[R-SIG-Finance] [R-sig-finance] robust portfolio optimization

[Patrick Burns]

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 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")


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 at stat.math.ethz.ch
> [mailto:r-sig-finance-bounces at stat.math.ethz.ch] Im Auftrag von ning zhang
> Gesendet: Dienstag, 1. Juli 2008 09:55
> An: Enrico Schumann
> Cc: r-sig-finance at 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 at 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 at stat.math.ethz.ch
>> [mailto:r-sig-finance-bounces at stat.math.ethz.ch] Im Auftrag von 
>> maratikus
>> Gesendet: Mittwoch, 27. Februar 2008 21:49
>> An: r-sig-finance at 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.
>>
>> _______________________________________________
>> R-SIG-Finance at stat.math.ethz.ch mailing list 
>> https://stat.ethz.ch/mailman/listinfo/r-sig-finance
>> -- Subscriber-posting only.
>> -- If you want to post, subscribe first.
>> No virus found in this incoming message.
>> Checked by AVG.
>>
>> 28.06.2008
>> 19:42
>>
>> _______________________________________________
>> R-SIG-Finance at stat.math.ethz.ch mailing list 
>> https://stat.ethz.ch/mailman/listinfo/r-sig-finance
>> -- Subscriber-posting only.
>> -- If you want to post, subscribe first.
>>
>>     
>
> 	[[alternative HTML version deleted]]
>
>
> No virus found in this incoming message.
> Checked by AVG. 
>
> 30.06.2008
> 08:43
>
> _______________________________________________
> R-SIG-Finance at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-finance
> -- Subscriber-posting only.
> -- If you want to post, subscribe first.
>
>
>