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.

Description

Keywords

Covariance matrix estimation, Covariance regression, Portfolio management, Positive definiteness

Citation

Source

Journal of the American Statistical Association

Type

Journal article

Book Title

Entity type

Access Statement

Open Access

License Rights

CC BY-NC-ND

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