Dear Jen,
The reason I have not answered is that you post one question, then
instead of patiently waiting for an answer, you very shortly post more
and more followups. As I said in a previous email, the likelihood of
answering, at least on my part, will depend on the effort shown to at
least try to do your own research and the framing of the question. You
also seem to be cross-posting to stackexchange.
With regards to your specific question, you are wrong and this is seen
by your own code:
resdi<-as.numeric(residuals(mydata,standardize=TRUE))
This is NOT the standardized residuals of the model but the
observations, so that when you compare to the Acf plot you are comparing
the observations (before the estimation) to the standardized residuals
(after the ARMA filtration).
You probably wanted to write:
resdi<-as.numeric(residuals(modgarch,standardize=TRUE))
The plots of the results from rugarch are the same with what you get
with the Forecast package (which is actually a wrapper for the stats
package 'plot.acf').
I'm going to politely ask you to please take some more care when posting
and making such grand statement as "plot are not useable anymore". You
are quickly burning through any remaining goodwill left on the part of
this developer. Finally, I would suggest an excellent reference such as
Zivot and Wang ("Modeling Financial Time Series with S-PLUS") or Tsay
("Analysis of Financial Time Series") which may help you answer some of
your many questions.
Regards,
Alexios
On 22/05/2013 08:10, Jen Bohold wrote:
Although it seems that there is no feedback and you do not want to comment on
me, I thought I should share this to the list, maybe someone else is some time
wondering about this (maybe I did a mistake, but no one of the list or you told
me in the previous mail). Also, I do not want to offend you, I like your
package it's great! Especially I liked the acf plots, they have a better
design, although
you will see in the following text, that the "ACF of Squared Standadrized
Residuals" plot are not useable anymore.
The plot of the ACF of the squared standardized residuals in rugarch output
(you get it via plot(yourmodel) and choosing number 11) is wrong.
However, the corresponding Q-Statistics of the rugarch output are correct!
Consider the following (I attached my data and the plots). I fitted the
following model (output extracted to the relevant parts):
*---------------------------------*
* GARCH Model Fit*
*---------------------------------*
Conditional Variance Dynamics
-----------------------------------
GARCH Model : sGARCH(1,1)
Mean Model : ARFIMA(5,0,5)
Distribution : norm
Optimal Parameters
------------------------------------
Estimate Std. Error t value Pr(>|t|)
ar1 0.000000 NA NA NA
ar2 0.000000 NA NA NA
ar3 0.000000 NA NA NA
ar4 -0.292207 0.019550 -14.9467 0.0e+00
ar5 -0.745887 0.018488 -40.3436 0.0e+00
ma1 0.000000 NA NA NA
ma2 0.000000 NA NA NA
ma3 0.000000 NA NA NA
ma4 0.309446 0.026659 11.6073 0.0e+00
ma5 0.718856 0.021208 33.8952 0.0e+00
omega 0.000006 0.000001 4.2106 2.5e-05
alpha1 0.093397 0.011308 8.2591 0.0e+00
beta1 0.892404 0.012437 71.7563 0.0e+00
Q-Statistics on Standardized Residuals
------------------------------------
statistic p-value
Lag[1] 7.898 4.949e-03
Lag[p+q+1][11] 21.627 3.312e-06
Lag[p+q+5][15] 27.133 5.374e-05
d.o.f=10
H0 : No serial correlation
Q-Statistics on Standardized Squared Residuals
------------------------------------
statistic p-value
Lag[1] 1.274 0.258961
Lag[p+q+1][3] 9.351 0.002229
Lag[p+q+5][7] 12.135 0.032980
d.o.f=2
As you can see in the "Q-Statistics on Standardized Squared Residuals" there is
clearly correlation in the standardized squared residuals. BUT if you look at the plot
with the plot method and choosing number 11 you can see, that NO spike is significant.
This plot is not correct, I controlled it via the Acf plot of the forecast
package and clearly, the spikes are larger! So the second spike is now
significant. I control the calculations via the Box.test method using d.o.f.=2
and choosing the lag 3 and 7 and the calculations in the rugarch package are
correct! So the p-values are indeed 0.002229 and 0.032980. So why is the plot
of the rugarch package wrong?
One further notice: In a previous mail, I asked, why the lags in the
Q-Statistics on Standardized Squared Residuals are different to the lags used
in Q-Statistics on Standardized Residuals. Of course, I have now seen, that the
second uses the GARCH parameters, so it is clear, that this has to be equal to
two (1+1). I also have to say, that I think, that the ACF of observations plot
e.g. is indeed correct (number 4), so it seems, that the plot number 11 uses
different scaled residuals? Maybe it uses the non-standardized squared
residuals? Could that be the reason?
Thanks a lot for your notice.
My code:
library(rugarch)
modsp<-ugarchspec(variance.model = list(model = "sGARCH", garchOrder = c(1, 1)),
mean.model = list(armaOrder = c(5, 5), include.mean = FALSE),
distribution.model =
"norm",fixed.pars=list(ar1=0,ar2=0,ar3=0,ma1=0,ma2=0,ma3=0))
modgarch<-ugarchfit(spec=modsp,data=mydata)
plot(modgarch)
residuals(mydata,standardize=TRUE)
resdi<-as.numeric(residuals(mydata,standardize=TRUE))
library(forecast)
Acf(resdi^2)
Box.test(resdi^2, lag = 3, type = "Ljung-Box", fitdf = 2)
Box.test(resdi^2, lag = 7, type = "Ljung-Box", fitdf = 2)
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