Forecasting Bitcoin volatility using machine learning techniques

Zih-Chun Huang, Ivan Sangiorgi and Andrew Urquhart

Journal of International Financial Markets, Institutions and Money, 2024, vol. 97, issue C

Abstract: This paper studies the Bitcoin volatility forecasting performance between popular traditional econometric models and machine learning techniques. We compare the 1-day to 2-month ahead forecasting performance of the Long Short-Term Memory (LSTM) and a hybrid Convolutional Neural Network-LSTM (CNN-LSTM) model to the traditional models. We find that neural networks outperform Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models for all forecasting horizons. Furthermore, the LSTM model outperforms the Heterogeneous Autoregressive (HAR) model and by integrating the Markov Transition Field (MTF) into the CNN-LSTM model, we achieve superior forecasting results in the short-term, particularly for the 7-day forecasts.

Keywords: Bitcoin; Volatility forecasting; Machine learning (search for similar items in EconPapers)
JEL-codes: C45 C53 G17 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfin:v:97:y:2024:i:c:s1042443124001306

DOI: 10.1016/j.intfin.2024.102064

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