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An Analysis of Exchange Rate Volatility and Trade in Mauritius *Polodoo. V. Mphil/Phd Candidate Faculty of Law & Management, University of Mauritius Reduit Email: [email protected] Seetanah.B, Faculty of Law & Management, University of Mauritius Reduit Email: [email protected] Sanassee. R.V Faculty of Law & Management, University of Mauritius Reduit Email: [email protected] Brooks. C ICMA Centre, University of Reading, U.K Email: [email protected] ABSTRACT This paper gives an analysis of the impact of exchange rate volatility on manufacturing trade flows in Mauritius. The econometric analysis employs yearly data for the period spanning 1980-2011 and two measures of exchange rate volatility, viz, the Z-score and the EGARCH are used. When using the Z-score, it is found that exchange volatility does not affect real manufacturing exports. However, exchange rate volatility is found to be significantly affecting real manufacturing imports. Also, the Vector Autoregression estimates suggest that exchange rate volatility has not been a determining factor influencing real manufacturing imports. Keywords: Manufacturing trade, EGARCH, Z-Score, VECM, VAR, Causality. *For correspondences and reprints 1 1.0. Introduction Subsequent to the floating of the US dollar in 1973, liberalization of capital flows and the associated exponential growth of cross-border financial transactions during the last three decades, important volatility and uncertainty has been seen in exchange rates (Arize, 1998). Academicians, policy makers, researchers and economists have always raised eyebrows with regards to the potential impact of exchange rate volatility on trade. The recent currency crisis emanating from the Europe and the ensuing economic instability as well as economic crises in the past in various countries due to exchange rate volatilities such as that of the Asian crisis, the peso crisis among others substantiate that tribulations in the foreign exchange market have some sort of domino effect and can spread their tentacles to the entire economy and fuel large-scale crises. Therefore, from a macroeconomic viewpoint, a stable and sound exchange rate helps to resist financial turmoil and helps to bring stability and preserve employment in an economy. Taking into account the recent volatility in world known currencies and given that Small Island Developing States like Mauritius rely heavily on manufacturing trade for its survival, this paper analyses the impact of exchange rate volatility on manufacturing trade for Mauritius. An analysis of exchange rate movements on trade to Mauritius is deemed pertinent as the latter has recently experienced negative shocks in its terms of trade owing to the phasing out of the MultiFiber Agreement in 2004, cuts in sugar price under the Lome Convention by the European Union in 2006 and skyrocketing world oil and commodity prices. The phasing out of the Africa Growth and Opportunity Act also had some severe repercussions, which means that Mauritius has been gradually losing its comparative advantage in textiles from low cost producing economies of the like of the Vietnam and Bangladesh. Furthermore, the manufacturing sector has been hardly hit with the closure of several firms, more specifically, in the textile sector. Moreover, the Mauritian trade has always been vulnerable to real exchange rate variability, notably during the last few years on account of a depreciating euro but exchange rate risk hedging facilities are virtually nonexistent. Although hedging facilities are available, they are considered as expensive for small exporting firms. As a result, exporters bear the consequences of unexpected changes in the exchange rates. Even though the effect of exchange rate movements has pertinent policy implications, attempts to analyse these issues in developing countries and more specifically Small Island Developing States like Mauritius are scarce. Moreover, the study employs a Vector Autoregression framework to take 2 into account the dynamism and endogeneity aspect of the relationship between trade and exchange rate volatility. It is noteworthy that most studies pertaining to exchange rate volatility and trade employ are inconclusive on account of the sensitiveness to the choices of sample period, econometric modelling, problems regarding proxies for exchange rate volatility as well as countries in panel data sets. This study is conducted using time series econometrics so as to provide a fresh insight as regards the impact of exchange rate volatility on manufacturing trade flows. The paper is organised as follows: Section 2 reviews existing literature on trade and exchange rate volatility; Section 3 gives the situational analysis regarding trade and exchange rates in Mauritius; Section 4 specifies the model and provides the data source; section 5 explains the methodology employed; section 6 analyses the results from econometric estimation and finally section 7 concludes the paper. 2.0. Review of Literature Since the floating of the US dollar in 1973, several theories have been developed to explain the channel through which exchange rate volatility affects trade. The theories propounded have been broadly classified into two channels. The first one concerns channels through which only exchange rate influence trade- the partial equilibrium theory- to put it in the words of Côté (1994). Here, the impact of exchange rate volatility on trade is explained and dependent on the following: the risk profile of exporters, the presence/absence of hedging, the market structure, costs and pertinence of hysteresis in trade, alterations of production volume and export destination, assuming that the only variable that influence trade is the exchange rate. A second channel, more technically known as the general equilibrium theory Côté (1994) explains the channel through which exchange rate volatility impacts on trade by combining all the probable factors that might influence trade. All the channels through which exchange rate volatility influences trade in goods are discussed below. Partial equilibrium theories ignore the availability of ‘nonlinear hedges (options and portfolio of options) or takes the prices of hedge instruments (or some of the determinants of these prices) as given’, Sercu and Uppal (1995, pg 5). In the same vein, Clark et al (2004) criticise the partial theories and aver that most of these theories assume that revenues are convex and assumes that demand and cash flow functions as given. The partial models, according to them, ignore the dynamism pertaining to the link between exchange rate volatility and trade and ignore the fact that changes in the macro economy may affect exchange rate and hence influence demand or cash flow function. 3 Because of the limitations of the partial equilibrium theories, neo classical trade models employ general equilibrium approach to explain the channel through which exchange rate volatility affects trade. Yet, according to Sercu and Uppal (1995), the main problem with the neo classical models is the assumption that Commodity Price Parity (CPP) holds for all goods and at all times. General equilibrium models, however, allow for deviations from CPP and changes in real exchange rates. General equilibrium models demonstrate that factors that influence exchange rates may also result in changes in other macroeconomic variables, substantiating why partial equilibrium models are flawed in some respects, ignoring the effect of changes in other macroeconomic variables in explaining the link between exchange rate volatility and trade. According to Chit et al (2008), general equilibrium models are founded on the ‘New Open Economy Macroeconomics’ that combines the impact of the market structure or trade, inflexibilities and intertemporal decision making. It is postulated by Sercy and Uppal (1995), Clark et al (2004), Chit et al (2008) among others that the general equilibrium framework endogenises exchange rate volatility such that a better insight as regards the link between exchange rate volatility and trade can be gained as it includes the impact on other macroeconomic variables to explain the link between exchange rate volatility and trade. It is a question of what causes exchange rate to be volatile that explains the link between exchange rate and trade. For example, a fall in shipping costs might result in a fall in exchange rate volatility, a situation which might improve trade volume. Similarly, higher shipping costs might result in an increase in exchange rate volatility, a situation which might depress trade volume. Similarly, for an economy with imperfectly tradable good, shocks to risks associated with output results in increased exchange rate volatility which increases the projected trade volume as economic agents respond by trading more given the uncertainty. Also, if the degree of segmentation between the commodity markets (traded internationally) falls, expected trade increases leading to a fall in exchange rate volatility. So far, we have looked at the impact of exchange rate volatility on trade. Most of the theories discussed so far take exchange rate volatility as an exogenous variable and disregards the impact of trade on exchange rate volatility. However, the general equilibrium models make it possible to endogenise exchange rate volatility and consider the impact of trade on exchange rate volatility. Rodriguez (1980) explains in a general equilibrium portfolio balance of rational expectations, the part that trade flows play in determining exchange rates. The author postulates that the current and the future time path of the exchange rate is influenced essentially by trade flows. Rodriguez (1980) further postulates that at any point in time, the difference between spot exchange rate and its long 4 run intrinsic value is comparative to the present value of the normalised trade balance. The conclusion is centred on the absorption approach to trade balance but the causality runs from trade balance to exchange rates. The foregoing theories explain the impact of trade balance /volume on exchange rate. However, Ortega and di Giovanni (2005) demonstrate through a simple Ricardian trade model that exchange rate volatility increases with trade costs such that there exists a negative link between exchange rate volatility and trade volume. Review of empirical evidences Studies regarding the impact of exchange rate volatility on manufacturing trade are provided by Campa and Goldberg (1993), Samson et al (2003), Aguirre et al (2007), Hayakawa and Fukunari (2008) among others. For instance, Campa and Goldberg (1993) examined the impact of exchange rate volatility on US manufacturing sector trade using annual sectoral data for the period 1972 to 1986. The authors employ real exchange rate and two measures of exchange rate volatility in their study; the coefficient of variation and the standard deviation of the first differences of the logarithm of exchange rate over the twelve previous quarters. The authors employ a panel investment equation and apply in the first instance Two Stage Least Squares to take into account endogeneity problems. The equation is also regressed with fixed effects and for robustness purposes, GMM is applied. The empirical findings reveal that for the period 1972 to 1983, exchange rate volatility led to a fall in investment in the manufacturing sector and hence trade. However, for the period 1984 to 1986, there was an increase in investment in the manufacturing sector and hence trade. Sekkat and Varoudakis (2002) investigated the impact of exchange rate on manufactured exports in North Africa using an export demand function having as independent variables; real exchange rate, volatility of exchange rate as measured by ARCH, trade liberalisation and other determinants of exports. The authors find a negative significant effect of exchange rate volatility on manufactured exports in North Africa. However, empirical results using export demand functions are mixed. Further, Samson et al (2003) examined the impact of exchange rate volatility on manufacturing trade for South Africa using annual data for the period 1990-2002. The authors employ both nominal and real exchange rate, the standard deviation of changes in exchange rates both for one and three years and a structural demand and supply model in their study. To account for 5 endogeneity, the authors employ instrumental variables approach and find no significant effect of exchange rate volatility on the manufacturing trade for South Africa. Moreover, Aguirre et al (2007) examined the impact of exchange rate volatility on manufactured exports in Brazil using quarterly data for the period 1986 to 2002. Two general measures of volatility are employed; the standard deviation of real effective exchange rate and the GARCH model. The empirical findings reveal that when the standard deviation is used, there is a significant dampening effect on Brazilian manufactured exports. However, when the GARCH model is employed, no statistically significant effect is found. Besides, Hayakawa and Fukunari (2008) examined the impact of exchange rate volatility on international trade in East Asia using bilateral trade data for 60 countries for a period spanning from 1992 to 2005. They employ a gravity function having real GDP, geographical distance, language, adjacency, real exchange rates, colony and tariffs as the main independent variables and standard deviation of the first difference of the monthly natural logarithm of bilateral real exchange rate for five years. The empirical findings reveal the following. In the first place, exchange rate volatility depresses trade within Asia much more acutely than in other areas. In addition, the larger impact is on intermediate goods which are deemed to take a significant portion of trade in East Asia in relation to other types of goods. Moreover, the impact of volatility is much more pronounced than tariffs but less pronounced than distance related costs in East Asia. 3.0. The Link between exchange rates and trade. The link between manufacturing exports as well as manufacturing imports and the main countries’ currencies are depicted in the diagrams below. From the diagram, ME is manufacturing exports, MI is manufacturing imports, EURO is the euro currency, GBP is pound sterling, USD is United States dollar, RENMINBI yuan, Chinese currency and INR is Indian rupee. As explained earlier, prior to 2005, the euro, GBP and USD had a general appreciating trend vis-à-vis the MUR and after 2005, they all depreciated slightly. Increased manufacturing exports led to appreciating euro, GBP and USD prior to the year 2005 although Mauritian main export markets remained to be Europe over the period considered. However, despite the fact that manufacturing exports kept on increasing after a fall in the 2000-2005 period, the euro, GBP and USD depreciated vis-à-vis the MUR. The movement of the currencies is mostly explained by factors other than trade. The renminbi and INR 6 as mentioned earlier, appreciated vis-à-vis the MUR after 1995 despite mounting manufacturing exports. Figure 3.1. Relationship between manufacturing exports (ME), imports (MI) and exchange rates (MUR. Million) 90,000 80,000 70,000 60,000 50,000 40,000 30,000 20,000 10,000 0 1970 1975 1980 1985 1990 ME 1995 2000 2005 2010 2005 2010 MI 70 60 50 40 30 20 10 0 1970 1975 1980 1985 EURO RENMINBI 1990 1995 2000 GBP INR USD Source:Drawn from Eviews 7 As far as manufacturing imports are concerned, it continuously grew over the period considered. It is pertinent to note that increased manufacturing imports which accelerated as from the mid-1990’s led to appreciating INR and renminbi as Mauritius started to diversify its import sources and ultimately China and India became Mauritius main trading partners. 7 As regards the exchange rate of the rupee, starting from a parity of USD1=MUR.5.29 in 1968, it reached USD1=MUR.31.01 as of end of 2012. The USD continued to appreciate in the 1980’s and was being traded at an average rate of MUR.14.83/USD. The depreciation of the rupee in that period is explained mostly by growing current account deficits as the Mauritian economy started to industrialise and adopted and export led growth strategy which meant that a lot of machineries and equipment need to be imported. However, consequent upon the liberalization of the capital account in July 1994, the appreciation of the USD accelerated. From a parity of MUR.18.44 in 1995, it reached MUR.30.45 in the year 2011, chiefly on account of record growth rates experienced by the USA leading to an overheated US economy, which mandated raising interest rates. In spite of the subprime crisis from the US starting in 2007, the rupee depreciated up to the year 2011on account of skyrocketing world oil prices, imported food price inflation and revitalized buybacks to relieve Greece’s troubles. Generally, over the period 1968 to 2011, the MUR depreciated against the GBP. From an average rate of MUR.12.703/GBP in 1968, the GBP was being traded at an average rate of MUR.45.33/GBP as of end of year 2011 The GBP continued to appreciate significantly in the early 2000’s to reach an average all time high of MUR.66.95/GBP in the year 2006. A reversal of the trend was noted after 2006, where the GBP started to depreciate against the MUR. The MUR was being traded at an average rate of MUR45.33/GBP by the end of 2011. A fall in Mauritian imports from the UK also contributed to the depreciation of the GBP vis-à-vis the MUR. Europe is also a major trading partner for Mauritius. The euro was introduced in the year 2000. Due to unavailability of data for euro prior to the year 2000 and given that the Mauritian economy is deemed to be euro-centric as regards its export markets, the ECU is used as a proxy for the euro prior to the year 2000. An analysis of the currency vis-à-vis the MUR for the period 1968 to 2011 reveals that the euro appreciated remarkably from a parity of 5.56 MUR/EUR in 1968 to reach a high of MUR.43.27/EUR in 2007, after which it depreciated on an average basis. In the early 2000s, the appreciating trend of the euro vis-à-vis may be explained by international factors, viz, problems regarding the Corporate Accounting scandal in the USA, cuts in rate of interest in USA as the US economy got overheated. In the late 2000’s, however, the rupee gained a lot vis-à-vis the euro on the back of a slowdown of the European economy caused principally by the subprime crisis emanating from the USA. Also, the sovereign debt crisis also weakened the euro against the major world currencies. However, the euro started to appreciate slightly in the year 2012 as the Bank of 8 Mauritius started to intervene frequently in the foreign exchange market to prevent the euro from further depreciation and to stabilize exporters’ revenue Generally, the INR has slightly depreciated against the MUR since independence from a par value of MUR.0.74/INR in 1968 to MUR.0.553/INR in the year 2011. After 1995, the INR reversed its trend and started to appreciate as the Indian government gave its automatic approval of foreign direct investment in many sectors. The appreciation of the INR in the late 2000s may also be explained by increased Mauritian imports from India as India became the leading import partner for the island. China has emerged as the second leading import source for Mauritius after India over the recent years. Generally, the renminbi appreciated against the MUR from 1968 to 2011. In the 2000s, given the rise of China as an economic superpower and that it became the second largest source of Mauritian imports, the renminbi appreciated significantly against the MUR. It was trading at an average exchange rate of MUR.3.41/RMB in the year 2000 and appreciated to MUR.4.58/RMB in the year 2011. 4.0. Model Specification. In order to analyse the impact of exchange rate on trade in manufacturing goods, it is pertinent to note that there are several factors apart from exchange rate volatility which influence imports and exports and that these factors are accounted for as per existing economic theory. As elucidated in the empirical literature, various types of trade models have been employed in the literature, viz, export equations, import equations, import and export equations and trade balance equations. Given that the gist of the study is to investigate the impact of exchange rate volatility on trade, the import and export equations will be employed. The latter have been widely employed by Siregar and Rajan (2002), Marquez and Schindler (2006), Garcia-Herrero and Koivu (2007), Lee and Kim (2010), Huchet-Bourdon and Korinek (2012) among others. However, so far, no studies have been conducted for Mauritius using this model, albeit recent studies in Africa include Rey (2006), Kiptui (2007), Sekantsi (2010), Olayungbo et al (2011) among others. 4.1. The Import and Export Equation Taking all the different factors that influence exchange rate volatility and as employed by Siregar and Rajan (2002), Marquez and Schindler (2006), Garcia-Herrero and Koivu (2007), Lee and Kim 9 (2010) Huchet-Bourdon and Korinek (2012) among others, the export and import equations may be modelled as follows: X t 11 21Yt foreign 31EERt 41VOLt 51 X t 1 1t (4.1.1) M t 12 22Yt local 32 EERt 42VOLt 52 M t 1 2t (4.1.2) From equations 4.1.1 and 4.12, Xt is the natural logarithm of volume of manufacturing exports Mt is the natural logarithm of the volume of manufacturing imports Yt foreign Yt local is the natural logarithm of foreign real income(GDP) to world GDP; is the natural logarithm of real GDP for the local economy; EERt is the effective exchange rate and VOLt is real exchange rate volatility. 21 and 22 are Income elasticities of exports and imports respectively 31 and 32 are the Price elasticities of exports and imports respectively Logarithmic transformations to the above functions are deemed pertinent as according to HuchetBourdon and Korinek (2012), such transformation enables both exports and imports to respond proportionately to changes in the factors mentioned. It is also mentioned by Siregar and Rajan (2002) that logarithmic transformation of the export and import functions helps to prevent the issue of radical decreases in elasticities. The authors postulate that for a small open economy, it is important to include lagged terms for the dependent variables as independent variables in order to account for partial adjustment behaviour. Hence lagged exports and imports are included in logarithmic forms in the equations. The equations 4.1.1 and 4.1.2 will be used for estimating the impact of exchange rate volatility on Manufacturing trade for Mauritius. 10 4.1.2. Definition of the Variables (a) Exports and Imports As far as exports and imports are concerned, volumes rather than values are used. In other words, real exports and imports figures are employed. Siregar and Rajan (2002) stresses the importance of using real exports as against values as regards trade figures. They reported that value comparisons are sometimes biased due to price factors. Similar statements were made by Learner and Stern (1970). . Following Yao and Zhang (2003), Clark et al (2004) Klaassen (2004) and Chit (2008) the real export for Mauritius is measured as: VEX ti X t USDEF i (4.1.3) i Where X t is the volume of exports / real exports for Mauritius to country i. VEX ti is the value of exports to country i converted in US dollar and USDEF is the US GDP deflator. Similarly, real import for Mauritius is calculated as VIM ti M t USDEF i (4.1.4) i Where M t is the volume of imports / real imports for Mauritius from country i. VIM ti is the value of imports from country i converted in US dollar and USDEF is the US GDP deflator. The data for value of exports is obtained from the Central Statistical Office, various issues of the annual reports Bank of Mauritius (BoM) and the World Bank database for US GDP deflator. Exchange rate data is obtained from the various issues of the annual reports of the BoM, www.oanda.com and www.fxtop.com. 11 (b) Income Income as a variable has been used widely by almost all authors in the literature. Income is deemed to be positively related to both real exports and real imports. Foreign Income is measured by foreign GDP and domestic income is measured by domestic GDP. A higher level of income from a trading partner means that the demand for domestic produced goods increases. In other words, a rise in the GDP of Mauritian trading partner tends to increase Mauritian real exports. On the other hand, higher domestic income means higher standard of living and for small island economies like Mauritius, having high propensity to import, real imports would rise. In other words, an increase in domestic GDP is positively related to Mauritian real imports. The data for GDP for both Mauritius and the trading partners is taken in US dollar from World Bank database. (c) Effective Exchange Rate. An effective exchange rate, sometimes referred to as trade weighted exchange rate, pertains to an index of a country’s currency vis-à-vis a basket of other currencies, usually of its trading partners. The currencies in the basket are weighted according to the proportion of trade with that country. In the literature, there are two types of effective exchange rates used: Nominal Effective Exchange Rate (NEER) and Real Effective Exchange Rate (REER). Whilst some authors employ either NEER (for example Dell ‘Ariccia (1998), De Grauwe and Schnabl (2004), Kandilov (2007) and Schnabl (2007) among others) or REER (for example Arize et al (2000), Marquez and Schindler (2006), Sheldon et al (2011) among others), others like Samson et al (2003) employed both the measures to analyse the impact of exchange rate volatility on trade flows. The NEER is the weighted mean of the nominal exchange rate of the domestic currency, without reflecting changes in the domestic prices vis-à-vis the domestic country’s main trading partners. The NEER has, however, been criticised on various fronts. For instance, it does not reflect changes in the purchasing power of a country’s currency nor does it show changes in a country’s trade competitiveness. As such, to take into account changes in competitiveness and purchasing power, the REER is mostly used in the literature. The REER thus accounts for changes in purchasing 12 power and competitiveness. In other words, the REER refers to weighted average of a country’s real exchange rate vis-à-vis its main trading partners’ currencies and is calculated as follows: ei t * Pi t REER Wit P 1 dt n (4.1.5) From equation 4.1.5, ei t is the bilateral exchange rates expressed in nominal terms Pi t is the trading partner i’s index of retail prices Pdt is the domestic economy’s index of retail prices and n Wit is the proportion of trade of the domestic economy with country i. Note that W =1. i 1 Wit is calculated as follows: Xi Mi Wi t n n X1 M i 1 1 (4.1.6) From equation 4.1.6, M i is the import of Mauritius for country i X i is the export of Mauritius to country i n M i is total imports from all trading partners 1 n X 1 is total exports to all trading partners. 1 The main reason for employing index of retail prices for Pit and Pdt is that data for either producer price index nor wholesale price index are not available for the whole sample period. We expect the price elasticity of exports for Mauritius to be negative given that fierce competition prevails on the 13 world market for Mauritian manufactured trade. However, the degree of price elasticities will differ. REER is being employed to immune ourselves from misleading inferences pertaining to the evolution of Mauritius’ extent of competitiveness and to take into account the dynamic nature of trade patterns between Mauritius and its trading partners. (d) Volatility There is a plethora of models developed to date. Here also, some studies employ only one measure whilst others employ more than one measure.For the purpose of this study the E-GARCH and Zscore are employed. The Z-score measure is a measure combines movements in exchange rate around a constant level as well as around a steady depreciation/appreciation rate. GARCH (Generalised Conditional Heteroskedasticity) was developed by Bollerslev in 1986. According to Chit et al (2008), in these models, exchange rate volatility is calculated by taking the variance of exchange rate as ‘a linear function of the expected squares of the lagged value of the error term from an auxiliary regression determining the mean’. Chit et al (2008, pg 100). EGARCH (exponential generalized autoregressive conditional heteroscedasticity) which has certain advantages over the simple GARCH model. In the literature, the EGARCH allows for asymmetries and there is no violation of the non-negativity condition. 4.1.3. Data Sources Data for the above variables were obtained from the Bank of Mauritius annual reports, Central Statistical Office, World Bank database, International Monetary Fund, International Financial Statistics, www.oanda.com and www.fxtop.com. The data starts from 1980 to end 2011 due to unavailability of data for some variables. The data are then converted into natural logarithm to process into time series. As such, the coefficients are merely elasticities. The following table gives a summary of the variables and their sources. 14 5.0 Analysis of Results In order to assess the impact of exchange rate volatility on manufacturing trade for Mauritius, time series econometrics is employed. We initially test for stationarity using the Augmented DickeyFuller test and the Phillips Perron test. The tests confirmed that our data series are stationary only in its first difference (ie I(1)). To test for cointegration, the Johansen cointegration technique is subsequently employed. To check for the presence of heteroskedasticity in the residuals, the study checks for the ARCH effects of the residuals, two different methodologies are applied. Firstly, graphs for the residuals. Second, the t-statistic as well as the p values are used. As a rule of thumb, when investigating the graph and it is found that low periods of volatility is followed by another low periods of volatility or high periods of volatility is followed by another high periods of volatility, ARCH effects is present. As far as p-value is concerned, ARCH effects is present if p values are less than 5% (0.05). The tests above substantiated the presence of ARCH effects. As a measure of volatility of exchange rate, there is a plethora of ARCH models to choose from. Hence, a choice has to be made between GARCH, EGARCH, PARCH and Component ARCH (1,1) bases on Akaike Information Criteria (AIC) and Shwarz Information Criteria (SIC). The ARCH family with lower absolute value of AIC and SIC is preferred. EGARCH is preferred as it has the lowest AIC and SIC for all equations with AICs of,-1.704396 and 2.1543 respectively and SICs of 1.433107 and 2.0764 respectively for the real manufacturing export and real manufacturing import equations. Once having verified for ARCH effects and chosen EGARCH from the family of GARCH, now proceed with the regression techniques to be applied. In fact, studies in the 1970’s and 1980’s such as Hooper and Kohlhagen (1978), Caballero and Corbo (1989) among others conducted to investigate the impact of exchange rate volatility on trade were inconclusive, spurious and inefficient on account of the employment of plain Ordinary Least Squares in their estimations and failed to test for unit root and the order of integration of the regression variables. Following recent development in time series econometrics, some authors such as Arize et al (2000), Cheong (2004), Rey (2006) among others, aver that international trade variables are not stationary in nature and those previous studies examining the impact of exchange rate volatility on trade neglect to examine 15 the integration order of the variables. In view thereof, to address these time series issues, the authors employ cointegration and error correction models to capture stationarity and cointegration of the variables. In the presence of cointegration, it is understood that there is a long run equilibrium relationship between them. As a result, Vector Error Correction Model (VECM), is applied with a view to appraise the short run properties of the series. On the other hand, if no cointegration is found, VECM is not resorted to, but rather VAR methodology is used. VAR methodology is considered better than ordinary least squares and instrumental variables approach as it does capture stochastic trends and does not require instrumental variables. It is also considered to be a better method to separate long run and short run properties of a time series data set and lend itself to the dynamic nature of the variables by treating all variables endogenous as a priori, albeit restrictions as well as exogeneity of some of the variables may be imposed. VAR methodologies also produce better forecasts and subjective factors are not required by the VAR models like the Instrumental variables approach. It is important to note that volatility is estimated in the first stage and same is used as a regressor in the equations. The latter is expected to have an errors-in variable problem. 5.1. Vector Error Correction Estimates- Z Score as Measure of Volatility 5.1.1. Real Manufacturing Export (RME) Model Given the presence of cointegration, Vector Error Correction methods are applied to the Real Manufacturing equation.The VECM estimates for RME equation when Z score is used as a measure of volatility are provided in Table 1: 16 Table 1: Vector Error Correction Estimates with Z-Score as Measure of Volatility Estimation Methods-Ordinary Least Squares Independent Variables Coefficient P value Standard t-statistic Error D(Real Manufacturing Equation) -0.701073 0.064 0.248942 -2.816208 170.003 0.0019 52.5967 3.232198 -0.688597 0.038 0.316108 -2.178358 First Differenced REER D(Foreign Income Equation) First Difference Foreign Income First Difference REER Adjusted R-Square 0.478411 R-Square 0.683321 Source: Eviews 7.1 The above table reports only the coefficients deemed to be significant regarding the VECM estimates. The insignificant coefficients are not reported here for expositional ease. From table 1, put of the error correction terms, only the first differenced of REER, and of foreign income are significant with p-values of 0.064, 0.019 and 0.0328 respectively. However the third difference of REER is not considered significant despite having a p-value of less than 0.05 as it has a positive and large coefficient (170.003), contrary to what econometric theory suggests. The significance of the lagged REER implies that REER adjusts very fast (70%) to equilibrium state of RME. It is noted that the lagged values of foreign income significantly influence real manufacturing exports, which means that foreign income increases, real manufacturing exports increase in the next periods and vice. The Mauritian manufacturing exports is indeed very dependent on income of foreigners. Falling incomes due to the European crisis in recent years have had a dampening effect on our manufacturing exports. As expected the first difference of REER is significant in explaining changes in real manufacturing exports with a negative coefficient of -0.70173 when the equation of real manufacturing exports with z score is used as a measure of volatility. The result is consistent with the findings of Campa 17 and Goldberg (1993), Sekkat and Varoudakis (2002), Clark et al (2004), Aguirre et al (2007) but is not in conformity with Samson et al (2003), This means that there is a negative link between the real price of rupee and manufacturing exports as expected. In other words, the result shows that a real depreciation of the rupee helps to boost manufacturing exports while a real appreciation adversely affects exports. It also means that Mauritian exporters are adversely affected in terms of export earnings in case of swings in real exchange rates as they are unable immune themselves from the adverse movements in real exchange rates for simple reason that hedging is deemed to be costly. This has always been the case in Mauritius. For example, presently, the textile sector is surviving thanks only to the large established players. Small players are having much difficulty to operate owing to frequent swings in the exchange rate. Moreover, a real depreciation of the Mauritian rupee makes imports dearer and raises the cost of production of firms in the manufacturing firms, which rely heavily on imports of their raw materials. As a result of increased cost of production, competitiveness is eroded on the world markets characterized by fierce competition from emerging players from China, Pakistan, Sri Lanka, India, Brazil and other Asian economies. Thus, a real depreciation of the rupee adversely impact on Mauritian manufacturing exports. 18 5.1.2. Real Manufacturing Import (RMI) Model Both Trace and Maximum eigen value for cointegration result in one cointegrating equation. The VECM estimates for the RMI equation with Z-Score as a measure of volatility are provided below: Table 2: Vector Error Correction Estimates with Z-Score as Measure of Volatility Estimation Methods-Ordinary Least Squares Independent Variable D (Real Manufacturing Import Equation) First Differenced(Real Manufacturing Imports) Coefficient Std. Error t-Statistic Prob. -0.138264 0.042674 -3.240009 0.0018 Second Differenced(Domestic Income) 0.047769 0.011408 4.187143 0.0001 Third Differenced(REER) -0.033969 0.009938 -3.418225 0.001 Third Differenced(Volatity Z-Score)) 0.045544 0.014831 3.070962 0.003 0.521673 0.251834 2.071497 0.0417 Second Differenced Real Manufacturing Imports -4.743137 2.366611 -2.00419 0.0486 Second Differenced(Volatity Z-Score)) -0.405053 0.194618 -2.081267 0.0408 Third Differenced(Volatity Z-Score)) -0.45244 0.195285 -2.31682 0.0232 D(Domestic Income Equation Second Differenced(Domestic Income D(Volatility Equation) R-squared Adj. R-squared Source: Eviews 7.1 0.645717 0.477899 19 From Table 2, the coefficients of the error correction terms are depicted by D(MRI) and is significant with a p-value of 0.0018 and a coefficient of -0.138264, suggesting that the independent variables adjust moderately (13.8%) towards long run equilibrium values of RMI. The second difference of domestic income the third difference of REER the third difference of volatility, are also significant coefficients from the estimates with p values of 0.001, 0.001 and 0.003 respectively. It is also noted that the second difference of real manufacturing imports significantly influence exchange rate volatility as evidenced by a p-value of 0.0486. The results are consistent with the findings of Siregar and Rajan (2002), Marquez and Schindler (2006), Hayakawa and Fukunari (2008) and Huchet-Bourdon and Korinek (2012) but not consistent with the findings of Samson et al (2003). The positive coefficient of domestic income (0.047769) means that an increase in domestic income encourages more consumption and more import given that Mauritius has a high propensity to import. It also means that higher income encourages more investment and capital goods not produced locally have to be purchased from foreign countries. Further, as expected, the real effective exchange rate coefficient is negative (-0.033969) implying a negative impact of real exchange rate on imports. The latter means that real exchange rate changes raised the cost of imports for Mauritius, which has an export led growth strategy and relies heavily on primary inputs and an adverse movement in real exchange rate, had a dampening effect on imports. Last, but not least, as expected, higher exchange rate volatility increases imports as Mauritian importers find their cost of imports increase and become risk averse. This is explained by the fact that that agreement regarding exchange rate at the time of trade contract is done much earlier than payments, which are made upon delivery. Hence, Mauritian importers cannot predict future rates accordingly, creating a profit uncertainty environment thereby reducing their imports with time. 20 5.2. Vector Error Correction Estimates- EGARCH as Measure of Volatility 5.2.1 Real Manufacturing Export (RME) Model The Trace test results show 3 cointegrating equations. The VECM estimates for the RME equation with EGARCH as a measure of volatility are provided below. Table 3:Vector Error Correction Estimates with EGARCH as Measure of Volatility Estimation Methods-Ordinary Least Squares Real Manufacturing Exports Independent Variable D(Real Manufacturing Exports Equation) Third Differenced(REER) Coefficient 0.857483 Std. Error t-Statistic 0.419412 Prob. 2.044486 0.0448 D(Foreign Income Equation) First Differenced(Real Manufacturing Exports) -0.003719 0.001655 -2.246812 0.0279 First Differenced(Foreign Income -1.206151 0.410337 -2.939415 0.0045 Third Differenced(Foreign Income 1.46369 0.65192 2.2452 0.028 Third Differenced(REER) 0.006747 0.002629 2.565951 0.0125 R-squared Adj. R-squared Source: Eviews 7.1 0.607509 0.353544 21 The adjusted R-squared stands at 35.35% of the parsimonious equation, which is considered acceptable. From equations 4.158 to 4.1.61, only the first difference of real manufacturing exports and the first difference of foreign income are significant with p-values of 0.0279 and 0.0045 and coefficients of -0.03719 and -1.206151 respectively suggesting that real manufacturing exports adjusts very slowly to long run equilibrium value of foreign income (0.3%) and lagged foreign income adjusts very fast to equilibrium (120%). The significance of the second difference of REER means that it accounts for 85.75% of real manufacturing exports. The significance of the second difference of exchange rate volatility means that it accounts for 0.67% of foreign income and the significance of the second difference of real manufacturing exports means that it accounts for about 0.0018% of exchange rate volatility. 5.2.2. Real Manufacturing Import (RME) Model- VAR Estimates Given that no cointegration is found in the real manufacturing import equation when EGARCH is used as a measure of volatility, the model is estimated using an unrestricted VAR model. The Vector Autoregression Estimates (lag length 1) for the Real Manufacturing equation with EGARCH as exchange rate volatility measure is provided in the following table: 22 Table 4: Vector Autoregression Estimates Sample (adjusted): 1981 2011 Included observations: 31 after adjustments Standard errors in ( ) & t-statistics in [ ] MRI MRI(-1) 0.953237 -0.0905 [ 10.5328] 0.442353 -1.49443 [ 0.29600] 0.665355 -1.30691 [ 0.50910] DY(-1) 0.003025 0.886434 0.159282 -0.00723 [ 0.41853] -0.11935 [ 7.42730] -0.10437 [ 1.52609] 0.008517 -0.01241 -0.05025 -0.20489 0.219201 -0.17918 EGARCH 0.0000658 -0.000069 [-0.95602] 0.0000073 7 0.0000055 [-1.34144] 0.0000043 1 0.0000094 [ 0.68643] 127.1796 [-0.24526] 890.7892 [ 1.22335] 1338.628 [ 0.45630] 0.810056 -81.124 [ 1.56772] 0.017196 -0.06638 [ 0.25905] -1339.58 [ 0.66498] -0.422986 -1.09614 [-0.38589] -1171.49 [ 1.14267] -0.601511 -0.9586 [-0.62749] -0.0617 [ 13.1287] 0.0000567 -0.00005 [ 1.12273] REER(-1) EGARCH(-1) C DY REER D(MRI) 0.835053 D(DY) 0.706992 D(REER) 0.153837 D(EGAR CH) 0.908964 Adj. Rsquared 0.809676 Source: Eviews 7.1 Output 0.661914 0.023659 0.894959 R-squared Table 4 depicts the VAR estimates for the real manufacturing import with EGARCH as a measure of exchange rate volatility. In order to know the significance of the estimates, it is very important to order the system by variable and then estimate the equations so obtained to obtain the p-values. When the system is ordered by variable, the estimates for the p-values are as follows: 23 Table 5: Vector Autoregression Estimates with EGARCH as Measure of Volatility Real Manufacturing Imports Independent Variable Coefficient Std. Error t-Statistic Prob. First Differenced(Real Manufacturing Imports) 0.976252 0.116292 8.394808 0.0000 First Differenced(Domestic Income) 0.842061 0.114375 7.362274 0.0000 0.732252 0.095098 7.699952 0.0000 D(Domestic Income Equation) D(Volatility Equation) First Differenced Volatility R-squared 0.835053 Adj. R-squared Source: Eviews 7.1 0.809676 From Table 5, the first difference of manufacturing imports and first difference domestic income and are significant with a p-values of 0.000 each and coefficients of 0.976252 and 0.842061 respectively,, suggesting that the independent variables adjust quite rapidly towards long run equilibrium values of RMI.. 5.3. Granger Causality/Block Exogeneity Wald Tests Results Having analysed the long run relationship between the variables, this section confines itself to analysing possible causality between the variables. Using Z-Score as a measure of volatility, as far as the results for real manufacturing export equation is concerned,(RME), there is no evidence of bidirectional causality. However, causality runs from FY to MRE, from REER to FY and from REER, FY and VOL simultaneously to FY as the Block Exogeneity Wald test p values stand at 0.0288, 0.0188 and 0.0343 respectively. 24 The results for the real manufacturing import equation shows unidirectional causality for the RMI equation as causality runs only from DY to RMI, from REER to RMI and from VOL to RMI as shown as p values of 0.0001, 0.0024 and 0.0085. All the variables taken together also granger-cause RMI as evidenced by a p-value of 0.0000. When EGARCH is used as a measure of volatility no causality is found the RME and RMI equations given that all the p values are greater than 0.05 6.0. Summary, Implications and Recommendations. This paper gives an analysis of the impact of exchange rate volatility manufacturing trade flows in Mauritius. The econometric analysis employs yearly data for the period spanning 1980-2011 and two measures of exchange rate volatility, viz, the Z-score and the EGARCH are used. When using the Zscore, it is found that exchange volatility does not affect real manufacturing exports. However, exchange rate volatility is found to be significantly affecting real manufacturing imports. When the EGARCH is used, the Vector Autoregression estimates suggest that exchange rate volatility has not been a determining factor influencing real manufacturing imports. The findings of the paper are intended to have important implications for the Mauritian manufacturing sector. In the first instance, it means that the Mauritian manufacturing sector is adversely affected in terms of earnings in case of high swings in real exchange rates. It may also mean that frequent swings in the MUR might bring uncertainty on the part of foreign buyers such that in the long run, they might contemplate to switch to cheaper markets, which means that given the elasticity of the foreign demand for manufactured goods, Mauritian manufacturers cannot increase their prices and hence suffer in terms of earnings. The results also mean that hedging facilities are not so much in place to immune manufacturers in Mauritius from frequent swings in real exchange rates. This is due to high costs associated with such transactions and acute competition facing the Mauritian manufacturing sector. As far as real manufacturing imports are concerned, the results confirm that Mauritius has a very high propensity to import as evidenced by the significance of domestic income with respect to real manufacturing imports. Movements in real exchange rates as well as volatility are also significant in explaining real manufacturing imports which means that Mauritius should try to bring strategies to promote import substitution, albeit at a lower cost. This shall also help to reduce our current account deficits given that a large proportion of our manufacturing imports are meant for production as Mauritius practices an export led growth strategy. The results also warrant an analysis of the constraints facing the Mauritian export sector as 25 well as the level of imports as well as what informed decision policy makers should make to address the problems facing our international trade sector. Further, it can also be a case for exporters who can argue their case to Government by lobbying for an export-friendly macroeconomic environment and assist them to plan their export activities more effectively. I believe it is high time for the Mauritian authorities and exporters to start thinking about payment in dollar for our exports. We pay most of our imports in dollar, yet we receive payment for most of our imports in euro. This is a situation which requires some attention among authorities. In essence, Asian countries such as Hong-Kong, India, Pakistan, Bangladesh, China etc exporting to Europe are paid in dollar when claiming payment from France or Great Britain.. However, it is important to note that while using the dollar as a single currency for our imports and exports would be very practical, this policy would not eliminate the risks of currency fluctuation, for the dollar is also prone to high level of depreciation. On the regional scene, it is also high time for the regional bodies like the SADC, COMESA and the African Union to start thinking about a regional currency pegged to a major world currency to immune members from frequent fluctuations in the exchange rates and thus stabilize export revenues for their export operators. Moreover, in order to boost export performance and reduce costs, it is suggested that the existing stimulus package be continued and a special stimulus package for the manufacturing sector sectors be put in place to smooth them out of their financial difficulties they might face in the light of adverse movements of the rupee. As regards the appreciating USD which is another burden for imports of raw materials, it would be appropriate that import substitution strategies are in place such that investors may be encouraged to produce the imported raw materials in Mauritius at lower cost. Further, the results also suggest a diversification of both of our export products and markets. We note that manufacturing exports for Mauritius, are concentrated in Europe. Bilateral and regional agreements should be intensified with a view to diversify our markets further. Besides, to boost export performance in the manufacturing sector, constraints facing our exporters should be reduced to a desirable level. For example, lower electricity charges, export processing efficiency in terms of reduced time for processing exports and lower rates of interest through the stimulus package. It is believed that these additional measures will give the export operators some breathing space in the light of external shocks facing their enterprises. 26 References Aguirre.A, Ferreira.A and Notini. H (2007). ‘The Impact of Exchange Rate Volatility on Brazilian Manufactured Exports’. Económica, La Plata, Vol. LIII. Arize, A.C., (1998). ‘The effects of Exchange Rate Volatility on U. S. Imports: An Empirical Investigation’. Journal of International Economics Vol 2(3), pp. 31-40. Arize A.C, Thomas. O, Slottje. D.J (2000). ‘Exchange-Rate Volatility and Foreign Trade: Evidence from Thirteen LDC's’: Journal of Business & Economic Statistics, American Statistical Association Vol. 18, No. 1, pp. 10-17 Ortega B. C and di Giovanni.J (2005): ‘Remoteness and Real Exchange Rate Volatility’. IMF Staff Papers, Vol. 53, pp. 115-132. Campa. J and Goldberg L.S (1993). ‘ Investment in Manufacturing, Exchange Rates and External Exposure’. NBER Working Paper no 4378, National Bureau of Economic Research Inc. Caballero. R.J. and Corbo, V.(1989). ‘The effects of real exchange rate uncertainty on exports: Empirical evidence’, The World Bank Economic Review, Vol 13 Issue 2. Clark, P., Tamirisa, N., Wei, S., Sadikov, A., Zeng, L., (2004). ‘A new look at exchange rate volatility and trade flows’. International Monetary Fund, Washington DC, Occasional Paper, No. 235. Chit.M. M.,Rizov.M, and Willenbockel. D. (2008). ‘Exchange Rate Volatility and Exports:New Empirical Evidence from the Emerging East Asian Economies’. The WorldEconomy. Vol 33 (2). pp. 239–263 Côté, A (1994), ‘Exchange Rate Volatility and Trade’ Working Paper No. 94-5, Bank of Canada. 27 Cheong C.(2004). ‘Does the risk of exchange rate fluctuation really affect international trade flows between countries?’ Economics Bulletin, Vol. 6, No. 4 pp. 1−8 Dell’Ariccia, G. (1998). ‘Exchange Rate Fluctuations and Trade Flows: Evidence from the European Union’. IMF Staff Papers No 46(3), pp. 315-334. De Grauwe. P and Schnabl G (2004): ‘Nominal versus Real Convergence with Respect to EMU Accession – EMU Entry Scenarios for the New Member States’. Kyklos Vol 58.Issue 4. pg481-499 Garcia-Herrero.A. and Koivu.T (2007). ‘China’s exchange rate policy and Asian trade Monetary and Economic Department’, BIS Working Papers No 282. Bank for International Settlements Hayakawa. K and Fukunari K. (2008). ‘The effect of exchange rate volatility on international trade in East Asia’. Journal of the Japanese and International Economies. Elsevier. Vol 23 issue no 4. pg 395406. Huchet-Bourdon, M. and J. Korinek (2012). ‘To What Extent Do Exchange Rates and their Volatility Affect Trade?’. OECD Trade Policy Working Papers, No. 119, OECD Publishing Hooper. P and Kohlhagen S.W (1978). ‘The Effect of Exchange Rate Uncertainty on the Prices and Volume of International Trade’. Journal of International Economics Vol 8(4): pg 483–512. Kandilov. I.T (2007). ‘The Effects of Exchange Rate Volatility on Agricultural Trade’-American Journal of Agricultural Economics, NC State, Raleigh, NC 27695-8109 Kiptui. M (2007). ‘Does the Exchange Rate Matter for Kenya’s Exports? A Bounds Testing Approach’ Prepared for presentation at the African Econometric Society 2007 Conference, Cape Town, July 4th to 6th 2007. 28 Klaassen, F. (2004). ‘Why it is so Difficult to Find an Effect of Exchange Rate Risk on Trade?’. Journal of International Money and Finance. Vol 23 Issue 5. pg. 817-39. Learner, E.E and Stern R.M (1970) :’ Quantitative International Economics: Boston, Allyn and Bacon, 1970. Lee M.H and Kim Y.J.(2010). ‘The Kalman Filter Estimation of the Effects of the Exchange Rate and its Volatility on the Korean Trade’. Pusan National University, Busan, Korea Working Paper Marquez, J. and Schindler, J. W. (2006). ‘Exchange-Rate Effects on China's Trade: An Interim Report’. Board of Governors of the Federal Reserve System. International Finance Discussion Papers No. 861. Olayungbo.D, Yinusa.O. and Akinlo A. (2011) .’Effects of Exchange Rate Volatility on Trade in Some Selected Sub-Saharan African Countries Modern Economy’, Published Online September 2011 (http://www.SciRP.org/journal/me) Rey. S (2006): ‘Effective Exchange Rate Volatility and MENA Countries’ Exports to the EU’, Journal of Economic Development’. Volume 31, Number 2, 23-54 Rodriguez, Carlos Alfredo, (1980)-‘The Role of Trade Flows in Exchange Rate Determination’- A Rational Expectations Approach’, Journal of Political Economy, University of Chicago Press, vol 88(6), pg 1148-58, December. Sekkat, K. and Varoudakis. A (2002). ‘Exchange-Rate Management and Manufactured Exports in Sub-Saharan Africa’. OECD Development Center Technical Papers No 134 Siregar, R. and Rajan R.S. (2002). ‘Impact of Exchange Rate Volatility on Indonesia’s Trade Performance in the 1990s’. Journal of the Japanese and International Economies, Vol 18, pg 218240. 29 Samson. M, Samson. A, Kenneth. M. Q, Ndlebe. A and Niekerk I.V (2003). ‘The volatility of the rand and its impact on the manufacturing sector: the impact on exporters and investors’ . Economic Policy Research Institute Research Paper No 35. A research report commissioned by: the External Relations and Policy Coordination Unit, Research Management Directorate Schnabl.G(2007). ‘Exchange Rate Volatility and Growth in Small Open Economies at the EMU Periphery’. Working Paper Series, No. 773 Sercu, P. and R. Uppal, (1995), “International Financial Markets and the Firm”, South-Western Publishing, Cincinnati, Ohio. Sekantsi L. (2010).’The Impact of Real Exchange Rate Volatility on South African Exports to the United States (U.S): A Bounds Test Approach’ F.R.E.I.T Working Paper, Trade Patterns 2010 Sheldon.M. , Mishra.S.K, Pick.D. and Thompson.S.(2011). ‘Exchange Rate Uncertainty and US Bilateral Fresh Fruit and Fresh Vegetable Trade: An Application of the Gravity’, Ohio State University Working Paper, AEDE Economics. Yao, R. and H. H. Zhang (2003), \Optimal Consumption and Portfolio Choices with Risky Housing and Borrowing Constraints," Working Paper 30