  In the past decade, Bitcoin as an emerging asset class has gained widespread
public attention because of their extraordinary returns in phases of extreme
price growth and their unpredictable massive crashes. We apply the log-periodic
power law singularity (LPPLS) confidence indicator as a diagnostic tool for
identifying bubbles using the daily data on Bitcoin price in the past two
years. We find that the LPPLS confidence indicator based on the daily Bitcoin
price data fails to provide effective warnings for detecting the bubbles when
the Bitcoin price suffers from a large fluctuation in a short time, especially
for positive bubbles. In order to diagnose the existence of bubbles and
accurately predict the bubble crashes in the cryptocurrency market, this study
proposes an adaptive multilevel time series detection methodology based on the
LPPLS model and finer (than daily) timescale for the Bitcoin price data. We
adopt two levels of time series, 1 hour and 30 minutes, to demonstrate the
adaptive multilevel time series detection methodology. The results show that
the LPPLS confidence indicator based on this new method is an outstanding
instrument to effectively detect the bubbles and accurately forecast the bubble
crashes, even if a bubble exists in a short time. In addition, we discover that
the short-term LPPLS confidence indicator highly sensitive to the extreme
fluctuations of Bitcoin price can provide some useful insights into the bubble
status on a shorter time scale - on a day to week scale, and the long-term
LPPLS confidence indicator has a stable performance in terms of effectively
monitoring the bubble status on a longer time scale - on a week to month scale.
The adaptive multilevel time series detection methodology can provide real-time
detection of bubbles and advanced forecast of crashes to warn of the imminent
risk.
