Forecasting cryptocurrency volatility: a novel framework based on the evolving multiscale graph neural network

Yang Zhou, Chi Xie (), Gang-Jin Wang, Jue Gong and You Zhu
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Yang Zhou: Hunan University
Chi Xie: Hunan University
Gang-Jin Wang: Hunan University
Jue Gong: Hunan University
You Zhu: Hunan University

Financial Innovation, 2025, vol. 11, issue 1, 1-52

Abstract: Abstract Cryptocurrency is a remarkable financial innovation that has affected the financial system in fundamental ways. Its increasingly complex interactions with the conventional financial market make precisely forecasting its volatility increasingly challenging. To this end, we propose a novel framework based on the evolving multiscale graph neural network (EMGNN). Specifically, we embed a graph that depicts the interactions between the cryptocurrency and conventional financial markets into the predictive process. Furthermore, we employ hierarchical evolving graph structure learners to model the dynamic and scale-specific interactions. We also evaluate our framework’s robustness and discuss its interpretability by extracting the learned graph structure. The empirical results show that (i) cryptocurrency volatility is not isolated from the conventional market, and the embedded graph can provide effective information for prediction; (ii) the EMGNN-based forecasting framework generally yields outstanding and robust performance in terms of multiple volatility estimators, cryptocurrency samples, forecasting horizons, and evaluation criteria; and (iii) the graph structure in the predictive process varies over time and scales and is well captured by our framework. Overall, our work provides new insights into risk management for market participants and into policy formulation for authorities.

Keywords: Cryptocurrency; Volatility forecasting; Graph neural network; Deep learning; Multiscale (search for similar items in EconPapers)
JEL-codes: C45 C53 G15 G17 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1186/s40854-025-00768-x

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