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I saw in the documentation that rolling window forecast can be applied with parameter first_obs
and last_obs
, while I am looking for an approach with minimal runtime overhead to
- demean the series in rolling basis, i.e.
returns[first_obs:last_obs] - mean(returns[first_obs:last_obs])
, and - fit the GARCH model
I wonder if the constant mean is applied on the rolling basis, or actually on the whole timeseries of argument y
.
model = arch_model(tseries, vol="GARCH", mean="Constant", ...)
for i in range(len(tseries) - rolling_window):
model.fit(first_obs=i, last_ob=i + rolling_window - 1, ...)
I tried to look into the source code but could not conclude it in a glance. Could you help address it?
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