Modelling COVID-19 Fluctuation in Nigeria Using Fractional Integrated Garch Model

Abstract

This study applied a fractionally integrated GARCH (FIGARCH) process in modelling daily cases of COVID-19 in Nigeria from 28 February 2020 to 23 March 2022. The time plot of the series showed the constant fluctuation in the study variable. The daily COVID-19 data was tested for stationarity using Augmented Dickey-Fuller (ADF), the series was not stationary. The Geweke and Porter-Hudak (GPH) method was used to estimate the long memory parameter d of the FIGARCH model. The daily series was stationary at a fractional differencing of order (d=0.97). The presence of long memory was also detected using the autocorrelation function. The fractionally integrated GARCH model was used to detect the period of high and low crisis. The crisis period was identified by volatility clustering and the leverage effect process. However, four models were estimated for FIGARCH models. The best model was selected based on the information criteria. Finally, the most adequate model for estimating the volatility of COVID-19 in Nigeria was the FIGARCH (1,0.97, 1) model.