The package features a framework for working with high-dimensional shrinkage optimal portfolios. It allows constructing those in two ways: 1) by applying shrinkage directly to the portfolio weights (function MVShrinkPortfolio) and 2) by obtaining shrinkage estimates of mean returns and covariance matrices (function MeanVar_portfolio).
The latest stable release is always on CRAN:
install.packages('HDShOP')
The latest development version can be installed in the following way:
library("remotes")
u<-"Otryakhin-Dmitry/"
r<-"global-minimum-variance-portfolio"
re <- paste(u,r,sep="")
remotes::install_github(repo=re, subdir="")
In this example, returns of assets from S&P500 are loaded and an MV portfolio is created, for which methods summary and plot are called.
library(HDShOP)
# loading S&P daily asset returns
data("SP_daily_asset_returns")
assets <- t(SP_daily_asset_returns[2:301, 2:201])
gamma<-1
p <- nrow(assets)
b<-exp(-0.1*(1:p))
# creating an MV shrinkage portfolio
sh_mv_port <- MVShrinkPortfolio(x=assets, gamma=gamma,
type='shrinkage', b=b, beta = 0.05)
# Making a summary and plotting the portfolio
summary(sh_mv_port)
plot(sh_mv_port)
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