Neural Generalised AutoRegressive Conditional Heteroskedasticity

• URL: http://arxiv.org/abs/2202.11285v1
• Date: Wed, 23 Feb 2022 03:23:02 GMT
• Title: Neural Generalised AutoRegressive Conditional Heteroskedasticity
• Authors: Zexuan Yin and Paolo Barucca
• Abstract summary: We propose Neural GARCH, a class of methods to model conditional heteroskedasticity in financial time series. We allow the coefficients of a GARCH model to be time varying in order to reflect the constantly changing dynamics of financial markets. We propose two variants of our model, one with normal innovations and the other with Students t innovations. We find that the Neural Students t model consistently outperforms the others.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We propose Neural GARCH, a class of methods to model conditional heteroskedasticity in financial time series. Neural GARCH is a neural network adaptation of the GARCH 1,1 model in the univariate case, and the diagonal BEKK 1,1 model in the multivariate case. We allow the coefficients of a GARCH model to be time varying in order to reflect the constantly changing dynamics of financial markets. The time varying coefficients are parameterised by a recurrent neural network that is trained with stochastic gradient variational Bayes. We propose two variants of our model, one with normal innovations and the other with Students t innovations. We test our models on a wide range of univariate and multivariate financial time series, and we find that the Neural Students t model consistently outperforms the others.

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