I'm trying to forecast a stock index with daily data from 1990 to today (over 7000 data points) with ARIMA, after correlogram, information criterion (prioritizing Akaike) and auto selection (either with Eviews and R), I end up with 10 parameters (5,1,5) (I bet it doesn't respect the principle of parsimony).
On the log returns of the train sample it's, indeed, the best model, but when I do the forecast on the test sample, it's clearly not the best, even adding a garch(1,1).
So I guess the problem is the length of my time series, or that I shouldn't go for Akaike's information criterion with that length? What are your thoughts on that ?
Thank you very much.