Machine learning approaches to forecasting cryptocurrency volatility: Considering internal and external determinants

Yijun Wang*, Galina Andreeva, Belen Martin-Barragan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Given the volatile nature of cryptocurrencies, accurately forecasting cryptocurrency volatility and understanding its determinants are crucial. This paper applies machine learning (ML) techniques to forecast cryptocurrency volatility using internal determinants (e.g., lagged volatility, previous trading information) and external determinants (e.g., technology, financial, and policy uncertainty factors). Both Random Forest and Long Short-Term Memory (LSTM) networks significantly outperform traditional volatility models such as GARCH. Furthermore, we explore two optimization models—Genetic Algorithm and Artificial Bee Colony—to tune the hyper-parameters of LSTM. Our results indicate that the application of these optimization models substantially improves forecasting performance. Moreover, using SHapley Additive exPlanations, an interpretation method, we find that internal determinants play the most important roles in volatility forecasts. Finally, our results show that models trained with determinants from multiple cryptocurrencies outperform those trained with determinants from a single cryptocurrency, suggesting that considering a broader range of determinants can capture the complex dynamics in the cryptocurrency market.

Original languageEnglish
Article number102914
Pages (from-to)1-21
Number of pages21
JournalInternational Review of Financial Analysis
Early online date14 Sept 2023
Publication statusPublished - Nov 2023

Keywords / Materials (for Non-textual outputs)

  • cryptocurrency volatility forecasting
  • deep learning techniques
  • determinants
  • machine learning techniques
  • time-series forecasting


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