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Inferring short-term volatility indicators from Bitcoin blockchain

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Listed:
  • Nino Antulov-Fantulin
  • Dijana Tolic
  • Matija Piskorec
  • Zhang Ce
  • Irena Vodenska

Abstract

In this paper, we study the possibility of inferring early warning indicators (EWIs) for periods of extreme bitcoin price volatility using features obtained from Bitcoin daily transaction graphs. We infer the low-dimensional representations of transaction graphs in the time period from 2012 to 2017 using Bitcoin blockchain, and demonstrate how these representations can be used to predict extreme price volatility events. Our EWI, which is obtained with a non-negative decomposition, contains more predictive information than those obtained with singular value decomposition or scalar value of the total Bitcoin transaction volume.

Suggested Citation

  • Nino Antulov-Fantulin & Dijana Tolic & Matija Piskorec & Zhang Ce & Irena Vodenska, 2018. "Inferring short-term volatility indicators from Bitcoin blockchain," Papers 1809.07856, arXiv.org.
    • Handle: RePEc:arx:papers:1809.07856
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    References listed on IDEAS

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    Cited by:

    1. Nazmiye Ceren Abay & Cuneyt Gurcan Akcora & Yulia R. Gel & Umar D. Islambekov & Murat Kantarcioglu & Yahui Tian & Bhavani Thuraisingham, 2019. "ChainNet: Learning on Blockchain Graphs with Topological Features," Papers 1908.06971, arXiv.org.
    2. Nino Antulov-Fantulin & Tian Guo & Fabrizio Lillo, 2021. "Temporal mixture ensemble models for probabilistic forecasting of intraday cryptocurrency volume," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 905-940, December.

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