The impact of COVID-19 on global stock markets: early linear and non-linear evidence for Italy

The scientific community still struggles to understand the magnitude of the worldwide infections and deaths induced by COVID-19, partly ignoring the financial consequences. In this paper, using the autoregressive fractionally integrated moving average (ARFIMA)—general autoregressive conditional heteroskedasticity (GARCH) model, we quantify and show the impact of the COVID-19 spread in Italy, utilizing data for the stock market. Using information criteria and forecasting accuracy measures, we show that the COVID-19 confirmed cases contribute with statistically significant information to the modeling of volatility, and also increase the forecasting ability of the volatility of the Italian stock market index, leading to a decrease in the mean stock index.