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

[Enrico Schumann]

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!
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30.06.2008
08:43