Estimating High Dimensional Covariance Matrices and Its Applications
Bai
Jushan
author
Columbia University. Economics
Shi
Shuzhong
author
Columbia University. Economics
originator
contributor
text
Working papers
New York
Department of Economics, Columbia University
2011
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.
Economic theory
Department of Economics Discussion Papers
1112-03
http://hdl.handle.net/10022/AC:P:11064
English
NNC
NNC
2011-09-01 14:57:26 -0400
2011-09-01 15:01:17 -0400
5066
eng