class_docstr: StatsForecast GARCH estimator.
  
  Direct interface to ``statsforecast.models.GARCH``.
  
  This implements the Generalized Autoregressive Conditional
  Heteroskedasticity (GARCH) model.
  
  Constructs a GARCH(p, q) model.
  
  Parameters
  ----------
  p: int (default 1)
      AR parameter - number of auto-regressive lags.
  q: int (default 1)
      GARCH heteroskedasticity lag parameter - number of lags for variance term.
class_docstr_tok: ["StatsForecast","GARCH","estimator",".","Direct","interface","to","``","statsforecast.models.GARCH","``",".","This","implements","the","Generalized","Autoregressive","Conditional","Heteroskedasticity","(","GARCH",")","model",".","Constructs","a","GARCH","(","p",",","q",")","model",".","Parameters","--","--","--","--","--","p",":","int","(","default","1",")","AR","parameter","-","number","of","auto-regressive","lags",".","q",":","int","(","default","1",")","GARCH","heteroskedasticity","lag","parameter","-","number","of","lags","for","variance","term","."]
code: def __init__(
      self,
      p=1,
      q=1,
  ):
      self.p = p
      self.q = q
  
      super().__init__()
  
file_path: sktime/sktime/forecasting/arch/_statsforecast_arch.py
imports: from sktime.forecasting.base.adapters._generalised_statsforecast import _GeneralisedStatsForecastAdapter
label_desc: Use image node_id 1 to create a new StatsForecastGARCH object from inherited base classes: _GeneralisedStatsForecastAdapter with example: obj = StatsForecastGARCH(p, q)