Engineering Proceedings (Jun 2023)
Econometric Modeling of the Impact of the COVID-19 Pandemic on the Volatility of the Financial Markets
Abstract
The purpose of this paper is to identify econometric models likely to highlight the impact of the COVID-19 pandemic on the financial markets. The Markov-switching “GARCH and EGARCH” models are suitable for analyzing and forecasting the series of daily returns of the major global stock indices (i.e., SSE, S&P500, FTSE100, DAX, CAC40, and NIKKEI225) during the pre-COVID-19 period, from 1 June to 30 November 2019, and the post-COVID-19 period, from 31 December 2019, to 1 June 2020. The Markov-switching “GARCH and EGARCH” models allow good modeling of the conditional variance. The estimated conditional variance values by these models highlight the increase in volatility for the stock markets in our sample, during the post-COVID-19 period compared to that pre-COVID-19, with a peak in volatility in “early January 2020” for the Chinese stock market and in “March 2020” for the other five stock markets (i.e., New York, Paris, Frankfurt, London, and Tokyo). The stock exchange of Frankfurt has shown great resilience compared to other international stock exchanges (i.e., the stock exchanges in Paris, London, and New York). The modeling of the impact of the COVID-19 pandemic on the financial markets by the Markov-switching “GARCH and EGARCH” models makes it possible to simultaneously take into consideration the nonlinearity at the level of the mean and the variance, and to obtain the results of the transition probabilities, the unconditional probabilities and the conditional anticipated durations during the pre-COVID-19 period and the post-COVID-19 period.
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