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- W2132027803 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." @default.
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- W2132027803 date "2011-01-01" @default.
- W2132027803 modified "2023-10-18" @default.
- W2132027803 title "Estimating High Dimensional Covariance Matrices and Its Applications" @default.
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- W2132027803 doi "https://doi.org/10.7916/d8rj4sgp" @default.
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