mean_bop19 {HDShOP} | R Documentation |
BOP shrinkage estimator
Description
Shrinkage estimator of the high-dimensional mean vector as suggested in Bodnar et al. (2019). It uses the formula
where
and
are shrinkage coefficients given by
Eq.(6) and Eg.(7) of Bodnar et al. (2019) that minimize
weighted quadratic loss for a given target vector
(shrinkage target).
stands for the sample mean vector.
Usage
mean_bop19(x, mu_0 = rep(1, p))
Arguments
x |
a p by n matrix or a data frame of asset returns. Rows represent different assets, columns – observations. |
mu_0 |
a numeric vector. The target vector used in the construction of the shrinkage estimator. |
Value
a numeric vector containing the shrinkage estimator of the mean vector
References
Bodnar T, Okhrin O, Parolya N (2019). “Optimal shrinkage estimator for high-dimensional mean vector.” Journal of Multivariate Analysis, 170, 63–79.
Examples
n<-7e2 # number of realizations
p<-.5*n # number of assets
x <- matrix(data = rnorm(n*p), nrow = p, ncol = n)
mm <- mean_bop19(x=x, mu_0=rep(1,p))
[Package HDShOP version 0.1.5 Index]