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Was the 2017 Crash of the Crypto-currency Market Predictable?

Author

Listed:
  • Élise Alfieri

    (CERAG - Centre d'études et de recherches appliquées à la gestion - UGA [2016-2019] - Université Grenoble Alpes [2016-2019])

  • Radu Burlacu

    (CERAG - Centre d'études et de recherches appliquées à la gestion - UGA [2016-2019] - Université Grenoble Alpes [2016-2019])

  • Geoffroy Enjolras

    (CERAG - Centre d'études et de recherches appliquées à la gestion - UGA [2016-2019] - Université Grenoble Alpes [2016-2019])

Abstract

The cyber-space of crypto-currency market is a main issue in terms of security and stability. The novelty and the high volatility of crypto-currencies question their speculative nature. Recently, the crypto-currency price exponentially increased and reached an important burst in the end of 2017. The objective of this article is to detect and test the prediction of this crypto-currency market crash using the Log-Periodic Power Law model (LPPL). We consider 2 main crypto-currencies, Bitcoin and Ether. We find that the LPPL model allows to estimate the date of the crash in the crypto-currency market depending on the window sensitivity.

Suggested Citation

  • Élise Alfieri & Radu Burlacu & Geoffroy Enjolras, 2019. "Was the 2017 Crash of the Crypto-currency Market Predictable?," Post-Print hal-02952123, HAL.
    • Handle: RePEc:hal:journl:hal-02952123
    Note: View the original document on HAL open archive server: https://hal.science/hal-02952123
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    References listed on IDEAS

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    1. Li LIN & Ruo En REN & Didier SORNETTE, 2009. "A Consistent Model of ‘Explosive’Financial Bubbles With Mean-Reversing Residuals," Swiss Finance Institute Research Paper Series 09-14, Swiss Finance Institute.
    2. Didier Sornette & Wei-Xing Zhou, 2002. "The US 2000-2002 market descent: How much longer and deeper?," Quantitative Finance, Taylor & Francis Journals, vol. 2(6), pages 468-481.
    3. 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.
    4. Zhou, Wei-Xing & Sornette, Didier, 2003. "2000–2003 real estate bubble in the UK but not in the USA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 329(1), pages 249-263.
    5. 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.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Crypto-Currencies; Bitcoin; Bubble; LPPL; Speculation;
    All these keywords.

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