Expected utility and global minimum variance shrinkage portfolios.

Description

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).

Installation

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="")

Example

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)