arch.univariate.base.ARCHModelResult

class arch.univariate.base.ARCHModelResult(params: ndarray, param_cov: ndarray | None, r2: float, resid: ndarray, volatility: ndarray, cov_type: str, dep_var: pandas.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

Estimated parameters

param_cov: ndarray | 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

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

volatility: ndarray

Conditional volatility from model

cov_type: str

String describing the covariance estimator used

dep_var: pandas.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