[R-SIG-Finance] Question about garchSim and garch
Spencer Graves
spencer.graves at pdf.com
Sun Feb 3 17:53:04 CET 2008
* Can 'garchFit' estimate the degrees of freedom for Student's t
shocks?
It has arguments "include.skew, include.shape", but I don't know
what they do (and haven't taken the time to read the code or ask
Diethelm to find out).
This question relates to the "two-pass estimation" discussed in
this thread, because Tsay (2005, sec. 3.5) fits arima(0,0,0)+garch(1,1)
to monthly excess returns of the Standard and Poor's 500 from 1926
through 1991 using (a) simultaneous estimation, (b) simultaneous
estimation with Student's t shocks, and (c) two-pass estimation. He
gets similar answers.
The current 'script\ch03.R' includes estimating models 'a' and 'c'
but not 'b'. If someone can help me figure out how to estimate the
degrees of freedom for Student's t shocks, I'd happily add that to
'scripts\ch03.R'.
Best Wishes,
Spencer
Spencer
tom soyer wrote:
> 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 at burns-stat.com
> <mailto:patrick at 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 at burns-stat.com
> <mailto:patrick at burns-stat.com>
> > <mailto:patrick at burns-stat.com <mailto:patrick at 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 at burns-stat.com <mailto:patrick at burns-stat.com>
> <mailto:patrick at burns-stat.com <mailto: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")
> >
> > >
> > >On 2/2/08, Spencer Graves <spencer.graves at pdf.com
> <mailto:spencer.graves at pdf.com>
> > <mailto:spencer.graves at pdf.com
> <mailto:spencer.graves at 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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