[R-SIG-Finance] rugarch

[pantea hafezian]

    Dear all
    I have modified my
    program according to your recommendation but
    unfortunately I still face same problem. I attached the program, data and
    out put for your consideration.
    Thank you very much and waiting
    to hera from you.
    With many kind,
    
   
  
  
 


 





--- On Fri, 2/8/13, alexios ghalanos <alexios at 4dscape.com> wrote:





                                                              

From: alexios ghalanos <alexios at 4dscape.com>

Subject: Re: [R-SIG-Finance] Question

To: "pantea hafezian" <pantea_hafezian at yahoo.com>

Cc: r-sig-finance at r-project.org

Date: Friday, February 8, 2013, 1:19 AM





Please
follow the guidelines and post a reproducible example next time.

1. What is data(Gold), where can we find it?

2. There is no model called "egarch" in the specification. It is
"eGARCH"



You are starting the model with 100 data points using the eGARCH model and the
normal distribution. It is quite likely, that the solution converges to one
which is on the boundary of covariance stationarity. I have replicated this :

########################################

set.seed(10)

X = rnorm(1000)

spec =ugarchspec(variance.model=list(model="eGARCH"),distribution.model
= "norm")

roll = ugarchroll(spec, data = X, n.start = 100,  refit.every = 500,
refit.window = "moving", solver = "solnp", fit.control =
list(),

calculate.VaR = TRUE, VaR.alpha = c(0.01, 0.025, 0.05), keep.coef = TRUE)

as.data.frame(roll)

########################################

See the NaNs in the sigma.



Solutions:

1. Use more more data for the estimation start (e.g. 200).

2. Use a different model (e.g. sGARCH, gjrGARCH etc).



Also, read some of the older posts in this forum on the amount of data to use
and why it is a bad idea to use so little data.



-Alexios

 

 

 

 

On 08/02/2013
01:34, pantea hafezian wrote:

> Dear Sir/Ms

> Thank you for your valuable and very helpful
package (rugarch). Actually

> I am going to compare different GARCH models
(VaR) by using this package

> but unfortunately  I face a problem. The
problem is related  to the back

> testing method. For instance, in Qupiec 
test, the value of K (LRUC)  is

> amounting to 0 and consequently p-value is
equal to 1 for all models. I

> performed this procedure several times with
various data but the results

> is same as I mentioned even for your own
example. It would be appreciate

> if let me know the result is supposed to be
like this or the problem

> rises from my programming.

> My program is enclosed to this email.

> Thank you again and I look forward hearing
from you about that.

> Sincerely Yours,

> Pantea Hafezian

> PhD candidate in Finance,

> University Technology Malaysia (UTM)

> 
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