AFRICA S TRADE AND VOLATILITY IN EXCHANGE RATE: AN ECONOMETRIC CALCULATION

AFRICA S TRADE AND OLATILITY IN EXCHANGE RATE: AN ECONOMETRIC CALCULATION DAID UMORU 1 Edo Universiy Iyamho, Edo Sae, Nigeria david.umoru@edouniversiy.edu.ng MUSTAFA ISEDU Ambrose Alli Universiy, Ekpoma, Edo Sae, Nigeria Absrac The sudy esimaes effecs of volailiy in muual exchange rae of a selecion of African counries in relaion o US dollar on aggregae expors of Africa counries from March 1, 005 o March 30, 018 using GMM esimaor. This is indebed by he fears being upsreched as wheher he variaion in exchange rae of Africa currencies vis-à-vis US dollar in curren ime have simulaed expor growh. The resuls show ha curren and hisorical volailiy had negaive and significan effecs on aggregae expors of all seleced Africa counries in he sudy. Also, speculaion effec of exchange rae volailiy is negaive and significan for all counries excep Liberia. The sudy advices Africa moneary auhoriies o cerify oal adherence o implemenaion of exchange rae sabilizaion policies. Keywords: Africa counries, expor, exchange rae volailiy, E-GARCH model JEL classificaion: Q4, H30, M16 1. BACKGROUND TO THE STUDY Trading relaions are umos imperaive aspecs of naions and exchange rae is fundamenal facor ha influence rade and hence economic aciviies. This could be reason why volailiy in foreign exchange marke has always araced subsanial consideraion in economics. In many of Africa counries, exchange rae adminisraion has endured subsanial changes over four decades. This according 1 AERC, FMNES, Associae Professor of Economics, Deparmen of Economics, Faculy of Ars, Mg. & Social Sciences, Edo Universiy Iyamho, Edo Sae, Nigeria. Deparmen of Banking & Finance, Ambrose Alli Universiy, Ekpoma Edo Sae, Nigeria 18 OLUME 10 NUMBER JULY 018

o Mordi (006) is paricularly due o exisence of many parallel marke operaions exising side-by-side he officially acknowledged foreign exchange marke. To heory, i all depends on he role played by marke agens, exchange rae volailiy may adversely or posiively influence expors. Thus, considering wo exporing counries and assuming absence of forward marke for foreign exchange such ha he exporers canno lock a price, such exporers become vulnerable o risk a he poin of he conversion. A floaing rae is randomly volaile and i relies on changes in currency. So, a higher degree of currency change poins o greaer volailiy in exchange rae which is an indicaion of he exen o which he currency varies over he period. In effec, exchange rae's volailiy consiues he global rade speculaion hrea effec as i increases exchange rae risk. The purchasing counry has o go for he purchase of foreign currency like US dollar or currencies of hose counries where ransacions need o be done. Exchange rae volailiy is worrisome as i exposes imporers and exporers o exchange rae risk (Umoru, 018). Equally, umos changes in exchange rae do no reflec changes in incomes, prices and pivoal facors of comparaive. However, overall evidence is mos ofen adjudged as mixed given he sensiiviy o he choices of proxies for exchange rae volailiy, and counries considered. Hence, moivaion o advance research ino effec of exchange rae insabiliies on expor is imperaive for a developing resource-based economy like Nigeria. Moreover, euro-zone currency and sovereign deb crises, US dollar volailiy among ohers seems o provoke curren debaes. Hence, we esimaed precisely, he effecs of condiional volailiy in muual exchange rae of seleced African counries and he speculaion effec of exchange rae volailiy on aggregae expors of hese counries o he US. The sixeen African counries are Nigeria, Madagascar, Sudan, Cameroun, Tanzania, Zambia, Namibia, Uganda, Senegal, Sierra Leone, Gambia, Mauriania, Niger, Mozambique, Malawi and Swaziland. The nex presens some sylized facs and expor profile of seleced Africa counries. The hird secion reviews he heoreical and empirical exs. The fourh secion is composed of heoreical framework and mehodology. Resuls and hence empirical esimaes are analysed in he fifh secion. The sixh concludes he sudy wih policy references.. OERIEW OF SELECTED AFRICA COUNTRIES Many Africa counries have experienced periods of rade expansion, bu hese have generally no been susained. According o he World Bank (005), Africa s expor has flucuaed mosly as expors o Asian counries accouned for OLUME 10 NUMBER JULY 018 19

jus 10% of oal merchandise while expor in services has been so insignifican. This shows ha rade rends are changing quie rapidly for Africa and such performances have no me he exporaion policy of he Africa governmens. Also, Africa counries have been bedevilled wih slackening growh which raises fears over heir capabiliy o survive he weak global economy due o reversal of foreign invesors risk enhusiasm coupled wih loss of confidence which makes such invesors fly capial from he African markes o more sable markes. Hence, he deficiency makes hese counries o go for rade and he relevan research quesion becomes, how can Africa circumven he volailiy risk effec of exchange rae? This requires an economeric evaluaion of relevan daa across he seleced Africa counries wih a view o maximize Africa s gains from foreign rade o boos economic growh. The poor rade performance of Africa counries in world rade is he consequence of he fac ha Africa s GDP per-capia has grown slower as analysed in he previous secion compared o Lain America or Eas Asia and also oupu elasiciy of rade in hese regions of he world exceeds uniy, hence, as he regions grow, heir rade volumes expanded more han equiably. The cross-naional variaion in rade performance wihin Africa shown in Table 1 reveals ha Nigeria, Gambia, Namibia, Uganda, Swaziland and Mozambique recorded highes expor growh of 11.05, 18.37, 9.73, 7.76, 6.98 and 19.13 respecively in dollar value over he period, 009 o 016 while Madagascar, Mauriania, Malawi and Niger recorded negaive expor growh rae of 13.54, -.41, -4.48 and -7.56 respecively. Table 1. Growh of Toal Expors Counries 1964-94 1964-75 1975-85 1985-94 Madagascar 19.44 55.89 9.01-13.54 Sudan 10.3 18.06 8.19.91 Senegal 11.3 15.4 13.15 4.1 Nigeria 9.5 13.37 3.11 11.05 Mauriania 9.77 3.85 5.4 -.41 Gambia 8.13 13.9-7.44 18.37 Malawi 7.51 8.78 16.91-4.48 Zambia 8.79 1.90 8.04 4.61 Niger 4.80 10.36 9.83-7.56 Cameroun 5.87 7.0 7.66.48 Namibia 4.77 7.66 -.86 9.73 Uganda - - 7.10 7.76 Sierra Leone 4.09 5.80 4.93 1.04 Burkina-Faso - - 0.33 1.0 Mozambique 5.47 8.01 1.3 6.98 Swaziland 3.40.83-10.13 19.13 0 OLUME 10 NUMBER JULY 018

Source: UN COMTRADE Table shows he developmen of expors and expor diversificaion in Africa counries for 000 and 017. Real expors of goods and services grew by more han 0 percen in Chad, Zambia and Sudan and by 10-0 percen in Mozambique, Ehiopia, Uganda and Lesoho. The bigges exporer in 008 was Souh Africa, followed by Coe d Ivoire, Sudan, Chad, Kenya, Zambia and Boswana. 50% 45% 40% 35% 30% 5% 0% 15% 10% 5% 0% Manufacured Producs Energy Producs Figure. Srucure of Africa s Expors Source: IMF World Economic Oulook, 016 The srucure of expors in Africa counries as shown in Figure above is such ha primary producs dominaed wih 44% o he Europe, no manufacured nor energy producs. The deducion is ha Africa counries are jus developing and hence hardly manufacure somehing subsanial. Table. Expors and Expor Diversificaion, 000 conrased wih 017 Primary Producs (Excl. Energy) To European Union % 34% 44% To African 39% 31% 30% Counry Real Expors HHI: Marke 000 017 CAGR 000 017 CAGR Madagascar 1 586 7.8% 0.1 0.11-0.5% Cameroun 3,53 5,608 3.6% 0.1 0.16 3.9% Tanzania 1,410 3,511 7.3% 0.09 0.13.7% Gambia 739 905 1.6% 0.1 0.13.3% Sudan 946 1,61 4.% 0.18 0. 0.8% Malawi 378 3-3.7% 0.09 0.1 1.0% Mauriania 57 1,14 5.3% 0.15 0.19 1.6% Liberia,151 3,464 3.7% 0.19 0.1 0.6% Mozambique 37,805 16.8% 0.1 0.14.9% Namibia 1,554,887 4.9% 0.01 0.1 19.0% Senegal 1,77 1,797.7% 0.15 0.15 0.% Malawi 7,951 49,410 4.5% 0.06 0.09 3.3% Nigeria 481 5,087 19.9% 0.09 0.33 11.0% OLUME 10 NUMBER JULY 018 1

Swaziland 84,058 7.3% 0.1 0.06-8.7% Zambia 1,517,84 4.9% 0.06 0.07 0.9% Uganda 40 1,69 11.4% 0.09 0.14 3.4% Source: U.N. Comsa, HS 1988/9 and World Developmen Indicaor, NB: Expors a 000 prices. A lower HHI denoes more diversificaion. *Base year 1999. **Base year 1996, *** Base year 1997 As shown in Char 3, Souh Africa, Coe d Ivoire and Nigeria drives he growh in expors beween 009 and 016. The poin is ha here is a significan variaion wihin Wes African counries in erms of rade performance. Nigeria Souh Africa Togo Equaorial Guinea Cape erde Ghana Mali Burkina Faso Coe d'lvoire Mauriius Boswana Rwanda Figure 3. Share of Expor Growh by Counry, 009 016 Source: IMF World Economic Oulook, 016 The apprehension has been ha Africa s condiions in erms of poor infrasrucure, is layou, or is dependence on primary producs make i an excepional scenario in which expors are no responsive o prices as well as radiional insrumens of commercial policy..1. LITERATURE ON EXCHANGE RATE OLATILITY AND ITS EFFECTS So many sudies have projeced a posiive relaionship beween exchange rae volailiy and inernaional rade while ohers have anicipaed negaive relaionship since he 1990s wih many sudies yielding lile or no suppor for neiher a posiive nor a negaive effec. Obiora and Igue (006) and Aliyu (009) repored negaive effec of exchange rae volailiy on Nigeria s expors o he US. Similarly, Isiua and Neville (006) repored ha exchange rae volailiy had a negaive and significan effec on Nigeria s expor hough heir research concenraed only on oil expors. Yinusa and Akinlo (008), Omojimie and Akpokodje (010) and Akinlo and Adejumo (014) found posiive volailiy effec of exchange rae on expor in OLUME 10 NUMBER JULY 018

Nigeria. Opaluwa, Umeh and Ameh (010) found ha flucuaion in exchange rae adversely affeced oupu of he manufacuring secor in Nigeria. Sudies by Ibikunle and Akhanolu (011) and Akinlo and Adejumo (014) obained negaive and insignifican relaionship beween oal expor and exchange rae volailiy in Nigeria. Umoru and Oseme (013) empirically found cyclical rade effec of exchange rae shocks in Nigeria. There are sudies ha have been conduced for oher counries namely Pakisan, Souh Africa, Poland, Korea, Hungary, Bangladesh, Pakisan, India, Tunisia, Egyp, Israel, Morocco, Algeria, UK, EU, Turkey, Romania and numerous Asian counries. For example, an economeric sudy by Arinze, Malindreos and Kasibhala (003) of a sample of en LDCs (including Souh Africa) found ha exchange rae variaion exers a significan negaive effec on expor in mos of he counries sudied. However, Souh Africa was an imporan excepion where exchange rae volailiy impaced posiively on expors. De ia and Abbo (004) used he ARDL o remark ha UK s expor o he EU are unaffeced by exchange rae flucuaion. Feensra and Kendall (005) provide no evidence of a single insance in which variaion in exchange rae had a negaive and significan impac on rade. Gheong, Mehari, and Williams (005) esablished ha unexpeced variaion in exchange rae escalaed expor prices in UK and consequenly decreased rade volume. This is similar o resuls of Abolagba e al. (010). The sudy by Rey (006) indicaed a significan relaionship, negaive for four counries (Algeria, Egyp, Tunisia, and Turkey) and posiive for wo (Israel and Morocco). Humayon, Ramzan, and Ahmed (007) observed ha uncerainy in exchange rae caused decline in Pakisan s expor. Shah, Mehboob and Raza (010) found ha flucuaion of exchange rae causes reducion in Pakisan s rade. To Hasan (013), uncerainy of exchange rae highly affeced rade beween Pakisan and UK and beween US and UAE while Sandu and Ghiba (011) repored ha exchange rae volailiy discouraged expors in Romania. In he sudy by Yousaf and Sabi (015), when exchange rae appreciaed by one percen, expor declines by 0.1 percen... LITERATURE GAP The conroversy regarding he effec of volailiy on expor shows he need o furher embark on a similar research. While, some of he above menioned sudies focused only on oil expors, ohers examined he J-curve effec of exchange rae devaluaion (see Umoru and Oseme, 013). Also, some of he previous sudies esimaed he effec of exchange rae uncerainy in place of volailiy on expor. Wha makes he presen sudy unique is is focus on oal expors, devises he appropriae measure of volailiy in exchange rae and he speculaion effec of exchange rae volailiy. OLUME 10 NUMBER JULY 018 3

3. THEORETICAL FRAMEWORK AND MODEL SPECIFICATION Theoreical Framework: Building on Mundell-Fleming heoreical framework, he sudy builds he heory ha volailiy in exchange rae increases risk of rade and so depresses rade flows (Chowdhury, 1993). The underlying heory provides ha an overvalued currency exchanges a a low rae for foreign currency while an undervalued currency exchanges a a high rae for he foreign currency. This induces expecaions of devaluaion which lead o capial fligh. Given ha he Mundell-Fleming model describes an open economy by classically porraying relaionship beween nominal exchange rae and an economy s oupu of a counry such ha he IS curve equaion becomes: Y= C + I + G + NX (1) Where Y is GDP, C is consumpion, I is invesmen, G is governmen spending and NX is ne expors. The LM curve equaion becomes: M/P= L (i, Y) () Where M is money supply, P is average price, L is liquidiy and i is ineres rae. A higher ineres rae or a lower GDP lowers money demand. Also, BoP equaion is: BoP= CA + KA Where CA is curren accoun and KA is capial accoun. Bringing ogeher IS componens of we have: C= C[Y T, i E(π)] (4) Where T is ax, E(π) is he expeced rae of inflaion and i E(π) is Fisher s ideniy which capures he difference beween nominal ineres and expeced inflaion raes. Relaively, invesmen is also a funcion of real ineres rae and income as show in equaion (5). I= I [i E(π), Y] (5) Where Y -1 is GDP in previous day and governmen spending is an exogenous variable as given in equaion (6). G= G (6) NX= NX [e, Y, Y*] (7) Where e is real exchange rae of he local currency, Y* is GDP of USA. Higher GDP of SSA counries leads o more spending on impors and hence leads o lower expors, while higher GDP of America leads o higher expors by SSA counries. 4 OLUME 10 NUMBER JULY 018

The underlying heory provides ha an wih overvalued currency, he counry s expors is relaively expensive and impors cheaper while wih an undervalued currency, he counry s expors is relaively cheaper and impors expensive. In oher words, he overvalued currency exchanges a a high rae for foreign currency while he undervalued currency exchange a a low rae for foreign currency. Theoreically, expor of SSA counries provides valuaion of he currency of sub-sahara counries. The demand for he currency for purchase of goods influences exchange rae of he currency before he goods. Modelling Condiional olailiy of Exchange Rae: The Exponenial Generalized Auoregressive Condiional Heeroscedasiciy (E-GARCH) model was specified in his sudy. According o Hansen and Lunde (005), volailiy deermined hrough E-GARCH (1, 1) model is a weighed average of pas squared residuals as specified. p 0 j j j1 Ln( ) b b Ln( ) e q i 1 bi b i 1 i 1 e (8) Where is he condiional volailiy of real exchange rae, e i represens ARCH (1,1) componen which measures volailiy in exchange rae of he previous day while is he GARCH (1,1) componen which measures lagged forecas j variance, b are effecs of forecas variance, j bi are volailiy effecs. We included he speculaion componen such ha b becomes he parameer ha measures exchange rae speculaion effec and b0 is he nuisance parameer. We included he speculaion componen such ha b becomes he parameer ha measures exchange rae speculaion effec such ha b < 0, i indicaes ha good news (posiive shocks) as regards exchange rae speculaion generaes lesser volailiy while b > 0 indicaes ha bad news (negaive shocks) generaes enormous volailiy and wha causes foreign exchange speculaion is he exisence of oo many parallel markes popularly known as he black markes. All hese joinly cause he volaile behaviour of exchange rae. b0 is he mean (nuisance) parameer. Following Hassen and Lunde (005) and Zivo (009), E-GARCH model is covariance saionary only when bj 1. The corresponding variance and mean equaions are hus specified: OLUME 10 NUMBER JULY 018 5

Ln( ) b0 be 1 b1 Ln( 1) r, b 0, b 0, b 0 and z 0 1 Where z is sandardized residual reurns, p is number of lagged componens of and q is number of lagged e erms, r is daily exchange rae, is average reurns and is residuals. The consrains b 0, b 0 1 are needed o ensure is sricly posiive (Poon, 005). Condiional variance equaion becomes a specificaion of a funcion of consan erm, volailiy news from previous day measured as lag of squared residuals from mean equaion, (ARCH variable) and previous period forecas variance, (GARCH variable). In effec, equaion (9) shows ha condiional volailiy is explained by curren variance (ARCH coefficien) and pas variances (GARCH coefficien). Emanaing from he preceding, is our desire for adoping E-GARCH model as i sponaneously ess for ARCH effecs in he sequence of exchange raes (Shephard and Andersen, 009; Dahiru and Asemoa, 013). ariables, Daa Descripion and Sources Toal expor (e) of Africa counries, calculaed as he log of he percenage raio of he daily nominal expors of a Africa counry o he US divided by he counry s expor uni (expor value index of he Africa counry). Oher variables include, exchange rae volailiy ( ), inernaional financial crisis (f), inernaional income (y) (see Umoru, 018). Daa for he sample of seleced Africa counries were sourced from World Bank daabase and daa span from March 1, 005 o March 30, 018. Mehods of Daa Analysis The GMM esimaor was used in esimaion under he disribuional assumpions ha sandardized residuals obey generalized error disribuion was used o esimae he influence of exchange rae volailiy and he speculaion effecs of exchange rae volailiy on oal expors of SSA counries. JOURNAL OF ACADEMIC RESEARCH IN ECONOMICS GMM 1 N N ' Z X Z y 1 1 (10) The GMM esimaor uses he weighed marix in esimaion o rack he full descripion of he DGP and model specificaion saus. Also, i uses insrumens o idenify parameer esimaes based on a non-singular C marix while simulaneously minimizing he esimaion disance o zero. The Eviews economeric package was uilized. (9) 6 OLUME 10 NUMBER JULY 018

4. ECONOMETRICS RESULTS 4.1. RESULTS OF TIME SERIES PROPERTIES Table 3 presens he saionariy paerns of he series in he sudy based on Phillips Perron (PP) echnique. According o he resuls, none of he series could gain saionariy a level bu a firs difference wih ime rend. Thus, all he variables are inegraed of order one a he 5% level wih criical value of -3.896. Table 3. Saionary Tes of ariable (wih Time Trend) ariables Nigeria Madagascar Sudan Cameroun f -6.73-9.158-5.71-9.58 y -9.54-14.013-7.3-6.053-10.58-7.65-9.51-11.341 e -6.79-8.599-7.039-9.149 ariables Tanzania Zambia Namibia Uganda f -5.63-6.463-9.138-5.13 y -6.094-8.059-6.15-8.351-7.534-5.63-1.54-9.45 e -10.69-6.591-8.791-6.93 ariables Senegal Sierra Leone Gambia Malawi f -4.13-9.461-8.31-7.498 y -10.114-1.563-6.947-6.86-1.68-1.495-14.34-13.978 e -15.143-1.1-8.93-7.456 ariables Mozambique Niger Swaziland Mauriania f -9.143-14.189-5.349-16.71 y -4.98-7.15-9.153-10.89-8.31-6.130-7.34-6.71 e -6.75-9.54-10.355-17.546 OLUME 10 NUMBER JULY 018 7

Figures in parenheses are he lag order The co-inegraion resuls for each couny are repored in Table 4. The number of co-inegraion relaion(s) was deermined on he basis of maximum and race eigenvalue s. Overall, we found a leas one co-inegraion relaion for counries. Specifically, in he case of Nigeria, he resuls of race es esablished wo coinegraion relaions a 5% significan level since he compued values of 13.735 and 119.96 exceeds he 5% criical values of 10.465 and 106.37 respecively. Consisen wih he race resul is he maximum eigenvalue s of 98.39 and 78.346 which exceeds he criical values of 7.351and 65.17 respecively a he 5% level. For Zambia, he race es esablished wo co-inegraion relaions while he maximum eigenvalue repored one co-inegraion relaion a 5% significan level. For Namibia, race es s, 93.35 and 85.16 exceeds he 5% criical values of 90.165 and 80.37 respecively. Consisenly, he maximum eigenvalue s of 73.19 and 58.745 exceeds he criical values of 65.345 and 50.37 respecively a he 5% level. Hence, he Namibian economy has wo co-inegraion relaions. In he case of Tanzania, boh he race and maximum eigenvalues es provided evidence of one co-inegraion relaion a 5% significan level since he calculaed race and maximum eigenvalue s of 86.34 and 60.39 exceeds he criical values of 7.665 and 55.145 respecively. In he cases of Madagascar, Uganda, Mozambique and Malawi boh he race and maximum eigenvalues es esablished four co-inegraion relaions a 5% significan level. For Senegal, race es resuls esablished zero co-inegraion relaion while he maximum eigenvalue es repored one co-inegraion relaion as he calculaed value of 43.19 exceeds he 5% criical value of 35.645. In he Sierra Leone case, here is one co-inegraion relaion. In Gambia, race es s, 46.451 and 39.850 exceeds he 5% criical values of 37.115 and 6.37respecively. Correspondingly, maximum eigenvalue es s of 6.190 and 3.456 exceeds he 5% criical values of 1.645 and.73 respecively. These imply wo co-inegraion relaions for he Gambian economy. For Swaziland, he race es repored wo co-inegraion relaions while he maximum eigenvalue es repored same co-inegraion relaions. Oher counries wih wo co-inegraing vecors include Cameroun, Niger and Mauriania. 8 OLUME 10 NUMBER JULY 018

Table 4. Co-inegraion Resuls Nigeria Hypohesized 13.735 98.39 10.465 7.351 None* 119.96 78.346 106.37 65.17 A mos 1* 86.347 50.91 94.367 51.06 A mos * 67.56 3.50 7.41 49.87 A mos 3* Zambia Hypohesized 113.15 78.19 100.165 7.593 None* 10.136 67.56 9.461 69.35 A mos 1* 7.147 58.14 85.57 59.36 A mos * 56.56 39.530 65.43 40.389 A mos 3* Namibia Hypohesized 93.35 73.19 90.165 65.345 None* 85.16 58.745 80.37 50.37 A mos 1* 70.547 43.56 74.36 46.063 A mos * 5.56.130 60.45 9.85 A mos 3* Tanzania Hypohesized 86.34 60.39 7.665 55.145 None* 55.56 48.746 56.547 49.33 A mos 1* 43.14 33.56 44.365 36.63 A mos * 5.56 19.136 8.153 0.585 A mos 3* Madagascar Hypohesized 95.45 89.336 83.65 64.345 None* 89.59 7.14 66.54 51.37 A mos 1* 73.143 53.57 45.36 6.93 A mos * 55.51 6.975 1.13 18.584 A mos 3* Uganda Hypohesized 66.04 5.39 5.665 45.345 None* 45.16 38.741 46.347 39.75 A mos 1* 33.13 3.53 34.365 6.61 A mos * 15.056 10.139 15.753 13.95 A mos 3* Mozambique JOURNAL OF ACADEMIC RESEARCH IN ECONOMICS Hypohesized 57.934 46.79 3.495 0.385 None* 35.46 30.43 6.345 18.45 A mos 1* 3.15 16.55 14.367 1.63 A mos * 9.46 4.53 5.75 3.78 A mos 3* OLUME 10 NUMBER JULY 018 9

Cape erde Hypohesized 56.34 36.39 33.665 5.31 None* 43.356 8.746 6.547 17.33 A mos 1* 1.74 13.56 19.365 13.63 A mos * 9.56.136 10.153 5.585 A mos 3* Senegal Hypohesized 71.434 43.19 71.965 35.645 None* 53.856 30.756 66.947 4.33 A mos 1* 3.94 1.95 45.365 33.43 A mos * 10.51.135 9.153 7.51 A mos 3* Sierra Leone Hypohesized 53.451 43.19 45.115 5.645 None* 3.853.356 36.37.73 A mos 1* 15.94 11.94 5.165 13.55 A mos * 6.51 5.35 7.113 16.43 A mos 3* Gambia Hypohesized 46.451 6.190 37.115 1.645 None* 39.850 3.456 6.37.73 A mos 1* 0.94 11.4 5.165 13.13 A mos * 5.51 5.05 7.113 9.03 A mos 3* Swaziland Hypohesized 89.435 76.450 75.345 5.645 None* 79.8 65.336 66.31 49.73 A mos 1* 45.971 31.194 55.85 3.693 A mos * 33.49 3.300 47.473 5.9 A mos 3* Cameroun Hypohesized 11.56 53.19 65.965 45.645 None* 15.856 46.156 56.947 3.33 A mos 1* 3.94 11.345 4.165 4.43 A mos * 10.51 3.635 3.53.51 A mos 3* Niger JOURNAL OF ACADEMIC RESEARCH IN ECONOMICS Hypohesized 9.46 49.143 65.115 6.341 None* 6.851 3.456 46.37 3.63 A mos 1* 18.94 13.94.165 14.19 A mos * 7.51.35 5.38 8.45 A mos 3* Sudan Hypohesized 30 OLUME 10 NUMBER JULY 018

66.451 46.190 35.45 6.75 None* 49.850 3.456 7.37 3.41 A mos 1*.94 15.134 3.165 16.13 A mos * 4.51 8.36 5.83 3.15 A mos 3* Mauriania Hypohesized 76.135 56.560 63.145 5.645 None* 65.8 53.36 4.31 49.73 A mos 1* 1.371 11.194 5.685 3.194 A mos * 1.49 0.300 0.173 0.01 A mos 3* 4.. EMPIRICAL RESULTS In Table 5, effecs of condiional volailiy on exchange raes are repored. The GMM resuls show ha he coefficien of curren exchange rae volailiy for Nigeria is -0.015 wih -raio of -.539, for Zambia, i is -0.017 wih -raio of - 3.119, for Namibia, i is -0.45 wih -raio of -9.150, for Tanzania, i is -0.196 wih -raio of -6.345, for Gambia, i is -0.078 wih -raio of -5.41, for Swaziland, i is -0.096 wih -raio of -5.389, for Madagascar, he coefficien is -0.015 wih -raio of -.344, for Uganda i is -0.007 wih -raio of -11.991, for Mozambique, he coefficien is -0.03 wih -raio of -.755, for Malawi, i is -0.04 wih -raio of - 1.963, for Sudan, he coefficien is 0.006 wih -raio of -7 and for Mauriania, he coefficien is -0.177 wih -raio of -9.456. Similarly, he coefficien is -0.01 wih -raio of -.519 for Senegal, -0.017 wih -raio of -1.19, -0.015 wih -raio of -.344 for Cameroun, -0.17 wih -raio of -5.941 for Niger. By implicaion, bad news regarding exchange rae speculaion also impac negaively on oal expors of majoriy of Africa counries excep Zambia while boh hisorical and curren volailiy in exchange rae also had adverse effecs on expors of all counries. Similarly, he resul suggess negaive relaionship beween expors and global financial crisis in all he counries. This resul denoes ha global financial crisis led o decline expors from Africa o Unied Saes. However, his finding does no apply for Liberia where he resuls are insignifican. The resuls furher sugges negaive relaionship beween expors o he US and exchange rae volailiy. The ARCH & GARCH erms are significan a he 5% level. The significance of boh he ARCH and GARCH erms indicaes ha, lagged condiional variance and lagged squared sochasic disurbance have an impac on he condiional variance. Table 5. GMM Resuls of Toal Expor JOURNAL OF ACADEMIC RESEARCH IN ECONOMICS ariables Nigeria Zambia Namibia Tanzania Δf -0.003*** -0.14-1.003*** -1.01* (-1.99) (-1.489) (-1.789) (-3.480) OLUME 10 NUMBER JULY 018 31

Δy 0.51** (.380) Δ -0.015** (.539) 0.05* (6.33) -0.017 (-3.119) 0.04** (.050) -0.45* (-9.150) 0.04 (1.330) -0.196* (-6.345) Δ -0.01* 1 (17.49) -0.13 (-3.799) C 0.639* 1.03 (5.000) (1.354) ariance Esimaes ARCH (1,1) -0.05* -0.01*** (-5.465) (-1.971) GARCH (1,1) 1.379* 0.06** (9.758) (.015) -0.013* (-3.587) 0.60** (.970) -0.01* (-19.67) 1.071** (.350) -0.19* (4.568) 1.134*** (1.956) -0.03* (-3.67) 0.351*** (1.986) J-Saisic F- sa DW-Sa Adjus R 59.367 1.90 0.73 6.177.005 0.9 0.00 39.465.90 0.63 17.353 1.994 0.59 ariables Madagascar Uganda Mozambique Malawi Δf -*** -0.061** -0.167-0.05 Δy (-1.984) 0.0 (4.11) Δ -0.015** (-.344) (-.911) 0.009 (.50) -0.007* (-11.991) (-3.18) (1.998) -0.03** (-.755) (-.00) 0.007 (3.100) -0.04*** (-1.963) Δ -1.005* 1 (-1) -0.174* (-11.935) C 0.031 0.015*** (1.890) (1.995) ariance Esimaes ARCH (1,1) -0.3* -* (4.016) (3.00) GARCH (1,1) 0.71* 0.399** (6.33) (.730) J-Saisic F- sa DW-Sa Adjus R 1.55.00 0.961 106.47.955 0.75 ariables Senegal Sierra Leone Δf 1.003** -0.14 (.156) (-1.489) Δy 0.01*** 0.015* (1.980) (6.43) Δ -0.01** (.519) -0.017 (-1.19) -0.091** (-.455) 0.114 (1.333) -0.06*** (1.978) 0.316* (5.05) 56.73.09 0.875 Gambia -0.433** (-.189) 0.16* (4.60) -0.078* (-5.41) -0.014* (-3.0) 1.55* (5.660) -0.01* (7.695) 0.119** (.06) 99.43 1.999 0.65 Swaziland -0.118* (-5.71) 0.05* (3.997) -0.096 ** (-5.389) 3 OLUME 10 NUMBER JULY 018

Δ -1.01* 1 (6.493) -0.15* (-3.179) C 0.149* 1.03 (5.000) (1.054) ariance Esimaes ARCH (1,1) -0.07* -0.04*** (-5.145) (-1.951) GARCH (1,1) 1.379* 0.06** (9.758) (.015) -1.160** (-.167) 0.038* (9.450) -0.00* (-4.531) 1.571* (13.395) -0.03 (-1.000) 0.14* (3.975) -0.011*** (-1.839) 1.331* (4.650) J-Saisic F- sa DW-Sa Adjus R 19.367 1.78 0.93 6.157.005 0.978 94.168.00 0.56 45.39 1.18 0.94 ariables Cameroun Niger Sudan Mauriania Δf -0.011*** (-1.984) 0.01* Δy (9.571) Δ -0.015** (-.344) -0.081 (-.911) ** (.150) -0.17* (-5.941) -0.140 (-.081) 0.03 (.397) -0.006** (-7.000) -0.00* (-5.111) 0.36 (.491) -0.177** (-9.456) Δ -0.05* 1 (-3.479) -0.154* (-1.135) C 0.131 0.015*** (1.890) (1.965) ariance Esimaes ARCH (1,1) -0.14* -* (.301) (3.9) GARCH (1,1) 0.17* 0.139** (4.13) (.130) -** (-.345) 0.139* (3.798) -* (1.876) 1.179* (4.95) -0.013** (-.097) 0.00*** (1.879) -0.01* (4.003) 0.37*** (1.94) J-Saisic F- sa DW-Sa Adjus R 4.536.000 0.861 16.18.153 0.75 Insrumens: (-1),y(-1),y(-), f(-1), f(-) 9.6 1.891 0.77 ( ), e(-1), ( ) 88.300.16 0.65 l C p-value are in parenhesis below each coefficien esimae, *(**)(***) indicaes significance of coefficien @ 1% (5%) (10%) respecively The resuls of condiional volailiy based on E-GARCH (1, 1) model are repored in Table 6. The volailiy coefficien as measured by he E-GARCH variable passes he significance es implying ha volailiy in exchange rae is unpredicable in Africa counries. OLUME 10 NUMBER JULY 018 33

Table 6. Esimaes of Condiional olailiy based on E-GARCH (1, 1) Model ariables Nigeria Zambia Namibia Tanzania 0.360* () 0.04 0.034** 0.09 b 0 (1.538) (0.005) (1.8) b 1.00* 1.011*** 1.05** 1.010*** 1 (0.53) (0.04) (0.730) b 0.007* () b Log L AIC SC HQC B-P-G -0.785** () 1.035 -.834-1.360-1.064 1.678 0.065* () -0.039** (0.005) 7.356-1.35 1.164 1.085 1.984 0.04*** (0.367) -0.118*.310-1.033 1.156 1.447 1.657 0.05** (0.004) -0.1* 5.493-1.034.19 1.955 1.566 ariables Madagascar Uganda Mozambique Malawi b 0 JOURNAL OF ACADEMIC RESEARCH IN ECONOMICS 0.05* 0.011 (0.039) 0.007 (0.051) 0.01 (0.008) b 1.713* 1 () 1.016** (0.03) 1.05** (0.00) 1.037* b 0.13* 0.016 (0.584) -0.049* b -0.115* Log L 1.489 7.543 AIC SC 39.067 9.167 HQC 1.90.085 B-P-G 1.736 1.895 ariables Senegal Sierra Leone 0.007 0.134 b 0 (0.054) (0.009) * (0.007) -0.98* 9.451 9.167 1.90 1.655 Gambia 0.015** (0.014) 0.075*** (0.119) -0.0* 6.58 9.167 1.90 1.667 Swaziland 0.016** (0.019) b 0.016 1 (0.648) 1.130* 1.019*** (0.107) 1.035* b 0.017* 0.018* 0.019*** (0.013) 0.014* 34 OLUME 10 NUMBER JULY 018

b Log L AIC SC HQC B-P-G -0.145** (0.017) 1.15-0.055 0.16 1.54 1.189-0.159* 9.67 -.154 0.064 1.17 1.153-0.14** 1.061 9.067 1.0 1.38-0.169 *** (0.054) 6.49.157 1.93 1.144 ariables Cameroun Niger Sudan Mauriania 0.01* b 0 b 1.013* 1 JOURNAL OF ACADEMIC RESEARCH IN ECONOMICS 0.031 (0.039) 1.009** (0.03) 0.003 (0.051) 1.05* (0.00) (0.008) 1.049** (0.10) b 0.413* b Log L AIC SC HQC B-P-G 0.016 (0.54) * (0.007) -0.15** (0.004) -0.049* -0.98* 1.489 7.543 9.451 1.467.167 3.567 1.95.085 1.13 1.936 1.895 1.695 Figures in ( ) are p-values 0.05*** (0.019) -0.0* 6.58.167 1.95 1.667 In Table 7, Ljung-Box Q-es and Ljung-Box Q -es s of sandardized and squared sandardized residuals respecively shows auocorrelaion of residual sequence are ally insignifican a he 5% level for all lags. Table 7. Auocorrelaion of Sandardized and Squared Sandardized Residuals ariables Ljung-Box Q 0.5 Nigeria Zambia Namibia Tanzania Q 0.5 (6) 0.046 0.00 (0.735) (0.116) (0.100) (0.007) Q 0.5 (1).390 0.047 0.06 (0.735) (0.115) (0.006) (0.135) Q 0.5 (0) 0.008 0.016 0.06 (0.509) (0.005) (0.86) (0.135) ariables Ljung-Box Q 0.5 Madagascar Uganda Mozambique Malawi Q 0.5 (6) 0.071 0.06 0.06 (0.81) (0.135) (0.056) (0.095) Q 0.5 (1) 0.003 0.00 0.007 0.01 (0.540) (0.035) (0.006) (0.005) Q 0.5 (0) 0.00 0.046 0.014 OLUME 10 NUMBER JULY 018 35

(0.018) (0.09) (0.04) (0.03) ariables Ljung-Box Q 0.5 Senegal Sierra Leone Gambia Swaziland Q 0.5 (6) 0.015 0.09 0.01 (0.041) (0.176) (0.079) (0.005) Q 0.5 (1) 0.003 0.015 0.091 (0.068) (0.005) (0.03) (0.360) Q 0.5 (0) 0.00 0.06 (0.017) (0.857) (0.04) (0.113) ariables JOURNAL OF ACADEMIC RESEARCH IN ECONOMICS Ljung-Box Q 0.5 Cameroun Niger Sudan Mauriania Q 0.5 (6) 0.07 0.06 0.06 (0.14) (0.035) (0.046) (0.035) Q 0.5 (1) 0.003 0.004 0.007 (0.15) (0.035) (0.153) (0.467) Q 0.5 (0) 0.00 0.06 0.06 (0.173) (0.197) (0.089) (0.06) Diagnosically, here is absence of auocorrelaion in sandardized residuals of mean equaion. Therefore, esimaes are consisen and reliable. However, ARCH effecs are presen in he variance equaion 5. CONCLUSION Empirically, volailiy in exchange rae negaively and significanly affecs expors in all seleced Africa counries in he sudy. In fac, esimaed GMM resuls show ha curren and hisorical volailiy have negaive and significan effecs on oal expors of all he Africa counries in he sudy. Also, he speculaion effec of exchange rae volailiy is negaive and significan for all he Africa counries excep Zambia. The effecs of America s GDP, imporing counry is posiive for all counries. This conforms o heoreical expecaion ha expors and income are posiively relaed. This could be explained by he fac ha volailiy in exchange rae reduces aciviies of invesors by srenghening uncerainy over reurns of a given invesmen. Supplemenary producion coss are procreaed which firms pass on o consumers hrough price escalaion. The join effec is reducion in oal expors. Moneary auhoriies in Africa should cerify overall adherence o execuion of exchange rae sabilizaion policies. 6. ACKNOWLEDGEMENTS Firs auhor graefully acknowledge he financial suppor from Edo Universiy, Iyamho (EUI) and also appreciae all he consrucive commens of he anonymous referees as well as he conribuions of all he discussans a he 018 36 OLUME 10 NUMBER JULY 018

Academy of Economics and Finance (AEF) Conference in Houson, Texas, US. Remaining errors are our responsibiliy. REFERENCES Abolagba, E., Onyekwere, N., Agbonkpolor, B., & Umar, H. (010). Deerminans of agriculural expors. Kamala-Raji Journal of Human Economics, 9, 181-184. Akinlo, A., & Adejumo,. (014). Exchange rae volailiy and non-oil expors in Nigeria. Inernaional Business and Managemen, 9(), 70-79. Arinze, A., Malindreos J., & Kasibhala, K. (003). Does exchange rae volailiy depress? expor flows? The case of LDCs. Inernaional Advances in Economic Research, 9(1), 7-19. Chowdhury, A. R. (1993). Does an exchange rae flucuaion depress rade flows? Evidence from error-correcion models. Review of Economics and Saisics, 75, 700-706. Dahiru A. B. & J. O. Asemoa (013). Exchange Raes olailiy in Nigeria: Applicaion of GARCH Models wih Exogenous Break. CBN Journal of Applied Saisics 4 (1), June. Feensra, C., & Kendall, D. (005).Exchange rae volailiy and inernaional prices. NBER Working Series No. 3644. Humayon, A., Ramzan, S., & Ahmed, F. (007). Is he uncerainy in exchange rae his he expors in Pakisan? The Journal of Commerce, 6, 30-38. Ibikunle, J., & Akhanolu, A. (011). An empirical invesigaion of he link beween exchange rae volailiy and rade in Nigeria. Journal of Emerging Trends in Economics and Managemen Sciences,, 175-183. Isiua, K., & Neville, N. (006). Assessmen of exchange rae volailiy on macroeconomic performance in Nigeria. A paper presened a he 11h Annual Conference of he African Economeric sociey, Dakar, Senegal. Mordi, C.N.O. (006). Challenges of Exchange rae volailiy in Economic Managemen in Nigeria, CBN Bullion, 30, 17 5. Obiora, I., & Igue, N. (006). Exchange rae volailiy and U.S-Nigeria rade flows: An economeric appraisal of he evidence. Paper Presened a he 11h Annual Conference of he African Economeric Sociey. Dakar, Senegal. Omojimie, B.& Akpokodje, G. (010). The impac of exchange rae reforms on rade performances in Nigeria. Journal of social sciences, 3(1), 53-6. Opaluwa, D., Umeh, C., & Ameh, A. (010). The effec of exchange rae flucuaions on he Nigerian manufacuring secor. African Journal of Business Managemen, 4(14), 994-998. Rey, S. (006). Effecive exchange rae volailiy and MENA counries expors o he EU.Journal of Economic Developmen 3(31), -1. OLUME 10 NUMBER JULY 018 37

Shah, A., Mehboob, I., & Raza, S. (010). The exchange rae flucuaions on Pakisan expor secors: An empirical analysis based on he secorial daa. Asian Economic and Financial Review, (6), 658-677. Shephard, N. & Andersen, T.G. (009). Sochasic volailiy: origins and overview In Andersen, T.G., Davis, R.A., Kreiss, J.-P. and Mikosch, T.(ed):Handbook of Financial Time Series, 33 53. Todani, K., & Munyama, T. (005). Exchange rae volailiy and expors in Souh Africa. IMF Working Paper, 3-15. Umoru, D., & Oseme, A. (013). Trade flows and exchange rae shocks in Nigeria: An empirical resul. Asian Economic and Financial Review, 3(7), 948-977. Wilson, C., & Choga, I. (015). Exchange rae volailiy and expor performance in Souh Africa. Handbook on Economics, Finance and Managemen Oulooks, 3, 7-8. Yinusa, D., & Akinlo, A. (008). Exchange rae volailiy and he exen of currency subsiuion in Nigeria. Indian Economic Review, 3(), 161-181. Yousaf, M., & Sabi, A. (015). The effecs of exchange rae volailiy on ASEAN- China bilaeral rade. Journal of Economics, Business and Managemen, 3(5), 479-48. 38 OLUME 10 NUMBER JULY 018