Modelling and estimating volatilities in exchange rate return and the response of exchange rates to oil shock

Abstract

Developing countries have persistently witnessed volatile exchange. Such volatility triggered instability in their exchange rates which induced colossal fluctuations in currency rates leading to uncertainty for both the consumers and firms. All these have instigated changes in official exchange rates that are harmful to underlie trade patterns in these countries. This study estimated fluctuations in daily exchange rate returns of ten African countries using generalized autoregressive conditional heteroskedasticity (GARCH) models, having ascertained the significance of autoregressive conditional heteroskedasticity (ARCH) effects. Structural vector autoregression (SVAR) estimator was utilized. Results showed Kenya shilling is the most relatively stable currency, whereas the Malawian kwacha is the most volatile among the currencies. There had been a series of random spikes in the exchange rate of Ghanaian cedi. Ghana and Kenya exchange rates are best projected using EGARCH, whereas SGARCH may be more efficient in estimating the volatility of Morocco and Zambia exchange rates. Leverage effects indicated a considerable magnitude of the adverse impact of bad news in the foreign exchange (FX) markets of Ghana and Zambia. Volatility shocks are expected to last in the future in those countries.