Hi,

As far as I am concerned, with conditional heteroscedasticity models,
ARCH(1) is same as GARCH(0,1) - not GARCH(1,0), so you should try:


> m.arch1 <- garch(m.ar1$residual, order=c(0,1))

 ***** ESTIMATION WITH ANALYTICAL GRADIENT *****


> m.arch1

Call:
garch(x = m.ar1$residual, order = c(0, 1))

Coefficient(s):
    a0      a1
2.1108  0.1882


Which gives some results.

PS.

> sessionInfo()
R version 2.2.1, 2005-12-20, i386-pc-mingw32

attached base packages:
[1] "methods"   "stats"     "graphics"  "grDevices" "utils"     "datasets"
[7] "base"

other attached packages:
 tseries      zoo quadprog
"0.10-0"  "1.0-3"  "1.4-7"

Best regards,
Wojtek



2005/12/30, Spencer Graves <spencer.graves@pdf.com>:
>
>          Have you received a reply to this post?  I haven't seen one.
>
>          I ran the script you included with your post.  For the garch(1,1)
> model, I got an answer with a message "FALSE CONVERGENCE".  For the
> arch(1,0) model, I got a message, "Warning: singular information" with
> parameter estimates that raise questions in my mind about what the
> algorithm did.  You say the first one "Seems to be okay",  but the
> second one "breaks."  What do you mean by "breaks"?
>
>          I also did 'RSiteSearch("garch")'.  This revealed that the
> fSeries
> package also has a garch modeling function.  Have you looked at that?
> I'm sorry, but I've done very little with garch, and I don't have the
> time now to study it more deeply.
>
>          If you'd like more help from this listserve, PLEASE do read the
> posting guide! "www.R-project.org/posting-guide.html".  Anecdotal
> evidence suggests that posts more consistent with this guide tend to get
> more useful replies quicker.
>
>          hope this helps.
>
>
> > sessionInfo()
> R version 2.2.0, 2005-10-06, i386-pc-mingw32
>
> attached base packages:
> [1] "methods"   "stats"     "graphics"  "grDevices" "utils"     "datasets"
> [7] "base"
>
> other attached packages:
> tseries      zoo quadprog
> "0.10-0"  "1.0-3"  "1.4-7"
> >
>
> Ajay Narottam Shah wrote:
>
> > I have attached a time-series r. I find that I am able to use the
> > garch() function in the tseries package to estimate a GARCH(1,1)
> > model. But it fails to get convergence for the GARCH(1,0) -- i.e.,
> > ARCH(1) -- model. I seem to think that the latter is actually a simple
> > likelihood function and it's much easier to get convergence for it.
> >
> > There's a mysterious statement towards the end of the time-series
> > chapter of the MASS book which says that the garch() function requires
> > the series to be mean 0 (a statement which I didn't find in the
> > documentation of tseries). So I also did this one more way: To first
> > estimate an AR(1) model, and focus on the residuals. Once again, I get
> > the same pathology: GARCH(1,1) works but ARCH(1) does not.
> >
> > I fed the same series to stata and it estimates AR(1)-ARCH(1) easily,
> > so this doesn't seem to be a pathological case of bad
> > data. Difficulties in convergence can, of course, always come about in
> > realworld data. I wondered if I'm missing something fundamental.
> >
> > Here's some code which illustrates my problem --
> >
> > library(tseries)
> > # take the definition of `r' ahead and then say
> >
> > m.ar1 <- arima(r, order=c(1,0,0))
> > m.garch11 <- garch(m.ar1$residual, order=c(1,1)) # Seems to be okay
> > # BUT
> > m.arch1 <- garch(m.ar1$residual, order=c(1,0))
> > # breaks.
> >
> > Alternatively, equally, one can do:
> >
> > m.garch11 <- garch(r, order=c(1,1)) # Seems to be okay
> > # BUT
> > m.arch1 <- garch(r, order=c(1,0))
> > # breaks.
> >
> >     -ans.
>
> >
>
> --
> Spencer Graves, PhD
> Senior Development Engineer
> PDF Solutions, Inc.
> 333 West San Carlos Street Suite 700
> San Jose, CA 95110, USA
>
> spencer.graves@pdf.com
> www.pdf.com <http://www.pdf.com>
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>
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