staticmpt {portn}

Static Portfolio Optimization

Description

The function generates portfolio weights for nature or conservation

Usage

staticmpt(mus, rbar, vmat)

Arguments

mus

An array of the expected values

rbar

An array of the observed mean returns

vmat

A variance and covariance matrix

Details

This function solves the series of the standard Markowitz portfolio analysis for nature or conservation, i.e., the quadratic problem without shorting.

min w' vmat w
s.t. w rbar = mu
w' 1 = 1 where w >= 0

where w is an array of non-negative portfolio weights, rbar is an array of the observed mean returns, vmat a matrix of variance-covariance matrix, and mu is an expected value.

Value

A list including the following component:

rbar An array of the observed mean returns

vmat A variance and covariance matrix

efdata A data.frame including:
- sd standard deviation
- er expected return in mus
- conv convergence status in optimization (0 = successful, otherwise: not an interior solution)
- w1, w2, ... portfolio weights
- ef 1 = on the efficient frontier and 0 = not on the efficient frontier

References

Ando, A. W. and M. L. Mallory. (2012) "Optimal Portfolio Design to Reduce Climate-related Conservation Uncertainty in the Prairie Pothole Region." Proceedings of the National Academy of Sciences (PNAS). 109 (17) pp. 6484-6489.

See Also

plotef

Examples

## No change likely scenario of CCI in Figure 2, Ando and Mallory (2012)
rs <- c(0.265,0.671,0.372)
vmat <- matrix(c(0.003,0.005,-0.006,0.005,0.013,-0.010,-0.006,-0.010,0.012),ncol=3)
mus <- seq(min(rs),max(rs),length.out=100)
cci <- staticmpt(mus,rs,vmat)