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A machine learning based regulatory risk index for cryptocurrencies

Author

Listed:
  • Xinwen Ni

    (Humboldt-Universität zu Berlin)

  • Taojun Xie

    (Singapore Management University)

  • Wolfgang Karl Härdle

    (Humboldt-Universität zu Berlin
    Singapore Management University
    Bucharest University of Economic Studies
    University of Edinburgh)

  • Xiaorui Zuo

    (Bucharest University of Economic Studies
    National University of Singapore)

Abstract

Cryptocurrency markets are highly sensitive to regulatory changes, often experiencing sharp price fluctuations in response to new policies and government interventions. Despite this, existing market indices fail to adequately capture the risks associated with regulatory uncertainty. In this paper, we introduce the Cryptocurrency Regulatory Risk Index (CRRIX), a machine learning-based index designed to quantify the impact of regulatory developments on cryptocurrency markets. Our methodology employs Latent Dirichlet Allocation (LDA) to classify policy-related news articles from major cryptocurrency news platforms, providing an objective measure of regulatory risk. We find that the CRRIX exhibits strong synchronicity with VCRIX, a cryptocurrency volatility index, suggesting that regulatory uncertainty plays a significant role in driving market fluctuations. Our results indicate that regulatory risk is a leading factor in market volatility, with major policy shifts triggering significant market movements. The proposed regulatory risk index provides a novel approach to quantifying policy uncertainty in the cryptocurrency sector, offering valuable insights for market participants navigating this rapidly changing environment.

Suggested Citation

  • Xinwen Ni & Taojun Xie & Wolfgang Karl Härdle & Xiaorui Zuo, 2025. "A machine learning based regulatory risk index for cryptocurrencies," Computational Statistics, Springer, vol. 40(7), pages 3563-3583, September.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:7:d:10.1007_s00180-025-01629-y
    DOI: 10.1007/s00180-025-01629-y
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