Sample covariance shrinkage for high dimensional dependent data

  • Authors:

  • Alessio Sancetta

  • Affiliations:

  • Faculty of Economics, Austin Robinson Building, Sidgwick Avenue, Cambridge CB3 9DE, UK

  • Venue:
  • Journal of Multivariate Analysis
  • Year:
  • 2008

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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.