new_MV_portfolio_traditional: Traditional mean-variance portfolio

In HDShOP: High-Dimensional Shrinkage Optimal Portfolios

Traditional mean-variance portfolio

Description

Mean-variance portfolios with the traditional (sample) estimators for the mean vector and the covariance matrix of asset returns. For more details of the method, see MVShrinkPortfolio. new_MV_portfolio_traditional is for the case p<n, while new_MV_portfolio_traditional_pgn is for p>n, where p is the number of assets and n is the number of observations.

Usage

new_MV_portfolio_traditional(x, gamma)

new_MV_portfolio_traditional_pgn(x, gamma)

Arguments

x	a p by n matrix or a data frame of asset returns. Rows represent different assets, columns – observations.
gamma	a numeric variable. Coefficient of risk aversion.

Value

an object of class MeanVar_portfolio

Element	Description
call	the function call with which it was created
cov_mtrx	the sample covariance matrix of asset returns
inv_cov_mtrx	the inverse of the sample covariance matrix
means	sample mean estimator of the asset returns
W_mv_hat	sample estimator of portfolio weights
Port_Var	portfolio variance
Port_mean_return	expected portfolio return
Sharpe	portfolio Sharpe ratio

Examples

n <- 3e2 # number of realizations
p <- .5*n # number of assets
gamma <- 1

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

test <- new_MV_portfolio_traditional(x=x, gamma=gamma)
str(test)

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