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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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    References listed on IDEAS

    as
    1. repec:rim:rimwps:18-14 is not listed on IDEAS
    2. Wang, Yijun & Andreeva, Galina & Martin-Barragan, Belen, 2023. "Machine learning approaches to forecasting cryptocurrency volatility: Considering internal and external determinants," International Review of Financial Analysis, Elsevier, vol. 90(C).
    3. Corbet, Shaen & Lucey, Brian & Yarovaya, Larisa, 2018. "Datestamping the Bitcoin and Ethereum bubbles," Finance Research Letters, Elsevier, vol. 26(C), pages 81-88.
    4. Podhorsky, Andrea, 2024. "Bursting the bitcoin bubble: Do market prices reflect fundamental bitcoin value?," International Review of Financial Analysis, Elsevier, vol. 93(C).
    5. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    6. Belloni, Alexandre & Chernozhukov, Victor & Chetverikov, Denis & Fernández-Val, Iván, 2019. "Conditional quantile processes based on series or many regressors," Journal of Econometrics, Elsevier, vol. 213(1), pages 4-29.
    7. Chen, Joseph & Hong, Harrison & Stein, Jeremy C., 2001. "Forecasting crashes: trading volume, past returns, and conditional skewness in stock prices," Journal of Financial Economics, Elsevier, vol. 61(3), pages 345-381, September.
    8. Bouri, Elie & Gupta, Rangan & Lau, Chi Keung Marco & Roubaud, David & Wang, Shixuan, 2018. "Bitcoin and global financial stress: A copula-based approach to dependence and causality in the quantiles," The Quarterly Review of Economics and Finance, Elsevier, vol. 69(C), pages 297-307.
    9. Marco Lambrecht & Andis Sofianos & Yilong Xu, 2025. "Does Mining Fuel Bubbles? An Experimental Study on Cryptocurrency Markets," Management Science, INFORMS, vol. 71(3), pages 1865-1888, March.
    10. Garnier, Josselin & Solna, Knut, 2019. "Chaos and order in the bitcoin market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 708-721.
    11. Jeong, Kiho & Härdle, Wolfgang K. & Song, Song, 2012. "A Consistent Nonparametric Test For Causality In Quantile," Econometric Theory, Cambridge University Press, vol. 28(4), pages 861-887, August.
    12. Wang, Yizhi, 2022. "Volatility spillovers across NFTs news attention and financial markets," International Review of Financial Analysis, Elsevier, vol. 83(C).
    13. Dilip Abreu & Markus K. Brunnermeier, 2003. "Bubbles and Crashes," Econometrica, Econometric Society, vol. 71(1), pages 173-204, January.
    14. Josselin Garnier & Knut Solna, 2018. "Chaos and Order in the Bitcoin Market," Papers 1809.08403, arXiv.org, revised Apr 2019.
    15. Huang, Zih-Chun & Sangiorgi, Ivan & Urquhart, Andrew, 2024. "Forecasting Bitcoin volatility using machine learning techniques," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 97(C).
    16. Yizhi Wang & Brian Lucey & Samuel Alexandre Vigne & Larisa Yarovaya, 2022. "An index of cryptocurrency environmental attention (ICEA)," China Finance Review International, Emerald Group Publishing Limited, vol. 12(3), pages 378-414, January.
    17. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    18. Panagiotidis, Theodore & Stengos, Thanasis & Vravosinos, Orestis, 2019. "The effects of markets, uncertainty and search intensity on bitcoin returns," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 220-242.
    19. Vaiva Pakštaitė & Ernestas Filatovas & Mindaugas Juodis & Remigijus Paulavičius, 2025. "Bitcoin Price Regime Shifts: A Bayesian MCMC and Hidden Markov Model Analysis of Macroeconomic Influence," Mathematics, MDPI, vol. 13(10), pages 1-25, May.
    20. Anusha Chari & Karlye Dilts Stedman & Christian Lundblad, 2023. "Risk-On Risk-Off: A Multifaceted Approach to Measuring Global Investor Risk Aversion," NBER Working Papers 31907, National Bureau of Economic Research, Inc.
    21. Alessandra Cretarola & Gianna Figà-Talamanca, 2021. "Detecting bubbles in Bitcoin price dynamics via market exuberance," Annals of Operations Research, Springer, vol. 299(1), pages 459-479, April.
    22. Elie Bouri & Rangan Gupta & Chi keung marco Lau & David Roubaud, 2021. "Risk aversion and Bitcoin returns in extreme quantiles," Economics Bulletin, AccessEcon, vol. 41(3), pages 1374-1386.
    23. Panagiotidis, Theodore & Stengos, Thanasis & Vravosinos, Orestis, 2018. "On the determinants of bitcoin returns: A LASSO approach," Finance Research Letters, Elsevier, vol. 27(C), pages 235-240.
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    Keywords

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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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