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Influencer detection meets network autoregression — Influential regions in the bitcoin blockchain

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  • Trimborn, Simon
  • Peng, Hanqiu
  • Chen, Ying

Abstract

Known as an active global virtual money network, the Bitcoin blockchain, with millions of accounts, has played a continually increasingly important role in fund transition, digital payment, and hedging. We propose a method to Detect Influencers in Network AutoRegressive models (DINAR) via sparse-group regularization to detect regions influencing others across borders. For a granular analysis, we analyse whether the transaction size plays a role in the dynamics of the cross-border transactions in the network. With two-layer sparsity, DINAR enables discovering (1) the active regions with influential impact on the global digital money network and (2) whether changes in the size of the transaction affect the dynamic evolution of Bitcoin transactions. In the analysis of real data of the Bitcoin blockchain from Feb 2012 to December 2021, we find that influence from certain regions is linked to the economic need to use BTC, such as to circumvent sanctions, avoid high inflation, and to carry out transactions through off-shore markets. The effects are robust to different groupings, evaluation periods, and choices of regularization parameters.

Suggested Citation

  • Trimborn, Simon & Peng, Hanqiu & Chen, Ying, 2024. "Influencer detection meets network autoregression — Influential regions in the bitcoin blockchain," Journal of Empirical Finance, Elsevier, vol. 78(C).
    • Handle: RePEc:eee:empfin:v:78:y:2024:i:c:s0927539824000641
    DOI: 10.1016/j.jempfin.2024.101529
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    Cited by:

    1. Kexin Zhang & Simon Trimborn, 2024. "Influential assets in Large-Scale Vector AutoRegressive Models," Tinbergen Institute Discussion Papers 24-080/III, Tinbergen Institute.

    More about this item

    Keywords

    Bitcoin blockchain; Network dynamics; Two-layer sparsity;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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