Efficient frontier mean variance portfolio

[galaad1]

Do you have a function to build the efficient frontier based on the risk portfolios and associated constraints ?

As an example with these two strategies:

Mean-variance portfolio without constraint and gamma = 0.89

Mean-variance portfolio with the gross constraint, gross constraint parameter = 1.2 and gamma = 0.89

library(RiskPortfolios)

Load returns of assets or portfolios

data("Industry_10") rets = Industry_10 rets = returns str(rets)

Mean estimation

mu = meanEstimation(rets)

Covariance estimation

Sigma = covEstimation(rets)

Semi-deviation estimation

semiDev = semidevEstimation(rets)

optimalPortfolio(mu = mu, Sigma = Sigma) optimalPortfolio(mu = mu, Sigma = Sigma, control = list(type = 'mv', constraint = 'gross', gross.c = 1.2))