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.
nobs (int) -- Length of series to simulate
burn (int, optional) -- Number of values to simulate to initialize the model and remove dependence on initial values.
initial_value (None) -- This value is not used.
x (None) -- This value is not used.
initial_value_vol ({ndarray, float}, optional) -- 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
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)