The Effect of Exchange Rate Volatility on International

The Journal of Business and Economic Studies, Vol. 21, No. 1/No. 2, Spring/Fall 2015

The Effect of Exchange Rate Volatility on International Trade in Selected MENA Countries Mohammad Sharif Karimi, Razi University, Iran Husyin Karamelikli, Karabuk University, Turkey

Abstract This paper investigates empirically the relationship between exchange rate volatility and the volume of international trade of six MENA countries over the period 1980–2012 using quarterly data. Estimates of the cointegrating relationships are obtained using different cointegration techniques. Estimates of the short-run dynamics are using cointegration and vector error correction model (VECM) techniques. The major finding shows that real exchange rate volatility exerts significant negative effects on exports both in the short run and the long run in each of the MENA countries. Overall, our results suggest that exporting activities of these countries can be further boosted up by policies aimed at achieving and maintaining a stable competitive real exchange rate. Keywords: Exchange Rate Variability, Exports, MENA countries, Cointegration, VECM JEL Codes: C32, F14, F17 Introduction Exchange rates across the world have fluctuated wildly particularly after the collapse of the Bretton Woods system of fixed exchange rates. Since then, there has been extensive debate about the impact of exchange rate volatility on international trade. A classic argument for a fixed exchange rate is its promotion of trade. Exchange rate volatility refers to the amount of uncertainty or risk regarding the size of changes in a currency's value. A higher volatility implies that a currency's value can potentially be spread out over a larger range of values. Subsequently the price of the currency can change dramatically over a short time period in either direction. A lower volatility means that a currency's value does not fluctuate drastically, but changes at a steady pace over a particular time period. Empirical support for this, however, is mixed. While one branch of research consistently shows a small negative effect of exchange rate volatility on trade, another, more recent branch presents evidence of a large positive impact of currency unions on trade.1 The exchange rate regime and the related issues are one of the important yardsticks of the macroeconomic management in striving for economic development through consistent improvements in the foreign sector of any economy. The issue is particularly important for countries that switched from a fixed to a flexible exchange rate regime due to a higher degree of variability associated with flexible exchange rates. Since many Middle East countries have moved to a flexible exchange rate regime at some point in the recent past, the relationship between exchange rate volatility and export flows has been studied in a large number of theoretical and empirical papers. 14


The main notion suggested by some theoretical models is that a rise in exchange rate volatility increases uncertainty of profits on contracts denominated in foreign currency and forces risk-averse agents to redirect their activity to the lower-risk domestic market. Other models suggest that higher levels of exchange rate movements offer greater opportunity for profit and therefore might lead to an increase in exports. Alternatively, some researchers have suggested that it is possible to offset potential unexpected movements of the exchange rate by investing in the forward market, causing producers to be unaffected by movements of the exchange rate. These different ranges of results have been supported by a large variety of empirical studies on the effects of exchange rate volatility on exports and this has evolved to be one of the most controversial topics of international trade. Trade from the overall MENA region have increased considerably over the past two decades, partly as a result of growing economic openness and the signing of trade agreements but mostly because of higher oil production and exports (IMF, 2011). On the other hand, it is important to know how exports in this region are affected by the fluctuation of exchange rates. Therefore, it would be interesting to see whether the exchange rate variability variable can adequately explain the MENA region’s trade performance. The purpose of this paper is to close this gap and provide estimates of the short- and longrun impact of exchange rate variability on export flows and to provide a contribution to the empirical debate on the relationship between exchange rate volatility and exports for six MENA countries’ economies which are Iran, Saudi Arabia, United Arabic Emirates, Jordan, Egypt and Algeria. The rest of the study is organized as follows. Section 2 contains the survey of some important past studies in order to give the theoretical and empirical evidence as a multidimensional support to the topic. In Section 3, we examine the specifications of our empirical model followed by a discussion of econometric methodology issues. Meanwhile, data sources and variable definitions are described in Section 4. In Section 5, we discuss the empirical results for the six countries. The final section concludes the study with some policy implications based on the empirical findings of the study. Literature Review It is often argued, since at least Ethier (1973), that exchange rate volatility should have a negative impact on international trade. The literature on the issue is quite large. Both theoretical as well as empirical studies provide ambiguous effects of volatility on exports. One of the earliest empirical studies of the relationship between trade and exchange rate volatility is that of Hooper and Kohlhagen (1978), who find limited evidence from the impact of volatility on bilateral trade prices and no evidence on bilateral trade volumes. Cushman (1983) used the moving average of the real exchange rate as his volatility measure and found a negative relationship between volatility and exports. In his 1988 study, Cushman added the absolute difference between spot, forward and current rates as an alternative measure of volatility and found mixed effects of volatility on exports. Koray and Lastrapes (1989) use VAR models to examine whether exchange rate volatility affects the volume of trade. They find that exchange rate volatility explains only a small part of imports and exports. In cross-sectional tests, Brada and Mendez (1988), using a gravity model of bilateral trade, find that even though exchange rate volatility reduces trade, its effect is smaller 15


than that of restrictive commercial policies. De Grauwe (1988) captured the ambiguity of the debate by modeling a producer who must decide between selling in the domestic or the foreign market. By providing some basic assumptions his model assumes that the only source affecting the exporter’s behavior is the local currency price of exports as well as his risk preferences. In his model, exchange rate is measured as the percentage change of export quantity as a measure of volatility. Even though new empirical statistical techniques are applied in the 1990’s, ambiguity of the estimated relationships continues to dominate the empirical literature. Chowdhury (1993) investigated the impact of exchange rate trade volatility on trade flows for the G-7 countries utilizing an error correction model. His study used an eight-period moving sample standard deviation of the growth rate of the real exchange rate as a measure of exchange rate volatility and found a significant negative impact. Despite all these developments the traditional measure of exchange rate volatility still remains the moving average of the standard deviation. Frankel and Wei (1993), using an instrumental-variables approach, also conclude that the effect of exchange rate volatility on trade is small. On the other hand, Asseery and Peel (1991) using an error-correction framework, and Kroner and Lastrapes (1993) using a multivariate GARCH-in-mean model, found that an increase in volatility may be associated with an increase in international trade, while McKenzie and Brooks (1997) found an even stronger positive association. Thus, the overall conclusion is that the effects of exchange rate volatility, if present, are small, and not always negative. Doroodian (1999) investigated the impact of exchange rate volatility on the export volume of three developing countries, i.e., India, South Korea, and Malaysia. Since the data were quarterly over the period 1973–1996, the measure of exchange rate volatility was generated using the GARCH approach. The empirical results supported the notion that the GARCH-based measure of exchange rate volatility had a significantly negative impact on the exports of all three countries. Arize et al. (2000) is another study that looked at the link between a measure of exchange rate uncertainty and again, export volume of 13 developing countries, one of which was Malaysia. Application of Johansen’s cointegration technique to quarterly data over the period 1973–1996 supports one cointegrating vector in each country. The estimate of that vector for each country reveals that indeed, exchange rate uncertainty has a significantly negative impact on export volume of all 13 countries, including Malaysia. Most empirical works have so far involved advanced economies, but recently researchers have chosen to study developing as well as emerging countries. Poon et al. (2005) examined the aggregate export data of five Asian countries (Indonesia, Japan, South Korea, Singapore, and Thailand) and reported that the exports of Indonesia and Thailand are positively affected by the exchange rate volatility in the long run and that the exports of Singapore is also positively affected in the short run. According to Choudhry (2008), the exchange rate volatility exerts a significantly positive effect on real exports by Canada, Japan, and New Zealand to the U.K. On the other hand, Arize et al. (2008) concluded that exchange rate volatility has a significantly negative effect on exports in eight Latin American countries both in the short and the long run. Similarly Chit et al. (2010) produced evidence on the negative effects of volatility on trade in five emerging East Asian Countries (China, Indonesia, Malaysia, the Philippines and Thailand). Hall et al. (2010) looked at ten emerging market countries and eleven developing 16


countries with the result that exports of developing countries is negatively impacted by exchange rate volatility, but that there was no significant relationship between export and volatility in emerging market countries. Summarizes the results of studies, published between 1973 and 2010, that focus exclusively on the relationship between exchange rate volatility and trade of developing countries. The overall thrust of these results is that exchange rate volatility had a negative and significant effect on the exports of the countries considered, Methodology for estimating the short- and longrun impact of exchange rate variability on export flows, we follow the approach introduced by Arize et al (2008) who examine the impact of exchange rate volatility on the export flows for eight Latin American economies. Methodology At the theoretical level, the effects of greater volatility of exchange rates on export flows are much debated. The literature gives results that contrast strongly. Using a traditional export demand model with an addition of a measure of exchange rate volatility, the long run export demand function can be written as: 𝐸𝑋𝑡 = 𝛽0 + 𝛽1 𝐼𝑁𝐶𝑡 + 𝛽2 𝑃𝑡 + 𝛽3 𝐸𝑋𝐶𝐻𝑡 + 𝜗𝑡

(1)

Where EX is the logarithm of a country's real exports, INC is real foreign income, P is relative price and EXCH is real exchange rate volatility. One difficulty in this study and any other research regarding exchange rate volatility for that matter is the measure of volatility itself. Since Engle (1982), the exchange rate volatility has essentially been defined by ARCH (Autoregressive Conditional Heteroscedasticity) models, and subsequent generalizations (GARCH, IGARCH, etc.). As Baillie and McMahon (1989) and others show, ARCH type effects remain very strong in high-frequency data, but diminish with monthly or quarterly series. We have constructed a GARCH measure of volatility as follows: REXC𝑡 = 𝜇0 + 𝜇1 REXC𝑡−1 + 𝜃𝑡 2 𝛿𝑡 = ∅0 + ∅1 𝜃𝑡−1 + ∅2 𝛿𝑡−1

(2) (3)

Where REXC𝑡 real exchange rate is expressed in natural logarithm and 𝜃𝑡 is a random error. The conditional variance equation in (3) is a function of three terms: (i) the mean, ∅0 ; (ii) news about volatility from the previous period, measured as the lag of the squared residual from the 2 mean equation, 𝜃𝑡−1 (the ARCH term); and (iii) the last period’s forecast error variance, 𝛿𝑡−1 (the GARCH term). Data and Variable Definitions The six MENA countries examined in this study are Iran, Saudi Arabia, United Arabic Emirates, Jordan, Egypt and Algeria. Data were obtained from the IMF's International Financial Statistics (IFS), IMF's Central Statistics Office, OECD Main Economic Indicators and the IMF's Directions of Trade (DOT) statistics and cover the 1980Q1 through 2012Q1 period. 17


Data for each individual country's export volume and unit values were taken from IFS, while the world export price index, PWt, is a geometric trade-weighted average of export prices. The weights are wji, and the base period is 1990 = 100. The relative price ratio was calculated as Pt = lnPXt−lnEt−lnPWt where PX is the export price in local currency and E is the exchange rate index. To compute measures for exchange rate volatility, trade weighted effective exchange rates (EXC) and real effective exchange rates (REXC) were computed. They were constructed as follows (for illustrative purposes, let Iran be country j). The period average exchange rates are in units of domestic currency per dollar. These period averages were then expressed in index form (1990 = 1.0). The EXC variable was calculated as: EXP [Σwji lnE(i, $, t)−lnE(j, $, t)] where EXP = exponent, ln = natural logarithm, E(i, $, t) = exchange rate index of country i at time t and E( j, $, t) = exchange rate index of Iran at time t. The real effective exchange rate was calculated as: REXC(j, t) = EXP[−lnP(j, t)+lnE( j, $, t)+ΣwjilnP(i, t)−ΣwjilnE(i, $, t)] where the exchange rate terms are in units of country i (or j) currency per U.S. dollars in index form (1990 = 1.0). P is the consumer price index of country i (or j) in index form (1990=1.0). Empirical Results Cointegration Analysis The first step in testing for cointegration in a set of variables is to test for stochastic trends in the autoregressive representation of each individual time series using conventional unit root tests. The first is the augmented Dickey and Fuller test, for which nonstationarity serves as the null hypothesis so that H0 : Xt ~I(1) H1 : Xt ~I(0). As it is usually done in the literature, we report the value of the ADF(k), where k is the minimum lag for white errors. According to Table 1, the ADF tests reject the null hypothesis of nonstationarity at the conventional levels, therefore we have considerable evidence that each variable is I (1) in each country. Table 1. Unit Root Test Country

ADF EX -2.65

INC -2.66

P -2.16

EXCH -2.57

Saudi Arabia UAE

-2.34

-2.66

-2.78

-2.34

-276

-2.66

-2.64

-2.23

Jordan

-2.89

-2.66

-2.56

-2.48

Egypt

-2.4

-2.66

-2.77

-2.88

Algeria

-1.79

-2.66

-1.89

-2.99

Iran

EX −2.67 (0.01) −4.18 (0.00) −3.13 (0.00) −4.19 (0.00) −3.11 (0.00) −2.35 (0.01)

INC −2.76 (0.01) −4.66 (0.00) −5.05 (0.00) −4.99 (0.00) −4.40 (0.00) −2.45 (0.01)

Lobato Robinson P −4.84 (0.00) −4.77 (0.00) −3.22 (0.00) −3.59 (0.00) −2.97 (0.01) 2.34 (0.02)

EXCH −2.35 (0.01) −3.62 (0.00) −3.10 (0.00) −4.97 (0.00) −4.29 (0.00) −1.87 (0.06)

EX = real exports, INC = real foreign income, P = relative price and EXCH = real exchange rate volatility. The augmented Dickey-Fuller (ADF) test statistics are from a model that includes a constant, trend and eight lags of the first difference of the regressand. The 5% critical value is −3.42. These test values have been adjusted using White's Heteroskedastic consistent standard errors. For the Lobato-Robinson test, the values in brackets are the p-values.

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To cross-check these results, we report also the results of applying the Lobato and Robinson tests which takes stationarity as the null hypothesis, where H0 : Xt ~I(0) H1 : Xt ~I(1). The Lobato and Robinson tests again support the conclusions of the ADF tests with regards to the integration properties of the variables. Since a unit root has been confirmed for the data series, the question is whether there exists some long-run equilibrium relationship among real exports, foreign economic activity, relative price, and exchange rate volatility for each country in our sample. As can be seen in Table 2, the Shin's test is conducted with three, six and eight lags. The null of stationarity (or cointegration) is not rejected in all lag lengths in the case of Iran, Algeria and Egypt for this test. However, as the authors suggest, some degree of augmentation in the tests is needed for better results. As the data show in all cases at higher lags, the null hypothesis of cointegration is accepted. Also, these results are further corroborated by the Johansen test results which indicate not only presence of cointegration but also the presence of a single cointegrating relationship. Table 2. Cointegration Tests Country

Iran Saudi Arabia UAE Jordan Egypt Algeria

3 0.247 0.162**

Shin's Test 4 0.154** 0.106**

Johansen 6 0.108** 0.088**

λmax 34.35 26.12

Trace 52.21 43.21

0.149** 0.068** 0.201 0.290

0.047** 0.023** 0.135** 0.177

0.087** 0.073** 0127** 0.145**

30.32 34.21 36.91 32.79

51.62 55.02 52.63 50.41

For the Shin test the critical value is 0.159 at the 5% level. The critical values for p−r = 4 in the case of Johansen are 27.07for λmax and 47.21 for the Trace test.

Vector Error-Correction Model The Granger representation theorem proves that if a cointegrating relationship exists among a set of I(1) series, then a dynamic error-correction representation of the data also exists. Since the cointegration tests in the previous section detected one long run equilibrium relationship for each of the export equation, the vector error correction models (VECMs) have been estimated to see the stability of the long run equilibrium relationship. VECM can be developed as: ∆𝑙𝑛 𝐸𝑋𝑡 = 𝐶 + 𝛼𝐸𝐶𝑇𝑡−1 + 𝜑𝑖 ∑𝑘𝑖=1 ∆𝑙𝑛𝐸𝑋𝑡−𝑖 + 𝛽𝑖 ∑𝑘𝑖=0 ∆𝑙𝑛𝐼𝑁𝐶𝑡−𝑖 (4) +𝜃𝑖 ∑𝑘𝑖=0 ∆𝑙𝑛𝑃𝑡−𝑖 + 𝛿𝑖 ∑𝑘𝑖=0 ∆𝑙𝑛𝐸𝑋𝐶𝐻𝑡−𝑖 + 𝜗𝑡 Where ECTt−1 is a lagged error correction term which captures the adjustment toward the long-run equilibrium. The coefficient α denotes the proportion of disequilibrium in exports in one period corrected in the next period. 19


Table 3 lists the summary results from the VECMs. We should first note that the onelagged error-correction term, ECT(-1) appears with a statistically significant coefficient and displays the appropriate (negative) sign in the equations for all the six countries. The results are summarized in Table 3. Table 3. Results from VECMs Iran Constant

𝐸𝐶𝑇𝑡−1 Δ𝐸𝑋𝑡−1

0.1258 (3.15) −0.21 (2.91) −0.20 (2.11)

Saudi Arabia 0.0094 (4.02) −0.33(2.79)

Δ𝐸𝑋𝑡−2

0.29 (3.86)

Δ𝐸𝑋𝑡−3

−0.13 (1.58) 0.18 (2.81)

Δ𝐸𝑋𝑡−4 Δ𝐼𝑁𝐶𝑡−1

0.52 (3.01) 2.35 (1.92)

2.09 (2.19)

UAE

1.07 (1.58)

3.17 (1.90)

0.68 (4.73) -0.0315 (-2.73) -0.3761 (-2.18) 0.0471 (0.45)

−0.27 (2.04) -0.0940 (-0.94) 0.1883 (1.25)

Δ𝐸𝑋𝐶𝐻𝑡−1

-0.0388 (-1.50)

Δ𝐸𝑋𝐶𝐻𝑡−2

Δ𝐼𝑁𝐶𝑡−4 Δ𝑃𝑡−1 Δ𝑃𝑡−2 Δ𝑃𝑡−3 Δ𝑃𝑡−4

Δ𝐸𝑋𝐶𝐻𝑡−3 Δ𝐸𝑋𝐶𝐻𝑡−4 Adj. 𝑅2

Egypt

Algeria

0.015 (3.37)

0.21008 (2.11)

0.0305 (2.92)

0.0195 (5.23)

−0.29 (4.65) 0.16 (1.62) 0.26 (3.66)

−0.18 (3.31) −0.312 (2.38)

−0.15 (2.81) −0.15 (1.64)

−0.13 (4.44)

0.56 (3.06) 2.37 (2.09)

Δ𝐼𝑁𝐶𝑡−2 Δ𝐼𝑁𝐶𝑡−3

Jordan

−0.24 (2.61) −0.20 (2.29) 0.25 (4.21) 1.51 (1.63) 2.31 (1.06) 2.93(2.77)

−1.15 (2.52) 2.3876 (2.33) 0.0557 (0.26) -0.0641 (-1.72)

−1.73 (0.41) -0.3129 (-1.53)

-0.09045 (-2.67)

-0.0153 (-3.77)

0.02203 (-1.15)

-0.0105 (-.012) -0.0001 (-0.06) −0.001 (-4.15)

-0.0791 (-.0.33) 0.08031 (0.41)

0.005 (1.22)

0.42

0.45

−2.16 (2.95) -0.0427 (-2.40)

0.04471 (-2.56)

-0.0011 (-2,93)

−0.002 (-2.18)

-0.008 (-3.61)

-0.005 (-2.73)

0.44

0.78

0.53

Figures in parentheses are the absolute t−statistics.

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−0.07 (4.35)

0.48


Considering that each regressand in Table 3 is cast in first-difference, the empirical results suggest that the statistical fit of each model to the data is satisfactory, as indicated by the values of adjusted R2, which ranged from a low of 0.42 in Iran to a high of 0.78 in Jordan. Moreover, the results show that the error-correction term's coefficient is statistically significant in each of the six cases and is always negative, as expected. These findings support the validity of an equilibrium relationship among the variables in each cointegrating equation. This implies that overlooking the cointegrating relationships among the variables would have introduced misspecification in the underlying dynamic structure. Also, the change in real exports per quarter that is attributed to the disequilibrium between the actual and the long-run equilibrium levels is measured by the absolute values of the errorcorrection term of each equation. There is substantial inter-country variation in the adjustment speed to the last period's disequilibrium, with Saudi Arabia having the largest value and Algeria the smallest. The results point to the existence of market forces in the export market that operate to restore long-run equilibrium after a short-run disturbance. On the other hand, since the sum of the estimates on current and lagged values of Δ𝐸𝑋𝐶𝐻𝑡 is negative for all countries, we conclude that exchange rate volatility has a negative short-run effect on foreign trade in addition to its adverse long-run effect established earlier. Summary and Conclusions Our results concerning the effects of exchange rate volatility on export flows suggest that there is a negative and statistically significant long-run relationship between export flows and exchange rate volatility in each of the six MENA countries. In addition, we also find evidence for a negative short-run effect of exchange rate volatility on export flows in all MENA countries studied. Our results have several policy implications. First, and foremost, economic policies that aim to stabilize the exchange rate (of which the establishment of a common currency area would be the most pronounced) are likely to increase the volume of trade among MENA countries. In this direction, the need is to establish a transparent exchange rate system under which the stability of the real exchange rate is achieved and maintained, and getting the targeted exchange rate right should be the essential part of the overall trade and economic growth strategy. Secondly, the data set for each country covers the current floating exchange rate era and thus allows us to address the stability over time of the estimated dynamic models during this period. This is essential for appropriate policy conclusions to be inferred from the estimated results. Third, by considering a vector error correction model, this study provides estimates of the speed of adjustment or the average time lag for adjustment of exports to changes in the explanatory variables as well as the short-run effects of exchange rate volatility on exports. Finally, each estimated model satisfies several recently-developed econometric tests in the analysis of timeseries data for issues such as cointegration and stationarity. Endnote 1

There is literature in which exchange rate volatility is used as a regressor in import and export equations and generally, the coefficients on exchange rate volatility are either insignificant or small enough to suggest

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that a reduction in exchange rate volatility has a small effect on trade. See, for example, Cushman (1988), Gotur (1985), Kenen and Rodrik (1986), Klein (1990), and Thursby and Thursby (1987).

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