I wanted to be able to generally include a non-linear constraint on an optimization. I was using maxdrawdown, var, or any risk measure as examples. Real world example might be when one needs to optimize a portfolio with weight constraints (max size to one or more securities). We might choose a utility function such as info ratio, calmar ratio, etc, but the resulting solution, while wonderful from utility perpective, has poor real world pnl and isn't simply scalable due to weight constraints. One can modify the utility function, or constrain the risk (or profit) to some minimum. So generally investigating optimization with potentially non-linear constraints. I think a penalty function into utility is best but wondering if there were other approaches. Ian McDonald ----- Original Message ----- From: Patrick Burns [patrick at burns-stat.com] Sent: 12/30/2008 09:01 PM GMT To: Ian McDonald Cc: r-sig-finance at stat.math.ethz.ch Subject: Re: [R-SIG-Finance] Optimization Question 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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