Comments (2)
You are seeing constant values because for a constant mean and you are seeing the mean forecasts (which are by construction constant). If you want to look at the forecast variances, you should look at the variances
attribute.
In your case:
You are looking at the wrong value. result.variance
is what you want.
print(result.variance.values[-1])
[530.57831213 534.01591322 537.4535143 540.89111539 544.32871648]
Or in a more traditional dataset:
from arch.data import sp500
y = sp500.load()["Adj Close"].pct_change().dropna()
model = arch_model(y, rescale=True)
model = model.fit(disp='off')
result = model.forecast(horizon=5)
print(result.variance)
h.1 h.2 h.3 h.4 h.5
Date
2018-12-31 3.59647 3.568502 3.540887 3.513621 3.486701
from arch.
Oh! Thanks for clarification.
from arch.
Related Issues (20)
- Issues with the 'rescale' parameter HOT 3
- Rolling window forecast with rolling demean HOT 3
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- Tests fail: ImportError while loading conftest '/usr/ports/science/py-arch/work-py39/arch-6.1.0/arch/conftest.py'. HOT 3
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- [DOC] Links to example notebooks for unit root tests and cointegration testing analysis HOT 1
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from arch.