arch.univariate.base.ARCHModelResult¶

class arch.univariate.base.ARCHModelResult(params: ndarray[Any, dtype[float64]], param_cov: ndarray[Any, dtype[float64]] | None, r2: float, resid: ndarray[Any, dtype[float64]], volatility: ndarray[Any, dtype[float64]], cov_type: str, dep_var: Series, names: list[str], loglikelihood: float, is_pandas: bool, optim_output: OptimizeResult, fit_start: int, fit_stop: int, model: ARCHModel)[source]¶

Results from estimation of an ARCHModel model

Parameters:¶

params: ndarray[Any, dtype[float64]]¶

Estimated parameters

param_cov: ndarray[Any, dtype[float64]] | None¶

Estimated variance-covariance matrix of params. If none, calls method to compute variance from model when parameter covariance is first used from result

r2: float¶

Model R-squared

resid: ndarray[Any, dtype[float64]]¶

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

volatility: ndarray[Any, dtype[float64]]¶

Conditional volatility from model

cov_type: str¶

String describing the covariance estimator used

dep_var: Series¶

Dependent variable

names: list[str]¶

Model parameter names

loglikelihood: float¶

Loglikelihood at estimated parameters

is_pandas: bool¶

Whether the original input was pandas

optim_output: OptimizeResult¶

Result of log-likelihood optimization

fit_start: int¶

Integer index of the first observation used to fit the model

fit_stop: int¶

Integer index of the last observation used to fit the model using slice notation fit_start:fit_stop

model: ARCHModel¶

The model object used to estimate the parameters

Methods

arch_lm_test([lags, standardized])	ARCH LM test for conditional heteroskedasticity
conf_int([alpha])	Parameter confidence intervals
forecast([params, horizon, start, align, ...])	Construct forecasts from estimated model
hedgehog_plot([params, horizon, step, ...])	Plot forecasts from estimated model
plot([annualize, scale])	Plot standardized residuals and conditional volatility
summary()	Constructs a summary of the results from a fit model.

Properties

aic	Akaike Information Criteria
bic	Schwarz/Bayesian Information Criteria
conditional_volatility	Estimated conditional volatility
convergence_flag	scipy.optimize.minimize result flag
fit_start	Start of sample used to estimate parameters
fit_stop	End of sample used to estimate parameters
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
optimization_result	Information about the convergence of the loglikelihood optimization
param_cov	Parameter covariance
params	Model Parameters
pvalues	Array of p-values for the t-statistics
resid	Model residuals
rsquared	R-squared
rsquared_adj	Degree of freedom adjusted R-squared
scale	The scale applied to the original data before estimating the model.
std_err	Array of parameter standard errors
std_resid	Residuals standardized by conditional volatility
tvalues	Array of t-statistics testing the null that the coefficient are 0