class_docstr: Inherits time series methods from TSM class.
  
  **** GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (GARCH) MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.array
      Field to specify the time series data that will be used.
  
  p : int
      Field to specify how many GARCH terms the model will have.
  
  q : int
      Field to specify how many ARCH terms the model will have.
  
  target : str (pd.DataFrame) or int (np.array)
      Specifies which column name or array index to use. By default, first
      column/array will be selected as the dependent variable.
docstr: Creates the negative log-likelihood of the model
  
  Parameters
  ----------
  beta : np.array
      Contains untransformed starting values for latent variables
  
  Returns
  ----------
  The negative logliklihood of the model
docstr_tok: ["Creates","the","negative","log-likelihood","of","the","model","Parameters","--","--","--","--","--","beta",":","np.array","Contains","untransformed","starting","values","for","latent","variables","Returns","--","--","--","--","--","The","negative","logliklihood","of","the","model"]
code: def neg_loglik(self, beta):
      """Creates the negative log-likelihood of the model
  
      Parameters
      ----------
      beta : np.array
          Contains untransformed starting values for latent variables
  
      Returns
      ----------
      The negative logliklihood of the model
      """
  
      sigma2, Y, __ = self._model(beta)
      parm = np.array(
          [
              self.latent_variables.z_list[k].prior.transform(beta[k])
              for k in range(beta.shape[0])
          ]
      )
      return -np.sum(
          ss.norm.logpdf(
              Y,
              loc=parm[-1] * np.ones(sigma2.shape[0]),
              scale=np.sqrt(sigma2),
          )
      )
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  from ..None import families
  from ..None import output
  from ..None import tests
  from ..None import tsm
  from ..None import data_check
  from .garch_recursions import garch_recursion
label_desc: Use image node_id 10 for calling the GARCH obj's underlying member method code with example usage: obj.neg_loglik(beta) and returns: unknown