  Here, we use Machine Learning (ML) algorithms to update and improve the
efficiencies of fitting GARCH model parameters to empirical data. We employ an
Artificial Neural Network (ANN) to predict the parameters of these models. We
present a fitting algorithm for GARCH-normal(1,1) models to predict one of the
model's parameters, $\alpha_1$ and then use the analytical expressions for the
fourth order standardised moment, $\Gamma_4$ and the unconditional second order
moment, $\sigma^2$ to fit the other two parameters; $\beta_1$ and $\alpha_0$,
respectively. The speed of fitting of the parameters and quick implementation
of this approach allows for real time tracking of GARCH parameters. We further
show that different inputs to the ANN namely, higher order standardised moments
and the autocovariance of time series can be used for fitting model parameters
using the ANN, but not always with the same level of accuracy.
