Investigating the impact of the number of daily cases of the Coronavirus pandemic on the stock market indices of developed countries with the approaches of ARIMA and ARCH models

Document Type : Original Article

Authors

1 Department of Financial Engineering, Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran

2 Graduate School of Management and Economics, Sharif University of Technology, Tehran, Iran

3 Department of Financial Engineering, Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

Abstract

The global economy is grappling with heightened uncertainty due to the ongoing Coronavirus pandemic. The daily reported cases of the virus have emerged as a critical factor shaping market sentiment and investor behavior. As the virus continues to spread, impacting various sectors, stock markets are witnessing considerable volatility. It becomes imperative for investors, policymakers, and analysts to comprehend the intricate relationship between daily COVID-19 cases and stock market indices. ARIMA, a widely-used time series analysis technique, proves invaluable in modeling and predicting future movements in stock market indices. Particularly effective for data exhibiting non-stationarity, seasonality, and autocorrelation, ARIMA leverages historical data on daily COVID-19 cases and stock market indices to identify patterns, trends, and make forecasts about future market movements. Complementing ARIMA, ARCH models are tailored to capture the volatility clustering and heteroskedasticity often observed in financial time series data. Given the heightened market volatility induced by the Coronavirus pandemic, ARCH models prove useful in modeling this volatility and making forecasts about future market volatility based on daily COVID-19 cases. By synergistically employing ARIMA and ARCH models, a comprehensive understanding of the intricate relationship between daily COVID-19 cases and stock market indices emerges. This paper delves into the impact of COVID-19 spread in Japan, Australia, France, Britain, and the United States, employing autoregressive integrated moving average (ARIMA) and autoregressive conditional heteroskedasticity (ARCH) methods. Statistical significance of COVID-19 confirmed cases is established, contributing to volatility modeling, as demonstrated by information criteria and forecasting accuracy measures.

Keywords


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