Patrick, I define good simulation as the one that preserves the structure of
autocorrelations and serial correlations.  For example, if two assets are
highly correlated, simulation should reflect that with noise for those
assets having some kind of correlation.  Same with autocorrelation.  If an
asset is highly mean-reverting (VIX), I don't want to simulate it as a very
trendy asset.  I can't give you a rigorous definition of a good simulation,
only descriptive.
Enrico, I'm familiar with block bootstraping and I'm looking for an
alternative solution.  Thanks for your suggestions!  Maximizing quantile
(VaR-like approach), unfortunately, is not convex because VaR itself is not
convex.  I've tried maximizing minimum value of Sharpe, Sortino, etc but the
result is highly dependent on the simulation.  Therefore, I'm focusing on
defining an acceptable simulation that preserves data structure.
On Tue, Jul 1, 2008 at 4:44 AM, Patrick Burns <patrick@burns-stat.com>
wrote:

> I pretty much understand all of the solutions
> that have been offered.  What I don't understand
> is the original question.
>
> How do you know if your solution is
> good or not?  Given that you have two years of data
> and you are talking about samples of one year, a
> reasonable plan would be to test an out-of-sample
> period (a day, a week, ...) and then move the in-sample
> data that amount.  Only a year of out-of-sample data
> seems rather short to me.
>
> You want to get good returns.  I think it is safe to say
> that the amount of predictive power in a year of daily
> returns is infinitesimal, no matter how much fancy footwork
> you do.  A test of optimization technology without a good
> predictive model for returns is going to be driven by noise
> unless you are creating minimum variance portfolios (or
> some other minimum risk).
>
>
> Patrick Burns
> patrick@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")
>
>
> Enrico Schumann wrote:
>
>>  there are lots of choices how to obtain the `robust solution' you want.
>> maybe optimise the weights to give the *mean* sharpe/sortino whatever, or
>> to
>> maximise a quantile (or the lowest) of the objective functions of your
>> 1,000
>> data sets.
>>
>> -----Ursprüngliche Nachricht-----
>> Von: r-sig-finance-bounces@stat.math.ethz.ch
>> [mailto:r-sig-finance-bounces@stat.math.ethz.ch] Im Auftrag von ning
>> zhang
>> Gesendet: Dienstag, 1. Juli 2008 09:55
>> An: Enrico Schumann
>> Cc: r-sig-finance@stat.math.ethz.ch
>> Betreff: Re: [R-SIG-Finance] [R-sig-finance] robust portfolio optimization
>>
>> you could sign the random weight to each assets first, and then calculated
>> portfolio variance as well as portfolio return. Finally, you could use
>> monte
>> carlo simulation to optimise the weight of each asset, which gives you the
>> best sharp ratio.
>>
>>
>>
>> On Tue, Jul 1, 2008 at 8:02 AM, Enrico Schumann <enricoschumann@yahoo.de>
>> wrote:
>>
>>
>>
>>> how about bootstrapping? keeping the cross-sectional correlation in the
>>> data is fairly simple by sampling whole rows from your returns matrix
>>> (assumed of dimension observations times returns), but the serial dependence
>>> is more difficult. if you have an idea how this serial dependence looks like
>>> (or, say, you know what parts you want to reproduce in your scenario sets)
>>> you may fit a regression model capturing this dependence and then resample
>>> from the residuals. if you want a rather non-parametric approach, block
>>> bootstrapping may be a technique to look at.
>>>
>>> i think patrick burns has a tutorial on bootstrapping on his homepage
>>> http://www.burns-stat.com/
>>>
>>> enrico
>>>
>>> -----Urspr|ngliche Nachricht-----
>>> Von: r-sig-finance-bounces@stat.math.ethz.ch
>>> [mailto:r-sig-finance-bounces@stat.math.ethz.ch] Im Auftrag von
>>> maratikus
>>> Gesendet: Mittwoch, 27. Februar 2008 21:49
>>> An: r-sig-finance@stat.math.ethz.ch
>>> Betreff: [R-SIG-Finance] [R-sig-finance] robust portfolio optimization
>>>
>>>
>>> I am exploring robust portfolio optimization.  I have historical daily
>>> data for 20 stocks over 2 year period.  i'd like to simulate 1,000 datasets
>>> of 1 year each that have autocorrelation and cross-correlation properties
>>> similar to those of the historical data.  Then I'd like to find allocation
>>> that maximizes minimum risk-adjusted return over 1,000 datasets.  All
>>> suggestions are appreciated!
>>> --
>>> View this message in context:
>>>
>>> http://www.nabble.com/robust-portfolio-optimization-tp15722777p1572277
>>> 7.html Sent from the Rmetrics mailing list archive at Nabble.com.
>>>
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>>> 28.06.2008
>>> 19:42
>>>
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>> 30.06.2008
>> 08:43
>>
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