ModSharpe: Modified Sharpe Ratio function

In FilippoPic/DEoptimPIC: DEoptimPIC provides some interesting tools and benchmark functions to test three different versions of the differential evolution algorithm

Description

Function that calculates the modified version of the Sharpe ratio, more coherent when the expected excess portfolio return is negative.

Usage

ModSharpe(x, mu, rf, Sigma)

Arguments

x	a vector or matrix - if it is a matrix, the function will return the value of the ModSharpe for each row.
mu	vector of the expected returns of the n risky assets
rf	risk-free rate
Sigma	estimator of the covariance matrix

Value

ModSharpe() returns as object the value of the function modified sharpe ratio calculated in x. If x is a matrix, it returns a column vector and calculates the modified sharpe for each row.

Examples

r <- matrix( ,nrow =60 ,ncol=25)
for (i in 1:60) {
 r[i,] <- runif(25,-2.5,2.5)
}
mu <- rowMeans(t(r))
Sigma <- cov(r)
xx <- runif(25,0.01,0.30)
ModSharpe(xx,mu,0,Sigma)

FilippoPic/DEoptimPIC documentation built on Feb. 14, 2022, 5:12 a.m.