About Garch models
Jaime, I'm not a big fan of testing, especially this form of testing. My prior is that all market data experience volatility clustering. The question is more whether the heteroscedasticity has an affect on what you are trying to do with the results of your model. But to answer your question: You should never believe a Ljung-Box test on squared residuals no matter what the p-value. The reason is because the Ljung-Box test (which is really robust) is not robust enough when used on the squares of a long-tailed distribution. The outliers can mess with the p-value as they wish. You can trust Ljung-Box tests on the ranks of squared residuals. I don't know enough about other tests of autocorrelation to comment. But if they don't agree with a rank Ljung-Box test, then I'd be suspicious. Pat
On 18/09/2012 12:52, jaimie villanueva wrote:
OK Patrick, Thanks.
I'm not sure if I've understood this sentence:
".If you are used to looking at p-values from goodness of fit tests, you
might notice something strange. The p-values are suspiciously close to
1. The tests are saying that we have overfit 1547 observations with 4
parameters. That is 1547 really noisy observations. I don?t think so.
A better explanation is that the test is not robust to this extreme
data, even though the test is very robust. It is probably
counter-productive to test the squared residuals. An informative test
is on the ranks of the squared standardized residuals."
it is exactly what is happening to me. p-values close to 1.
What does the sentence mean:
- Whenever Ljung -Box p-values close to 1 , never belive it.? or
- Should I run other type of autocorrelation test?
best
Jaimie
2012/9/18 Patrick Burns <patrick at burns-stat.com
<mailto:patrick at burns-stat.com>>
You should *not* believe the Ljung-Box
test. For an explanation of why, see:
http://www.portfolioprobe.com/__2012/07/06/a-practical-__introduction-to-garch-__modeling/
<http://www.portfolioprobe.com/2012/07/06/a-practical-introduction-to-garch-modeling/>
Pat
On 18/09/2012 11:55, jaimie villanueva wrote:
Hi R users,
I'm trying to fit an ARMA or GARCH or ARMA/GARCH model over a
financial
time series of daily Log returns.
I've followed the same procedure as most texts are recommending
in order to
check whether an autocorrelation structure exist (either on
residuals or
squared residuals) or not. After run the Ljung-Box and LM ARCH
test over
squared residuals and I realise that NO autocorrelation
structure exist, I
supposed that, if i try to fit a GARCH model the fitting results
would be
quite useless.
Instead of that, I've found that the fitting was pretty good.
The question is: Should I go ahead with the GARCH model or
Should i belive
the Ljung-Box and LM ARCH test ?.
Thanks in advance.
Jaimie
[[alternative HTML version deleted]]
_________________________________________________
R-SIG-Finance at r-project.org <mailto:R-SIG-Finance at r-project.org>
mailing list
https://stat.ethz.ch/mailman/__listinfo/r-sig-finance
<https://stat.ethz.ch/mailman/listinfo/r-sig-finance>
-- Subscriber-posting only. If you want to post, subscribe first.
-- Also note that this is not the r-help list where general R
questions should go.
--
Patrick Burns
patrick at burns-stat.com <mailto:patrick at burns-stat.com>
http://www.burns-stat.com
http://www.portfolioprobe.com/__blog
<http://www.portfolioprobe.com/blog>
twitter: @portfolioprobe
Patrick Burns patrick at burns-stat.com http://www.burns-stat.com http://www.portfolioprobe.com/blog twitter: @portfolioprobe