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Journal of Development Economics , Vol. CrossRef Google Scholar. Branson , William H. Chicago , pp. Google Scholar. In: Rudiger Dornbusch, Jacob A. Frenkel Eds.
Buttle, Edward F. International Economic Review , Vol. Chopra, Ajai, Peter J. Cooper , Richard N. Essays in International Finance No. Princeton Denoon , David B. India, Indonesia, and Ghana. Cambridge Diaz-Alejandro, Carlos F. The Journal of Political Economy , Vol.
Donovan, Donal J. Dornbusch , Rudiger , Open Economy Macroeconomics. New York Review of Economics and Statistics , Vol. Economic Development and Cultural Change , Vol. Goldstein, Morris, Peter J. Journal of International Economics , Vol. Princeton Studies in International Finance No. The Canadian Journal of Economics , Vol. European Economic Review , Vol. Stockholm Canberra Substituting Equation 6 into Equation 4 where and solving for V;.
The system of coefficients and sign attached to each variable represent the elasticities and direction of effect of the respective variables on exchange rate volatility. In a similar way many macroeconomics variables could be incorporated into the model to investigate their relationship with the exchange rate volatility. It is summarized as follows:. Equation 13 will be stationary if the persistent of volatility shocks, is lesser than 1 and in the case it comes much closer to 1, volatility shocks will be much more persistent. The normality assumption of the error was adopted in the study.
There is no consensus in the empirical literature concerning the factors that influenced exchange rate volatileity. According to , real exchange rate volatility is due to slow adjustment of commodity prices and rapid response of nominal exchange rates to exogenous shocks.
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He discovered a significant long and short-run negative effect of exchange-rate volatility on the volume of imports. They found that exchange rate volatility has a significant negative effect on the volume of imports of G-7, countries whereas for Canada, it is positive and significant. He observed that depreciation of the level of the real exchange rate reduced the output. He observed that the rate of inflation and exchange rate explained each other.
He concluded that among other key factors, exchange rate movements explained inflation in the three countries. Empirical supports for domestic monetary policy as a potential source of real exchange rate variability is provided by [27,28]. Exchange rate volatility is not regime neutral. The switch to flexible exchange rate system resulted in significant increase in real exchange rate volatility see . Hviding et al. Greater variability in real productivity shocks usually proxied by variability in the rate of growth of real GDP results in higher exchange rate variability [34,35], applying two different econometric approaches—a theoretical vector autoregression and a structural production function—concluded that the net effect of a decline in the value of the dollar is a temporary increase in inflation and real output, followed by a permanent reduction in output and level of real wages.
It was explicitly concluded that exchange rate volatility is a major factor for the upsurge of inflation  in Uganda,  in South America;  in Africa;  in Colombia;  in Nigeria and South Africa and  in Nigeria. Also [39,40] and  provided evidence of negative relationship between exchange rate volatility and foreign direct investment in African countries. The study was conducted in Nigeria; the country is situated on the Gulf of Guinea in the sub Saharan Africa.
Nigeria has a total land area of , Data covered the period to In time series analysis, stationarity of the series is examined by unit root tests. These two tests were used in this study for determining the stationarity level of series.
PC-Give 10 and gretl econometric softwares were used to carry out the tests and the result is presented in Table 2. The result implies that the time series should be tested for the existence of a cointegration [42,43]. The concept of co-integration as developed by Granger involved the determination of the static or longrun associations among non-stationary time series. The pre-condition for applying the standard procedure of the co-integration tests to any series is that the variables in consideration must be integrated of the same order or non-stationary individually.
The study applied the Engle and Granger two-step technique and Johansen cointegration approach to examine cointegration relationship among time series. The result of the Engle and Granger two-step technique of the cointegration regression and the stationarity tests for the residual ECM generated in Equation 15 is presented in Table 2. Hence, there exists a long run equilibrium relationship between the exchange rate volatility and some major macroeconomic variables in Nigeria.
For the Johansen cointegration approach, the trace and maximum eigenvalue test statistics were significant at various rank levels. This implies that there is cointegration relationship among the specified variables. Table 4 presents the result of the long run equation of exchange rate volatility in Nigeria as specified in Equation The test result as shown in Table 5 reveals that the optimum lag length appropriate for the specified variables is at the second lag indicated by the asterisks among the information criteria.
This means that in generating the short run dynamic model for the exchange rate volatility in Nigeria, the optimum lag length of time series should be 2 in order to obtain a more interpretable parsimonious ECM model. The primary reason for estimating the ECM model is to capture the dynamics in the exchange rate volatility equation in the short-run and to identify the speed of adjustment as a response to departures from the long-run equilibrium.
The general specification of the ECM that was estimated for the exchange rate volatility in Nigeria is shown below:. Table 2. Result of the unit root test for variables used in the analysis. Table 3. Results of johansen cointegration test unrestricted constant. The result of the exercise is presented in Table 6. The result validates the existence of a long-run equilibrium relationship among the time series in the exchange rate volatility equation, and also indicates that the exchange rate is sensitive to the departure from it equilibrium value in the previous periods.
The slope co-. Table 4. Long-run equation of exchange rate volatility in Nigeria. Table 5. Optimal lag length of variables used in the analysis. The ECM t value of 0. The diagnostic test for the ECM model revealed R 2 value of 0.
The F-statistic of 4. The Durbin-Watson value of 2. Table 6. ECM estimates of the exchange rate volatility equation in Nigeria. This means that in the short run, increase in the external reserves would lead to decrease in exchange rate volatileity. In the long run model, the relationship is neutral.
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The finding is similar to the empirical results reported by  for Nigeria and South Africa and  in Zimbabwe. The result implies that increase in the inflation reduces the tendency of increase exchange rate volatility in Nigeria. The result might be explained by the fact that increase in inflation would constrain cash circulation and this would reduce activities in the foreign exchange market.
Contrary in the long run, the impact of inflation on exchange rate volatility becomes neutral probably due to the learning process or adaptation to the economy process by the investors. The result also revealed that exchange rate volatility has a significant negative elastic association with the lending interest rate of commercial banks in both short and long run exchange rate volatility models in Nigeria.
However the negative association between exchange rate volatility and interest rate could be attributed to the frequent intervenetion of the Central Bank of Nigeria on the operations of commercial banks in the country.
Orthodox Models for Adjustment and Growth
In the short run, foreign private investment FPI reduces exchange rate volatility in Nigeria. The reason for the result could be attributed to the fact that in the short run government provides incentive to investors such as tax holiday, tax cut and lower tariff on imported machineries among others. These incentives reduce the frequency and volume of transaction by the investors in the foreign exchange market, thereby reducing volatility in the sub sector.
In the long run, the coefficient of FPI is significant and positively correlated to exchange rate volatility. The finding could suggests that foreign investtors in Nigeria in the long run are exposed to importation cost probably imposed by increasing depreciation of machineries and other assets. This increases the activities and perhaps the volatility in the foreign exchange market.