arch.univariate.ConstantVariance.forecast¶

ConstantVariance.
forecast
(parameters, resids, backcast, var_bounds, start=None, horizon=1, method='analytic', simulations=1000, rng=None, random_state=None)¶ Forecast volatility from the model
 Parameters
parameters ({ndarray, Series}) – Parameters required to forecast the volatility model
resids (ndarray) – Residuals to use in the recursion
backcast (float) – Value to use when initializing the recursion
var_bounds (ndarray, 2d) – Array containing columns of lower and upper bounds
start ({None, int}) – Index of the first observation to use as the starting point for the forecast. Default is len(resids).
horizon (int) – Forecast horizon. Must be 1 or larger. Forecasts are produced for horizons in [1, horizon].
method ({'analytic', 'simulation', 'bootstrap'}) – Method to use when producing the forecast. The default is analytic.
simulations (int) – Number of simulations to run when computing the forecast using either simulation or bootstrap.
rng (callable) – Callable random number generator required if method is ‘simulation’. Must take a single shape input and return random samples numbers with that shape.
random_state (RandomState, optional) – NumPy RandomState instance to use when method is ‘bootstrap’
 Returns
forecasts – Class containing the variance forecasts, and, if using simulation or bootstrap, the simulated paths.
 Return type
VarianceForecast
 Raises
If method is not supported
If the method is not known
Notes
The analytic
method
is not supported for all models. Attempting to use this method when not available will raise a ValueError.