Financial COVID-19 Crisis: an Empirical Study and Prediction of Some Stock Market Indices

The main purpose of this paper is to highlight the direct linear relationship between the financial markets crash and the COVID-19 pandemic daily data. Moreover, we propose efficient modeling able to predict the values of some stock market indices without statistical learning. To achieve this goal, we first carry out a statistical study of the effects of COVID- 19 daily data on some European, American and Chinese stock indices during the period from December 31, 2019 until July 31, 2020 which will be divided into two phases. This study is accomplished particularly by evaluation of the correlations between these various stock market indices, between these indices and the pandemic daily data of the concerned countries as well as by the study of changes in stock market indices returns and daily volatility during the pandemic. We also establish linear regression models using stepwise method, then predictions successfully made. The obtained results by applications on real data show that the first period is marked by a linear causal relationship between the values of the studied stock indices, the Chinese COVID- 19 data and the values of CSI300 index. While in the second period, the evolution of the studied indices is characterized by a linear causal relationship with the American COVID-19 data and the NYSE index evolution. [ABSTRACT FROM AUTHOR] Copyright of IAENG International Journal of Applied Mathematics is the property of Newswood Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)