Michael Brandt provides an excellent survey on modern portfolio theory
http://home.uchicago.edu/~lhansen/handbook.htm. See Chapter "Portfolio
Choice Problems."
 In the standard mean-variance framework estimation risk is a very important
component of optimal portfolio allocations, as has been discussed. Con gave
the correct reference (Chopra and Ziemba). As a consequence, portfolio
optimization is firstly a statistical problem and secondly an optimization
problem.
 Section 3 (Traditional Econometric Approach) in Brandt covers these issues,
e.g. James Stein estimation that I mentioned yesterday.
 As I also mentioned, it may well be useful to apply benchmark related
optimization that gives relative over- and underweights with respect to *any
* benchmark portfolio. Hence, the weights sum to zero. I posted a code for
that to the list a few months ago.
 Regards,
Hannu Kahra


On 10/9/05, Silvia Marelli <silmarelli@yahoo.co.uk> wrote:
>
> Hi,
> I am trying to build some realistic efficient
> portfolios using some mean/variance techniques
> (Markowitz, CAPM etc...).
> I normally end up with an unrealistic concentration of
> the wealth in a too limited number of assets.
> I heard about Monte Carlo techniques to account for
> the unaccuracy of the information available.
> What would be a good starting point?
> I am not experienced, so I need to keep it as simple
> as possible.
> Should I simply optimize many ptfs, by sampling the
> return of each asset from a distribution which I
> assume to be a Gaussian centered on the expected
> return of the asset?
> Is it possible to introduce some "noise" also in the
> covariance matrix?
> Then how should I "average out" the results?
> I am not very familiar with these techniques, so if
> anyone can suggest some online resources, I would be
> very grateful.
> Regards
>
> Silvia
>
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