In the past decade, Bitcoin has become an emerging asset class well known to
most people because of their extraordinary return potential in phases of
extreme price growth and their unpredictable massive crashes. We apply the
LPPLS confidence indicator as a diagnostic tool for identifying bubbles using
the daily data of Bitcoin price in the past two years. We find that the LPPLS
confidence indicator based on the daily data of Bitcoin price 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. 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 the adaptive multilevel time series detection
methodology have not only an outstanding performance to effectively detect the
bubbles and accurately forecast the bubble crashes, but can also monitor the
development and the crash of bubbles even if a bubble exists in a short time.
In addition, we discover that the short-term LPPLS confidence indicator greatly
affected by the extreme fluctuations of Bitcoin price can provide some useful
insights into the bubble status on a shorter time 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. The adaptive multilevel
time series detection methodology can provide real-time detection of bubbles
and advanced forecast to warn of an imminent crash risk in not only the
cryptocurrency market but also the other financial markets.