<div dir="ltr">Peter,<div><br></div><div>I suspected this might be the case and did confirm it for the results in the mean-StdDev space. The plot is using the objective measures from the extractStats output of the EqmETL.RND object and these are calculated with clean="boudt" whereas the buoys.portfmeas as well as MinSD.ROI are calculated with clean="none".</div>
<div><br></div><div>From the graph, it looks like the minimum StdDev for the random portfolios is 0.0095. This is close to the result I get with the following test:</div><div><br></div><div><div>> R.clean <- Return.clean(R=R, method="boudt")</div>
<div>> </div><div>> tmpSD <- vector("numeric", length=nrow(rp))</div><div>> tmpSDcleanR <- vector("numeric", length=nrow(rp))<br></div><div>> for(i in 1:nrow(rp)){</div><div>+ tmpSD[i] <- StdDev(R=R, weights=rp[i,])</div>
<div>+ tmpSDcleanR[i] <- StdDev(R=R.clean, weights=rp[i,])<br></div><div>+ }</div><div>> </div><div>> min(tmpSD)</div><div>[1] 0.01020511</div><div>> min(tmpSDcleanR)</div><div>[1] 0.009514874</div><div>> as.numeric(MinSD.ROI$objective_measures)</div>
<div>[1] 0.01009001</div></div><div class="gmail_extra"><br>Ross<br><br><div class="gmail_quote">
On Sat, Sep 28, 2013 at 1:04 PM, Peter Carl <span dir="ltr"><<a href="mailto:peter@braverock.com" target="_blank">peter@braverock.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex">
I think I might have found the issue. In the RP solvers, I was using<br>
clean="boudt", but wasn't passing in anything into the ROI solvers. That<br>
would be a big difference...<br>
<br>
I'll confirm that is the issue later, and come back if it wasn't.<br>
<br>
Thanks for listening.<br>
<div><div><br>
pcc<br>
--<br>
Peter Carl<br>
<a href="http://www.braverock.com/peter" target="_blank">http://www.braverock.com/peter</a><br>
<br>
> Whoops, I hit "send" too early.<br>
><br>
> That's the gist of the message, though. I'll check in all my recent<br>
> changes and the RP result so that you can reproduce these results. Let me<br>
> know if you have any other ideas or see issues in how I've laid out the<br>
> constraint objects.<br>
><br>
> Thanks in advance,<br>
><br>
> pcc<br>
> --<br>
> Peter Carl<br>
> <a href="http://www.braverock.com/peter" target="_blank">http://www.braverock.com/peter</a><br>
><br>
> I hit a bump in the road a couple of days ago when I looked at the two<br>
> attached charts. One shows the mean-ETL of a set of different objectives<br>
> against a cloud of random portfolios. The objectives that can be are<br>
> calculated through ROI; I used RP for most of the others and DE for the<br>
> risk budget objective.<br>
><br>
> Note that the cloud of RP portfolios shows portfolios with lower mETL than<br>
> the indicated MinmETL portfolio. Well, maybe the mETL space isn't convex?<br>
> Turns out, you can see the same issue in the attached mean-SD space with<br>
> the minSD portfolio.<br>
><br>
> I went back and re-calculated the RP portfolios to eliminate the wiggle we<br>
> usually give the boundaries (a two percent leeway to speed up portfolio<br>
> generation) reasoning that those might be the issue. I generated 10K<br>
> RP's, of which about 3K summed exactly to 1.0 (which is plenty). So I<br>
> know that it isn't that those portfolios are out of bounds relative to the<br>
> constraints objects.<br>
><br>
> I've tried to make sure that every objective is using the same<br>
> constraints, and I've double-checked my post processing to make sure I<br>
> wasn't damaging the output from the optimization runs to get things into<br>
> charts.<br>
><br>
> This suggests to me that it's a deeper problem, although there still just<br>
> might be an issue in my code somewhere.<br>
> --<br>
> Peter Carl<br>
> <a href="http://www.braverock.com/peter" target="_blank">http://www.braverock.com/peter</a><br>
><br>
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