Modelling the Volatility of Exchange Rates in Rwandese Markets

Abstract

This work applied Generalized Autoregressive Conditional Heteroskedasticity (GARCH) approachto modellingvolatility in Rwanda Exchange rate returns. The Autoregressive (AR) model with GARCH errorswas fittedto the daily exchange rate returns using Quasi-Maximum Likelihood Estimation (Q-MLE) method to get the current volatility. Asymptotic consistency and asymptotic normality of estimated parameterswere given.Akaike Information criterion was used for appropriate GARCH model selection whileJarque Bera test used for normality testing revealed that both returns and residuals have fat tails behaviour. Itwas shown thatthe estimated model fits Rwanda exchange rate returns data well.