Hi,
I am completely new to GARCH models and trying to fit a multivariate time
series model using DCC GARCH model and forecast it.
The data looks like this:
> head(datax)
x vibration_x Speed
1 2017-05-16 17:53:00 -0.132 421.4189
2 2017-05-16 17:54:00 -0.296 1296.8882
3 2017-05-16 17:55:00 -0.572 0.0000
4 2017-05-16 17:56:00 -0.736 1254.2695
5 2017-05-16 17:57:00 0.000 0.0000
6 2017-05-16 17:58:00 0.000 0.0000
> garch11.spec = ugarchspec(mean.model = list(armaOrder = c(1,1)),
variance.model = list(garchOrder = c(1,1),
model = "sGARCH"),
distribution.model = "norm")> dcc.garch11.spec = dccspec(uspec = multispec( replicate(2, garch11.spec)
),
dccOrder = c(1,1), distribution =
"mvnorm")> fit.a = dccfit(dcc.garch11.spec, data = datax[,c(2,3)], out.sample = 100,
fit.control =
list(eval.se=T))> dcc.focast=dccforecast(fit.a, n.ahead = 100)
May I know how to get the forecast values from 'dcc.focast' ? when i
plot
the model using,
> plot(dcc.focast, which = 1)
I get different plots such as.
Make a plot selection (or 0 to exit):
1: Conditional Mean (vs Realized Returns)
2: Conditional Sigma (vs Realized Absolute Returns)
3: Conditional Covariance
4: Conditional Correlation
5: EW Portfolio Plot with conditional density VaR limits
May i know what i should do with "Conditional covariance" and
"conditional
correlation" forecast. I know this is for volatility prediction. I am
interested to know what things i can interpret from this conditional
covariance ?
Any help is much appreciated. Thanks.,
Regards
Dhivya
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