The 2021 Bitcoin Bubbles and Crashes – Detection and Classification

Abstract

In this study, we adopted the Log-Periodic Power Law Singularity (LPPLS) model for real-time identification and monitoring of Bitcoin bubbles and crashes using different time scale data and proposed the modified Lagrange regularization method to alleviate the impact of potential LPPLS model over-fitting to better estimate bubble start time and market regime change. We also aimed to determine the natures of the bubbles and crashes – be it endogenous due to its own price evolution or exogenous due to external market and/or policy influences. We performed a systematic market event analysis and correlated which to Bitcoin bubbles detected. Based on the daily LPPLS confidence indictor from December 1, 2019 to June 24, 2021, we found that the Bitcoin boom from November 2020 to mid-January 2021 is an endogenous bubble, stemming from the self-reinforcement of cooperative herding and imitative behaviors of market players, while the price spike from mid-January 2021 to mid-April 2021 is likely an exogenous bubble driven by extrinsic events including a series of large-scale acquisitions and adoptions by well-known institutions such as Visa and Tesla. We have also demonstrated the utilities of multi-resolution LPPLS analysis in revealing both short-term changes and long-term states.

Keywords: Bitcoin bubble, Log-periodic power law singularity (LPPLS), LPPLS confidence indicator, Cryptocurrency, Financial bubble and crash, Modified Lagrange regularization method

JEL Classification: G01, C02, C53