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Do collective emotions drive bitcoin volatility? A triple regime-switching vector approach

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  • Bourghelle, David
  • Jawadi, Fredj
  • Rozin, Philippe

Abstract

In this paper, we build an empirical specification that helps to explain bitcoin volatility and to characterize phases of the bitcoin bubble using information derived from investors’ emotions and sentiment that captures investment intentions and investors’ aversion to risk. To this end, we investigated the bilateral relations between bitcoin volatility and investor emotions between 2018 and 2021, a period characterized by significant changes in bitcoin prices as well as wide disparities in investor emotions, especially in the context of the ongoing COVID-19 pandemic. The study was based on a linear and nonlinear Vector Autoregressive (VAR) model that we applied to data related to bitcoin prices and market sentiment as expressed by the Fear and Greed index. Overall, our results evince the key role played by collective emotions in the formation and collapse of the bitcoin bubble. Two findings in particular stand out. First, our model shows significant time-varying lead-lag effects between bitcoin volatility and investor sentiment that come into play bilaterally and help to characterize the dynamics of bitcoin volatility. Second, these interactions exhibit asymmetry and nonlinearity as the sign and size of collective emotions (resp. bitcoin volatility) vary with the regime and market state under consideration (calm state versus period of bubble formation, etc.). In other words, the power of sentiment has a time-varying effect on the market. Indeed, in the first regime (“calm state”), where bitcoin volatility is relatively low and the market shows evidence of stability, collective emotions have a negative impact on bitcoin volatility, prompting a stabilizing strength. However, in the second regime (“bubble formation”), the effect of emotions turns significantly positive as investors gradually become less fearful and more reassured, which can simultaneously increase volatility and destabilize the market. Finally, in the third regime (“bubble collapse”), when bitcoin reaches a high level of value and experiences impressive volatility excess, the effect of emotions again turns negative, resulting in further switching behavior that pushes investor action to provoke a bitcoin price correction, moving it toward a new state of stability. Our conclusion helps improve predictions of bitcoin price dynamics informed by the information provided by investor emotions.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:jeborg:v:196:y:2022:i:c:p:294-306
    DOI: 10.1016/j.jebo.2022.01.026
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    as
    1. Jamal Bouoiyour & Refk Selmi & Aviral Kumar Tiwari & Olaolu Richard Olayeni, 2016. "What drives Bitcoin price?," Economics Bulletin, AccessEcon, vol. 36(2), pages 843-850.
    2. Terasvirta, T & Anderson, H M, 1992. "Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages 119-136, Suppl. De.
    3. Baig, Ahmed & Blau, Benjamin M. & Sabah, Nasim, 2019. "Price clustering and sentiment in bitcoin," Finance Research Letters, Elsevier, vol. 29(C), pages 111-116.
    4. Cathy Yi-Hsuan Chen & Christian M. Hafner, 2019. "Sentiment-Induced Bubbles in the Cryptocurrency Market," JRFM, MDPI, vol. 12(2), pages 1-12, April.
    5. Corbet, Shaen & Lucey, Brian & Yarovaya, Larisa, 2018. "Datestamping the Bitcoin and Ethereum bubbles," Finance Research Letters, Elsevier, vol. 26(C), pages 81-88.
    6. Bruce Hansen, 1999. "Testing for Linearity," Journal of Economic Surveys, Wiley Blackwell, vol. 13(5), pages 551-576, December.
    7. Guégan, Dominique & Renault, Thomas, 2021. "Does investor sentiment on social media provide robust information for Bitcoin returns predictability?," Finance Research Letters, Elsevier, vol. 38(C).
    8. Christian Conrad & Anessa Custovic & Eric Ghysels, 2018. "Long- and Short-Term Cryptocurrency Volatility Components: A GARCH-MIDAS Analysis," JRFM, MDPI, vol. 11(2), pages 1-12, May.
    9. Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January.
    10. repec:men:wpaper:58_2015 is not listed on IDEAS
    11. Eom, Cheoljun & Kaizoji, Taisei & Kang, Sang Hoon & Pichl, Lukas, 2019. "Bitcoin and investor sentiment: Statistical characteristics and predictability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 511-521.
    12. Naeem, Muhammad Abubakr & Mbarki, Imen & Shahzad, Syed Jawad Hussain, 2021. "Predictive role of online investor sentiment for cryptocurrency market: Evidence from happiness and fears," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 496-514.
    13. Garman, Mark B & Klass, Michael J, 1980. "On the Estimation of Security Price Volatilities from Historical Data," The Journal of Business, University of Chicago Press, vol. 53(1), pages 67-78, January.
    14. Zeyu Zheng & Zhi Qiao & Tetsuya Takaishi & H Eugene Stanley & Baowen Li, 2014. "Realized Volatility and Absolute Return Volatility: A Comparison Indicating Market Risk," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-10, July.
    15. Jaroslav Bukovina & Matus Marticek, 2016. "Sentiment and Bitcoin Volatility," MENDELU Working Papers in Business and Economics 2016-58, Mendel University in Brno, Faculty of Business and Economics.
    16. Ardia, David & Bluteau, Keven & Rüede, Maxime, 2019. "Regime changes in Bitcoin GARCH volatility dynamics," Finance Research Letters, Elsevier, vol. 29(C), pages 266-271.
    17. Gurdgiev, Constantin & O’Loughlin, Daniel, 2020. "Herding and anchoring in cryptocurrency markets: Investor reaction to fear and uncertainty," Journal of Behavioral and Experimental Finance, Elsevier, vol. 25(C).
    18. Katsiampa, Paraskevi, 2019. "Volatility co-movement between Bitcoin and Ether," Finance Research Letters, Elsevier, vol. 30(C), pages 221-227.
    19. Hansen,B.E., 1999. "Testing for linearity," Working papers 7, Wisconsin Madison - Social Systems.
    20. Cheah, Eng-Tuck & Fry, John, 2015. "Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin," Economics Letters, Elsevier, vol. 130(C), pages 32-36.
    21. Hung, Jui-Cheng & Liu, Hung-Chun & Yang, J. Jimmy, 2021. "Trading activity and price discovery in Bitcoin futures markets," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 107-120.
    22. Aalborg, Halvor Aarhus & Molnár, Peter & de Vries, Jon Erik, 2019. "What can explain the price, volatility and trading volume of Bitcoin?," Finance Research Letters, Elsevier, vol. 29(C), pages 255-265.
    23. Anastasiou, Dimitrios & Ballis, Antonis & Drakos, Konstantinos, 2021. "Cryptocurrencies’ Price Crash Risk and Crisis Sentiment," Finance Research Letters, Elsevier, vol. 42(C).
    24. Conghui Chen & Lanlan Liu & Ningru Zhao, 2020. "Fear Sentiment, Uncertainty, and Bitcoin Price Dynamics: The Case of COVID-19," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 56(10), pages 2298-2309, August.
    25. Kalyvas, Antonios & Papakyriakou, Panayiotis & Sakkas, Athanasios & Urquhart, Andrew, 2020. "What drives Bitcoin’s price crash risk?," Economics Letters, Elsevier, vol. 191(C).
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    7. Dias, Ishanka K. & Fernando, J.M. Ruwani & Fernando, P. Narada D., 2022. "Does investor sentiment predict bitcoin return and volatility? A quantile regression approach," International Review of Financial Analysis, Elsevier, vol. 84(C).
    8. Anwer, Zaheer & Farid, Saqib & Khan, Ashraf & Benlagha, Noureddine, 2023. "Cryptocurrencies versus environmentally sustainable assets: Does a perfect hedge exist?," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 418-431.
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    More about this item

    Keywords

    Bitcoin volatility; Bitcoin bubble; Emotions; Sentiment; Regime-switching VAR model; Nonlinearity;
    All these keywords.

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • F10 - International Economics - - Trade - - - General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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