I am using the fGARCh package in R to analyze volatility of stock returns. More precisely I am using a garch(1, 1) fit. The code looks like this:

GARCH11<-garchFit(formula = ~garch(1, 1), data = Returns.zoo, trace = FALSE)

Returns.zoo is my time series.

Now I know the interpretation of GARCH@h.t and GARCH@sigma.t. But what does GARCH@fitted tell me in relation to the time series and why is the value equal at all times?

I would be very relieved if someone could enlighten me. Thanks very much!


1 Answer 1


When fitting a volatility model, you have two series - one describing the actual data and one the data's volatility.

In your code, the volatility part is modelled by a GARCH(1,1) model, while the data is simply modelled with a constant term, which is included by default. Hence, your "fitted" model is just a constant term and GARCH1@fitted provides a constant value, which is equal to the mean coefficient mu.

If you additionally want to model the data series, you can add an ARMA model to your specification:

GARCH11<-garchFit(formula = ~arma(1,1)+garch(1, 1), data = Returns.zoo, trace = FALSE)

Then, the @fitted output will be time-varying, following an ARMA(1,1) process. As you said that you model stock returns, using an ARMA model probably isn't necessary, though.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.