cov_estim_lwcc_sf: Ledoit-Wolf Covariance Estimation (Linear Shrinkage) IV

In antshi/CovEstim: (High-dimensional) Covariance Estimation

Ledoit-Wolf Covariance Estimation (Linear Shrinkage) IV

Description

Computes the Ledoit-Wolf linear shrinkage estimator of the covariance matrix towards the one-factor covariance matrix.

Usage

cov_estim_lwcc_sf(data, shrink_int = NULL, zeromean_log = FALSE)

Arguments

data	an nxp data matrix.
shrink_int	a double, indicating the shrinkage intensity. Default is the optimal shrinkage intensity as in \insertCiteledoit2003identity;textualcovestim.
zeromean_log	a logical, indicating whether the data matrix has zero means (TRUE) or not (FALSE). Default value is FALSE.

Details

The Ledoit-Wolf linear shrinkage estimator of the covariance matrix towards the one-factor covariance matrix is calculated with the following formula:

\hat{\Sigma}= s\Sigma_{T} + (1-s)\Sigma,

where \Sigma is the sample covariance matrix, s is the user-supplied or optimal shrinkage intensity and \Sigma_{T} is the covariance matrix estimator, given by a one-factor model, where the factor is equal to the cross-sectional average of all the variables. This covariance estimator assumes a zero correlation and variances of one as the underlying covariance structure of the data. A corresponding MATLAB code for the estimator can be accessed under https://www.econ.uzh.ch/en/people/faculty/wolf/publications.html.

Value

a list with the following entries

• a pxp estimated covariance matrix.

• an estimation specific tuning parameter, here the shrinkage intensity.

References

\insertAllCited

Examples

data(rets_m)
sigma_lwcc_sf <- cov_estim_lwcc_sf(rets_m)[[1]]

antshi/CovEstim documentation built on June 10, 2025, 3:11 a.m.