Comments (3)
bashtage
commented on August 11, 2024
Another good idea - the difficult with a general purpose forecast is that for h > 1 the forecast is not necessarily analytic (i.e., for any volatility model that isn't linear in the squares, such as EGARCH). However, the 1 step always is for any model, so this is a simple way to think about starting on the forecasting.
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bashtage
commented on August 11, 2024
#132 should provide this and more once merged.
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bashtage
commented on August 11, 2024
Fixed in #132
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Related Issues (20)
- Doc: Example in Parametric-VaR HOT 1
- Local block bootstrap
- Why the sqrt(2/pi) factor in EGARCH? HOT 3
- Issue with Pip install HOT 3
- fit() without parameter vs. warm start HOT 2
- Cannot reconcile simulation.values against simulation.residual_variances HOT 2
- The order of input data for engle_granger() function, what about the order of the cointegrating_vector? HOT 2
- Issues with the 'rescale' parameter HOT 3
- Rolling window forecast with rolling demean HOT 3
- Exogenous variable in the Volatility Equation HOT 1
- ENH: Add NGARCH specification to set of volatility processes
- Issue when using arch.bootstrap.MCS HOT 9
- My scratch implementation does not match the result for EGARCH HOT 1
- Tests fail: ImportError while loading conftest '/usr/ports/science/py-arch/work-py39/arch-6.1.0/arch/conftest.py'. HOT 3
- Probably bug in FIGARCH implementation with horizon > 1 HOT 1
- ModuleNotFoundError: No module named 'arch' when importing arch_model HOT 7
- add topic garch
- Clarification on `.simulated_variances` attribute of `ARCHModelForecast` object. HOT 3
- `arch_model` estimator is forecasting same value irrespective of the `horizon` parameter. HOT 2
- [DOC] Links to example notebooks for unit root tests and cointegration testing analysis HOT 1
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