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Does Mining Activity Drive Crash Risks in Cryptocurrency Markets? An Application to Bitcoin

Author

Listed:
  • Matteo Bonato

    (Department of Economics and Econometrics, University of Johannesburg, Auckland Park, South Africa; IPAG Business School, 184 Boulevard Saint-Germain, 75006 Paris, France)

  • Riza Demirer

    (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA)

  • Rangan Gupta

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

  • Abeeb Olaniran

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

Abstract

This paper explores the role of mining activity, proxied by growth rates of electricity consumption and cost of mining, as a driver of pricing inefficiencies in cryptocurrencies. Utilizing alternative measures of crash risk proxied by the realized negative coefficient of skewness and realized down-to-up volatility, derived based on 5-minute intraday Bitcoin data, nonparametric causality-in-quantiles tests, along with sign analysis, captured by the estimates of partial average derivatives, provide evidence that mining activity can, in general, predict an increase in the entire conditional distribution of crash risk, with the strongest impact associated over the normal (median) to moderately high (upper quantiles) levels of risk. Despite the emergence of these assets in international transactions and as an investment vehicle, our results suggest that decentralized mining process can contribute to inefficiencies in the pricing of cryptocurrencies, putting further doubt into the role of these assets as a medium of exchange, alternative to conventional assets.

Suggested Citation

  • Matteo Bonato & Riza Demirer & Rangan Gupta & Abeeb Olaniran, 2025. "Does Mining Activity Drive Crash Risks in Cryptocurrency Markets? An Application to Bitcoin," Working Papers 202530, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202530
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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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