  Starting on February 20, 2020, the global stock markets began to suffer the
worst decline since the Great Recession in 2008, and the COVID-19 has been
widely blamed on the stock market crashes. In this study, we applied the
log-periodic power law singularity (LPPLS) methodology based on multilevel time
series to unravel the underlying mechanisms of the 2020 global stock market
crash by analyzing the trajectories of 10 major stock market indexes from both
developed and emergent stock markets, including the S&P 500, DJIA, NASDAQ,
FTSE, DAX, NIKKEI, CSI 300, HSI, BSESN, and BOVESPA. In order to effectively
distinguish between endogenous crash and exogenous crash, we proposed using the
LPPLS confidence indicator as a classification proxy. The results show that the
apparent LPPLS bubble patterns of the super-exponential increase, corrected by
the accelerating logarithm-periodic oscillations, have indeed presented in the
price trajectories of the seven indexes: S&P 500, DJIA, NASDAQ, DAX, CSI 300,
BSESN, and BOVESPA, indicating that the large positive bubbles have formed
endogenously prior to the 2020 stock market crash, and the subsequent crashes
for the seven indexes are endogenous, stemming from the increasingly systemic
instability of the stock markets, while the well-known external shocks such as
the COVID-19 pandemic etc. only acted as sparks during the 2020 global stock
market crash. In contrast, the obvious signatures of the LPPLS model have not
been observed in the price trajectories of the three remaining indexes: FTSE,
NIKKEI, and HSI, signifying that the crashes in these three indexes are
exogenous, stemming from external shocks. The novel classification method of
crash types proposed in this study can also be used to analyze regime changes
of any price trajectories in global financial markets.
