OK. Thanks Patrick. I was thinking about the same yesterday! That's one of
the reasons that I thought R's garch still has potential. I was going to
test your theory, but then I got distracted when I was unable to reconcile
the different GARCH notations. But now I am thinking, why do it in multiple
steps when you can just do everything at once? Do you have any particular
scenarios in mind?

Thanks!


On 2/3/08, Patrick Burns <patrick@burns-stat.com> wrote:
>
> tom soyer wrote:
>
> > Patrick, I am sorry maybe I didn't explain it well. I was thinking
> > using arma to estimate the mean, and garch for the conditional
> > variance. Does that make sense?
>
>
> Yes, that makes sense, and that is what I was talking about:
> it seems to be the case that estimating the mean model and
> the conditional variance model separately tends to give you
> a similar answer as estimating them both in a single procedure.
>
> Pat
>
> >
> > With regard to comparing models, do you, or anyone else know how to
> > build news impact curves in R?
> >
> > Thanks!
> >
> >
> > On 2/3/08, *Patrick Burns* <patrick@burns-stat.com
> > <mailto:patrick@burns-stat.com>> wrote:
> >
> >     tom soyer wrote:
> >
> >     >Thnaks Spencer. I am glad I am not the only one that find garch
> >     strange. I
> >     >guess I will give up on it too. It seems that garchFit and
> >     garchSim are very
> >     >good. They have been giving me good results so far.
> >     >
> >     >Thanks for the tip on how to specify arma + garch model. I found
> >     this paper
> >     >also very
> >     helpful:http://www.itp.phys.ethz.ch/econophysics/R/pdf/garch.pdf.
> >     >
> >     >Do you know how to specify arma + egarch model in R? Is it even
> >     possible in
> >     >R without installing Ox?
> >     >
> >     >
> >
> >     In my experience ARMA estimation and garch estimation are
> >     suitably robust to each other.  It is definitely second prize to
> have
> >     to estimate one and then the other, but your results are unlikely to
> >     be all that different than if you did it "right".  (I'd love to
> >     hear of
> >     any counter-examples.)
> >
> >     Patrick Burns
> >     patrick@burns-stat.com <mailto: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")
> >
> >     >
> >     >On 2/2/08, Spencer Graves <spencer.graves@pdf.com
> >     <mailto:spencer.graves@pdf.com>> wrote:
> >     >
> >     >
> >     >>Hi, Tom:
> >     >>
> >     >>     The file 'scripts\ch03.R' in the FinTS package includes a
> brief
> >     >>description of attempts to use garch{tseries} and
> >     garchFit{fGarch}.  I
> >     >>don't understand either function very well, but I got answers from
> >     >>'garchFit' that seemed to match some of the published results in
> >     Tsay;
> >     >>I gave up on 'garch'.
> >     >>
> >     >>     Since 'garchSim' and 'garchFit' are both in 'fGarch', I would
> >     >>expect that it should be moderately easy to simulate something,
> >     plot the
> >     >>result, and see for yourself.  Chapter 3 of Tsay (2005) gives a
> >     >>reasonable overview of GARCH and related models with several
> >     examples.
> >     >>The companion script\ch03.R is far from complete but might help.
> >     >>
> >     >>     You may find the following example from 'ch03.R' of interest:
> >     >>
> >     >>library(FinTS)
> >     >>data(sp500)
> >     >>library(fGarch)
> >     >>spFit30.11 <- garchFit(sp500~arma(3,0)+garch(1,1), data=sp500)
> >     >>
> >     >>     This specifies an arma(3,0) mean model with garch(1,1) noise.
> >     >>This syntax is buried in the 'garchFit' help page.
> >     >>
> >     >>     Hope this helps.
> >     >>     Spencer
> >     >>
> >     >>tom soyer wrote:
> >     >>
> >     >>
> >     >>>Hi,
> >     >>>
> >     >>>I am new to GARCH and I am trying to figure out how to use R's
> >     garchSim
> >     >>>
> >     >>>
> >     >>and
> >     >>
> >     >>
> >     >>>garch, and I am a bit confused. I am hopeing that R finance
> >     experts can
> >     >>>
> >     >>>
> >     >>help
> >     >>
> >     >>
> >     >>>me understand them better. If we look at the definition of
> >     GARCH(1,1),
> >     >>>there should be two equations:
> >     >>>[1]: Y(t) = c + e(t), and
> >     >>>[2]: sigma^2(t) = a0 + a1*e^2(t-1) + b1*sigma^2(t-1)
> >     >>>
> >     >>>So, I would expect any garch simulation function to four
> >     parameters: c,
> >     >>>
> >     >>>
> >     >>a0,
> >     >>
> >     >>
> >     >>>a1, and b1. But take a look at the garchSim, it has only three
> >     >>>
> >     >>>
> >     >>parameters:
> >     >>
> >     >>
> >     >>>model = list(omega = 1.0e-6, alpha = 0.1, beta = 0.8). I assume
> >     here
> >     >>>
> >     >>>
> >     >>that
> >     >>
> >     >>
> >     >>>omega = a0 in [2], alpha=a1, and beta=b1. If so, then it seems
> >     that in
> >     >>>garchSim, c, the constant (or the mean) in [1], is always
> >     assumed to be
> >     >>>zero. Does anyone know if this is true? I just want to make
> >     sure that I
> >     >>>understand exactly what I should expect from the output of the
> >     garchSim
> >     >>>function.
> >     >>>
> >     >>>Also, I have a similar question about garch. It seems that the
> >     >>>
> >     >>>
> >     >>coefficients
> >     >>
> >     >>
> >     >>>estimated by garch(x,order=c(1,1)) are a0, a1, and b1. Like
> >     garchSim,
> >     >>>
> >     >>>
> >     >>there
> >     >>
> >     >>
> >     >>>is no c, the mean. So does this mean garch also assumes zero
> >     mean and
> >     >>>
> >     >>>
> >     >>thus
> >     >>
> >     >>
> >     >>>actually fits model [2] instead of both [1] and [2]?
> >     >>>
> >     >>>Thanks!
> >     >>>
> >     >>>
> >     >>>
> >     >>>
> >     >
> >     >
> >     >
> >     >
> >     >
> >
> >
> >
> >
> > --
> > Tom
>
>


-- 
Tom

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