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Sentiment-Induced Bubbles in the Cryptocurrency Market

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  • Chen, Cathy Yi-Hsuan
  • Hafner, Christian

Abstract

Cryptocurrencies lack clear measures of fundamental values and are often associated with speculative bubbles. This paper introduces a new way of testing for speculative bubbles based on StockTwits sentiment, which is used as the transition variable in a smooth transition autoregression. The model allows for conditional heteroskedasticity and fat tails of the conditional distribution of the error term, and volatility may depend on the constructed sentiment index. We apply the model to the CRIX index, for which several bubble periods are identified. The detected locally explosive price dynamics, given the specified bubble regime controlled by a smooth transition function, are more akin to the notion of speculative bubble that is driven by exuberant sentiment. Furthermore, we find that volatility increases as the sentiment index decreases, which is analogous to the commonly called leverage effect.
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Suggested Citation

  • Chen, Cathy Yi-Hsuan & Hafner, Christian, 2019. "Sentiment-Induced Bubbles in the Cryptocurrency Market," LIDAM Reprints ISBA 2019053, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvar:2019053
    Note: In : Journal of Risk and Financial Management, vol. 12, no. 2, p. 1-12 (2019)
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    Cited by:

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    5. Ramit Sawhney & Shivam Agarwal & Vivek Mittal & Paolo Rosso & Vikram Nanda & Sudheer Chava, 2022. "Cryptocurrency Bubble Detection: A New Stock Market Dataset, Financial Task & Hyperbolic Models," Papers 2206.06320, arXiv.org.
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    10. Christian M. Hafner, 2020. "Alternative Assets and Cryptocurrencies," JRFM, MDPI, vol. 13(1), pages 1-3, January.
    11. Luca Mungo & Silvia Bartolucci & Laura Alessandretti, 2023. "Cryptocurrency co-investment network: token returns reflect investment patterns," Papers 2301.02027, arXiv.org, revised Jan 2023.
    12. Shigeyuki Hamori, 2020. "Recent Advancements in Section “Financial Technology and Innovation”," JRFM, MDPI, vol. 13(12), pages 1-2, December.
    13. Ştefan Cristian Gherghina & Liliana Nicoleta Simionescu, 2023. "Exploring the asymmetric effect of COVID-19 pandemic news on the cryptocurrency market: evidence from nonlinear autoregressive distributed lag approach and frequency domain causality," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-58, December.
    14. Silvia Bartolucci & Fabio Caccioli & Pierpaolo Vivo, 2019. "A percolation model for the emergence of the Bitcoin Lightning Network," Papers 1912.03556, arXiv.org.
    15. Ozkan Haykir & Ibrahim Yagli, 2022. "Speculative bubbles and herding in cryptocurrencies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-33, December.
    16. Bowden, James & Gemayel, Roland, 2022. "Sentiment and trading decisions in an ambiguous environment: A study on cryptocurrency traders," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    17. Bourghelle, David & Jawadi, Fredj & Rozin, Philippe, 2022. "Do collective emotions drive bitcoin volatility? A triple regime-switching vector approach," Journal of Economic Behavior & Organization, Elsevier, vol. 196(C), pages 294-306.
    18. Kyriazis, Nikolaos & Papadamou, Stephanos & Corbet, Shaen, 2020. "A systematic review of the bubble dynamics of cryptocurrency prices," Research in International Business and Finance, Elsevier, vol. 54(C).
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    20. Chen, Cathy Yi-hsuan & Okhrin, Yarema & Wang, Tengyao, 2022. "Monitoring network changes in social media," LSE Research Online Documents on Economics 113742, London School of Economics and Political Science, LSE Library.
    21. Thomas E. Koker & Dimitrios Koutmos, 2020. "Cryptocurrency Trading Using Machine Learning," JRFM, MDPI, vol. 13(8), pages 1-7, August.
    22. Marco Ortu & Nicola Uras & Claudio Conversano & Giuseppe Destefanis & Silvia Bartolucci, 2021. "On Technical Trading and Social Media Indicators in Cryptocurrencies' Price Classification Through Deep Learning," Papers 2102.08189, arXiv.org, revised Feb 2021.
    23. Melisa Ozdamar & Levent Akdeniz & Ahmet Sensoy, 2021. "Lottery-like preferences and the MAX effect in the cryptocurrency market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-27, December.

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    More about this item

    JEL classification:

    • C - Mathematical and Quantitative Methods
    • E - Macroeconomics and Monetary Economics
    • F2 - International Economics - - International Factor Movements and International Business
    • F3 - International Economics - - International Finance
    • G - Financial Economics

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