arch.univariate.HARCH.forecast

HARCH.forecast(parameters, resids, backcast, var_bounds, start=None, horizon=1, method='analytic', simulations=1000, rng=None, random_state=None)

Forecast volatility from the model

Parameters
  • parameters ({ndarray, Series}) – Parameters required to forecast the volatility model

  • resids (ndarray) – Residuals to use in the recursion

  • backcast (float) – Value to use when initializing the recursion

  • var_bounds (ndarray, 2-d) – Array containing columns of lower and upper bounds

  • start ({None, int}) – Index of the first observation to use as the starting point for the forecast. Default is len(resids).

  • horizon (int) – Forecast horizon. Must be 1 or larger. Forecasts are produced for horizons in [1, horizon].

  • method ({'analytic', 'simulation', 'bootstrap'}) – Method to use when producing the forecast. The default is analytic.

  • simulations (int) – Number of simulations to run when computing the forecast using either simulation or bootstrap.

  • rng (callable) – Callable random number generator required if method is ‘simulation’. Must take a single shape input and return random samples numbers with that shape.

  • random_state (RandomState, optional) – NumPy RandomState instance to use when method is ‘bootstrap’

Returns

forecasts – Class containing the variance forecasts, and, if using simulation or bootstrap, the simulated paths.

Return type

VarianceForecast

Raises

Notes

The analytic method is not supported for all models. Attempting to use this method when not available will raise a ValueError.