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Time-varying network structure and volatility prediction in the cryptocurrency market

Author

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  • Zhou, Fan
  • Guo, Wenjing

Abstract

This paper constructs time-varying correlation networks of the cryptocurrency market using five-minute high-frequency trading data and examines their predictive value for return volatility. We analyze nine major cryptocurrencies from December 2019 to January 2025, extracting key network metrics—including density, clustering coefficient, and betweenness centrality—and incorporating them into a random forest model. The results show that the network structure is highly dynamic, with major cryptocurrencies occupying central roles in risk transmission. Network measures are strongly linked to volatility, and the model achieves superior forecasting performance relative to ARIMA benchmarks, especially in capturing downside risks. Robustness checks further confirm the reliability of both the model specification and the network construction. From a structural perspective, this study broadens the research horizon of cryptocurrency price forecasting by integrating network topology into predictive modeling with high-frequency data, thereby enhancing the understanding and management of market risks.

Suggested Citation

  • Zhou, Fan & Guo, Wenjing, 2026. "Time-varying network structure and volatility prediction in the cryptocurrency market," Finance Research Letters, Elsevier, vol. 87(C).
    • Handle: RePEc:eee:finlet:v:87:y:2026:i:c:s1544612325022779
    DOI: 10.1016/j.frl.2025.109028
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    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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