arch.univariate.EGARCH¶

class arch.univariate.EGARCH(p: int = 1, o: int = 0, q: int = 1)[source]¶

EGARCH model estimation.

Parameters:¶

p: int = 1¶

Order of the symmetric innovation. Must be non-negative.

o: int = 0¶

Order of the asymmetric innovation. Must be non-negative.

q: int = 1¶

Order of the lagged (transformed) conditional variance. Must be non-negative.

Raises:¶

• TypeError – If p, o or q is not an integer.

• ValueError – If p, o or q is negative, or p and o are both 0.

Notes

In this class of processes, the variance dynamics are

\[\ln\sigma_{t}^{2}=\omega +\sum_{i=1}^{p}\alpha_{i} \left(\left|e_{t-i}\right|-\sqrt{2/\pi}\right) +\sum_{j=1}^{o}\gamma_{j} e_{t-j} +\sum_{k=1}^{q}\beta_{k}\ln\sigma_{t-k}^{2}\]

where \(e_{t}=\epsilon_{t}/\sigma_{t}\).

Examples

>>> from arch.univariate import EGARCH

Symmetric EGARCH(1,1)

>>> egarch = EGARCH(p=1, q=1)

Standard EGARCH process

>>> egarch = EGARCH(p=1, o=1, q=1)

Exponential ARCH process

>>> earch = EGARCH(p=5)

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.