[R-SIG-Finance] 130/30 Portfolio Optimization
Attiglah, Mama
Mama_Attiglah at ssga.com
Tue Apr 15 10:45:43 CEST 2008
This is a simple non-linear optimisation with linear constraints in a
convex set. Go to www.r-project.org, download the package Rdonlp2, and
use the optimiser donlp2.
I would advise you to start with initial values being half way through
the feasible set of each of the control variables( the weights), then
reuse the optimal weights as the initial values in order to secure st
stability of the optimum.
Hope this will help.
-----
Mama Attiglah, PhD
Quantitative Research analyst
Advanced Research Center
State Street Bank
+44(0)20 7698 6290 (Direct Line)
+44 (0)207 004 2968 (Direct Fax)
Please visit our Web site at
www.ssga.com
-----Original Message-----
From: r-sig-finance-bounces at stat.math.ethz.ch
[mailto:r-sig-finance-bounces at stat.math.ethz.ch] On Behalf Of Guy Yollin
Sent: 14 April 2008 19:35
To: Shlomo Katchmalik; r-sig-finance at stat.math.ethz.ch
Subject: Re: [R-SIG-Finance] 130/30 Portfolio Optimization
Hi Shlomo,
On Feb 5th, David Basterfield gave a webcast on the differential
evolution algorithm (http://www.icsi.berkeley.edu/~storn/code.html) in
which he works through a few 115/15 portfolio optimization examples.
The webcast can be downloaded from the events area of the Insightful
website (www.insightful.com).
The DE algorithm is quite elegant and has been implemented in the
package DEoptim.
Best,
-- G
-----Original Message-----
From: r-sig-finance-bounces at stat.math.ethz.ch
[mailto:r-sig-finance-bounces at stat.math.ethz.ch] On Behalf Of Shlomo
Katchmalik
Sent: Monday, April 14, 2008 11:05 AM
To: r-sig-finance at stat.math.ethz.ch
Subject: [R-SIG-Finance] 130/30 Portfolio Optimization
Hi All,
Does anybody have an idea as to how one would find an optimal 130/30
portfolio using R?
More specifically, for a given return covariance matrix Q, vector of
expected security returns mu, and risk tolerance tau, the problem is
to find the portfolio vector x that minimizes
x' * Q * x - tau * mu' * x
subject to the following constraints:
A * x = b for given constraint matrix A and vector b,
x >= L,
x <= U,
the sum of the positive elements of x is 1.3,
the sum of the negative elements of x is -0.3,
If not for the last two nonlinear constraints, solve.QP in
library(quadprog) would be applicable. Unfortunately, these two
constraints are central to the problem.
I'd appreciate any help.
Thanks,
Shlomo.
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