Real-time Prediction of Bitcoin bubble Crashes

by   Min Shu, et al.

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.


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