Covariance Regression Analysis
Date
2017
Authors
Zou, Tao
Lan, Wei
Wang, Hansheng
Tsai, Chih-Ling
Journal Title
Journal ISSN
Volume Title
Publisher
American Statistical Association
Abstract
This article introduces covariance regression analysis for a p-dimensional response vector. The proposed method explores the regression relationship between the p-dimensional covariance matrix and auxiliary information. We study three types of estimators: maximum likelihood, ordinary least squares, and feasible generalized least squares estimators. Then, we demonstrate that these regression estimators are consistent and asymptotically normal. Furthermore, we obtain the high dimensional and large sample properties of the corresponding covariance matrix estimators. Simulation experiments are presented to demonstrate the performance of both regression and covariance matrix estimates. An example is analyzed from the Chinese stock market to illustrate the usefulness of the proposed covariance regression model.
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Keywords
Covariance matrix estimation, Covariance regression, Portfolio management, Positive definiteness
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Source
Journal of the American Statistical Association
Type
Journal article
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Access Statement
Open Access
License Rights
CC BY-NC-ND
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