arch.univariate.ZeroMean.simulate

ZeroMean.simulate(params, nobs, burn=500, initial_value=None, x=None, initial_value_vol=None)[source]

Simulated data from a zero mean model

Parameters
params{ndarray, DataFrame}

Parameters to use when simulating the model. Parameter order is [volatility distribution]. There are no mean parameters.

nobsint

Length of series to simulate

burnint, optional

Number of values to simulate to initialize the model and remove dependence on initial values.

initial_valueNone

This value is not used.

xNone

This value is not used.

initial_value_vol{ndarray, float}, optional

An array or scalar to use when initializing the volatility process.

Returns
simulated_dataDataFrame

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

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)
Return type

DataFrame