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Blockchain analytics for intraday financial risk modeling


  • Matthew F. Dixon

    () (Illinois Institute of Technology)

  • Cuneyt Gurcan Akcora

    (University of Manitoba)

  • Yulia R. Gel

    (University of Texas at Dallas)

  • Murat Kantarcioglu

    (University of Texas at Dallas)


Abstract Blockchain offers the opportunity to use the transaction graph for financial governance, yet properties of this graph are understudied. One key question in this direction is the extent to which the transaction graph can serve as an early-warning indicator for large financial losses. In this article, we demonstrate the impact of extreme transaction graph activity on the intraday volatility of the Bitcoin prices series. Specifically, we identify certain sub-graphs (‘chainlets’) that exhibit predictive influence on Bitcoin price and volatility and characterize the types of chainlets that signify extreme losses. Using bars ranging from 15 min up to a day, we fit GARCH models with and without the extreme chainlets and show that the former exhibit superior value-at-risk backtesting performance.

Suggested Citation

  • Matthew F. Dixon & Cuneyt Gurcan Akcora & Yulia R. Gel & Murat Kantarcioglu, 2019. "Blockchain analytics for intraday financial risk modeling," Digital Finance, Springer, vol. 1(1), pages 67-89, November.
  • Handle: RePEc:spr:digfin:v:1:y:2019:i:1:d:10.1007_s42521-019-00009-8
    DOI: 10.1007/s42521-019-00009-8

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    More about this item


    Blockchain; Cryptocurrencies; Graph analysis; GARCH; Intraday financial risk;

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation


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