Time Series Volatility Forecasting of the Zimbabwean Stock Exchange

Abstract

This paper attempts to explore the comparative ability of different statistical and econometric volatility forecasting models in the context of Zimbabwe stock market. Two different models were considered in this study. The volatility of the ZSE industrial index returns have been modeled by using a univariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models including both symmetric and asymmetric models that captures most common stylized facts about index returns such as volatility clustering, fat tails and leverage effect, these models are GARCH (1,1) and exponential GARCH (1,1). The first model is used for capturing the symmetry effect whereas the second model is for capturing the asymmetric effect.

The study used a stock market average daily industrial index from Zimbabwe (ZSE index), a country not previously considered in the volatility literature, for the period 19 February 2009 to 31 December 2014. Basing on the empirical results presented, the following can be concluded. ZSE data showed a significant departure from normality and existence of conditional heteroskedasticity in the residuals series. For all periods specified, the empirical analysis evidenced that asymmetric EGarch (1;1) model outperform the symmetric Garch (1;1) model on forecasting future volatility after using two different evaluation techniques of Error measures statistics and regression based analysis.

Future studies should consider the general extensions of Garch models, in particular symmetric and asymmetric Garch (p; q) rather than symmetric and asymmetric Garch (1;1) model. This general extension is helpful because such higher order-models are often useful when long span of data is used. Therefore, with additional lags such models allow both fast and slow decay of information.