Modelling Exchange Rate Volatility Using Asymmetric GARCH Models (Evidence from Sierra Leone)

article examines the accuracy and forecasting performance of volatility models for the Leones/USA dollars exchange rate return, including the ARMA, Generalized Autoregressive Conditional Heteroscedasticity (GARCH), and Asymmetric GARCH models with normal and non-normal (students t and skewed Student t) distributions. In fitting these models to the monthly exchange rate returns data over the period January 2004 to December 2013, we found that, the Asymmetric (GARCH) and GARCH model better fits under the non-normal distribution than the normal distribution and improve the overall estimation for measuring conditional variance. The GJR-GARCH model using the skewed Student t- distribution is most successful and better forecast the Sierra Leone exchange rate volatility. Finally, the study suggests that the given models are suitable for modeling the exchange rate volatility of Sierra Leone and the Asymmetric GARCH models shows asymmetric in exchange rate returns, resulting to the presence of leverage effect. Given the implication of exchange rate volatility, the study would be of great value to policy makers, investors and researchers at home and abroad in promoting development of the capital market and foreign exchange market stability in emerging economies.