Can machine learning models better volatility forecasting? A combined method
File(s)
Author(s)
Han, Beining
Liu, Anqi
Chen, Jing
Knottenbelt, William
Type
Journal Article
Abstract
Volatility forecasting for Bitcoin has garnered increasing attention due to heightened investment interest and the inherent risks associated with cryptocurrencies. Traditional forecasting models, such as the Generalised Autoregressive Conditional Heteroskedasticity (GARCH) family models, are widely employed. However, there is a need for careful consideration regarding their ability to capture extreme shocks and the long-term volatile features. In this study, we fit several GARCH models, with the Exponential GARCH model demonstrating the best goodness of fit. We further utilise their volatility observations for an automated forecasting solution, using the Long Short-Term Memory (LSTM) neural network for predictions. Our results indicate a significant clear improvement in volatility forecasting regarding both the model's in-sample and out-of-sample accuracy. Notably, the LSTM model optimises information intake through its short- and long-memory states. Overall, our novel LSTM neural network model is more robust in responding to market shocks and regime changes.
Date Issued
2026-08-01
Date Acceptance
2025-08-12
Citation
European Journal of Finance, The, 2026, 32 (13)
ISSN
1351-847X
Publisher
Taylor and Francis Group
Journal / Book Title
European Journal of Finance, The
Volume
32
Issue
13
Copyright Statement
© 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/ licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
Identifier
10.1080/1351847X.2025.2553053
Subjects
Bitcoin
BITCOIN
Business & Economics
Business, Finance
CRYPTOCURRENCIES
forecasting
GARCH
GARCH MODELS
LSTM
RISK
Social Sciences
VARIANCE
volatility
Publication Status
Published
Date Publish Online
2025-09-14