arch.univariate.base.ARCHModelFixedResult¶

class arch.univariate.base.ARCHModelFixedResult(params: ndarray[tuple[int], dtype[float64]], resid: ndarray[tuple[int], dtype[float64]], volatility: ndarray[tuple[int], dtype[float64]], dep_var: Series, names: list[str], loglikelihood: float, is_pandas: bool, model: ARCHModel)[source]¶

Results for fixed parameters for an ARCHModel model.

Parameters:¶

params: ndarray[tuple[int], dtype[float64]]¶

Estimated parameters.

resid: ndarray[tuple[int], dtype[float64]]¶

Residuals from model. Residuals have same shape as original data and contain nan-values in locations not used in estimation.

volatility: ndarray[tuple[int], dtype[float64]]¶

Conditional volatility from model.

dep_var: Series¶

Dependent variable.

names: list[str]¶

Model parameter names.

loglikelihood: float¶

Loglikelihood at specified parameters.

is_pandas: bool¶

Whether the original input was pandas.

model: ARCHModel¶

The model object used to estimate the parameters.

Notes

The results are created by ARCHModel.fix. The arguments of the constructor are not validated.

Methods

arch_lm_test([lags, standardized])	Perform an ARCH LM test for conditional heteroskedasticity.
forecast([params, horizon, start, align, ...])	Construct forecasts from estimated model.
hedgehog_plot([params, horizon, step, ...])	Plot forecasts from estimated model.
hill_estimator([k])	Compute the Hill tail-index estimator of standardized residuals.
plot([annualize, scale])	Plot standardized residuals and conditional volatility.
summary()	Construct a summary of the results from a fit model.
var_ratio([level])	Compute the ratio of Gaussian VaR to model-distribution VaR.

Properties

aic	Akaike Information Criteria.
bic	Schwarz/Bayesian Information Criteria.
conditional_volatility	Estimated conditional volatility.
excess_kurtosis	Sample excess kurtosis of standardized residuals.
loglikelihood	Model loglikelihood.
model	Model instance used to produce the fit.
nobs	Number of data points used to estimate model.
num_params	Number of parameters in model.
params	Model parameters.
resid	Model residuals.
std_resid	Standardized residuals, the residuals divided by the conditional volatility.