Update of rugarch package yields different results / questions on stationarity conditions
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. -----Urspr?ngliche Nachricht----- Von: alexios ghalalanos [mailto:alexios at 4dscape.com] Gesendet: Mittwoch, 24. September 2014 19:47 An: J?schke, Stefan; r-sig-finance at r-project.org Cc: alexios at 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$stationarity=0 2. can be changes to whatever you want by using the setbounds<- method on the specification. As far as I know, you can take the ARMA and GARCH stationarity conditions separately, as you can also estimate them in 2 steps without too much loss in efficiency. If you want to see the degree of interaction between the 2, then use the ugarchdistribution method which includes a number of interesting parameter interaction plots (and you can also investigate others by working with the returned parameter distribution data). If you feel there is some bug somewhere in the code or you have some suggestion how to make the estimation of a certain model 'better', then by all means feel free to contribute a detailed patch. -Alexios On 24/09/2014 20:01, Stefan.Jaeschke at rwe.com wrote:
Hi there,
1) I have recently updated the rugarch package to version 1.3-3 (I
do not remember the previous number) and I am surprised to see
different results when fitting a dataset, the loglikelihood is lower
than before and the beta parameters have changed significantly. Below
I put the code from the fit
Data <- read.csv("WTI_logreturnsUS.csv", header = TRUE, sep = ";",
dec=".")
renditen <- Data$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 Gr??en / Kind regards
*Stefan J?schke*
RWE Supply & Trading GmbH
Performance Controlling CAO Gas & VAC (MFC-GV)
Altenessener Str. 27
45141 Essen
Germany
Phone +49 201 5179-1674
Email stefan.jaeschke at rwe.com
<mailto:stefan.jaeschke at rwe.com>
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