sigma_estim_lwone_cpp: Ledoit-Wolf Linear Shrinkage Covariance Estimation II (CPP)

Description Usage Arguments Details Value References Examples

View source: R/RcppExports.R

Description

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

Usage

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sigma_estim_lwone_cpp(data, shrink_int = -1, 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 \insertCiteledoit2004oneparam;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 diagonal matrix of equal variances is calculated with the following formula:

\hat{Σ}= sΣ_{T} + (1-s)Σ,

where Σ is the sample covariance matrix, s is the user-supplied or optimal shrinkage intensity and Σ_{T} is a diagonal matrix with the average sample variance \bar{σ}^2 on the diagonal. This covariance estimator assumes a zero correlation and equal variances 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

References

\insertAllCited

Examples

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data(sp200)
sp_rets <- sp200[,-1]
sigma_lwone <- sigma_estim_lwone_cpp(as.matrix(sp_rets))

antshi/CovEstim documentation built on Nov. 13, 2020, 2:25 p.m.