q-fin updates on arXiv.org
Sat, 01 Jun 2019 10:00:18 GMT language
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' times $t_2$ before the crashes, we employ a clustering method to
group the predicted critical times $t_c$ of the LPPLS fits over different time
scales, where $t_c$ is the most probable time for the ending of the bubble.
Each cluster is proposed as 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.