Hi,
1. You can easily check whether you are getting the forecast at the date
you want by inspecting the returned forecast density data.frame:
as.data.frame(roll, which = "density")
OR VaR:
as.data.frame(roll, which = "VaR")
If you provided an xts object, then the dates in the data.frame rownames
will provide you with the answer.
Have you tried help('uGARCHroll-class') ?
2. "show(roll,which=4)". There is no documented method 'show' which
takes on additional arguments 'which'.
3. 'report(roll,type="VaR",VaR.alpha=0.01,conf.level=0.99)'
This is a formal test of the conditional coverage. You ask WHY your
model does not pass the test. ONLY YOU can answer that question given
you knowledge of YOUR data.
However, it is usually unlikely that the normal distribution provides
for a good fit to the observed security return dynamics in financial
markets (try distribution.model='jsu').
Also, if you search previous postings you may see that a data length of
255 may not be adequate for modelling the volatility process
persistence. There is a blog post on this question you may find useful
(http://www.unstarched.net/2012/12/26/garch-parameter-uncertainty-and-data-size/).
Regards,
Alexios
On 04/06/2013 14:25, Alexandra Bridges wrote:
Hi,
I am using the rugarch package and especially the command ugarchroll
to do a rolling forecasting to calculate the VaR.
I am using the sp500ret of the rugarch package:
library(rugarch)
data(sp500ret)
This is daily data. I now want to fit a GARCH model every 100th day.
The window size should be 255 observations. So my GARCH model should
take the last recent 255 observations. Therefore the first VaR
forecast belongs to the 256th day (this is in this dataset the
11.03.1988).
My code is:
# model specification
spmodel<-ugarchspec(variance.model = list(model = "sGARCH", garchOrder
= c(1, 1)),
mean.model = list(armaOrder = c(0, 0), include.mean = FALSE),
distribution.model = "norm")
# model fit
spgarchmodel<-ugarchfit(spec=spmodel,data=sp500ret)
# now rolling forecasts with ugarchroll
# observations available in total:
length(sp500ret[,1])
roll = ugarchroll(spmodel, sp500ret, n.start=255,
refit.every = 100, refit.window = 'moving', window.size = 255,
calculate.VaR = TRUE, keep.coef = TRUE)
show(roll)
# or the following alternatively also works:
roll = ugarchroll(spmodel, sp500ret, forecast.length=(length(sp500ret[,1]))-255,
refit.every = 100, refit.window = 'moving', window.size = 255,
calculate.VaR = TRUE, keep.coef = TRUE)
show(roll,which=4)
First: Is this right what I am doing? Since both methods lead to the
same result I think I am correct, right?
Second:
The backtest shows the following:
report(roll,type="VaR",VaR.alpha=0.01,conf.level=0.99)
That means, I have far more exceedances than expected. So my model is
not good, why? What am I doing wrong? Is this due to a bad model
specification or due to an error in my code?
--
Alexa Bridges
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