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Sample Covariance Shrinkage for High Dimensional Dependent Data

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Author Info
Sancetta, A.

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Abstract

For high dimensional data sets the sample covariance matrix is usually unbiased but noisy if the sample is not large enough. Shrinking the sample covariance towards a constrained, low dimensional estimator can be used to mitigate the sample variability. By doing so, we introduce bias, but reduce variance. In this paper, we give details on feasible optimal shrinkage allowing for time series dependent observations.

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File URL: http://www.econ.cam.ac.uk/dae/repec/cam/pdf/cwpe0637.pdf
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Publisher Info
Paper provided by Faculty of Economics, University of Cambridge in its series Cambridge Working Papers in Economics with number 0637.

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Length: 25
Date of creation: May 2006
Date of revision:
Handle: RePEc:cam:camdae:0637

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Related research
Keywords: Sample Covariance Matrix; Shrinkage; Weak Dependence;

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Find related papers by JEL classification:
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods

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  1. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-58, May. [Downloadable!] (restricted)
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  2. Ledoit, Olivier & Wolf, Michael, 2003. "Improved estimation of the covariance matrix of stock returns with an application to portfolio selection," Journal of Empirical Finance, Elsevier, vol. 10(5), pages 603-621, December. [Downloadable!] (restricted)
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This page was last updated on 2009-11-16.

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