arch.univariate.FixedVariance¶

class arch.univariate.FixedVariance(variance: ndarray[tuple[int, ...], dtype[float64]], unit_scale: bool = False)[source]¶

Fixed volatility process.

Parameters:¶

variance: ndarray[tuple[int, ...], dtype[float64]]¶

Array containing the variances to use. Should have the same shape as the data used in the model. This is not checked since the model is not available when the FixedVariance process is created. The variances must be finite and positive.

unit_scale: bool = False¶

Flag whether to enforce a unit scale. If False, a scale parameter will be estimated so that the model variance will be proportional to variance. If True, the model variance is set to variance.

Raises:¶

• TypeError – If variance cannot be converted to a float64 array or unit_scale is not a bool.

• ValueError – If variance is not 1-d, or contains NaN, inf, or a value that is not positive.

Notes

Allows a fixed set of variances to be used when estimating a mean model, allowing GLS estimation.

Methods

backcast(resids)	Construct values for backcasting to start the recursion.
backcast_transform(backcast)	Transform user-provided backcast values.
bounds(resids)	Return bounds for parameters.
compute_variance(parameters, resids, sigma2, ...)	Compute the variance for the ARCH model.
constraints()	Construct parameter constraints arrays for parameter estimation.
forecast(parameters, resids, backcast, ...)	Forecast volatility from the model.
parameter_names()	Return the names of the model parameters.
simulate(parameters, nobs, rng[, burn, ...])	Simulate data from the model.
starting_values(resids)	Return starting values for the ARCH model.
update(index, parameters, resids, sigma2, ...)	Compute the variance for a single observation.
variance_bounds(resids[, power])	Construct loose bounds for conditional variances.

Properties

name	The name of the volatility process.
num_params	The number of parameters in the model.
start	Index to use to start variance subarray selection.
stop	Index to use to stop variance subarray selection.
updateable	Flag indicating that the volatility process supports update.
volatility_updater	Get the volatility updater associated with the volatility process.