A Model on a Comparative Cross-Regime Analysis on Modelling and Forecasting of the Zimbabwe Stock Market Volatility Using ARCH (1), GARCH (1,1) and EGARCH (1,2) as the Extension to Account for Leverage Effects from April 2012 to April 2024

Abstract

In order to account for the uncertainty underlying financial asset investments, volatility is crucial. Because of this, politicians, mutual fund managers, individual and institutional investors, and regulators of the financial industry are worried about volatility. In the context of the Zimbabwean stock market, this study aims to investigate how well three econometric models ARCH, GARCH, and EGARCH compare in terms of modelling and volatility predictions. The symmetry impact was estimated using the GARCH and ARCH models, while the asymmetric effect was captured by the EGARCH model, the third model. In light of this, this study employed daily averages for the mining and industrial indices over the period of 16 April 2012 to March 2024. This country was regarded in the literature for stock market volatility modelling and forecasting as efficient and having no volatility. The findings indicate that volatility persists, demonstrating that it takes time for the market to properly assimilate information into pricing and that shocks to conditional variance take longer to fade. Additionally, there is an asymmetry, which suggests that positive and negative news have distinct effects on the stock market and that positive news causes more volatility than negative news. Thus, we draw the conclusion that equally good and negative news have greater effects. During the epidemic, investors rely more on the good news to make wise judgments that increase their profits. Taking the outcomes into account, any measure meant to lessen the pandemic’s effects is beneficial for investment.