A machine learning search for optimal GARCH parameters

Luke De Clerk and Sergey Savl'ev

Papers from arXiv.org

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.

Date: 2022-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-ets and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2201.03286

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