CovarEstim: Covariance matrix estimator

In HDShOP: High-Dimensional Shrinkage Optimal Portfolios

Covariance matrix estimator

Description

It is a function dispatcher for covariance matrix estimation. One can choose between traditional and shrinkage-based estimators.

Usage

CovarEstim(x, type = c("trad", "BGP14", "LW20"), ...)

Arguments

x	a p by n matrix or a data frame of asset returns. Rows represent different assets, columns – observations.
type	a character. The estimation method to be used.
...	arguments to pass to estimators

Details

The available estimation methods are:

Function	Paper	Type
Sigma_sample_estimator		traditional
CovShrinkBGP14	Bodnar et al 2014	BGP14
nonlin_shrinkLW	Ledoit & Wolf 2020	LW20

Value

an object of class matrix

Examples

n<-3e2 # number of realizations
p<-.5*n # number of assets

x <- matrix(data = rnorm(n*p), nrow = p, ncol = n)

Mtrx_trad <- CovarEstim(x, type="trad")

TM <- matrix(0, p, p)
diag(TM) <- 1
Mtrx_bgp <- CovarEstim(x, type="BGP14", TM=TM)

Mtrx_lw <- CovarEstim(x, type="LW20")

HDShOP documentation built on May 29, 2024, 2:20 a.m.