[R-SIG-Finance] Optimization Question

[Hyun-U Sohn]

Well, for BM/GBM, you could calculate the expected value of MDD over
the investment horizon according to Magdon-Ismail et al. 2003 (?) or
their article in Risk and use that. That shouldn't be too hard, but
depending on how the parameters are determined, it's unlikely to be
very useful for risk management. Which is why I would be interested in
which specific distributional assumptions the original question was
aiming at. Indeed I'd be grateful for references on sampling
distributions of MDD for processes other than BM, or for that matter
even what form the sampling distribution of MDD of BM takes. Or do you
plan to use some Monte Carlo method to calculate MDD or any of the
other risk measures in the post?

On Tue, Dec 30, 2008 at 10:01 PM, Patrick Burns <patrick at burns-stat.com> wrote:
> I suspect that including Maximum drawdown in an
> optimization is unlikely to be useful.  However, I'm
> anxious to be proven wrong.
>
> It's not clear to me what specifically you are wanting
> to do, but Manfred Gilli has done a fair amount on
> VaR in optimization. (But I don't think you'll get any
> R code off him.)
>
> Patrick Burns
> patrick at burns-stat.com
> +44 (0)20 8525 0696
> http://www.burns-stat.com
> (home of S Poetry and "A Guide for the Unwilling S User")
>
> ian.mcdonald at malbecpartners.com wrote:
>>
>> I am interested in doing a portfolio optimization, but I need to impose
>> non-linear, functional constraints (i.e Max Drawdown, or VaR, etc). I've
>> looked into several of the optimization routines (nlminb, constrOptim and
>> optim with SANN/L-BFGS-B) but it appears that all of them are geared to
>> linear constraints. Other than using a penalty function method, is anyone
>> aware of any other approaches available in  R?
>>
>> Thanks
>> Ian McDonald
>> Malbec Partners
>>
>>        [[alternative HTML version deleted]]
>>
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