presented by warren tibesigwa, makerere university business school will kaberuka, makerere...

PRESENTED BY WARREN TIBESIGWA, MAKERERE UNIVERSITY

BUSINESS SCHOOL WILL KABERUKA, MAKERERE UNIVERSITY

BUSINESS SCHOOL

16/10/2014ORSEA PAPER

Volatility analysis of exchange rate of emerging economies: A case of East African countries

(1990-2010).

Introduction


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

Objectives of the Study


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.

Literature Review


Literature related to exchange rate volatility was reviewed and the relevant models noted.

Methodology


Data usedMonthly 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.


Data AnalysisData 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 α0 being the intercepts, P and Q being equal monthly lags of exchange rates, Øi, Øj, αi and βj are the coefficients to be determined . αi≥0, βj≥0 and also ∑ (αi+βj) <1.

In equation 1, Zt is the current exchange rate and ut is the stochastic error term which is assumed to be normally distributed ie E(ut)=0 and Variance given by equation 2

t = +1 1

p q

i t i j t j ti j

u

,ut~ (0, 2

t )…………………………………..(1)

q

i

p

jjtjitit

1 1

220

2 ………………………………………………………(2)

Study Findings


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


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


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

Country Volatility Model Long run average of volatility

Uganda ARIMA(1,1,0):GARCH(1,1) 151.35

Kenya ARIMA(1,1,0):GARCH(1,1) 0.25

Tanzania ARIMA(1,1,0):GARCH(1,1) 4.92

Rwanda ARIMA(3,1,4):GARCH(1,1) -0.05

Burundi ARIMA(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.

FORECASTING


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.

Graphic comparison of trend of the exchange rates


Static forecasts


RWANDAN FRANC

The static forecasts indicate that the volatility model fitted the data pretty wellThe mean Absolute percentage Error(MAPE) resulting from the model was 1.25% which is too small implying that the model is good

Dynamic forecasts


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


The forecasts obtained during the period when Rwanda joined EAC showed that volatility started reducing from the time the country joined

Trends of exchange rate volatility of the other countries


The trends shown by the forecast graphs of the other countries after joining EAC are shown in table fiveTable 5:Trends of exchange rate volatility of the other countriesCountry Volatility Trend after joining EAC

Uganda Decrease

Kenya Decrease

Tanzania Decrease

Burundi Decrease


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.

Structual break tests


The structural break tests during times suspected to have been affected by NEWS yielded the results in table 6.Table 6:Structual break test resultsCountry Suspected Dates affected Null hypotheses Prob. for the test Decision

Uganda March 1993

July 2000

Ho: Data has no structural

breaks at the stated points

0.1703 Reject the Null

Kenya March 1993

July 2000

Date for the bombs




Tanzania March 1993

July 2000




Rwanda April 1994

July 2007




Burundi April 1994

July 2007





The results in table 6 show that NEWS affected the exchange rate volatility of the countries during the affected periods.

Summary of findings


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.

CONCLUSION


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.

Policy Implication


The countries should open their economies to the various regional market groupings especially those that are in their immediate neighbourhood like the COMESA.

Recommendation for further Research


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.


…..END…..

Appendix


Tests carried outSerial 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 homoscedasticJaque Bera test for normality Ho: Residuals are normally distributed.


Test resultsThere 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 distributedThe residuals were homoscedastic(had

constant spread)

presented by warren tibesigwa, makerere university business school will kaberuka, makerere...

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