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I want to simulate hypothetical assets so I can control all aspects of
the tests, from parameters to correlations across assets. I can
construct correlations based on minimum variance hedge ratios that will
allow me to create hedge portfolios with higher weights on some assets
than others. This way I can also look at hedging aspects within the
VAR calculation and the problems with violating the models assumptions.<br>
<br>
I have used garchsim and armasim, but as I understand their
implementation, I am simulating the independent process, not a
correlated process.<br>
<br>
Including the modified cornish VAR is a really good idea as a benchmark
case as well. <br>
<br>
thanks for that suggestion, if nothing else you are entitled to a
footnote for it.<br>
<br>
Thanx<br>
Joe<br>
<br>
<br>
Brian G. Peterson wrote:
<blockquote cite="mid200608281204.52922.brian@braverock.com" type="cite">
<pre wrap="">On Monday 28 August 2006 10:40, Joe Byers wrote:
</pre>
<blockquote type="cite">
<pre wrap="">Rmetrics group,
I am working on a project to determine the errors associated with
structural assumptions underlying a companies Value at Risk
calculation. Normal VAR calculations using a covariance matrix for the
portfolio assume constant mean or zero mean if the returns are mean
adjusted. This project calls for creating 4-5 hypothetical assets, 1
constant mean and variance, 1 seasonal mean and constant variance, 1
constant mean and seasonal variance, 1 time varying mean (AR or Garch
in mean), 1 time varying variance (GARCH type). I want to provide the
hypothetical parameters for these assets and simulate returns. I can
simulate each of these assets as independent but really need correlated
errors.
These returns will be used to calculate a benchmark risk metrics type
VAR and then progess through correcting the VAR calculations for each
case of asses type.
Anyone that is interested, I would appreciate suggestions. I am also
favoring co-authorship for this help.
</pre>
</blockquote>
<pre wrap=""><!---->
I've had very good success using Modified Cornish-Fisher VaR to handle the
non-normality of the distribution, occasionally with a weighted average
of since-inception VaR and rolling period VaR.
Why wouldn't you choose existing (real) assets with the characteristics
that you want to use in your simulated portfolios?
If you want to simulate assets, there are several simulation functions in
RMetrics and in other R packages, and I'd suggest that you start there.
However, I don't find that these end up looking much like the
distributions of real assets in practice, so I don't tend to use them
very often.
Regards,
- Brian
</pre>
</blockquote>
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