Modelling Stock Market Volatility Using Univariate GARCH Models: Evidence from Sudan and Egypt

Suliman Zakaria Suliman Abdalla · Peter Winker

72 citations0/0 files storedInternational Journal of Economics and Finance

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Financial Risk and Volatility Modeling Financial Markets and Investment Strategies Market Dynamics and Volatility

Abstract

Stock market volatility in two African exchanges, Khartoum Stock Exchange, KSE (from Sudan) and Cairo and Alexandria Stock Exchange, CASE (from Egypt) is modelled and estimated. The analysis is based on using daily closing prices on the general indices in the two markets over the period of 2nd January 2006 to 30th November 2010. The paper employs different univariate specifications of the Generalized Autoregressive Conditional Heteroscedastic (GARCH) model, including both symmetric and asymmetric models. The empirical results show that the conditional variance (volatility) is an explosive process for the KSE index returns series, while it is quite persistent for the CASE index returns series. The results also provide evidence on the existence of a positive risk premium in both markets, which supports the hypothesis of a positive correlation between volatility and the expected stock returns. Furthermore, the asymmetric GARCH models find a significant evidence for asymmetry in stock returns in the two markets, confirming the presence of leverage effect in the returns series.

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References

1979Distribution of the Estimators for Autoregressive Time Series with a Unit Root 1986Generalized autoregressive conditional heteroskedasticity 1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 1981Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root 1991Conditional Heteroskedasticity in Asset Returns: A New Approach 1993On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks 1989The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis 1992ARCH modeling in finance

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