minMAD function

Compute Minimum Mean--Absolute-Deviation Portfolios

Compute minimum mean--absolute-deviation portfolios.

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.

Returns

a vector of portfolio weights

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.

Author(s)

Enrico Schumann

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))