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Blockchain analytics for intraday financial risk modeling

Author

Listed:
  • 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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    References listed on IDEAS

    as
    1. Yhlas Sovbetov, 2018. "Factors Influencing Cryptocurrency Prices: Evidence from Bitcoin, Ethereum, Dash, Litcoin, and Monero," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 2(2), pages 1-27.
    2. Peter Gomber & Jascha-Alexander Koch & Michael Siering, 2017. "Digital Finance and FinTech: current research and future research directions," Journal of Business Economics, Springer, vol. 87(5), pages 537-580, July.
    3. Caporale, Guglielmo Maria & Gil-Alana, Luis & Plastun, Alex, 2018. "Persistence in the cryptocurrency market," Research in International Business and Finance, Elsevier, vol. 46(C), pages 141-148.
    4. Akcora, Cuneyt Gurcan & Dixon, Matthew F. & Gel, Yulia R. & Kantarcioglu, Murat, 2018. "Bitcoin risk modeling with blockchain graphs," Economics Letters, Elsevier, vol. 173(C), pages 138-142.
    5. Cuneyt Akcora & Matthew Dixon & Yulia Gel & Murat Kantarcioglu, 2018. "Bitcoin Risk Modeling with Blockchain Graphs," Papers 1805.04698, arXiv.org.
    6. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    7. Neil Shephard & Kevin Sheppard, 2010. "Realising the future: forecasting with high-frequency-based volatility (HEAVY) models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(2), pages 197-231.
    8. Svetlana Borovkova & Diego Mahakena, 2015. "News, volatility and jumps: the case of natural gas futures," Quantitative Finance, Taylor & Francis Journals, vol. 15(7), pages 1217-1242, July.
    9. Dyhrberg, Anne Haubo, 2016. "Bitcoin, gold and the dollar – A GARCH volatility analysis," Finance Research Letters, Elsevier, vol. 16(C), pages 85-92.
    10. Jeffrey Chu & Stephen Chan & Saralees Nadarajah & Joerg Osterrieder, 2017. "GARCH Modelling of Cryptocurrencies," JRFM, MDPI, vol. 10(4), pages 1-15, October.
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    Cited by:

    1. Constandina Koki & Stefanos Leonardos & Georgios Piliouras, 2019. "A Peek into the Unobservable: Hidden States and Bayesian Inference for the Bitcoin and Ether Price Series," Papers 1909.10957, arXiv.org, revised Jul 2021.
    2. Jörg Osterrieder & Andrea Barletta, 2019. "Editorial on the Special Issue on Cryptocurrencies," Digital Finance, Springer, vol. 1(1), pages 1-4, November.

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    More about this item

    Keywords

    Blockchain; Cryptocurrencies; Graph analysis; GARCH; Intraday financial risk;
    All these keywords.

    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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