Hello,
In this program I’m trying to maximize portfolio skewness under constraints.
Nevertheless , I have one more problem. In my case, I have one linear constraint and one Nonlinear constraint (quartic constraint about portfolio kurtosis which is less than a scalar). As I understand while reading chapter 14 about Nonlinear optimization examples, I must use NLPQN instead of NLPNRA! IS that sufficient?
In the other hand, I still think about the mining of “optn”! and if x0 must take always only two initial values?
Finally, I want to state decision variables wi as positive, I question whether it been subject to a further constraint.
Sorry for those lots of questions. Thank you in advance!
proc iml;
M3 = {32 -7 -7 -8,
-7 -8 -8 17};
start skpf(p) global(M3);
w = p`; /* w is column vector */
return w` * M3 * (w@w);
finish;
/* specify linear constraints */
con = { 0 0 . ., /* min w[i] */
1 1 . ., /* max w[i] */
1 1 0 1}; /* sum(w) = 1 */
x0 = {0.5 0.5};
optn = {1 /* maximize objective function */
1 }; /* summarize iteration history */
call nlpnra(rc, xOpt, "skpf", x0, optn, con);
maxVal = skpf(xOpt);
print rc, xOpt maxVal;
Yes. If you have nonlinear constraint , you have to switch into NLPQN.
if you have three assets , then you have to have three initial value for x0.
con={} already constraint 0<= w1 ,w2 <=1,
if you have nonlinear constrain condition,you need add one more option in it.
Hello,
Thank you very much! Your recommandations are highly appreciated. I'm still trying to solve the problem.
Hello,
Sorry, but what about if I have 500 assets, should I have 500 initial values for X0?
thank you in advance
Yes. I think so .
You can easily define it as
x0=j{1,500,1}/500;
Great!!! Thank you so much!
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