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Forecasting Bitcoin Spikes: A GARCH-SVM Approach

Department of Economics, Democritus University of Thrace, 69100 Komotini, Greece
Author to whom correspondence should be addressed.
Academic Editor: Konstantinos Nikolopoulos
Forecasting 2022, 4(4), 752-766;
Received: 9 August 2022 / Revised: 16 September 2022 / Accepted: 17 September 2022 / Published: 22 September 2022
(This article belongs to the Section Forecasting in Economics and Management)
This study aims to forecast extreme fluctuations of Bitcoin returns. Bitcoin is the first decentralized and the largest, in terms of capitalization, cryptocurrency. A well-timed and precise forecast of extreme changes in Bitcoin returns is key to market participants since they may trigger large-scale selling or buying strategies that may crucially impact the cryptocurrency markets. We term the instances of extreme Bitcoin movement as ‘spikes’. In this paper, spikes are defined as the returns instances that outreach a two-standard deviations band around the mean value. Instead of the unconditional historic standard deviation that is usually used, in this paper, we utilized a GARCH(p,q) model to derive the conditional standard deviation. We claim that the conditional standard deviation is a more suitable measure of on-the-spot risk than the overall standard deviation. The forecasting operation was performed using the support vector machines (SVM) methodology from machine learning. The most accurate forecasting model that we created reached 79.17% out-of-sample forecasting accuracy regarding the spikes cases and 87.43% regarding the non-spikes ones. View Full-Text
Keywords: forecast; cryptocurrency; Bitcoin; machine learning; support vector machines; spikes; GARCH forecast; cryptocurrency; Bitcoin; machine learning; support vector machines; spikes; GARCH
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MDPI and ACS Style

Papadimitriou, T.; Gogas, P.; Athanasiou, A.F. Forecasting Bitcoin Spikes: A GARCH-SVM Approach. Forecasting 2022, 4, 752-766.

AMA Style

Papadimitriou T, Gogas P, Athanasiou AF. Forecasting Bitcoin Spikes: A GARCH-SVM Approach. Forecasting. 2022; 4(4):752-766.

Chicago/Turabian Style

Papadimitriou, Theophilos, Periklis Gogas, and Athanasios Fotios Athanasiou. 2022. "Forecasting Bitcoin Spikes: A GARCH-SVM Approach" Forecasting 4, no. 4: 752-766.

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