Re-examining Moroccan Stock Market Volatility Through the GARCH Lens

Abstract

Abstract : This paper models and estimates the volatility of daily stock returns at Casablanca Stock Exchange (MASI index), using a battery of symmetric and asymmetric GARCH models with alternative innovative distributions. We employ GARCH, GARCH-M, EGARCH, GJR-GARCH and APARCH using secondary data over the period September 2, 2019 through December 31, 2022. The findings show that the volatility shocks are highly persistent, with leverage effect confirming that negative news raise future risk more than positive news. APARCH (1,1) was also found to be more accurate in predicting stock returns based on information criterion and log likelihood. Furthermore, our findings show that the Moroccan Stock market exhibited near-normal return behavior before the onset of major shocks. However, the crisis and subsequent recovery periods were characterized by strong departures from normality, consistent with the presence of fat tails and volatility clustering that were captured by the APARCH model results.

Keywords : Volatility, GARCH Models, MASI, Covid 19