A machine learning search for optimal GARCH parameters

TL;DR

This paper introduces a machine learning approach, specifically using an Artificial Neural Network, to efficiently estimate GARCH model parameters for financial time series, enabling real-time tracking and improved fitting accuracy.

Contribution

The paper presents a novel ML-based fitting algorithm for GARCH-normal(1,1) models, enhancing speed and flexibility over traditional methods.

Findings

ANN can predict GARCH parameters with high speed.

Different input features affect accuracy of parameter estimation.

Real-time GARCH parameter tracking is feasible with this approach.

Abstract

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.