Mean Models¶

All ARCH models start by specifying a mean model.

`ZeroMean`([y, hold_back, volatility, ...])	Model with zero conditional mean estimation and simulation
`ConstantMean`([y, hold_back, volatility, ...])	Constant mean model estimation and simulation.
`ARX`([y, x, lags, constant, hold_back, ...])	Autoregressive model with optional exogenous regressors estimation and simulation
`HARX`([y, x, lags, constant, use_rotated, ...])	Heterogeneous Autoregression (HAR), with optional exogenous regressors, model estimation and simulation
`LS`([y, x, constant, hold_back, volatility, ...])	Least squares model estimation and simulation

(G)ARCH-in-mean Models¶

(G)ARCH-in-mean models allow the conditional variance (or a transformation of it) to enter the conditional mean.

`ARCHInMean`([y, x, lags, constant, ...])	(G)ARCH-in-mean model and simulation

Special Requirements¶

Not all volatility processes support application to AIM modeling. Specifically, the property updateable must be True.

In [1]: from arch.univariate import GARCH, EGARCH

In [2]: GARCH().updateable
Out[2]: True

In [3]: EGARCH().updateable
Out[3]: True

Writing New Mean Models¶

All mean models must inherit from :class:ARCHModel and provide all public methods. There are two optional private methods that should be provided if applicable.

`ARCHModel`([y, volatility, distribution, ...])	Abstract base class for mean models in ARCH processes.