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Estimating High Dimensional Covariance Matrices and Its Applications

Jushan Bai; Shuzhong Shi

Title:
Estimating High Dimensional Covariance Matrices and Its Applications
Author(s):
Bai, Jushan
Shi, Shuzhong
Date:
Type:
Working papers
Department:
Economics
Permanent URL:
Series:
Department of Economics Discussion Papers
Part Number:
1112-03
Publisher:
Department of Economics, Columbia University
Publisher Location:
New York
Abstract:
Estimating covariance matrices is an important part of portfolio selection, risk management, and asset pricing. This paper reviews the recent development in estimating high dimensional covariance matrices, where the number of variables can be greater than the number of observations. The limitations of the sample covariance matrix are discussed. Several new approaches are presented, including the shrinkage method, the observable and latent factor method, the Bayesian approach, and the random matrix theory approach. For each method, the construction of covariance matrices is given. The relationships among these methods are discussed.
Subject(s):
Economic theory
Item views:
1055
Metadata:
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