arch.univariate.base.ARCHModelResult

class arch.univariate.base.ARCHModelResult(params, param_cov, r2, resid, volatility, cov_type, dep_var, names, loglikelihood, is_pandas, optim_output, fit_start, fit_stop, model)[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 (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 covergence 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