# 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 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