Blockchain analytics for intraday financial risk modeling

Matthew F. Dixon (), Cuneyt Gurcan Akcora, Yulia R. Gel and Murat Kantarcioglu
Additional contact information
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

Digital Finance, 2019, vol. 1, issue 1, No 5, 67-89

Abstract: 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.

Keywords: Blockchain; Cryptocurrencies; Graph analysis; GARCH; Intraday financial risk (search for similar items in EconPapers)
JEL-codes: C58 C63 G18 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s42521-019-00009-8

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