arch.arch_model

import arch

# 使用help函数查看arch.arch_model的文档
help(arch.arch_model)

输出结果为：

Help on function arch_model in module arch.univariate.mean:
arch_model(y, x=None, mean='Constant', lags=0, vol='Garch', p=1, o=0, q=1, power=2.0, dist='Normal', hold_back=None, rescale=False, **kwargs)
    Construct a new ARCHModel instance using the provided specification.
    
    Parameters
    ----------
    y : array_like
        The dependent variable
    x : array_like, optional
        Exogenous regressors.  Ignored if model does not permit exogenous
        regressors.
    mean : str, optional
        Name of the mean model.  Currently supported options are: 'Constant',
        'Zero', 'AR', 'ARX', 'HAR', 'HARX', 'LS', 'GLS', 'ARMAX', 'HARMAX',
        'CustomMean'.  Default is 'Constant'.
    lags : int or list[int], optional
        Either a scalar integer value indicating lag length or a list of
        integers specifying lag locations.  Used in the construction of
        the selected mean model.  Default is 0.
    vol : str, optional
        Name of the volatility model.  Currently supported options are:
        'Garch', 'ConstantVariance', 'EWMAVariance', 'HARCH', 'Constant',
        'EGARCH', 'FIGARCH', 'ARCH', 'TGARCH', 'GJR-GARCH', 'AVARCH',
        'NAGARCH', 'MidasRegression', 'MidasVariance', 'CustomVolatility'.
        Default is 'Garch'.
    p : int, optional
        Order of the symmetric innovation.  Used in the construction of the
        selected volatility model.  Default is 1.
    o : int, optional
        Order of the asymmetric innovation.  Used in the construction of the
        selected volatility model.  Default is 0.
    q : int, optional
        Order of lagged volatility terms.  Used in the construction of the
        selected volatility model.  Default is 1.
    power : float, optional
        Power to use in the case of an ARCH in mean model.  Default is 2.0.
    dist : str, optional
        Name of the distribution.  Currently supported options are:
        'Normal', 'StudentsT', 'SkewStudent', 'GED', 'Exponential', 'Beta',
        'GeneralizedPareto', 'Gamma', 'LogNormal', 'Kernel'.  Default is
        'Normal'.
    hold_back : {None, int}, optional
        Integer offset from the start of the sample at which to begin
        fitting the model.  Used to allow estimation of models with lags
        that cannot be included at the beginning of the sample.  Default is
        None.
    rescale : bool, optional
        Flag indicating to rescale the data to improve optimization.  Default
        is False.
    **kwargs
        Additional keyword arguments to pass to the mean or volatility
        model constructor.
    
    Returns
    -------
    model : ARCHModel
        Configured ARCHModel instance.