| dc.description.abstract |
A sudden fall of stock prices happens during a pandemic due to the panic sell-off by the investors. Such a sell-off may continue for more than a day, leading to a significant crash in the stock price or, more specifically, an extreme event (EE). In this paper, Hilbert–Huang transformation and a structural break analysis (SBA) have been applied to identify and characterize an EE in the stock market due to the COVID-19 pandemic. The Hilbert spectrum shows a maximum energy concentration at the time of an EE, and hence, it is useful to identify such an event. The EE’s significant energy concentration is more than four times the standard deviation above the mean energy of the normal fluctuation of stock prices. A statistical significance test for the intrinsic mode functions is applied, and the test found that the signal is not noisy. The degree of nonstationarity test shows that the indices and stock prices are nonstationary. We identify the time of influence of the EE on the stock price by using SBA. Furthermore, we have identified the time scale (τ) of the shock and recovery of the stock price during the EE using the intrinsic mode function obtained from the empirical mode decomposition technique. The quality stocks with V-shape recovery during the COVID-19 pandemic have definite τ of shock and recovery, whereas the stressed stocks with L-shape recovery have no definite τ. The identification of τ of shock and recovery during an EE will help investors to differentiate between quality and stressed stocks. These studies will help investors to make appropriate investment decisions |
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