nonlin_shrinkLW: nonlinear shrinkage estimator of the covariance matrix of...

In HDShOP: High-Dimensional Shrinkage Optimal Portfolios

nonlinear shrinkage estimator of the covariance matrix of Ledoit and Wolf (2020)

Description

The nonlinear shrinkage estimator of the covariance matrix, that minimizes the minimum variance loss functions as defined in Eq (2.1) of \insertCiteLW2020;textualHDShOP.

Usage

nonlin_shrinkLW(x)

Arguments

x	a p by n matrix or a data frame of asset returns. Rows represent different assets, columns – observations.

Value

an object of class matrix

References

\insertAllCited

Examples

n<-3e2
c<-0.7
p<-c*n
mu <- rep(0, p)
Sigma <- RandCovMtrx(p=p)

X <- t(MASS::mvrnorm(n=n, mu=mu, Sigma=Sigma))
Sigma_shr <- nonlin_shrinkLW(X)

HDShOP documentation built on Nov. 10, 2022, 5:12 p.m.