Forecasting Bitcoin Spikes: A GARCH-SVM Approach

Abstract

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.

Time-Series Neural Network: A High-Accuracy Time-Series Forecasting Method Based on Kernel Filter and Time Attention

“…In recent decades, many researchers and practitioners have tried to predict stock prices using various methods, including time-series-based prediction methods [7,8], machine learningbased prediction methods [9,10], deep learning-based prediction methods [11][12][13][14], and so on. However, due to the characteristics of stock prices, such as non-linearity, high noise, and variability, it is often difficult to achieve the desired prediction results with these methods [15][16][17][18][19][20][21].…”

Understanding Cryptocurrency

“…Erfanian et al (2022) delved into predicting prices through economic theories, Gadi and Sicilia (2022) analyzed the characteristics of heterogeneous cryptocurrencies, and Gupta and Chaudhary (2022) focused on volatility within the cryptocurrency market. Additional studies, such as those by Hsu (2022) on spillover effects in cryptocurrencies, Papadimitriou et al (2022) on forecasting spikes, and Yan et al (2022), who considers bitcoin as a safe-haven asset and a medium of exchange, further emphasize the breadth of this research area. Manimuthu et al (2019) reviewed scholarly articles to understand how Bitcoin was addressed in the literature.…”