arch.univariate.EWMAVariance¶

class arch.univariate.EWMAVariance(lam=0.94)[source]¶

Exponentially Weighted Moving-Average (RiskMetrics) Variance process

Parameters: lam ({float, None}, optional) -- Smoothing parameter. Default is 0.94. Set to None to estimate lam jointly with other model parameters

num_params¶

The number of parameters in the model

Type: int

Examples

Daily RiskMetrics EWMA process

>>> from arch.univariate import EWMAVariance
>>> rm = EWMAVariance(0.94)

Notes

The variance dynamics of the model

\[\sigma_t^{2}=\lambda\sigma_{t-1}^2 + (1-\lambda)\epsilon^2_{t-1}\]

When lam is provided, this model has no parameters since the smoothing parameter is treated as fixed. Set lam to None to jointly estimate this parameter when fitting the model.

Methods

`backcast`(resids)	Construct values for backcasting to start the recursion
`backcast_transform`(backcast)	Transformation to apply to user-provided backcast values
`bounds`(resids)	Returns 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`()	Names of model parameters
`simulate`(parameters, nobs, rng[, burn, ...])	Simulate data from the model
`starting_values`(resids)	Returns starting values for the ARCH model
`variance_bounds`(resids[, power])	Construct loose bounds for conditional variances.

Properties

`name`	The name of the volatilty process
`start`	Index to use to start variance subarray selection
`stop`	Index to use to stop variance subarray selection