def plot_fit(self, **kwargs):
        """ Plots the fit of the model

        Returns
        ----------
        None (plots data and the fit)
        """
        import matplotlib.pyplot as plt
        import seaborn as sns

        figsize = kwargs.get('figsize',(10,7))

        if self.latent_variables.estimated is False:
            raise Exception("No latent variables estimated!")
        else:
            plt.figure(figsize=figsize)
            date_index = self.index[max(self.p, self.q):]
            t_params = self.transform_z()
            sigma2, Y, ___ = self._model(self.latent_variables.get_z_values())
            plt.plot(date_index, np.abs(Y-t_params[-1]), label=self.data_name + ' Absolute Demeaned Values')
            plt.plot(date_index, np.power(sigma2,0.5), label='GARCH(' + str(self.p) + ',' + str(self.q) + ') Conditional Volatility',c='black')
            plt.title(self.data_name + " Volatility Plot")  
            plt.legend(loc=2)   
            plt.show()
Plots the fit of the model

        Returns
        ----------
        None (plots data and the fit)