[R-SIG-Finance] [R-sig-finance] robust portfolio optimization
enricoschumann at yahoo.de
Tue Jul 1 09:02:18 CEST 2008
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
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
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