arch.univariate.ConstantMean.simulate¶
-
ConstantMean.
simulate
(params, nobs, burn=500, initial_value=None, x=None, initial_value_vol=None)[source]¶ Simulated data from a constant mean model
- Parameters
params (ndarray) -- Parameters to use when simulating the model. Parameter order is [mean volatility distribution]. There is one parameter in the mean model, mu.
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 a constant mean and volatility
>>> import numpy as np >>> from arch.univariate import ConstantMean, GARCH >>> cm = ConstantMean() >>> cm.volatility = GARCH() >>> cm_params = np.array([1]) >>> garch_params = np.array([0.01, 0.07, 0.92]) >>> params = np.concatenate((cm_params, garch_params)) >>> sim_data = cm.simulate(params, 1000)