[R-SIG-Finance] Update of rugarch package yields different results / questions on stationarity conditions

[alexios ghalalanos]

If I were to actually use your previous estimates and filter the data
you provided:


>cf=list(mxreg1=-0.3644,
omega=-0.3081,
alpha1=-0.1288,
alpha2=-0.1308,
gamma1=0.2575,
gamma2=0.2340,
beta1=-0.6917,
beta2=0.9764,
beta3=0.6738,
skew=0.9659,
shape=1.9107)

>setfixed(spec)<-cf
> likelihood(ugarchfilter(spec, nrenditen))
[1] 1051.357

This seems much lower and considerably far from what the estimated model
gives. Are you sure you provided us with the correct parameter values
and the same dataset you used before?

-Alexios


On 24/09/2014 20:29, alexios ghalalanos wrote:
> Unless you tell us what the previous version you had installed was, I
> really can't say for sure.
> 
> 1. Is for the stationarity of the eGARCH model.
> 2. Are the parameter bounds.
> 
> You are free to change both:
> 1. can be switched off by setting fit.control$stationarity=0
> 2. can be changes to whatever you want by using the setbounds<- method
> on the specification.
> 
> As far as I know, you can take the ARMA and GARCH stationarity
> conditions separately, as you can also estimate them in 2 steps
> without too much loss in efficiency. If you want to see the degree of
> interaction between the 2, then use the ugarchdistribution method which
> includes a number of interesting parameter interaction plots (and you
> can also investigate others by working with the returned parameter
> distribution data).
> 
> If you feel there is some bug somewhere in the code or you have some
> suggestion how to make the estimation of a certain model 'better', then
> by all means feel free to contribute a detailed patch.
> 
> -Alexios
> 
> 
> On 24/09/2014 20:01, Stefan.Jaeschke at rwe.com wrote:
>> Hi there,
>>
>>  
>>
>> 1)     I have recently updated the rugarch package to version 1.3-3 (I
>> do not remember the previous number) and I am surprised to see different
>> results when fitting a dataset, the loglikelihood is lower than before
>> and the beta parameters have changed significantly. Below I put the code
>> from the fit
>>
>>  
>>
>> Data <- read.csv("WTI_logreturnsUS.csv", header = TRUE, sep = ";", dec=".")
>>
>> renditen <- Data$LogReturnsWTI
>>
>> Data_WTI <- renditen
>>
>> nrenditen = renditen - mean(renditen)
>>
>> external <- Data$LogReturnsStocks
>>
>> dim(external) <- c(length(external),1)
>>
>> mean_WTI <- mean(renditen)
>>
>>  
>>
>> spec = ugarchspec(variance.model = list(model = "eGARCH", garchOrder =
>> c(2,3), submodel = NULL, external.regressors = NULL, variance.targeting
>> = FALSE), mean.model =list(armaOrder = c(0, 0), include.mean = FALSE,
>> external.regressors = external), distribution.model = "sged")
>>
>> fit <- ugarchfit(spec,nrenditen)
>>
>>  
>>
>>  
>>
>> likelihood         2326.425          2319.141
>>
>>                        
>>
>> mxreg1             -0.3644             -0.3124
>>
>> omega              -0.3081                -0.0474
>>
>> alpha1              -0.1288                -0.1090
>>
>> alpha2              -0.1308                0.0644
>>
>> gamma1           0.2575                  0.2189
>>
>> gamma2           0.2340                  -0.1441
>>
>> beta1                -0.6917             0.9999
>>
>> beta2                0.9764              0.4135
>>
>> beta3                0.6738              -0.4199
>>
>> skew                0.9659             0.9708
>>
>> shape               1.9107              1.9373
>>
>>  
>>
>> Why do I see these differences?
>>
>>  
>>
>> 2)     Why do we need the following two conditions for strict
>> stationarity of an EGARCH(q,p) model? I do refer to the ARMA
>> representation in Nelson (1991), Equation (2.3)
>>
>>  
>>
>> a)     min(Mod(polyroot(c(1, -betas)))) > 1
>>
>>  
>>
>> b)    |beta_i| < 1, i = 1,…,p
>>
>>  
>>
>> Whereas condition a) is clear to me (stationarity of AR processes), I
>> don’t see we should restrict the parameter |beta_i| < 1. Could somewhen
>> help on that? Why are the parameters regarding q not involved in the
>> conditions at all?
>>
>>  
>>
>> 3)     In general, I am aware of conditions for stationarity for
>> conditional mean processes (e.g. ARMA-models) or conditional variance
>> processes (e.g. GARCH-models). I am struggling a bit to find sufficient
>> conditions for (strikt) stationarity in case of combinations. For
>> instance, an ARMA(1,0)-GARCH(1,1) or ARMA(0,1)-EGARCH(2,3) model. Can I
>> take the conditions for mean/variance separately and join them in the
>> end? They should interact somehow, shouldn’t they? If anybody could help
>> me on that, I would be very pleased.
>>
>>  
>>
>> Many thanks in advance!
>>
>>  
>>
>> Mit freundlichen Grüßen / Kind regards
>>
>>  
>>
>> *Stefan Jäschke*
>>
>> RWE Supply & Trading GmbH
>>
>> Performance Controlling CAO Gas & VAC (MFC-GV)
>>
>> Altenessener Str. 27
>>
>> 45141 Essen
>>
>> Germany
>>
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>>
>> Email                      stefan.jaeschke at rwe.com
>> <mailto:stefan.jaeschke at rwe.com>
>>
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