high-dimensional covariance matrices is an interesting and important research topic. In this article, we propose a dynamic structure and develop an estimation procedure for high-dimensional covariance matrices. Asymptotic properties are derived to justify the estimation procedure and simulation studies are conducted to demonstrate its performance when the sample size is finite. By exploring a financial application, an empirical study shows that portfolio allocation based on dynamic high-dimensional covariance matrices can significantly outperform the market from 1995 to 2014. Our proposed method also outperforms portfolio allocation based on the sample covariance matrix, the covariance matrix based on factor models, and the shrinkage estimator of covariance matrix. Supplementary materials for this article are available online.