Hi Alexios
I am modelling the same data and getting vastly different estimates using
"rugarch" package and "fGarch" package (all installs and packages have been
recently downloaded and should be up to date). The data is 242 monthly log
returns in raw log return format (i.e. not multiplied by 100 to get percent
format). I have attached the file for reproducible results.
In estimating an ARMA(2,2)-GARCH(1,1) with "normal" errors, I get the
following output using:
(1) "rugarch" package:
spec <- ugarchspec(variance.model=list(model="sGARCH",garchOrder=c(1,1),
submodel=NULL,external.regressors=NULL,variance.targeting=F),
mean.model=list(armaOrder=c(2,2),include.mean=T,external.regressors=NULL),
distribution.model="norm")
tempgarch <- ugarchfit(spec=spec,data=ALSI.reg.log.ret,solver="hybrid")
show(tempgarch)
Output:
Optimal Parameters
------------------------------------
Estimate Std. Error t value Pr(>|t|)
mu 0.015107 0.003310 4.56480 0.000005
ar1 0.561145 0.577557 0.97158 0.331258
ar2 -0.303709 0.526705 -0.57662 0.564195
ma1 -0.629355 0.555266 -1.13343 0.257033
ma2 0.430343 0.511488 0.84135 0.400149
omega 0.000257 0.000343 0.75094 0.452687
alpha1 0.197622 0.148026 1.33505 0.181862
beta1 0.730256 0.228730 3.19265 0.001410
(2) "fGarch" package
garch.fit=garchFit(formula=~arma(2,2)+garch(1,1),data=ALSI.reg.log.ret,cond.dist="norm",trace=F,include.mean=T)
summary(garch.fit)
Output:
Error Analysis:
Estimate Std. Error t value Pr(>|t|)
mu 0.0368275 0.0070622 5.215 0.000000184 ***
ar1 -0.4909910 0.0955405 -5.139 0.000000276 ***
ar2 -0.8424945 0.0538062 -15.658 < 2e-16 ***
ma1 0.4733331 0.0904955 5.230 0.000000169 ***
ma2 0.8844500 0.0550194 16.075 < 2e-16 ***
omega 0.0003106 0.0001838 1.690 0.0910 .
alpha1 0.3696620 0.1496237 2.471 0.0135 *
beta1 0.5808720 0.1434332 4.050 0.000051267 ***
I am confused as to why the output differs to such a great extent. Any idea
why this is happening?
Thank you very much
Gareth
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