I tried to fit auto.arima() with a ts data. But it is not giving the right forecast. For many it is coming as arima(0,1,0) model which is not good at all. Can I fit a GARCH model to the original series in this case? How do you get fitted and forecasted values of original data using garch(1,1,) or some other model? I tried to use code for GARCH but it is not giving the fitted and forecast of original values.

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    $\begingroup$ (0,1,0) may be a disappointing model, but it may also be right. Do the diagnostics on the residuals (e.g. ACF and PACF) look decent? $\endgroup$ – zbicyclist Nov 17 '12 at 23:55

auto.arima in package forecast is using AIC, AICc or BIC values in order to select the model. It does not mean that it is the best model, although it normally gives a good model. You should first check if the garch model is a good candidate or not. Just because auto.arima cannot give you a good model is not a good reason to go for garch! In R, to check conditional heteroscedascity, you can use McLeod-Li test in package TSA.
Garch models the variance of the series so the fitted values are not going to change unless you model the mean as well. Take a look at this for example.


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