1. PRESENTED BY WARREN TIBESIGWA, MAKERERE UNIVERSITY BUSINESS SCHOOL WILL KABERUKA, MAKERERE UNIVERSITY BUSINESS SCHOOL 16/10/2014 ORSEA PAPER Volatility analysis of exchange rate of emerging economies: A case of East African countries (1990-2010).

2. Introduction 16/10/2014 ORSEA PAPER Most currrencies of emerging economies are not stable because they are affected by both local and global events. This makes it difficult to properly plan for future development. It becomes imperative therefore that the volatilities in these currencies are studied with a view of predicting their future movements

3. Objectives of the Study 16/10/2014 ORSEA PAPER The main objectives of this paper are to: Examine whether there exists volatility periods in the exchange rate data of these economies. Establish whether joining the East African common market had a significant effect on the exchange rate volatility of the currencies of the five East African countries. Establish whether NEWS affects the exchange rate volatility of each country. Test which of the five currencies is more vulnerable to NEWS.

4. Literature Review 16/10/2014 ORSEA PAPER Literature related to exchange rate volatility was reviewed and the relevant models noted.

5. Methodology 16/10/2014 ORSEA PAPER Data used Monthly data of the exchange rates of each of the five countries against the US Dollar for a period of 20 years from 1990M1 to 2010M12 was used. The data was obtained from IMF data base and from the publications of each of the five central banks.

6. 16/10/2014 ORSEA PAPER Data Analysis Data was analysed using Eviews 7 and the usual econometric tests carried out. The following models were fitted Equation 1 is the mean model which is the Box-Jenkins (ARIMA) model and equation(2) is the variance model equation which is the GARCH model with µ and α 0 being the intercepts, P and Q being equal monthly lags of exchange rates, Ø i, Ø j, α i and β j are the coefficients to be determined. α i ≥0, β j ≥0 and also ∑ (α i +β j ) <1. In equation 1, Z t is the current exchange rate and u t is the stochastic error term which is assumed to be normally distributed ie E(u t )=0 and Variance given by equation 2

7. Study Findings 16/10/2014 ORSEA PAPER ADF(Augumented Dickey Fuller) test was used to test for trend in both differenced and non- differenced data of each of the five countries. The results of the test are indicated in table 2

8. 16/10/2014 ORSEA PAPER The results in table 2 show that the data is non- stationary for the non differenced data and stationary for the first difference. Table 2 :Results of ADF test on the data of the five countries

9. 16/10/2014 ORSEA PAPER Lag determination was then done and the results were deduced from the ACF and PACF. The correlograms (Partial autocorrelation and Autocorrelation functions) of all the countries’ exchange rate data yielded the ARIMA models with the corresponding Volatility(GARCH)model shown in the table 3 CountryVolatility ModelLong run average of volatility UgandaARIMA(1,1,0):GARCH(1,1)151.35 KenyaARIMA(1,1,0):GARCH(1,1)0.25 TanzaniaARIMA(1,1,0):GARCH(1,1)4.92 RwandaARIMA(3,1,4):GARCH(1,1)-0.05 BurundiARIMA(1,1,1):GARCH(1,1)22.42 Table 3. Showing the volatility model and the long run average of volatility of the five countries.

10. FORECASTING 16/10/2014 ORSEA PAPER The models fitted for the five countries were then used for forecasting the exchange rate volatility of all the countries and the results shown in the following tables.

11. Graphic comparison of trend of the exchange rates 16/10/2014 ORSEA PAPER

12. Static forecasts 16/10/2014 ORSEA PAPER RWANDAN FRANC The static forecasts indicate that the volatility model fitted the data pretty well The mean Absolute percentage Error(MAPE) resulting from the model was 1.25% which is too small implying that the model is good

13. Dynamic forecasts 16/10/2014 ORSEA PAPER RWANDAN FRANC Forecasts were obtain for the period when the country was experiencing Genocide(1994) and the period when the country joined EAC. Forecasts obtained during Genocide period showed that the volatility was increasing

14. 16/10/2014 ORSEA PAPER The forecasts obtained during the period when Rwanda joined EAC showed that volatility started reducing from the time the country joined

15. Trends of exchange rate volatility of the other countries 16/10/2014 ORSEA PAPER The trends shown by the forecast graphs of the other countries after joining EAC are shown in table five Table 5:Trends of exchange rate volatility of the other countries CountryVolatility Trend after joining EAC Uganda Decrease Kenya Decrease Tanzania Decrease Burundi Decrease

16. 16/10/2014 ORSEA PAPER The forecasts of the volatility of the exchange rates of all the currencies of the five countries showed that joining the common market group(good NEWS) reduced the volatility while the genocide in Rwanda(bad NEWS) increased volatility.

17. Structual break tests 16/10/2014 ORSEA PAPER The structural break tests during times suspected to have been affected by NEWS yielded the results in table 6. Table 6:Structual break test results CountrySuspected Dates affectedNull hypothesesProb. for the testDecision Uganda March 1993 July 2000 H o : Data has no structural breaks at the stated points 0.1703Reject the Null Kenya March 1993 July 2000 Date for the bombs H o : Data has no structural breaks at the stated points 0.5215Reject the Null Tanzania March 1993 July 2000 H o : Data has no structural breaks at the stated points 0.9923Reject the Null Rwanda April 1994 July 2007 H o : Data has no structural breaks at the stated points 0.3653Reject the Null BurundiApril 1994 July 2007 H o : Data has no structural breaks at the stated points 0.6018Reject the Null

18. 16/10/2014 ORSEA PAPER The results in table 6 show that NEWS affected the exchange rate volatility of the countries during the affected periods.

19. Summary of findings 16/10/2014 ORSEA PAPER Significant volatility models are obtained for exchange rate data of all the EAC countries implying that the concept of volatility has relevance in these economies. Forecasts of variance indicate that 1994 genocide increased the exchange rate volatility of Rwandan Franc while integration of the economies into the East African Community reduced the volatility of all the five countries that form EAC. The structural break test showed that NEWS affected the exchange rates of all EAC countries. The long run averages of the exchange rate volatilities of all the countries showed the Ugandan shilling was the most vulnerable currency.

20. CONCLUSION 16/10/2014 ORSEA PAPER The concept of volatility is applicable in the exchange rate data of all the five countries of EAC. NEWS were found to affect the exchange rate of the EAC countries and The GARCH family models were found to capture the volatility of the exchange rates of the all the countries of East African Community.

21. Policy Implication 16/10/2014 ORSEA PAPER The countries should open their economies to the various regional market groupings especially those that are in their immediate neighbourhood like the COMESA.

22. Recommendation for further Research 16/10/2014 ORSEA PAPER Apart from NEWS, macro economic factors such as inflation, interest rate could be used to establish their effect on the exchange rate volatility of the currencies of these countries.

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24. Appendix 16/10/2014 ORSEA PAPER Tests carried out  Serial autocorrelation LM test(Lagrange Multiplier)- Breusch Godfrey test. Ho:Residuals exhibit no serial autocorrelation  Homoscedastic test by use of the white’s Heteroscedasticity test Ho: residuals are homoscedastic  Jaque Bera test for normality Ho: Residuals are normally distributed.

25. 16/10/2014 ORSEA PAPER Test results  There was no evidence of autocorrelation of residuals because the prob for the test was 0.004 and therefore the null is accepted at 5% level of significance. Further more the D-W test statistic was 2.00157 which is approx 2 implying no autocorrelation  The residuals were normally distributed  The residuals were homoscedastic(had constant spread)