minMAD: Compute Minimum Mean-Absolute-Deviation Portfolios

In enricoschumann/NMOF: Numerical Methods and Optimization in Finance

Compute Minimum Mean–Absolute-Deviation Portfolios

Description

Compute minimum mean–absolute-deviation portfolios.

Usage

minMAD(R, wmin = 0, wmax = 1,
       min.return = NULL, m = NULL, demean = TRUE,
       method = "lp",
       groups = NULL, groups.wmin = NULL, groups.wmax = NULL,
       Rglpk.control = list())

Arguments

R	a matrix of return scenarios: each column represents one asset; each row represents one scenario
wmin	minimum weight
wmax	maximum weight
min.return	a minimum required return; ignored if NULL
m	a vector of expected returns. If NULL, but min.return is not NULL, then column means are used as expected returns.
demean	logical. If TRUE, the columns of R are demeaned, corresponding to an objective function xxxx
method	string. Supported are lp and ls.
groups	group definitions
groups.wmin	list of vectors
groups.wmax	list of vectors
Rglpk.control	a list

Details

Compute the minimum mean–absolute-deviation portfolio for a given scenario set.

The function uses Rglpk_solve_LP from package Rglpk.

Value

a vector of portfolio weights

Author(s)

Enrico Schumann

References

Konno, H. and Yamazaki, H. (1991) Mean-Absolute Deviation Portfolio Optimization Model and Its Applications to Tokyo Stock Market. Management Science. 37 (5), 519–531.

See Also

minvar, minCVaR

Examples

na <- 10
ns <- 1000
R <- randomReturns(na = na, ns = ns,
                   sd = 0.01, rho = 0.8, mean = 0.0005)

minMAD(R = R)
minvar(var(R))

enricoschumann/NMOF documentation built on April 13, 2024, 12:16 p.m.