Bayesian State-space modelling of Impact of COVID-19 on stock Markets in G7 Countries

Abstract

Abstract This work examines the impact of Corona virus disease (COVID-19) on the stock market of Group of Seven (G7) countries using daily data from March, 1st of 2020 to December, 31st of 2020. A Bayesian Structural Time Series Model (BSTSM) was used to capture the effects of COVID-19 on the stock market performance of these G7 countries through a Markov Chain Monte Carlo (MCMC) method. We considered an AR(p) model with time-varying parameters and local linear trend models to know if the stock price of these countries during the period of the first wave of COVID-19 is changing overtime. There was a stochastic trend in stock prices of G7 countries during the period of the first wave of COVID-19 while the autoregressive process itself was also changing overtime. The stock market of the USA followed by Japan performed well than other G7 countries during the first phase of the COVID-19 pandemic while the stock market of France was affected during the COVID-19 pandemic.