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RESEARCH PRODUCT
Contagion in Bitcoin Networks
José LagesCélestin CoquidéDima L. Shepelyanskysubject
CheiRankGoogle matrixMarkov chain[QFIN]Quantitative Finance [q-fin]Financial networksComputer science[PHYS.PHYS.PHYS-SOC-PH]Physics [physics]/Physics [physics]/Physics and Society [physics.soc-ph]Balance of trade01 natural sciences010305 fluids & plasmaslaw.inventionPageRankBankruptcylaw0103 physical sciencesHouse of cardsEconometrics010306 general physics[QFIN.TR]Quantitative Finance [q-fin]/Trading and Market Microstructure [q-fin.TR]ComputingMilieux_MISCELLANEOUSdescription
12 pages, 6 figures. Paper accepted in 2nd Workshop on Blockchain and Smart Contract Technologies (BSCT 2019), workshop satellite of 22nd International Conference on Business Information Systems (BIS 2019); International audience; We construct the Google matrices of bitcoin transactions for all year quarters during the period of January 11, 2009 till April 10, 2013. During the last quarters the network size contains about 6 million users (nodes) with about 150 million transactions. From PageRank and CheiRank probabilities, analogous to trade import and export, we determine the dimensionless trade balance of each user and model the contagion propagation on the network assuming that a user goes bankrupt if its balance exceeds a certain dimensionless threshold $\kappa$. We find that the phase transition takes place for $\kappa0.55$ almost all users remain safe. We find that even on a distance from the critical threshold $\kappa_c$ the top PageRank and CheiRank users, as a house of cards, rapidly drop to the bankruptcy. We attribute this effect to strong interconnections between these top users which we determine with the reduced Google matrix algorithm. This algorithm allows to establish efficiently the direct and indirect interactions between top PageRank users. We argue that this study models the contagion on real financial networks.
year | journal | country | edition | language |
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2019-12-17 |