arch.univariate.ConstantMean.simulate¶

ConstantMean.simulate(params: ndarray | Series | Sequence[float], nobs: int, burn: int = 500, initial_value: float | ndarray[Any, dtype[float64]] | None = None, x: ndarray | DataFrame | Series | None = None, initial_value_vol: float | ndarray[Any, dtype[float64]] | None = None) → DataFrame[source]¶

Simulated data from a constant mean model

Parameters:¶

params: ndarray | Series | Sequence[float]¶

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 = 500¶

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

initial_value: float | ndarray[Any, dtype[float64]] | None = None¶

This value is not used.

x: ndarray | DataFrame | Series | None = None¶

This value is not used.

initial_value_vol: float | ndarray[Any, dtype[float64]] | None = 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 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)