Thanks Stefan, email me off list and I'll help you recreate the results.

Cheers,

Alexios

Sent from my iPhone

> On 24 Sep 2014, at 22:22, <stefan.jaesc...@rwe.com> wrote:
> 
> It was not my intention to criticize, not at all. You shouldn't take this 
> personally. On the other hand, you are encouraging to use this mailing list 
> for asking for support. Anyway, it is a great job you are doing and the 
> community is definitely grateful for having the chance to apply the provided 
> functionality.
> 
> -----Ursprüngliche Nachricht-----
> Von: Alexios Ghalanos [mailto:alex...@4dscape.com] 
> Gesendet: Mittwoch, 24. September 2014 21:14
> An: Jäschke, Stefan
> Cc: <r-sig-finance@r-project.org>
> Betreff: Re: [R-SIG-Finance] Update of rugarch package yields different 
> results / questions on stationarity conditions
> 
> I'll need to check what you sent me then against the changes made. As for 
> being irritated (and saying so publicly)...well, I've strived over the years 
> to produce an open source alternative for GARCH modelling with very few 
> contributions, but many requests for help both publicly and privately. 
> Perhaps you'll be less irritated buying an off the shelf program with paid 
> support.
> 
> Alexios
> 
>> On 24 Sep 2014, at 22:02, <stefan.jaesc...@rwe.com> wrote:
>> 
>> Alexios, you provided me with this estimates back in a mail on Tues. 
>> 29.10.2013 13:46. And I could reproduce those values, now I am a bit 
>> irritated. Unfortunately, I am not able to rebuilt the "old" rugarch version.
>> 
>> -----Ursprüngliche Nachricht-----
>> Von: alexios ghalalanos [mailto:alex...@4dscape.com]
>> Gesendet: Mittwoch, 24. September 2014 19:47
>> An: Jäschke, Stefan; r-sig-finance@r-project.org
>> Cc: alex...@4dscape.com
>> Betreff: Re: [R-SIG-Finance] Update of rugarch package yields 
>> different results / questions on stationarity conditions
>> 
>> 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$stationarity=0 2. can 
>>> be changes to whatever you want by using the setbounds<- method on 
>>> the specification.
>>> 
>>> As far as I know, you can take the ARMA and GARCH stationarity 
>>> conditions separately, as you can also estimate them in 2 steps 
>>> without too much loss in efficiency. If you want to see the degree of 
>>> interaction between the 2, then use the ugarchdistribution method 
>>> which includes a number of interesting parameter interaction plots 
>>> (and you can also investigate others by working with the returned 
>>> parameter distribution data).
>>> 
>>> If you feel there is some bug somewhere in the code or you have some 
>>> suggestion how to make the estimation of a certain model 'better', 
>>> then by all means feel free to contribute a detailed patch.
>>> 
>>> -Alexios
>>> 
>>> 
>>>> On 24/09/2014 20:01, stefan.jaesc...@rwe.com wrote:
>>>> Hi there,
>>>> 
>>>> 
>>>> 
>>>> 1)     I have recently updated the rugarch package to version 1.3-3 (I
>>>> do not remember the previous number) and I am surprised to see 
>>>> different results when fitting a dataset, the loglikelihood is lower 
>>>> than before and the beta parameters have changed significantly. 
>>>> Below I put the code from the fit
>>>> 
>>>> 
>>>> 
>>>> Data <- read.csv("WTI_logreturnsUS.csv", header = TRUE, sep = ";",
>>>> dec=".")
>>>> 
>>>> renditen <- Data$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
>>>> 
>>>> Phone                      +49 201 5179-1674
>>>> 
>>>> Email                      stefan.jaesc...@rwe.com
>>>> <mailto:stefan.jaesc...@rwe.com>
>>>> 
>>>> ____________________________________________________
>>>> 
>>>> 
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>>>> 
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