A Dynamic Structure for High-Dimensional Covariance Matrices and Its Application in Portfolio Allocation

• 成果类型: Article
• 署名作者: Guo, Shaojun; Box, John Leigh; Zhang, Wenyang
• 署名单位: Renmin University of China; University of York - UK
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2015.1129969
• 发表日期: 2017
• 页码: 235-253
• 关键词: single-index models; longitudinal data; selection; rates

摘要

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.