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

arith

Implements 'Markowitz' Critical Line Algorithm ('CLA') for classical
mean-variance portfolio optimization, see Markowitz (1952) <doi:10.2307/2975974>.
Care has been taken for correctness in light of previous buggy implementations.

Martin Maechler

Critical Line Algorithm in Pure R

Yanhao Shi

MS function

<dl> <dt>weights_set</dt>
<dd>numeric matrix (\(n \times m\)) of optimal
 asset weights \(W = (w_1, w_2, \ldots, w_m)\), as
 resulting from <code>CLA()</code>.</dd> <dt>mu</dt>
<dd>expected (log) returns (identical to argument of
 <code>CLA()</code>).</dd> <dt>covar</dt>
<dd>covariance matrix of (log) returns (identical to
 argument of <code>CLA()</code>).</dd></dl>

Arguments

Author

Means (Mu) and Standard Deviations (Sigma) of the &#8220;Turning Points&#8221; from CLA — MS

<dl>

 <dt>weights_set</dt>
<dd>numeric matrix (\(n \times m\)) of optimal
 asset weights \(W = (w_1, w_2, \ldots, w_m)\), as
 resulting from <code>CLA()</code>.</dd>

 <dt>mu</dt>
<dd>expected (log) returns (identical to argument of
 <code>CLA()</code>).</dd>

 <dt>covar</dt>
<dd>covariance matrix of (log) returns (identical to
 argument of <code>CLA()</code>).</dd>

</dl>

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

Description

Usage

Value

Arguments

Author

Details

See Also

Examples