arch.univariate.HARX.simulate

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

Simulates data from a linear regression, AR or HAR models

Parameters

  • params (ndarray) -- Parameters to use when simulating the model. Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable.

  • 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 ({ndarray, float}, optional) -- Either a scalar value or max(lags) array set of initial values to use when initializing the model. If omitted, 0.0 is used.

  • x ({ndarray, DataFrame}, optional) -- nobs + burn by k array of exogenous variables to include in the simulation.

  • 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

>>> import numpy as np
>>> from arch.univariate import HARX, GARCH
>>> harx = HARX(lags=[1, 5, 22])
>>> harx.volatility = GARCH()
>>> harx_params = np.array([1, 0.2, 0.3, 0.4])
>>> garch_params = np.array([0.01, 0.07, 0.92])
>>> params = np.concatenate((harx_params, garch_params))
>>> sim_data = harx.simulate(params, 1000)

Simulating models with exogenous regressors requires the regressors to have nobs plus burn data points

>>> nobs = 100
>>> burn = 200
>>> x = np.random.randn(nobs + burn, 2)
>>> x_params = np.array([1.0, 2.0])
>>> params = np.concatenate((harx_params, x_params, garch_params))
>>> sim_data = harx.simulate(params, nobs=nobs, burn=burn, x=x)