volatility forecast in comparison with realized volatility about arch HOT 3 OPEN

Could you post more of your code? I isn't clear what result is coming from.

from arch.

resid_predict = return[-splitDate: ] - ARIMA_model.predict (start=splitDate, end=end_date)

y= pd.concat ([ ARIMA_model.resid.dropna() , resid_predict ])

model_set= arch_model(y=y, mean = 'zero', vol= 'GARCH', dist='t', p=1 , o=0, q=1)

result= model_set.fit(disp = 'off', last_obs=splitDate)

frcst = result.forecast(horizon=1, start=splitDate, method="simulation", simulations=30 *50)
frcst_variance=frcst.variance .squeeze()
realized_volatility= realized_volatility(n_period=30)

fig, ax = plt.subplots(figsize= (12,6))
ax.plot(result.conditional_volatility, "green",
np.sqrt(frcst_variance), "red" ,
realized_volatility, "blue")

if I set (7,7) for orders of GARCH then:

from arch.

I have another related question. it would be great if it got clear to me. I just change the length of out-of-sample set (test set). it was 30 (days) and I changed it to 5. then in-sample prediction of model which is in fact the conditionally variance again is illustrated by green line. It seems there has been no change in in-sample perdition in last 25 days of train set (in comparison to when I had set the length of test set equal to 30). is this can be considered as evidence that modeling process is taking the test set into the account when the model is building?

from arch.