arch.univariate.base.ARCHModel.fit¶
- ARCHModel.fit(update_freq=1, disp='final', starting_values=None, cov_type='robust', show_warning=True, first_obs=None, last_obs=None, tol=None, options=None, backcast=None)[source]¶
Estimate model parameters
- Parameters:
- update_freq
int
,optional
Frequency of iteration updates. Output is generated every update_freq iterations. Set to 0 to disable iterative output.
- disp{bool, “off”, “final”}
Either ‘final’ to print optimization result or ‘off’ to display nothing. If using a boolean, False is “off” and True is “final”
- starting_values
ndarray
,optional
Array of starting values to use. If not provided, starting values are constructed by the model components.
- cov_type
str
,optional
Estimation method of parameter covariance. Supported options are ‘robust’, which does not assume the Information Matrix Equality holds and ‘classic’ which does. In the ARCH literature, ‘robust’ corresponds to Bollerslev-Wooldridge covariance estimator.
- show_warningbool,
optional
Flag indicating whether convergence warnings should be shown.
- first_obs{
int
,str
,datetime
,Timestamp
} First observation to use when estimating model
- last_obs{
int
,str
,datetime
,Timestamp
} Last observation to use when estimating model
- tol
float
,optional
Tolerance for termination.
- options
dict
,optional
Options to pass to scipy.optimize.minimize. Valid entries include ‘ftol’, ‘eps’, ‘disp’, and ‘maxiter’.
- backcast{
float
,ndarray
},optional
Value to use as backcast. Should be measure \(\sigma^2_0\) since model-specific non-linear transformations are applied to value before computing the variance recursions.
- update_freq
- Returns:
- results
ARCHModelResult
Object containing model results
- results
Notes
A ConvergenceWarning is raised if SciPy’s optimizer indicates difficulty finding the optimum.
Parameters are optimized using SLSQP.
- Return type: