Silvia,
 David Jessop refers in a separate mail to Berndt Scher's book "Portfolio
Construction and Risk Budgeting"
http://www.amazon.com/exec/obidos/tg/detail/-/1904339301/qid=1128942250/sr=8-1/ref=sr_8_xs_ap_i1_xgl14/103-5833626-9777409?v=glance&s=books&n=507846
.
His latetes book is with Douglas Martin: " Introduction to Modern Portfolio
Optimization with NuOPT, S-PLUS and S+Bayes"
http://www.amazon.com/exec/obidos/tg/detail/-/0387210164/qid=1128942250/sr=8-2/ref=sr_8_xs_ap_i2_xgl14/103-5833626-9777409?v=glance&s=books&n=507846

and I remember that in this book he is against using portfolio resampling
with the Gaussian assumption.
 I recall (I do not have the references with me) that Ziemba and Kallberg
have found that errors in the means are 10-20 times more serious (for the
portfolio) than errors in the covariances. As a consequence the resulting
portfolios tend to be very consentrating such that a lot of assets have zero
weights.
 An old trick is to apply the so-called Bayes-Stein or James Stein
estimators that shrink the sample means towards some fixed value, e.g. the
grand mean or the return on the minumum variance portfolio. The latter is
the approach suggested by Jorion. The Black-Litterman "model" applies
shrinkage towards the CAPM equilibrium returns, since it applies the
inversion of the Markowitz problem.
 Instead of using standard Markowitz optimization, benchmark related
optimization (explained in Scherer's Portfolio Construction book) may be
usefull.
 I hope these help.
 Hannu Kahra
Turku Business School, Finland
and CeRP, Italy


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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