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Cryptocurrencies as financial bubbles: The case of Bitcoin

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  • Geuder, Julian
  • Kinateder, Harald
  • Wagner, Niklas F.

Abstract

We study bubble behavior in Bitcoin prices during the years 2016 to 2018 based on two distinct testing methodologies. The Phillips et al. (2015) PSY methodology is used to identify multiple bubble periods. The log-periodic power law (LPPL) approach by Filimonov and Sornette (2013) identifies bubble growth and potential critical bubble termination times. Our results underline that bubble behavior is clearly a common and reoccurring characteristic of Bitcoin prices. A critical time point is identified to be December 6, 2017, after which neither approach provides evidence of ongoing bubble behavior.

Suggested Citation

  • Geuder, Julian & Kinateder, Harald & Wagner, Niklas F., 2019. "Cryptocurrencies as financial bubbles: The case of Bitcoin," Finance Research Letters, Elsevier, vol. 31(C).
    • Handle: RePEc:eee:finlet:v:31:y:2019:i:c:s1544612318306846
    DOI: 10.1016/j.frl.2018.11.011
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    References listed on IDEAS

    as
    1. Peter C. B. Phillips & Yangru Wu & Jun Yu, 2011. "EXPLOSIVE BEHAVIOR IN THE 1990s NASDAQ: WHEN DID EXUBERANCE ESCALATE ASSET VALUES?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 52(1), pages 201-226, February.
    2. Ciaian, Pavel & Rajcaniova, Miroslava & Kancs, d'Artis, 2018. "Virtual relationships: Short- and long-run evidence from BitCoin and altcoin markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 52(C), pages 173-195.
    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. Anders Johansen & Olivier Ledoit & Didier Sornette, 2000. "Crashes As Critical Points," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(02), pages 219-255.
    5. Filimonov, V. & Sornette, D., 2013. "A stable and robust calibration scheme of the log-periodic power law model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3698-3707.
    6. Peter C. B. Phillips & Shuping Shi & Jun Yu, 2015. "Testing For Multiple Bubbles: Historical Episodes Of Exuberance And Collapse In The S&P 500," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56, pages 1043-1078, November.
    7. Zhang, Wei & Wang, Pengfei & Li, Xiao & Shen, Dehua, 2018. "Quantifying the cross-correlations between online searches and Bitcoin market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 657-672.
    8. Feng, Wenjun & Wang, Yiming & Zhang, Zhengjun, 2018. "Informed trading in the Bitcoin market," Finance Research Letters, Elsevier, vol. 26(C), pages 63-70.
    9. Vladimir Filimonov & Didier Sornette, 2011. "A Stable and Robust Calibration Scheme of the Log-Periodic Power Law Model," Papers 1108.0099, arXiv.org, revised Jun 2013.
    10. Didier SORNETTE & Peter CAUWELS, 2014. "Financial Bubbles: Mechanisms and Diagnostics," Swiss Finance Institute Research Paper Series 14-28, Swiss Finance Institute.
    11. Vidal-Tomás, David & Ibañez, Ana, 2018. "Semi-strong efficiency of Bitcoin," Finance Research Letters, Elsevier, vol. 27(C), pages 259-265.
    12. Didier Sornette & Peter Cauwels, 2014. "Financial bubbles: mechanisms and diagnostics," Papers 1404.2140, arXiv.org.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Bitcoin; Bubble periods; Cryptocurrencies; Explosive price behavior; Financial bubbles; Log-periodic power law;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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