arch.univariate.ZeroMean.simulate¶

Simulated data from a zero mean model

Parameters:¶

params: ArrayLike1D | collections.abc.Sequence[float]¶: Parameters to use when simulating the model. Parameter order is [volatility distribution]. There are no mean parameters.
nobs: int¶: Length of series to simulate
burn: int = 500¶: Number of values to simulate to initialize the model and remove dependence on initial values.
initial_value: None | float | Float64Array = None¶: This value is not used.
x: ArrayLike | None = None¶: This value is not used.
initial_value_vol: None | float | Float64Array = None¶: An array or scalar to use when initializing the volatility process.

Returns:¶

simulated_data – DataFrame with columns data containing the simulated values, volatility, containing the conditional volatility and errors containing the errors used in the simulation

Return type:¶

pandas.DataFrame

Examples

Basic data simulation with no mean and constant volatility

>>> from arch.univariate import ZeroMean
>>> import numpy as np
>>> zm = ZeroMean()
>>> params = np.array([1.0])
>>> sim_data = zm.simulate(params, 1000)

Simulating data with a non-trivial volatility process

>>> from arch.univariate import GARCH
>>> zm.volatility = GARCH(p=1, o=1, q=1)
>>> sim_data = zm.simulate([0.05, 0.1, 0.1, 0.8], 300)