MS {CLA}

Means (Mu) and Standard Deviations (Sigma) of the “Turning Points” from CLA

Description

Compute the vectors of means (\mu_i) and standard deviations (sigma_i), for all the turning points of a CLA result.

Usage

MS(weights_set, mu, covar)

Arguments

weights_set

numeric matrix (n \times m) of optimal asset weights W = (w_1, w_2, \ldots, w_m), as resulting from CLA().

mu

expected (log) returns (identical to argument of CLA()).

covar

covariance matrix of (log) returns (identical to argument of CLA()).

Details

These are trivially computable from the CLA()'s result. To correctly interpolate this, “hyperbolic” interpolation is needed, provided by the findSig and findMu functions.

Value

a list with components

Sig

numeric vector of length m of standard deviations, \sigma(W).

Mu

numeric vector of length m of means \mu(W).

Author(s)

Yanhao Shi

See Also

CLA.

Examples

## The function is quite simply
MS
## and really an auxiliary function for CLA().

## TODO:  add small (~12 assets) example