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

【Author】 Wang, Yijun; Andreeva, Galina; Martin-Barragan, Belen

【Source】INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS

【影响因子】8.235

【Abstract】Given the volatile nature of cryptocurrencies, accurately forecasting cryptocurrency volatility and under-standing 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 sub-stantially 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.

【Keywords】Time-series forecasting; Cryptocurrency volatility forecasting; Machine learning techniques; Deep learning techniques; Determinants

【发表时间】NOV Int. Rev. Financ. Anal.

【收录时间】2023-10-30

【文献类型】实证数据

【主题类别】

区块链治理-市场治理-价格预测