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The 2020 Global Stock Market Crash Endogenous or Exogenous?

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Document pages: 25 pages

Abstract: Starting on February 20, 2020, the global stock markets began to suffer theworst decline since the Great Recession in 2008, and the COVID-19 has beenwidely blamed on the stock market crashes. In this study, we applied thelog-periodic power law singularity (LPPLS) methodology based on multilevel timeseries to unravel the underlying mechanisms of the 2020 global stock marketcrash by analyzing the trajectories of 10 major stock market indexes from bothdeveloped and emergent stock markets, including the S&P 500, DJIA, NASDAQ,FTSE, DAX, NIKKEI, CSI 300, HSI, BSESN, and BOVESPA. In order to effectivelydistinguish between endogenous crash and exogenous crash, we proposed using theLPPLS confidence indicator as a classification proxy. The results show that theapparent LPPLS bubble patterns of the super-exponential increase, corrected bythe accelerating logarithm-periodic oscillations, have indeed presented in theprice trajectories of the seven indexes: S&P 500, DJIA, NASDAQ, DAX, CSI 300,BSESN, and BOVESPA, indicating that the large positive bubbles have formedendogenously prior to the 2020 stock market crash, and the subsequent crashesfor the seven indexes are endogenous, stemming from the increasingly systemicinstability of the stock markets, while the well-known external shocks such asthe COVID-19 pandemic etc. only acted as sparks during the 2020 global stockmarket crash. In contrast, the obvious signatures of the LPPLS model have notbeen observed in the price trajectories of the three remaining indexes: FTSE,NIKKEI, and HSI, signifying that the crashes in these three indexes areexogenous, stemming from external shocks. The novel classification method ofcrash types proposed in this study can also be used to analyze regime changesof any price trajectories in global financial markets.

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