Modelling and Forecasting Exchange-rate Volatility with ARCHtype Models

Abstract

This paper uses the Generalized Autoregressive Conditional Heteroskedasticity models to estimate volatility (conditional variance) in the monthly returns of the principal stock exchange of Sudan. over the period from January 1999 to December 2013. The models include both symmetric and asymmetric models that capture the most common stylized facts about index returns such as volatility clustering and leverage effect. The empirical results show that the conditional variance process is highly persistent (explosive process), and provide evidence on the existence of risk premium for the return series which support the positive correlation hypothesis between volatility and the expected stock returns. Our findings also show that the asymmetric models provide better fit than the symmetric models, which confirms the presence of leverage effect. These results, in general, explain that high volatility of return series is present in the Sudanese stock market over the period.