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Dissection of Bitcoin's Multiscale Bubble History

Author

Listed:
  • J-C Gerlach

    (ETH Zurich)

  • Guilherme Demos

    (ETH Zurich)

  • Didier Sornette

    (ETH Zurich and Swiss Finance Institute)

Abstract

We present a detailed bubble analysis of the Bitcoin to US Dollar price dynamics from January 2012 to February 2018. We introduce a robust automatic peak detection method that classifies price time series into periods of uninterrupted market growth (drawups) and regimes of uninterrupted market decrease (drawdowns). In combination with the Lagrange Regularisation Method for detecting the beginning of a new market regime, we identify 3 major peaks and 10 additional smaller peaks, that have punctuated the dynamics of Bitcoin price during the analyzed time period. We explain this classification of long and short bubbles by a number of quantitative metrics and graphs to understand the main socio-economic drivers behind the ascent of Bitcoin over this period. Then, a detailed analysis of the growing risks associated with the three long bubbles using the Log-Periodic Power Law Singularity (LPPLS) model is based on the LPPLS Confidence Indicators, defined as the fraction of qualified fits of the LPPLS model over multiple time windows. Furthermore, for various fictitious present analysis times t2, positioned in advance to bubble crashes, we employ a clustering method to group LPPLS fits over different time scales and the predicted critical times tc (the most probable time for the start of the crash ending the bubble). Each cluster is argued to provide a plausible scenario for the subsequent Bitcoin price evolution. We present these predictions for the three long bubbles and the four short bubbles that our time scale of analysis was able to resolve. Overall, our predictive scheme provides useful information to warn of an imminent crash risk.

Suggested Citation

  • J-C Gerlach & Guilherme Demos & Didier Sornette, 2018. "Dissection of Bitcoin's Multiscale Bubble History," Swiss Finance Institute Research Paper Series 18-30, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp1830
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    File URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3164246
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    Citations

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    Cited by:

    1. Peter Fratrič & Giovanni Sileno & Sander Klous & Tom Engers, 2022. "Manipulation of the Bitcoin market: an agent-based study," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-29, December.
    2. Stanisław Drożdż & Ludovico Minati & Paweł Oświȩcimka & Marek Stanuszek & Marcin Wa̧torek, 2019. "Signatures of the Crypto-Currency Market Decoupling from the Forex," Future Internet, MDPI, vol. 11(7), pages 1-18, July.
    3. Marian Gidea & Daniel Goldsmith & Yuri Katz & Pablo Roldan & Yonah Shmalo, 2018. "Topological recognition of critical transitions in time series of cryptocurrencies," Papers 1809.00695, arXiv.org.
    4. Dean Fantazzini & Stephan Zimin, 2020. "A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 47(1), pages 19-69, March.
    5. Ifeoma Christy Mba & Emmanuel Ikechukwu Mba & Jonathan Emenike Ogbuabor & Winnie Ogochukwu Arazu, 2018. "Mean Sojourn and Mean Return Time of the Buy-hoard-sell Strategy of Bitcoin Exchange Prices," International Journal of Economics and Financial Issues, Econjournals, vol. 8(5), pages 276-282.

    More about this item

    Keywords

    Cryptocurrency; Bitcoin; k-Means Clustering; Multiscale Bubble Indicator; Log-Periodic Power Law Singularity Analysis; Forecasting; Time Series Analysis; Market Crashes;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G1 - Financial Economics - - General Financial Markets
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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