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Economic Change and Restructuring ; 56(1):265-295, 2023.
Article in English | ProQuest Central | ID: covidwho-2209406


This paper aims to map the effects of the rapid spread of coronavirus (COVID-19) on stock price dynamics and markets selections based on data from March 22, 2021, to September 20, 2021. Options markets from 2020 to 2021, multiple kinds of critical COVID-19 data. The proposed hypothetical modal considers investors' behavior and errors caused by the level of sentiment elicited for stock markets and green categories. This paper another element (1) Covid-19 (2) feeling, and (3) networking websites, for example, Covid-19 influence on the green size, green direction, and impact on securities prices. This paper used google search data work also creates a proxy for emotions dependent on five main categories of Data: (1) Covid-19, pandemic effect (2) markets, (3) lockdown, (4) banking and government aid. Moreover, this paper Use (a) VIX index sentiment, (b) S& The P 500 index is a measure of how well a sentiment (c) Sentiment in the S& amp;P 500 bank index. The Projected to empirical Finding follow First Level during the Covid-19, effect on jump volatility, and variability level in persistence on the green stock market exceeds that on the options market. VIX index green financial level increases with the COVID green financial level increase with the COVID-19 market index, index banking index and lockdown index. Therefore, it concluded the Share market statistic, COVID-19 benchmark, and long-run volatility. The fraction of the leap government assistance reduced. We find that the outbreak of the Pandemic of COVID-19 effects of the S&P 500 Index and S&P 500 Banks Index decrease with highest values (39%) but only after a surge in volatility covid-19 Pandemic. These results comply with our model's expectations.

ENERGIES ; 15(13), 2022.
Article in English | Web of Science | ID: covidwho-1938742


It is generally known that violent oil price volatility will cause market panic;however, the extent to which is worthy of empirical test. Firstly, this paper employs the TVP-VAR model to analyze the time-varying impacts of oil price volatility on the panic index using monthly data from January 1990 to November 2021. Then, after using the SVAR model to decompose the oil price volatility, this paper uses the PDL model to analyze the heterogeneous impacts of oil price volatility from different sources. Finally, based on the results of oil decomposition, this paper uses the TARCH model to analyze the asymmetric impacts of oil price volatility in different directions. The results show that: (1) oil price volatility can indeed cause market panic, and these impacts exhibit time-varying characteristics;(2) oil price volatility from different sources has different impacts on the panic index, and the order from high to low is oil-specific demand shocks, supply shocks, and aggregate demand shocks;and (3) oil price volatility has asymmetric impacts on the panic index, and positive shocks have greater impacts than negative.