MODELLING AND FORECASTING VOLATILITY IN NIGERIA: EVIDENCE FROM THE STOCK MARKET

Abstract

Trades in stock market anywhere in the world is faced with intense volatility due to stocks prices instability in real time that is mostly driven by information and other market dynamics. This research examines two volatility models with two different error distributions innovations in modelling and forecasting the continuous compounded return series (CCRS) of Nigeria All Share Index (NGX ASI) spot prices spanning the period of January 30, 2012 to June 30, 2021. The Generalized Autoregressive Conditional Heteroscedastic (GARCH) and Asymmetric Power Autoregressive Conditional Heteroscedastic ARCH (APARCH) volatility models under Student-t Distribution (StD) and Generalized Error Distribution (GED) error innovations are utilized. The best-fitted model is determined using Akaike’s Information Criterion (AIC) while Mean Square Error (MSE) is used to evaluate forecast performance of the fitted volatility models. The results from the analysis showed that amongst competing models, APARCH (1,1)-GED was selected to be the best fitted volatility model with better forecasting power for the CCRS-NGX-ASI spot prices. This is because it produces the smallest AIC and MSE values