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Dynamic Return Connectedness Among Crypto-Mining Technology Firms and Major Cryptocurrencies: The Role of Sentiment Indices

Author

Listed:
  • Elie Bouri

    (School of Business, Lebanese American University, Lebanon; Korea University Business School, Seoul, Korea)

  • Oguzhan Cepni

    (Copenhagen Business School, Department of Economics, Porcelaenshaven 16A, Frederiksberg DK-2000, Denmark; Ostim Technical University, Ankara, Turkiye)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

  • Sibanjan Mishra

    (TAPMI Management Institute, Manipal Academy of Higher Education, Manipal, Karnataka, India)

  • Muhammed Enes Olgun

    (Central Bank of the Republic of Turkiye, Ankara, Turkiye)

Abstract

This study examines the dynamic return connectedness between leading crypto-mining firms and major cryptocurrencies, emphasizing the influence of investor sentiment across time and frequency domains. Using a time-varying parameter VAR model and quantile regression, we find strong, evolving interconnectedness, with cryptocurrencies, especially Bitcoin and Ethereum, acting as dominant transmitters of return shocks, while mining firms such as Hive Technologies, Marathon Digital, and Riot Platforms primarily absorb these shocks. Notably, connectedness is more pronounced in the short term, and shifts in directional spillovers occur post-2020. Sentiment indices, particularly the Compass SESAMm Crypto Sentiment Index, play a significant role in modulating return spillovers, especially during bearish and neutral market phases. These findings provide actionable insights for investors, risk managers, and policymakers monitoring systemic risks in the rapidly evolving digital finance ecosystem.

Suggested Citation

  • Elie Bouri & Oguzhan Cepni & Rangan Gupta & Sibanjan Mishra & Muhammed Enes Olgun, 2025. "Dynamic Return Connectedness Among Crypto-Mining Technology Firms and Major Cryptocurrencies: The Role of Sentiment Indices," Working Papers 202533, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202533
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    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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