Even though oil prices are not subject to manipulations by individual countries, instability in the same generates shocks that other variables respond to, yet amid these shocks, more units of local currencies in developing countries are needed to acquire foreign inputs for production. Fluctuating oil prices consequently imply that high prices would increase the cost of production and ultimately reduce the purchasing power of industries. This study ascertains threshold effects of exchange rate devaluation and changes in oil prices on the industrial output of thirty developing countries using threshold and nonlinear autoregressive distributed lag (NARDL) regressions. Results revealed percentage rise above the devaluation threshold caused a fall in production by 4.36 percent. Oil prices within this devaluation region negatively affected output. Below and within the devaluation threshold of 0.692, the relationship patterns switch with oil price variability attracting positive and significant effects, while devaluation impacted industrial output positively with a substantial magnitude of 0.334. A higher devaluation was met with lower output in the industrial sector. In this higher region, increased oil prices weaken devaluation effects by 91.882. When a currency falls more than it is obtainable in the threshold (6.9 percent), oil prices cut output by a larger magnitude than it stimulated positively when the devaluation rate did not surpass the threshold value.
Exchange rate volatility, or a continuous fluctuation in the currency rate has been a major concern in recent years due to its impact on economic activities. No wonder concerns have been raised regarding the connection between exchange rate fluctuations and their effects on the overall economy. The motivation for the study is based on the fact that most emerging economies experiencing inflationary tendencies are more likely to experience a high degree of exchange rate volatility persistence. Such a scenario seems catastrophic to developing economies where large currency movement are more frequent. BEKK-GARCH and DCC-GARCH models were utilized to estimate volatility transmission and persistence respectively in selected African countries. Results show there is presence of spill-over effect in exchange rates of all countries. BEKK-GARCH estimates show that negative effects of exchange rate of one country had deleterious effect on exchange rate of another. We found evidence in favour of bidirectional exchange rate volatility transmission amongst all exchange rates of countries in the study. Dynamic conditional correlation (DCC) model estimates further revealed Ghanaian cedi top list of countries exchange rate volatility persistence followed by naira with a value of 1.0974. Efficient structural transformation is needed to mitigate structural problems that generate inflation in these countries.
Given that volatility influences decisions about currency rates, monetary policy, and macroeconomic policy, it is crucial to predict and anticipate volatility in emerging economies. The study employed generalized autoregressive conditional heteroskedasticity (GARCH) asymmetric models to estimate and forecast exchange rate dynamics in developing countries. We found that South Africa model had similar variance and covariance proportion of 0.99356 percent and 0.995901 percent respectively and the exchange rate could rise or fall by 2 to 6 units of rand, in exchange for USD. In Kenya, exchange rates continually exhibited steady rise monthly with extremely low mean absolute percentage error of 0.01568 percent and this demonstrates how strongly the model predicts Kenya’s future currency rates while the variance chart supports absence of persistence. In Ghana, exchange rates are projected to increase significantly as 99.5 percent of unsystematic error was un accounted for in the model. Volatility is highly persistent in Nigeria; hence the forecasting model reported a high error rate by taking 1.06 percent of the symmetric error into cognizance. Kenya, Ghana, and Mauritius had asymmetry in currency volatility, revealing turbulence in exchange rates when the bad news hit the market. Hence, local currencies are rendered worthless in the foreign exchange market.
In the assessment of governments’ fiscal performance, exchange rates play some roles while capital movements could serve as determinant of fiscal discipline. This study examined the effects of exchange rate devaluation, and capital inflows, on budgetary spending, and the interactions among the variables using the Bayesian vector autoregression (BVAR) and sys-generalized method of moments (GMM) estimators with 1,184 panel observations. The study covers 37 emerging nations. The variables had a co-integrating connection, demonstrating a long-run link between the variables studied. Having executed the Gibbs sampling for simulation efficiently, our Markov Chain Monte Carlo (MCMC) simulation converged appropriately. The sampling efficiency parameter is equal to 0.96257, close to 1. The Monte Carlo standard errors (MCSE) are extremely low at 0.000 with an implication of adequate precision in the BVAR model estimation. The results disclose that a 1 percent devaluation shock compressed fiscal spending by 0.56 percent and a shock to capital inflows stimulated 0.99% growth in fiscal spending. The 95 percent credible interval suggests a considerable size of effects on devaluation and capital flows. Accordingly, managing the exchange rate can be a valuable tool for managing capital shortage in Africa. Rather than increase government spending, governments should concentrate on revenue generation by utilizing an effective exchange rate policy to influence the national pattern of product diversification.
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.
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