Dissection of Bitcoin's Multiscale Bubble History

38 Pages Posted: 18 Apr 2018 Last revised: 30 Apr 2018

See all articles by J-C Gerlach

J-C Gerlach

ETH Zürich - Department of Management, Technology, and Economics (D-MTEC)

Guilherme Demos

ETH-Zürich - Department of Management Technology and Economics (D-MTEC); Universidade Federal de Santa Catarina (UFSC)

Didier Sornette

ETH Zürich - Department of Management, Technology, and Economics (D-MTEC); Swiss Finance Institute; Southern University of Science and Technology; Tokyo Institute of Technology

Date Written: April 12, 2018

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.

Keywords: Cryptocurrency, Bitcoin, k-Means Clustering, Multiscale Bubble Indicator, Log-Periodic Power Law Singularity Analysis, Forecasting, Time Series Analysis, Market Crashes

JEL Classification: C2, C13, C32, C53, C55, C61, G1, G10

Suggested Citation

Gerlach, Jan-Christian and Demos, Guilherme and Sornette, Didier, Dissection of Bitcoin's Multiscale Bubble History (April 12, 2018). Swiss Finance Institute Research Paper No. 18-30, Available at SSRN: https://ssrn.com/abstract=3164246 or http://dx.doi.org/10.2139/ssrn.3164246

Jan-Christian Gerlach (Contact Author)

ETH Zürich - Department of Management, Technology, and Economics (D-MTEC) ( email )

Scheuchzerstrasse 7
SEC
Zürich, 8092
Switzerland

Guilherme Demos

ETH-Zürich - Department of Management Technology and Economics (D-MTEC) ( email )

Scheuchzerstrasse 7
SEC F3
Zürich, Zürich 8092
Switzerland

Universidade Federal de Santa Catarina (UFSC) ( email )

Campus Reitor João David Ferreira Lima
Bairro Trindade
Florianopolis, Santa Catarina 88040
Brazil

Didier Sornette

ETH Zürich - Department of Management, Technology, and Economics (D-MTEC) ( email )

Scheuchzerstrasse 7
Zurich, ZURICH CH-8092
Switzerland
41446328917 (Phone)
41446321914 (Fax)

HOME PAGE: http://www.er.ethz.ch/

Swiss Finance Institute

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Southern University of Science and Technology

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Shenzhen, Guangdong 518055
China

Tokyo Institute of Technology

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Tokyo 152-8550, 52-8552
Japan

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