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 Regularization Method
for detecting the beginning of a new market regime, we identify three major peaks and 10 additional smaller peaks, that have punctuated the dynamics of Bitcoin price during the analysed 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.
Maritime inventory routing problem with multiple time windows
