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I'm new to GARCH, but I've got daily data of TV Ratings. I've been trying to forecast this for future, and a quick background - the data is non-stationary, has high seasonality (weekly, monthly & yearly).

I've tried UCM, but forecasts for weekly data using UCM are easier to handle, and daily level of forecasts aren't making the cut. Which is when I turned to GARCH to see if I can quickly get some high level estimates into the future.

I'm stuck with trying to get the forecasts for both the "conditional mean" and the "conditional variance" for t periods in the future. I've got the estimates for the GARCH(1,1) model, but I'm stuck trying to forecast the series into the future.

            y(t) = constant + AR(1)coeff*y(t-1) + u(t)
            h(t) = ARCH0 + ARCH1*u(t-1) + GARCH1*h(t-1)

But I haven't been able to implement this to get the predicted conditional mean & variance values, and haven't really found a perfect step-by-step guide anywhere yet! I'm new to GARCH, so, I'm guessing this might be pretty basic, but any help would be much appreciated.

Thanks, Arun

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migrated from quant.stackexchange.com Aug 20 '13 at 15:44

This question came from our site for finance professionals and academics.

    
What software are you using to run your garch regression? R has some good packages that I have used in the past for this. I could probably dig up and post some code if needed to do a forecast and predict function for this. Your errors are going to grow as you forecast out this model how many periods is t that you are looking to forecast? – bjackfly Aug 21 '13 at 6:54

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