Comments (2)
simulations. Values
- These are simulated return.
simulations.residual_variances
- These are the simulated variances
These should not give the exact same values. The values have the mean in them if any. Even if the mean is 0, squaring and averaging will only give you a noisy estimate of the final residual variance.
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Closing as answered
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Related Issues (20)
- 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
- Are we using two-step approach for estimation? HOT 2
- Time series bootstrapping issues HOT 5
- volatility forecast in comparison with realized volatility HOT 3
- How to modify GARCH model to incorporate new terms ? HOT 1
- Source for Long Run Covariance Estimator
- There is no _version.py in arch.
- Details on Hansen's Skewed T
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