Osei-Assibey, Kwame Poku

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University of DundeeDOCTOR OF PHILOSOPHYExchange rate volatility in LDCssome findings from the Ghanaian, Mozambican and Tanzanian marketsOsei-Assibey, Kwame PokuAward date:2010Link to publicationGeneral rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            • Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            • You may not further distribute the material or use it for any profit-making activity or commercial gain            • You may freely distribute the URL identifying the publication in the public portalTake down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Download date: 17. Feb. 2017DOCTOR OF PHILOSOPHYExchange rate volatility in LDCssome findings from the Ghanaian, Mozambican and Tanzanian marketsKwame Poku Osei-Assibey2010Conditions for Use and DuplicationCopyright of this work belongs to the author unless otherwise identified in the body of the thesis. It is permittedto use and duplicate this work only for personal and non-commercial research, study or criticism/review. Youmust obtain prior written consent from the author for any other use. Any quotation from this thesis must beacknowledged using the normal academic conventions. It is not permitted to supply the whole or part of thisthesis to any other person or to post the same on any website or other online location without the prior writtenconsent of the author. Contact the Discovery team (discovery@dundee.ac.uk) with any queries about the useor acknowledgement of this work.“Having endeavoured to forecast exchange rates for more than half acentury, I have understandably developed significant humility about myability in this area…”Alan Greenspan,Remarks before the Euro 50 RoundTableWashington D.C., November 30, 2001A Guide to this ThesisInterest in the modelling of exchange rate volatility has remarkably increased in thepost Bretton Woods era.  This is because the current floating exchange rate systemwhich is being used by many countries in determining the level of exchange rate ischaracterised by large unexpected volatile deviations from their fundamentals’determined equilibrium. Flood (1981), Meese and Rogoff (1983a and 1983b) andalso Killian and Taylor (2003) observed that these large and unanticipated volatiledeviations make exchange rate very difficult to predict especially in the short run.Modelling exchange rate volatility enables us to forecast uncertainties (volatility)that are associated with forecasts from exchange rate models.Although exchange rate volatility is seen by many researchers to hold thepotential in affecting the economic welfare of any nation, yet  most of the studies onexchange rate volatility are focused mainly on developed and to an extent emergingmarkets with studies on Less Developed Countries (LDCs) almost non-existent.Exchange rate volatility is seen as a major influence on any LDC government’smonetary policies, especially when export growth, imports, debts obligations, aidand foreign direct investment are contributory factors to their economic growth. Inthis thesis, we analysed nominal exchange rate volatility in three LDCs namelyGhana, Mozambique and Tanzania. The reasons for using bilateral nominalexchange rates include; They are readily available (especially for LDCs) compared to otheralternatives (real exchange rates-RER, real effective exchange rates-REER, and nominal effective exchange rates-NEER). In flexibleexchange rate regime, nominal exchange rates and real exchange ratestend to move together in the short run. This means that there would be nosignificant difference in results if either is used in a trade model. Monthly (daily and even hourly) nominal exchange rates are easilyavailable for most countries compared to the other alternative exchangerate measures. High frequency exchange rates allow us to estimate muchmore optimal volatility forecasts (see Andersen et al, 1999). For governments, fluctuation in the nominal exchange rate volatilitywould in turn influence the behaviour of short-term capital flow andwhich would in a way have an effect on net foreign assets; the result is achange in the volume of currency in circulation which would requireprudent monetary policies to stabilize.Summaries of the six main chapters of our thesis are as follows:In Chapter One, we analysed the nature of exchange rate variability in theGhanaian cedi against US dollar (GHC/USD), the Mozambican metical against theUS dollar (MZM/USD) and the Tanzanian Shilling against the US dollar(TZS/USD). We observed that all three countries considered in our studies startedoperating an operational flexible exchange rate systems in the nineties after decadesof currencies overvaluation, political instability, fixed exchange rates regimes, longperiods of high inflationary rates and improper monetary and fiscal policies. We alsoobserved that exchange rate behaviour in the three LDCs under consideration isinfluenced by similar factors including external debts, foreign aid, import ofmanufactured and processed goods, and exports of primary commodities amongothers.Previous literature suggests that mastering the art of volatility modellinginvolves the utilizing of the time series properties of the data of interest as well as theempirical regularities of financial market volatility (see Engle and Patton 2001 aswell as Poon 2005). Many of these inherent common financial time series propertiesand empirical regularities were observed in studies in which data from highly tradedcurrencies or exchange rates from developed countries were used. In Chapter Two,we investigated empirically if GHC/USD, MZM/USD and TZS/USD exhibit theabove-mentioned properties. For each country, we used daily nominal bilateralexchange rate data from 01/01/2001 to 30/12/2005 and monthly data from 01/1995to 12/2005. We found that exchange rate series for the three LDCs underconsideration exhibit similar time series properties and empirical regularitiesobserved in previous studies in exchange rate markets of developed economies andhighly traded currencies. These findings therefore implies that volatility modellingand forecasting techniques that have been previously applied to  previous studies canalso be used on the Ghanaian, Mozambican and the Tanzanian nominal exchangerates. For instance, volatility models to be estimated should be able to cater for thenon-linear and non-normal characteristics observed in the nominal GHC/USD,MZM/USD, and TZS/USD exchange rates. Also, the observed volatility clustering inthe series under consideration implies that current volatility shocks (persistence)would influence the level of volatility many periods in the future. Asymmetrycharacteristics observed in our data suggests that volatility models that capturepositive and negative shocks differently can be applied to model the volatility in thethree exchange rates under consideration.A number of volatility measuring techniques such as EWMA (ExponentiallyWeighted Moving Averages) exist but none has arguably gained so manyapplications in research like the Engle’s ARCH (Autoregressive ConditionalHeteroscedastic) models and its other extensions. Engle’s (1982) ARCH modelrevolutionized the art of volatility modelling to such an extent that even after morethan two decades, the modelling technique remains very popular and shows no signof waning. Bera and Higgins (1993) suggested the following for the ARCH success. ARCH family of models are simple and easy to handle. They take care of clustered errors. They take care of non-linearities in financial data. They take care of the econometrician’s ability to forecast; that is thepower of forecast changes as new information becomes available.In Chapter Three, the econometrics of some ARCH family of models is discussed. Inparticular, we explained the different classes of popular members of the ARCHfamily of models, the properties of an ARCH process and estimation techniques thatare applied in ARCH modelling. Also, in this chapter, the evaluation criteria whichcan be applied in the comparison of the forecasting performances of competingvolatility models are explained.In Chapter Two, we observed that the nominal GHC/USD, MZM/USD andTZS/USD exchange rate series exhibit the non-linear and non-normal datacharacteristics, as well as the empirical regularities associated with financial markets.As already suggested and supported by Bera and Higgins (1993) the ARCHmodelling technique have been found in previous studies to accommodate theseproperties. In Chapter Four, we empirically applied the ARCH modelling to analysevolatility in the daily and monthly nominal GHC/USD, MZM/USD and TZS/USDexchange rates. For each country we used daily series from 01/01/2001 to30/12/2005 and monthly series from 01/1995 to 12/2005. These are the objectiveswe aimed to achieve in Chapter Four: Finding if we would need larger ARCH lags to estimate volatility in theGHC/USD, MZM/USD and TZS/USD nominal exchange rates (One ofthe main problem of ARCH models is that in practice, a large number oflags are required to model volatility in financial time series see Franke etal 2008, page 244 as well as Brooks 2008, page 452). Finding if the popular GARCH (1, 1) which have been observed in paststudies (see Hansen and Lunde 2002) to adequately model volatility inmost financial time series fits our three LDCs exchange rate series underconsideration. Empirically substantiating that the three LDCs exchange rate series underconsideration exhibit the inherent time series characteristics and theempirical regularities associated with exchange rate and other financialmarkets volatility. Comparing the forecasting abilities of our estimated ARCH family ofmodels for each exchange rate series to forecasts from volatilitiesgenerated using an alternative estimation technique.We observed that larger number of ARCH lags (ARCH (5) and ARCH (4)) wererequired to estimate daily nominal volatility in GHC/USD and TZS/USD exchangerates whereas an ARCH (2) appear to estimate  daily nominal volatility inMZM/USD exchange rate. The simple ARCH (1) model however successfullycaptured monthly nominal volatility in each of the three LDCs considered. Althoughwe were able to model daily nominal volatility in GHC/USD, MZM/USD, andTZS/USD with the GARCH (1, 1) model, yet the same model did not produce agood fit for the GHC/USD and MZM/USD monthly series. The GARCH (1, 1)however modelled monthly nominal volatility in TZS/USD exchange rate. Previousdaily volatility influence on future daily volatility in each of our three LDCsexchange rate series under consideration was observed to be asymmetric. In thecorresponding monthly exchange rate series under consideration the presence ofasymmetry depends on the series under consideration. We observed that theEGARCH (1, 1) was able to capture asymmetry in the conditional variance withinGhanaian and the Mozambican monthly exchange rate but the TARCH (1, 1) did notproduce a good fit for both series. The TARCH (1, 1) was able to model conditionalvolatility on the monthly Tanzanian exchange rate under consideration. EGARCH(1, 1) however failed to produce a good fit. The observation of different volatiledynamics in same series but having different frequencies is not unusual. Diebold(1988) explained that volatility clustering and other features in exchange rate seriesthat reflects market uncertainty tend to aggregate in low frequency data. Both dailyand monthly exchange rate series for all three countries however suggest thepresence of persistence.We compared goodness of fit of our estimated ARCH family volatilitymodels using the Mincer-Zarnowitz (1969) regression technique. Further, wecompute period ahead volatility forecasts for each series under consideration andcompare forecasting abilities using calculated Root Mean Square Errors (RMSE).We also compared our results to conditional volatility generated from an ExponentialWeighted Moving Averages (EWMA) method similar to the JP MorganRiskMetricsTM.  We observed that our estimated ARCH family of models did notalways produce the best fit and the best period ahead forecasts. For instance weobserved that the EWMA model produced the best fit for the daily GHC/USDexchange rates. We also observed that the EWMA was judged to forecast a monthahead conditional variance in MZM/USD and TZS/USD exchange rates.While flexible exchange rates facilitate stabilisation, exchange ratefluctuations can cause real volatility. This gives policy importance to the causalrelationship between exchange rate and its volatility. An exchange rate may beexpected to become more volatile when the underlying currency changes. Weconjecture that a reverse causation, which further influence the value of the currency,may be mitigated by astute macro-policies. In Chapter Five we used the VAR toolkitto analyse relationship between daily GHC/USD, MZM/USD and TZS/USD anddaily exchange rate uncertainty/volatility for the three LDCs under consideration.We generated exchange rate volatility for our period of study using asymmetricARCH models estimated in Chapter Four. The asymmetric ARCH models wereapplied because they handle good news (innovations/shocks) and bad news asdifferent inputs, and consequently, generate future conditional variance according tothe sign of shock they identify in a data.  We observe a bi-directional Granger causalrelationship between changes in exchange rates and its associated uncertainty for allthe three LDCs under consideration. Compared to Ghana and Mozambique, we findthat shocks to exchange rate uncertainty from innovations in the level of Tanzanianexchange rate take a shorter length of time to decay. We attribute this to the macro-policies undertaken by the Tanzanian policymakers which have helped to ensureprice and currency stability in our period of study. In the reverse Granger causalanalyses, we observed that the effect of uncertainty feedback to exchange rate isfleeting. We however conjectured from our results that whereas exchange rateintervention and other macro-policies might have contributed to quell marketuncertainty in the Tanzanian market, the Ghanaian and Mozambican exchange ratesmarkets however reacted with further uncertainty when stabilising policies areimplemented.In Chapter Six we analysed the relationship between trade and exchange ratevolatility for Ghana, Mozambique and Tanzania and some of their biggest tradingpartners. Our main objectives for this study are; To establish if exchange rate volatility negatively affects bilateral trade inLDCs as suggested by Arize et al (2000) and Saur and Bohara (2001) forour period of interest. To establish whether country data, estimation technique and volatilityproxies significantly affect our results (Baum and Caglayan 2006). Lastly, to ascertain whether the need to export primary goods and importmanufactured goods by LDCs overrides  other factors such as costassociated in trading ( measured in terms of distance ) as well as commonlanguage (which include cultural homogeneity and historical ties) that areknown to affect bilateral trade (De Grauwe 1988) .We focused our investigation in the post Bretton Woods era (specifically between1980 and 2005). We also included other variables that are assumed to affect bilateraltrade including incomes, populations, distances between trade partners and a dummyvariable component to represent same language between trade partners in ourempirical analyses. We generated three proxies of exchange rate volatility includingan ARCH family generated volatility series, an EWMA generated volatility seriesand exchange rate volatility estimated as annualised monthly variance over theperiod between 1980 and 2005 for each of the three LDCs. We applied a ‘standard’time series cross-sectional gravity trade model for our analyses. We also controlledfor heterogeneous trade relationship between each of the three LDCs and theirrespective trade partners by estimating fixed effects gravity model.Similar to Baum and Caglayan (2006) we observed that the effect ofvolatility, population sizes, distance between trading partners and same languagerespectively on trade depends on the estimation technique applied as well as thevolatility measure used. We however observed that Gross Domestic Product (GDP)as a measure of income positively and significantly influence bilateral tradeirrespective of the estimation technique or the volatility proxy used. Analyses offitness of models (based on R-Squares, AIC values and Durbin Watson statistics)suggest that the fixed effects gravity model estimate bilateral significantly better thanthe alternative ‘standard’ pooled gravity model for each of the three countriesconsidered. Using the estimated fixed effects gravity trade models for our analyses,we observed that GDP (irrespective of the volatility measurement used) is positivelyrelated to bilateral trade for Ghana, Mozambique and Tanzania and their respectivetrade partners considered in this study. We further observed that earnings from tradeincreases as population increases for Ghana and Tanzania and their bilateral tradepartners. In the case of Mozambique and her trade partners, we observed a negativeand significant relationship between bilateral trade and population size. We alsoobserved that for all three LDCs considered in our study, exchange rate volatility(irrespective of the measurement used), distance between trade partners and samelanguage do not appear to influence bilateral trade for the period between 1980 and2005

Exchange rate volatility in LDCs

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Exchange rate volatility in LDCs:some findings from the Ghanaian, Mozambican and Tanzanian markets

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