Should you use GARCH models for forecasting volatility? A comparison to GRU neural networks
Article
Pallotta, A. and Ciciretti, V. 2023. Should you use GARCH models for forecasting volatility? A comparison to GRU neural networks. Studies in Nonlinear Dynamics & Econometrics. https://doi.org/10.1515/snde-2022-0025
Type | Article |
---|---|
Title | Should you use GARCH models for forecasting volatility? A comparison to GRU neural networks |
Authors | Pallotta, A. and Ciciretti, V. |
Abstract | The GARCH model is the most used technique for forecasting conditional volatility. However, the nearly integrated behaviour of the conditional variance originates from structural changes which are not accounted for by standard GARCH models. We compare the forecasting performance of the GARCH model to three regime switching models: namely, the Markov Switching GARCH, the Hidden Markov Model, and the Gated Recurrent Unit neural network. We define the number of optimal states by means of three methods: piecewise linear regression, Baum–Welch algorithm and Markov Chain Monte Carlo. Since forecasting volatility models face the bias-variance trade-off, we compare their out-of-sample forecasting performance via a walk-forward methodology. Moreover, we provide a robustness check for the results by applying k-fold cross-validation to the original time series. The Gated Recurrent Unit network is the best suited for volatility forecasting, while the Hidden Markov Model is the best at discerning the market regimes. |
Keywords | volatility forecasting; GARCH; Hidden Markov Models; Markow switching GARCH; Gated Recurrent Unit; walk-forward |
Sustainable Development Goals | 9 Industry, innovation and infrastructure |
Middlesex University Theme | Creativity, Culture & Enterprise |
Publisher | De Gruyter |
Journal | Studies in Nonlinear Dynamics & Econometrics |
ISSN | 1081-1826 |
Electronic | 1558-3708 |
Publication dates | |
Online | 04 Dec 2023 |
Publication process dates | |
Submitted | 11 Mar 2022 |
Accepted | 09 Nov 2023 |
Deposited | 13 Feb 2024 |
Output status | Published |
Accepted author manuscript | File Access Level Open |
Copyright Statement | The final publication is available at www.degruyter.com. This is an Accepted Manuscript of an article published by De Gruyter in Studies in Nonlinear Dynamics & Econometrics on 4 December 2023, available at http://doi.org/10.1515/snde-2022-0025 |
Digital Object Identifier (DOI) | https://doi.org/10.1515/snde-2022-0025 |
Language | English |
https://repository.mdx.ac.uk/item/x71q3
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