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)

Results from estimation of an ARCHModel model

Parameters:

paramsndarray

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

r2float

Model R-squared

residndarray

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

volatilityndarray

Conditional volatility from model

cov_typestr

String describing the covariance estimator used

dep_varSeries

Dependent variable

nameslist (str)

Model parameter names

loglikelihoodfloat

Loglikelihood at estimated parameters

is_pandasbool

Whether the original input was pandas

optim_outputOptimizeResult

Result of log-likelihood optimization

fit_startint

Integer index of the first observation used to fit the model

fit_stopint

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

modelARCHModel

The model object used to estimate the parameters

Attributes:

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

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