exchange rate volatility and trade flows: a review article

Exchange rate volatility and tradeflows: a review article

Mohsen Bahmani-Oskooee and Scott W. HegertyThe University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA

Abstract

Purpose – Since the last review article by McKenzie, the literature has experienced a surge in thenumber of empirical articles. These new contributions, coupled with those that were overlooked byMcKenzie, set the stage for this review. Many of the recent studies have been empirical in nature andthese deserve specific attention. Thus, this paper aims to survey and review all of the studies bypaying attention to the attributes outlined in the text.

Design/methodology/approach – This paper examines the vast empirical literature, up to 2005, toassess the main trends in modeling and estimating these trade flows at the aggregate, bilateral, andsectoral levels.

Findings – The increase in exchange-rate volatility since 1973 has had indeterminate effects oninternational export and import flows. Although it can be assumed that an increase in risk may lead toa reduction in economic activity, the theoretical literature provides justifications for positive orinsignificant effects as well. Similar results have been found in empirical tests. While modelingtechniques have evolved over time to incorporate new developments in econometric analysis, no singlemeasure of exchange-rate volatility has dominated the literature.

Originality/value – An argument put forward by the opponents of the floating exchange rates isthat such rates introduce uncertainty into the foreign exchange market, which could deter trade flows.However, a theoretical argument is put forward by some to show that uncertainty could also boosttrade flows if traders increase their trade volume to offset any decrease in future revenue due toexchange rate volatility. The empirical literature reviewed in this paper supports both views.

Keywords Exchange rates, International trade

Paper type Case study

I. IntroductionAfter the post-war Bretton Woods system of fixed exchange rates collapsed in 1973,the relative prices of currencies began to fluctuate. These fluctuations broughtincreased uncertainty to traders; this risk may influence the volume of internationaltrade. Since the beginning of the current float, numerous theoretical papers have beenwritten to explain the effects of increased exchange-rate volatility on trade, and evenmore have been published evaluating these ideas empirically. These studies haveapplied different methods and obtained different results, but no consensus has beenreached regarding how to model, or even how to properly measure, exchange-ratevolatility.

Since the last review article by McKenzie (1999), the literature has experienced asurge in the number of empirical articles. These new contributions, coupled with thosethat were overlooked by McKenzie, set the stage for this review. Many of the recentstudies have been empirical in nature and these deserve specific attention. Thus, thispaper aims to survey and review all of the studies by paying attention to theirfollowing attributes. First, in section II we provide a brief overview of the theoreticalliterature that outlines the reasoning behind why increased exchange-rate volatilitymight hurt – or help – the volume of trade. Next, in section III the question of finding

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Exchange ratevolatility

211

Journal of Economic StudiesVol. 34 No. 3, 2007

pp. 211-255q Emerald Group Publishing Limited

0144-3585DOI 10.1108/01443580710772777

an “appropriate” measure of exchange-rate volatility is addressed. In section IV, V, andVI, the empirical literature is assessed at the aggregate, bilateral, and sectoral levels.Concluding remarks for future research are outlined in section VII.

II. Exchange rate volatility and trade flows: theory and empiricsThe theoretical backgroundThe first theoretical papers that explained the effects of exchange-rate riskhypothesized that, in the absence of any mechanism to reduce this risk, volatilitywill reduce the volume of trade. Ethier (1973) shows that, if traders were uncertain as tohow the exchange rate affects their firms’ revenue, the volume of trade will be reduced.This uncertainty could be alleviated, however: Clark (1973) notes that whilerisk-aversion among traders might depress the volume of a country’s exports, perfectforward markets might reduce this effect. Baron (1976) finds that forward markets maynot be sufficiently developed, and traders may still be unsure of how much foreignexchange they want to cover. Hooper and Kohlhagen (1978) further outline the theorybehind risk aversion in assessing the effects of exchange-rate volatility on trade pricesand quantities, incorporating both supply and demand effects. Gagnon (1993) putsforth a theoretical “upper bound” to the size of this negative effect.

Other theoretical papers, however, show that increased exchange-rate volatilitymight have the opposite effect and increase the volume of trade. Viaene and de Vries(1992) note that, because importers and exporters are on opposite sides of a riskytrading relationship, their respective roles are reversed, leading to a positive coefficienton a volatility variable for one partner. Franke (1991) demonstrates that, under verygeneral conditions, a firm might benefit from increased volatility and thus increase thevolume of its exports in response. Sercu (1992) also shows that volatility can increasetrade, as it increases the probability that the price a trader receives might exceed tradecosts. Sercu and Vanhulle (1992) theorize that increased volatility increases the value ofexporting firms, thus encouraging exports. Dellas and Zilberfarb (1993) use anasset-market approach to explain a positive effect. Broll and Eckwert (1999) concludethat volatility increases the value of a trader’s option to export; since this risk increasesthe potential gains from trade, the volume of trade will increase accordingly.

Still other papers conclude that volatility has no discernible impact on the volume ofinternational trade. Willett (1986), noting that the empirical evidence up to that timehad not revealed the expected reduction of trade, hypothesizes that this had beenbecause international risk increased in the floating period, but international anddomestic risk had behaved differently. He called for analyses of specific industries todetermine the specific industry-level risk effects. At the aggregate level, this adds tothe large body of theoretical literature that predicts that volatility will have a negativeeffect, a positive effect, or no effect at all on trade flows.

Estimation procedures: an overviewIt was not long before these theories were put to the test. Early empirical studies reliedon simple methods such as Ordinary Least Squares to evaluate the effects of exchangerate volatility. Over time, the techniques were refined, incorporating distributed lags tocapture dynamic effects; instrumental variables to account for endogeneity; andvarious procedures to control for autocorrelation. As time series analysis, rather thansimple regression, became more popular, such techniques as the Vector Autoregression

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(VAR) were applied to this area of research. Once co-integration analysis wasintroduced to time-series econometrics, economists were able to avoid many of theproblems – such as “spurious correlations” – created by relying on an overly simpletechnique such as OLS. Thus, the progressive refinement of econometric technique isapparent as the literature incorporates more and more complicated techniques.

While there are still different models of the determinants of trade flows – such asthose based on gravity models and those based on income and substitution effects –recent literature has employed simpler models than were used in older papers. Manypapers focus on export volumes as a function of the importer’s income, some measureof relative prices of export goods and competing domestic goods, and exchange-ratevolatility. Likewise, import volume is often modeled as a function of domestic income,relative prices, and exchange-rate volatility. While some agreement has been reachedregarding the basic empirical specifications, there has been no consensus as to a singlemeasure of volatility.

III. Measuring exchange rate volatilityThe methods of measuring volatility have evolved over time to reflect new advances ineconometric techniques. Nonetheless, there has not yet emerged a clearly dominantapproximation for uncertainty. The most common is some measure of variance, but theexact construction of this measure differs from study to study. The volatility variablemay be constructed as the standard deviation of a rate of change, or the level, of avariable; a moving standard deviation, or a within-period one; or employ the nominal,or the real, exchange rate.

Whether the nominal or the real exchange rate is used differs among studies. On theone hand, the real exchange rate captures the true relative price of a traded good; sincethe real exchange rate incorporates the price levels of the trading countries, however, itcaptures volatility in these price levels as well. Thus, nominal exchange rate volatilitywas often preferred at first. Akhtar and Hilton (1984) were among the first to measurethe effects of uncertainty, measuring the standard deviation of daily observations ofthe nominal exchange rate during each three-month period. Later papers have usedeither the nominal or real rate, or both rates with the aim of comparing the measures.Here, neither rate dominates the literature.

Also important are the properties of the method used to approximate volatilityitself. Later estimates involved using the moving standard deviation of the monthlychange in the exchange rate. Kenen and Rodrik (1986) brought attention to this method,which has the advantage of being stationary. Before the advent of co-integrationanalysis, this was a very desirable property. Such authors as Bleaney (1992) performeda similar analysis, using the level rather than the rate of change in the exchange rate.Some authors have introduced other methods of measuring volatility, which often tookadvantage of newer time-series methods. Autoregressive ConditionalHeteroskedasticity (ARCH), developed by Engle and Granger (1987) as a measure ofvolatility in time-series errors, is a popular measure of exchange-rate volatility in muchof the literature surveyed below. This procedure models the variance of each period’sdisturbance term as a function of the errors in the previous period(s), with the simpleARCH(1) modeled as follows:

s 2ð1tÞ ¼ a0 þ a112t21 ð1Þ


213

This model can be extended to an ARCH(p) model with the addition of more lags,where p indicates the number of total lags in the model:

s 2ð1tÞ ¼ a0 þ a112t21 þ :::þ ap1

2t2p ð1bÞ

A further extension is Generalized Autoregressive Conditional Heteroskedasticity(GARCH), which incorporates moving-average processes. Pattichis (2003), described inmore detail below, shows that for his sample, an ARCH-based volatility measure isstationary.

Other proxies have also been used. Thursby and Thursby (1987) use the deviationof the log real exchange rate from its predicted value based on the trend of the variable:

lnRijt ¼ fi

0 þ fi1t þ fi

2t2 þ 1ijt ð2Þ

The measure of volatility is the standard deviation of the actual minus the fitted value,using monthly data. Asseery and Peel (1991) use the squared residuals from anARIMA process to form another proxy for volatility; they find this measure to besuperior to ARCH, and the authors’ estimate had the desirable property of stationarity.Some papers have introduced their own measures of volatility as well. Table I outlinesthe main proxies for exchange-rate volatility:

While some measures are more popular than others, none stands out as the standardvolatility proxy.

Stochastic properties of the measureCertain studies have focused specifically on evaluating the properties of exchange-ratevolatility itself, rather than solely on its effects. Although these have brought to lightthe fact that volatility may not be modeled using simple methods, these conclusionshave not been applied in the empirical literature. Rana (1981) compares differentmeasures of risk for eight Asian countries and concludes that these variables conformto a non-normal probability distribution. The author thus questions whether the“conventional” volatility measures gave correct results. Pritchett (1991) examines realexchange-rate volatility for 56 developing countries and finds that the stochasticproperties of volatility, while often overlooked, are key in understanding uncertainty’seffects on trade flows. While the standard deviation (the second moment of thevariable) is the most-common basis for measures of volatility, Pritchett finds thatskewness and kurtosis (the third and fourth moments, respectively) are equallyimportant. The study also indicates that the non-normality of the volatility variablelimited the effectiveness of the standard-deviation-based volatility measure. Thus,these authors have found that exchange-rate volatility may not be correctly proxied inthe bulk of the literature, but later papers have not heeded their conclusions. Theycontinue to rely on the variance, rather than the higher moments of the variable.

Authors have also drawn attention to proper model specification as well as to thestochastic properties of the volatility term. Bini-Smaghi (1991) makes the case thatspecial attention should be paid to the precise methods of modeling both exchange-ratevolatility and the economic relationship itself. Using OLS to perform his analysis, headdresses problems that were apparent using this particular specification. Still, manyof his comments were insightful: He calls for the use of disaggregated, sectoral data; ameasure of competitors’ prices (rather than the WPI or GNP deflator) that is not

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217

correlated with volatility; proper specification that controls for autocorrelation; andapplying the best proxy for exchange-rate risk. Bini-Smaghi states that the nominal,rather than real, exchange rate should be used as the basis of this proxy, becausefluctuations in relative prices represent an additional, separate risk to traders. Theauthor calls for an instrumental variable method of constructing the volatility term,because of the potential problems associated with many of the popular proxies in theliterature. The Akhtar-Hilton within-period standard deviation, for example, fails tocapture long-run shifts in the exchange rate. The moving standard deviation ofpercentage changes in the exchange rate, used by Kenen and Rodrik, is “moresatisfactory,” (p. 933) but has stochastic properties that make it potentially flawed.Thus, Bini-Smaghi suggests further research into model specifications that couldcapture significant effects of exchange-rate volatility. West and Cho (1994) test thepredictive ability of a number of volatility measures and find that, for time horizonslonger than one week, it is difficult to choose between the various models.

Seabra (1995) tests several measures of exchange rate uncertainty in his study ofpurchasing power parity in Latin America. After conducting tests for PPP for 11 LatinAmerican countries, Seabra evaluates many of the volatility proxies that are prevalentin the literature. He concludes that ARCH-based methods are most efficient. Pattichis(2003) generates an ARCH-based measure of nominal and real exchange-rateuncertainty for the 15 EU countries to test whether it is stationary. Using this method,he performs the Dickey-Fuller test and finds that for the countries in his sample,exchange-rate volatility is I(0), or stationary. Thus, the author concludes that if tradeflows are I(1), there can be no co-integration between these flows and exchange-ratevolatility[1]. However, this need not be the case, as has been shown by the “boundstesting” approach to co-integration of Pesaran et al. (2001). In this relatively newapproach, there is no need for unit-root testing, and variables in a model can be I(1), I(0)or some combination of the two. Even though these studies have maderecommendations as to the “best” volatility proxies, the empirical analyses in thisarea have not settled on a single measure. Table I gathers all different measures ofvolatility and identifies studies that have used each measure.

Given what has been said about these different measures, where does the literaturestand in terms of the impact of exchange-rate volatility on trade flows? We turn to thisquestion we turn to next. For ease of exposure, we classify all existing empiricalstudies into three categories. Those that have used aggregate trade data between onecountry and the rest of the world; those that have used trade data between twocountries, (bilateral trade); and those that have disaggregated the bilateral trade databy sectors or by industries.

IV. Empirical studies using aggregate dataAggregate data measures the trade flows of a nation to all its trading partners or to therest of the world. Early studies used Ordinary Least Squares (OLS) to evaluate thesensitivity of the aggregate trade flows to a measure of exchange-rate uncertainty;more recent papers have applied newer, more sophisticated techniques, includingtime-series and panel-data methods. For convenience, we classify the studies in thisgroup by the type of method used in estimating their models.

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OLS-based studiesThe earliest studies performed basic regressions of trade flows on their determinants.While there are a variety of functional forms to choose from, the most basic structure isto set exports as a function of world GDP, relative prices, and a volatility term. Initialstudies, however, incorporated a number of additional variables.

Akhtar and Hilton (1984) employ a polynomial distributed lag method in their OLSestimate of the effects of exchange-rate volatility. These lags allow for delayed effects;traders might respond to volatility from a previous period just as they might react tocurrent conditions. Using a within-period volatility measure, the authors set exportvolume as a function of foreign income, foreign capacity utilization (to capturenon-price rationing), and relative prices. Likewise, they model import volume as afunction of domestic income, the ratio of foreign to domestic capacity utilization, andrelative prices. Using data for the USA and Germany, they estimate their models usingquarterly data over the period 1974-1981. The authors find that volatility had asignificantly negative effect on German exports, German imports, and US imports, butno effect on US exports. Thus, their conclusions conform to the theory that increasedrisk reduces trade flows.

This was quickly challenged by Gotur (1985), who re-specifies the equations toarrive at drastically different results. Using the same methodology as Akhtar andHilton, she re-works the study with certain modifications. First, she includes France,the UK, and Japan in his sample and repeats Akhtar and Hilton’s analysis. Germanexports and imports are found to have been negatively impacted, and Japanese exportsare shown to be positively affected, but the other seven trade flows register no effect.Next, Gotur assesses the robustness of the earlier paper’s results. She charges thatAkhtar and Hilton had applied the Cochrane-Orcutt procedure to control forautocorrelation, even in the cases in which the problem was not even present; Goturthus applies the procedure only when the Durbin-Watson statistic calls for it. Inaddition, she changes the sample period under investigation to account for lagstructure; imposes a polynomial lag; and adapts the volatility measure to use adifferent definition of the effective exchange rate, as well as to incorporate the rate ofchange, rather than the level, of the exchange rate. She thus finds that most resultswere insignificant, repudiating the results of Akhtar and Hilton’s paper. Thus, modelspecification seems to spell the difference between finding significant or insignificantresults for the same data. However, given the evidence of unit root in most macrovariables, the findings of both studies should be discounted since none accounted forintegrating properties of the variables, leaving the results to suffer from spuriousregression problem.

OLS studies that incorporate lag effects often find more significant effects thanthose that ignore them. The next major analysis, by Kenen and Rodrik (1986), uses OLSwith an Almon lag to estimate the following equation:

logVt ¼ a0 þk

Xak logRm

t2k þ a2 logYt þ a3T þ a4I1Amt ð3Þ

where V is import volume, R is the (lagged) real effective exchange rate, T is a trendterm, and the final term is one of the six measures of volatility described below. Theauthors’ choice of deterministic variables is admittedly simpler than that used byothers, most notably the bilateral study by Hooper and Kohlhagen (1978), but a


219

variation of this simple model has been used in the bulk of the studies that havefollowed. Still, debate about the best volatility measure led Kenen and Rodrik, likemany authors, to test the model with various proxies and then choose the one thatproduced the best results. The authors use three different standard deviation measures:the standard deviation of monthly percentage changes in the real effective exchangerate (REER) for 24- and 12-month periods are denoted as I1A and I1B, respectively; ofthe REER from a log-linear trend equation (I2A and I2B); and of the REER from theresiduals of an AR(1) process (I3A and I3B). The sample includes the G-7 countries plusBelgium, The Netherlands, Sweden, and Switzerland, using quarterly data over theperiod 1979-1984. The authors find that volatility had a negative impact on exportflows. Once again, given recent advances in time-series analysis, such aserror-correction modeling, the main criticism of specification (3) is that the dynamicsonly includes the lagged value of the exchange rate. Had they included the laggedvalues of other variables, their results could have been different. Furthermore, theresults could be, again, spurious because Kenen and Rodrik (1986) did not account forthe unit-root properties of the variables.

Using OLS (with corrections for serial correlation, but without lags) to estimateexport volumes as a function of foreign income, relative price, volatility and the exportearnings of oil-producing countries, Bailey et al. (1987) survey the theoreticalfoundations behind potential negative or positive effects of uncertainty on tradevolume, and empirically test them for 11 OECD countries. They find little support foreither hypothesis. The authors use four measures of volatility: absolute percentagechanges in the nominal and effective real exchange rates, as well as a moving standarddeviation of both rates. Of the 33 regressions they ran for the G-7 countries, volatilitycarried a significantly negative coefficient in only three. Still, without lags it may bequestioned whether the model specification itself led to a lack of significant results.

Rather than concentrating on modeling techniques, economists at this point stilltended to focus on finding an approximation that best captured uncertainty – ofteninventing their own. Peree and Steinherr (1989) devise a unique measure of long-termvolatility (see Table I) for their OLS estimate of the export volume of the UK, Belgium,Germany, Japan, and the USA, using annual data over the period 1960-1985. Theyincorporate two measures of relative price in their formulation of the following theequation:

Et ¼ a0 þ a1Y*

t þ a2Rt þ a3Wt þ a4Tt þ jt; ð4Þ

where export volume is a function of world demand (Y *), the real exchange rate (Rt),volatility (W), and the terms of trade (relative prices, T). The authors find that theirown volatility measure works no better than any other measure; thus, the mostaccurate proxy for uncertainty remained elusive. They also find that, with theexception of the United States, exchange-rate volatility has a significant negative effecton trade volume. Thus, for all its problems, OLS was able to capture significant effects.Most often, these were negative.

Most studies surveyed up to this point examine the exports of the developed world,but the range of countries studied began to be extended to less-developed countries.These analyses also led to mixed results. Corbo and Caballero (1989) surmise that arisk-neutral firm might benefit from increased risk as marginal return rises, but that arisk-averse firm may react negatively to uncertainty. The authors then apply this

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theory to less-developed countries in Asia and South America. Approximatingvolatility as a four-quarter moving standard deviation of the real exchange rate, theauthors use OLS and instrumental variables to estimate the export demand functionsof Chile, Colombia, Peru, Philippines, Thailand, and Turkey. In addition to otherdeterminants, they include lagged exports to capture learning by doing. The authorsstate that OLS will result in a downward bias for price elasticities. Therefore, the logsof the relative CPIs of DCs and LDCs, world demand, lagged exports, and time, as wellas the standard deviation of the log real exchange rate, were chosen as instruments.They find that when they use IV estimation, volatility is shown to have a clear,negative effect on export volumes.

Medhora (1990) applies OLS on the pooled import volume of the countries of theWest African Monetary Union: Benin, Cote d’Ivoire, Niger, Senegal, Togo, and BurkinaFaso. The author notes that the Union’s exports follow a completely differentspecification, which depends on such factors as government policies and weather.Volatility is measured using the Akhtar-Hilton definition: the standard deviation of thenominal effective exchange rate (NEER) within each year – testing various ranges ofpossible sub-periods (weekly, monthly, and quarterly). Estimating what has become apopular, yet simple model, of export volume as a function of world income, relativeprices, and volatility, the author sets up what has become a much-used equation:

lnXt ¼ b0 þ b1 lnWt þ b2 lnPt þ b3Ext ð5Þ

An analysis of the period 1976-1984 finds that not only did volatility have nosignificant effect, but there was no difference between the choice of sub-periods whenformulating a volatility proxy.

Bahmani-Oskooee and Ltaifa(1992) include additional variables in their OLSanalysis that included both developed and less-developed countries. They estimate thefollowing equation using annual data over the period 1973-1980:

logXi ¼ aþ b logYi þ c log Popi þ dDEVi þ e logsi þ 1i ð6Þ

where X ¼ export volume, Y ¼ foreign income, s ¼ the standard deviation ofpercentage changes in the REER, Pop¼population, and DEV¼the rate of devaluationrelative to the US dollar. The population variable was drawn from the gravity model ofBrada and Mendez (1988), but here it is used to proxy potential excess labor that couldbe embodied in exports. Assessing 86 countries (19 developed and 67 LDCs),Bahmani-Oskooee and Ltaifa find that LDCs are more sensitive to exchange-ratevolatility.

Bahmani-Oskooee and Payesteh (1993) continue this look at less-developedcountries, this time analyzing both imports and exports for Greece, Korea, Pakistan,the Philippines, Singapore, and South Africa from 1973-1990. Testing numerousspecifications of relative prices and dynamic effects, they incorporate an Almon Laginto some of their specifications. Trade flows are specified as functions of income,relative prices (lagged REER, or ratio of import prices to domestic prices), andvolatility (measured by the standard deviation of percentage changes in the REER).The authors find that for half of the cases, volatility had depressed trade flows, butwhen they apply co-integration (time-series) analysis, they find no long-runrelationship between trade flows and their determinants. This study was followedby Bahmani-Oskooee (1996), who uses the Johansen co-integration procedure to


221

evaluate the same countries that were covered in the 1993 paper, only using log ratherthan level variables. Using these transformed variables, as well as a time-seriesapproach, the results are significantly negative for both exports and imports.

Modern time-series studiesWhile the Ordinary Least Squares is widely used to estimate time-series,cross-sectional, and panel models, eventually modern and specific time-seriesanalysis began to surpass OLS as the main econometric tool in this part of theliterature. The main goal of these relatively new techniques is to account forintegrating properties of the variables so that the results are not considered spurious.ARCH has become a popular method of approximating volatility, and VAR andespecially error-correction models have become the most commonly utilized estimationtechniques. These methods, however, were not conclusive at first. Lastrapes and Koray(1990) apply the Vector Autoregression to their analysis of US trade. At the time, theyviewed the VAR to be superior because the procedure does not require the specificationof exogenous variables. Their VAR incorporates eight variables: export volume,import volume, volatility (measured as the 12-month moving standard deviation of theREER), the M1 money supply, the three-month T-bill rate, income, and the ConsumerPrice Index). Using monthly data (1973-1987), the authors find no effect on exports ofexchange rate volatility, but they find a small negative effect on imports.

Co-integration analysis continued to make inroads during the early 1990s. Asseeryand Peel (1991), in an often-cited study, adopt this method to evaluate the exports ofJapan, West Germany, the USA, the UK, and Australia over the period 1972-1987.Assessing export volume as a function of income and relative prices, they use quarterlydata and a measure of volatility that is based on the residuals from an ARIMA processfitted to the log real exchange rate. They find a positive effect of exchange ratevolatility for most countries.

Chowdhury (1993) incorporates co-integration analysis as well – and finds theopposite result. Export volume is determined by foreign GDP and relative prices, andvolatility is approximated by an eight-quarter moving standard deviation of thegrowth rate in the REER. For the G-7 countries over the quarterly period from1973-1990, volatility is shown to have a significant negative effect for all countries.Chowdhury concludes that past time-series models had not detected the same effectbecause of the properties of the models themselves, which had not takennon-stationarity into account. Thus, co-integration analysis corrected the flaws ofprevious models, and only refined techniques were able to capture relationships thatother studies had overlooked.

Kroner and Lastrapes (1993) find similar results using monthly data for the sameperiod, using additional variables in their specification as well as a multivariateGARCH proxy for volatility. The authors incorporate not only the exchange rate andthe relative price as determinants of the quantity of exports, but also labor costs, andthree lags of export volume itself. They find that exchange-rate uncertainty had asignificantly negative impact on the export volume of the USA, UK, and positiveimpact in the results for France and Germany. It had no significant impact on Japaneseexports. Also assessing the impact of volatility on export prices, they find that priceswere affected even more than export quantities.

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Qian and Varangis (1994) also use ARCH to approximate volatility. They estimatethe short-run effects of exchange-rate uncertainty by using the lagged differencedvariables in the following export equation, which is an extension of a long-run versionof equation (5):

DXt ¼ a0 þ a1DSt þ a2DP*

t þ a3DWt þ a4rt þ a5f ðhtþ1Þ þX12

i¼1

dxiDXt2i

þX12

i¼1

lxiDPt2i þ 1xt ð7Þ

where S ¼ nominal exchange rate, P* is the foreign price level, W is the real wage rate,r is the real interest rate, f(h) is the ARCH measure of volatility, and P is the domesticprice level. The model is estimated using OLS for Canada, Australia, Japan, the UK,The Netherlands, and Sweden, using monthly data over the period 1973-1990. Themodel is also extended to look at bilateral relationships. While these had manysignificant coefficients for volatility, only Sweden has a significant (positive) aggregateresult (although Japan-USA and Canada-USA exports are negatively affected at thebilateral level). These findings should be viewed with caution, due to the fact that thedynamics of many variables have been ignored in (7).

Arize and Ghosh (1994) use not only ARCH, but three additional measures ofvolatility in their study: a five-quarter moving-average standard deviation ofexchange-rate growth; the recursive residuals from an AR(4) model of exchange-rategrowth; and the residuals of an ARIMA (1,1,0) process fitted to the log of the exchangerate. The model incorporates income and relative price as additional determinants ofUS exports during the period 1970-1997, and finds that volatility had a negative andsignificant coefficient. Most importantly, after performing a number of structuralstability testes, the authors conclude that the general export flow equation, often usedwithout volatility in other studies, is unstable without a volatility term.

With the advent of error-correction modeling, economists have been further able toanalyze both the short-run and long-run effects of uncertainty. Increased volatility mayproduce a temporary effect, but this may be reduced or eliminated over time asequilibrium is again reached. Using this approach, Arize (1995) models export volumeas a function of income, relative price, and volatility which is approximated as aneight-quarter moving standard deviation of the log of the REER. An analysis of theThe Netherlands, Sweden, Denmark, and Switzerland over the period 1973:II-1992:IVfinds that volatility has had a significant negative effect for all countries for both theshort- and the long-run. Additional papers by this author, which generally use a verysimilar empirical specification as the 1995 paper, have provided a wealth ofinformation about numerous countries, the trade flows of most of which appear to havebeen depressed by exchange-rate volatility. These studies use either the Engle-Grangermethod of co-integration, which tests for stationarity in the residuals of an OLSestimation of stationary variables, or the Johansen approach, which tests for thepresence of one or more co-integrating vectors. Those studies that employ theEngle-Granger method of co-integration include Arize (1996), who assesses UK exportsand concurs with Arize and Ghosh (1994) that the trade-flow equation is incorrectlyspecified if it omits volatility. Arize (1997) obtains different results from previous


223

studies of G-7 trade flows, obtaining significantly negative results for all countries.Arize and Malindretos (1998) look at Australia and New Zealand, using ARCH as wellas a recursive method to capture volatility. They find mixed results: Volatility is shownto have had a positive effect for Australia, and a negative one for New Zealand. Arizeand Shwiff (1998) re-examine the import flows of the G-7 countries, per Kenen andRodrik (1986), using two additional measures of volatility: the log deviation of theREER from expected value given by an AR(4) process, as well as predicted changes inthe REER. They show positive results for Canada and insignificant results forGermany. Arize et al. (2003), who perform the classic study for Turkey, Korea,Malaysia, Indonesia, and Pakistan, obtaining negative results. Those papers thatemploy the Johansen co-integration procedure include Arize (1998), who analyzes theeffects of volatility on the import volume of Belgium, Denmark, Finland, France,Greece, The Netherlands, Spain, and Sweden; and Arize et al. (2000), who study theexport volume of 13 LDCs[2]. While most results support the adverse effect ofexchange-rate volatility, Finland’s volatility coefficient was shown to be insignificant;and Greece and Sweden showed positive and significant coefficients.

This body of literature outlined here shows mixed, but mostly negative, effects ofexchange-rate volatility on trade flows. It is important to note that these models, likemuch of the literature of the past ten years, also use simple specification – oftenemploying only income, relative prices, and volatility as determinants of trade flows.Most recent literature also makes use of similar methods of time-series analysis (eitherthe Engle-Granger or the Johansen method of co-integration), and similar modelspecifications that use income and relative prices, even if there is no single, consistentmeasure of volatility. Using the Engle-Granger method, Doroodian (1999) measuresvolatility with both ARMA residuals and a GARCH-based measure to study theexports of India, Malaysia, and South Korea over the period 1973:II-1996:III. Theauthor finds significantly negative effects of exchange-rate volatility on trade flows.Sukar and Hassan (2001) used a GARCH-based measure as well, and find a similarnegative result for U.S. exports from 1975:I to 1993:II. Doganlar (2002), approximatingvolatility as a four- or eight-quarter moving standard deviation of the real exchangerate, also finds that volatility depressed the exports of Turkey, South Korea, Malaysia,Indonesia, and Pakistan. Poon et al. (2005) achieves mixed results: Modeling the exportvolume of Indonesia, Japan, South Korea, Singapore, and Thailand as functions ofworld income, the REER, the terms of trade and volatility (approximated by the12-period moving standard deviation of the log REER) over the quarterly span from1973:II to 2002:II, the authors find that volatility depressed the exports for Japan, SouthKorea, and Singapore, and had a significantly positive effect on those of Thailand.

One study measures the effects of exchange-rate volatility using an entirelydifferent exchange rate. Using the Johansen method, Bahmani-Oskooee (2002)evaluates both the exports and imports of Iran. Rather than using the official exchangerate for his volatility proxy, he uses the black-market rate – noting that the gapbetween the two rates had become quite large. Employing a four-year moving standarddeviation of percentage changes in the black-market exchange rate as his volatilityproxy, Bahmani-Oskooee finds that black-market exchange-rate volatility hasdiscouraged Iranian trade flows. In the absence of any other study that uses blackmarket exchange rate volatility, this is one area where future research needs toconcentrate.

JES34,3

224

Panel studiesNot all papers have used time-series analysis to estimate their models. Sauer andBohara (2001) use a panel-data model to evaluate volatility’s influence on the exports of91 developed and less-developed countries. Three measures of volatility are tested: oneis ARCH-based, the second uses the eight-quarter moving standard deviation of theerrors from an AR(1) process on the log RER, and the third uses an eight-quartermoving standard deviation. Employing both the fixed- and random-effect models, theauthors model export volume as a function of world income, and one or both of tworelative-price measures: the real effective exchange rate (RER) and each country’sterms of trade (TOT) as in (8):

logXit ¼ ao þ a1 logY*

it þ a2 logRERit þ a3 logTOTit þ a4 logVOLit þ uit ð8Þ

The terms of trade measure of relative price appears to have been significant in allspecifications, but the real exchange rate – or both terms together – is also significantin most cases. When the countries are evaluated together, the effect of volatility issignificantly negative; when the countries are separated by region, the developedcountries and Asia register no effect, but Africa and Latin America show asignificantly negative effect. The authors attribute this regional disparity to thediffering opportunities for currency hedging in different countries. Thus, panel dataapproaches arrive at the same conclusion as purely time-series analyses. Table IIprovides summary and main features of all studies that have relied upon aggregatetrade data.

V. Empirical studies using bilateral dataWhile studies of countries’ aggregate trade flows produced important results – thatexchange-rate volatility generally depressed trade flows – there was still a possibilitythat significant results might be concealed. The well-known “aggregation bias” was apotential problem if a country’s bilateral trade flows with different partners producedoffsetting positive and negative effects that cancelled each other out at the aggregatelevel. Thus, bilateral studies may provide a more accurate analysis, as they evaluatethe bilateral exchange rate – which is the rate that is actually used by exporters andimporters. Just as has been shown to be the case with aggregate studies, over time theempirical methods used in bilateral studies have improved, but the results they havebeen consistent. These studies differ in another aspect, however – the modelspecification employed by has incorporated a wide range of explanatory variables.While the first studies made use of more variables than more recent ones have, gravitymodels are also popular in studies of exchange-rate uncertainty. In addition, otherpapers have measured the growth rates, rather than the levels, of the variables inquestions to make them stationary.

Early model specificationsThe first bilateral analyses of the effects of exchange-rate volatility also incorporatemore economic variables than do later studies. Hooper and Kohlhagen (1978)performed one of the first bilateral studies, using OLS (with a Cochrane-Orcuttcorrection) to assess the trade flows of the USA and Germany with their tradingpartners over the 1966I-1975IV. Volatility is proxied three different ways: using thestandard deviation of the 13 weekly spot and forward exchange-rate observations


225

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tion

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orts

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por

tslo

gv

ersi

onof

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man

i-O

skoo

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dP

ayes

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(199

3)

Lag

ged

RE

ER

,In

com

e,tr

end

Gre

ece,

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ea,

Pak

ista

n,

Ph

ilip

pin

es,

Sin

gap

ore,

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thA

fric

a

Qu

arte

rly

:19

73-1

990

Sig

nifi

can

tly

neg

ativ

e

Bah

man

i-O

skoo

ee(2

002)

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ese

ries

Joh

anse

nm

eth

odof

co-i

nte

gra

tion

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por

tv

olu

me

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ort

vol

um

e

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r-y

ear

mov

ing

stan

dar

dd

evia

tion

ofp

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nta

ge

chan

ges

inR

ER

RE

R,

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me,

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nd

term

Iran

Qu

arte

rly

:19

74:1

-199

4:4

Sig

nifi

can

tly

neg

ativ

e

(continued

)

Table II.


227

Au

thor

(yea

r)M

eth

odD

epen

den

tv

aria

ble

Mea

sure

ofv

olat

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ind

epen

den

tv

aria

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oun

trie

sT

ime

span

Res

ult

s/re

mar

ks

Bah

man

i-O

skoo

eean

dL

taif

a(1

992)

OL

SE

xp

ort

vol

um

eS

tan

dar

dd

evia

tion

ofp

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nta

ge

chan

ges

inR

EE

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Pop

ula

tion

,d

eval

uat

ion

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com

e86

cou

ntr

ies:

19d

evel

oped

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LD

Cs

Yea

rly

:19

59-1

989

LD

Cs

are

mor

ese

nsi

tiv

eto

exch

ang

e-ra

tev

olat

ilit

yB

ahm

ani-

Osk

ooee

and

Pay

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h(1

993)

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ese

ries

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odof

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nte

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nd

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dev

iati

onof

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arte

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com

e,T

ren

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73-1

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etw

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bso

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cen

tag

ech

ang

es,

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tion

ofb

oth

NE

ER

and

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me,

Rel

ativ

ep

rice

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xp

ort

earn

ing

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oil-

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73:1

-198

4:3

3/33

reg

ress

ion

ssi

gn

ifica

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yn

egat

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rest

un

affe

cted

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bo

and

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989)

OL

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um

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qu

arte

rm

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gst

and

ard

dev

iati

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Xþ

wor

ldin

com

e,L

agex

por

ts,

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eC

hil

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eru

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hil

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rly

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not

spec

ified

Neg

ativ

eef

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ry(1

993)

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ng

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eth

odof

co-i

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por

tv

olu

me

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ht-

qu

arte

rm

ovin

gst

and

ard

dev

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wth

rate

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R

For

eig

nin

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e,re

lati

ve

pri

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arte

rly

:19

73-1

990

Sig

nifi

can

tly

neg

ativ

efo

ral

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De

Vit

aan

dA

bb

ott

(200

4b)

Tim

ese

ries

AR

DL

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nd

sT

esti

ng

app

roac

hto

co-i

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gra

tion

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por

tv

olu

me

Six

-qu

arte

rm

ovin

gav

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and

ard

dev

iati

onof

log

lev

elof

RE

R

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ativ

ep

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sU

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arte

rly

:19

87:1

-200

1:2

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sig

nifi

can

tre

lati

onsh

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002)

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ese

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eth

odof

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por

tV

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me

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ror

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and

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ativ

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urk

ey,

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don

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arte

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egin

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yn

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ood

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(199

9)T

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seri

esA

RM

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um

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AR

CH

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por

tp

rice

s,p

d,

yIn

dia

,M

alay

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thK

orea

Qu

arte

rly

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73:2

-199

6:3

Sig

nifi

can

tly

neg

ativ

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ur

(198

5)O

LS

Ex

por

tv

olu

me

Sam

eas

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hta

r-H

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xp

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sig

nifi

can

tfo

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SA

and

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man

yw

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ifica

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and

all

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ade

flow

s

(continued

)

Table II.

JES34,3

228

Au

thor

(yea

r)M

eth

odD

epen

den

tv

aria

ble

Mea

sure

ofv

olat

ilit

yO

ther

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den

tv

aria

ble

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oun

trie

sT

ime

span

Res

ult

s/re

mar

ks

Hol

ly(1

995)

*T

ime

seri

esJo

han

sen

met

hod

ofco

-in

teg

rati

on

Ex

por

tv

olu

me

GA

RC

HW

orld

exp

orts

,rel

ativ

ep

rice

sU

KM

onth

ly:

1980

-199

6S

ign

ifica

ntl

yn

egat

ive

onsu

pp

ly,n

otd

eman

d

Ken

enan

dR

odri

ck(1

986)

OL

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/lag

sIm

por

tv

olu

me

3:st

and

ard

dev

iati

onof

mon

thly

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cen

tag

ech

ang

ein

RE

ER

I24-

and

12-m

onth

;st

and

ard

dev

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onof

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ER

from

log

-lin

ear

tren

deq

uat

ion

;st

and

ard

dev

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ER

from

AR

(1)

pro

cess

Inco

me,

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ged

RE

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nd

term

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plu

sB

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ium

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he

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her

lan

ds,

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eden

,Sw

itze

rlan

d

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arte

rly

:v

arie

s,19

79-1

984

Neg

ativ

e

Kro

ner

and

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trap

es(1

993)

GA

RC

H-i

n-M

ean

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por

tv

olu

me

GA

RC

HE

xch

ang

era

te,

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ativ

ep

rice

s,L

abor

cost

s,In

com

e,th

ree

lag

sof

exp

orts

US

A,

UK

,Ja

pan

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erm

any

,F

ran

ceM

onth

ly:

1973

-199

0S

ign

ifica

nt

imp

act

for

all

Las

trap

esan

dK

oray

(199

0)T

ime

seri

es8£

8V

AR

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por

tv

olu

me

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ort

vol

um

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12-m

onth

mov

ing

stan

dar

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tion

ofR

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eysu

pp

ly,

thre

e-m

onth

T-b

ill

rate

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com

e,P

rice

s

US

AM

onth

ly:

1973

:3-1

987:

12S

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ifica

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yn

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ive

for

imp

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otex

por

ts–

bu

tsm

all

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hor

a(1

990)

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por

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olu

me

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nd

ard

dev

iati

onof

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ER

w/i

nea

chy

ear

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eek

ly,

mon

thly

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dq

uar

terl

y

Inco

me,

Rel

ativ

ep

rice

sW

AM

U:

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in,

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ed

’Iv

oire

,N

iger

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eneg

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o,B

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ina

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o

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rly

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76-1

984

No

sig

nifi

can

tre

lati

onsh

ip

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tich

is(2

003)

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ese

ries

–co

-in

teg

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onon

lyG

AR

CH

EU

15M

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ly:

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mv

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olis

I(0)

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r(1

989)

OL

SE

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um

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wo

bas

edon

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par

itie

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ong

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m)

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me,

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erm

sof

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de

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elg

ium

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erm

any

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pan

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60-1

985

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net

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(200

5)T

ime

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met

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por

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me

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ard

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ER

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me,

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ER

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de

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ones

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an,

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gap

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nd

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arte

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-200

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ifica

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Kor

ea,

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gap

ore;

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nifi

can

tly

pos

itiv

efo

rT

hai

Ian

d (continued

)

Table II.


229

Au

thor

(yea

r)M

eth

odD

epen

den

tv

aria

ble

Mea

sure

ofv

olat

ilit

yO

ther

ind

epen

den

tv

aria

ble

sC

oun

trie

sT

ime

span

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ult

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mar

ks

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nan

dV

aran

gis

(199

4)O

LS

Fir

std

iffe

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ces

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por

tv

olu

me

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CH

NE

X,

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np

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s,R

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ged

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agg

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stra

lia,

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he

Net

her

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eden

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thly

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990

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rb

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ran

dom

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cts

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por

tv

olu

me

3:A

RC

H,e

igh

t-q

uar

ter

mov

ing

SE

from

AR

(1)

oflo

g(R

EE

R),

eig

ht-

qu

arte

rm

ovin

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reg

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ion

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g(R

EE

R)

ont,

t^2

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me,

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R,T

erm

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trad

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cou

ntr

ies:

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atin

Am

eric

an,

25A

fric

an,

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sian

,22

LD

Cs

Yea

rly

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73-1

993

Sig

nifi

can

tly

neg

ativ

efo

rL

atin

Am

eric

a,A

fric

a,n

otD

Cs

orA

sia;

Sig

nifi

can

tly

neg

ativ

efo

rte

rms

oftr

ade

Su

kar

and

Has

san

(200

1)T

ime

seri

esG

ran

ger

met

hod

ofco

-in

teg

rati

on

Ex

por

tv

olu

me

GA

RC

HIn

com

e,R

elat

ive

pri

ces

US

AQ

uar

terl

y:

1975

-199

3S

ign

ifica

ntl

yn

egat

ive

Table II.

JES34,3

230

within each period, and the absolute average difference over the 13-week period. Theauthors choose to limit the risk proxy to capture only exchange-rate risk and not othertypes of risk; they thus focus on the variance of the expected future spot rate. Afterattempting a nonlinear estimation technique, they choose a linear one, which ismodeled as follows:

q* ¼ d0 þ d1UC* þ d2UC þ d3PD þ d4Y þ d5CU þ d6EH * þ d7EH

þ d8d*s1=R1þ d9dsR1

ð9Þ

where asterisks denote the exporting country’s variables, UC is the unit cost ofproduction, PD represents domestic prices, Y is income, CU is capacity utilization, EHis an exchange-rate adjustment factor, s1=R1

is exchange-rate risk for the exportingfirm, and sR1

is exchange-rate risk for the importing firm. The authors find thatvolatility had no significant effect on the volume of trade; they conclude that their focuson short-run volatility may have neglected certain effects on quantity that may havebeen caused by long-run volatility. In addition, their study also formulates a priceequation that showed that exchange-rate volatility has had a significant impact onprices.

Similar, although somewhat simpler, specifications had different results in laterpapers. Cushman (1983), following Hooper and Kohlhagen (1978), models exportvolume as a function of the home and foreign unit cost of production (UC and UC*,respectively), nominal GNP (Y), the importer’s manufacturing capacity utilization (CU),the importers’ weighted real exchange rate (R), uncertainty (S), recent percentagechanges in R (M), and a dummy to capture the trade disruptions caused by a dockstrike (D):

Q ¼ a0 þ a1Y þ a2CU þ a3UC þ a4UC* þ a5R þ a6M þ a7S þ a8D ð10Þ

Volatility is approximated as a four-quarter standard deviation of the expected growthrate of the nominal exchange rate relative to inflation, and 14 trade flows – of the USAwith the UK, France, Germany, Canada, and Japan, and Germany with the UK, France,and Japan – over the quarterly period 1965-1977 are considered. Of these,exchange-rate risk has positive effect on trade flows (including two French flows)and negative effects on seven cases. In another paper, Cushman (1986) again uses OLSto test a similar specification for the export volume of the USA to the UK, TheNetherlands, France, Germany, Canada, and Japan over the floating period. The modeladds third-country risk, however – incorporating external effects. Cushman concludesthat these effects are necessary when formulating a trade model, in order to captureindirect as well as direct risk. Thus, those studies that had omitted this factor mayhave overstated the effects of direct (bilateral) risk. While Cushman finds that thenegative impact of uncertainty had been growing over the floating period,third-country effects need to be included. Few recent studies, however, do so;third-country effects are generally omitted.

Finally, Cushman (1988) tests a number of different volatility measures using thespecification of equation (10). They include: the four-quarter standard deviation ofpercentage changes in R, the 12-month moving standard deviation of the samemeasure, the nominal three-month exchange-rate expectations based on the forward


231

rate, and the 12-month moving standard deviation based on expectations. Quarterlydata for the floating period for the UK, The Netherlands, France, Germany, Canada,and Japan demonstrate negative results for ten of the 12 flows, and those volatilitymeasures based on the forward rate and assuming a “time-varying risk premium”appear to have a slightly better significance level. Trade flows to Japan show a positiveeffect. Thus, these early analyses find that exchange-rate uncertainty had had mixedresults.

Gravity models of bilateral tradeWhile these early models of bilateral trade used a number of purely economicvariables, gravity models use a more geographic approach. Trade might take placebecause of two countries’ proximity to each other, the size of their markets, commonborders, or common language between the two. Thus, favorable prices and exchangerates (substitution effects) may not matter as much as being right next door. This typeof model generally captures imperfect competition rather than completely free trade.Abrams (1980) uses a gravity-type model to assess the value (rather than the volume)of the bilateral exports of 19 countries using pooled OLS. He utilizes the standarddeviations of both the levels and the rate of change of the 12 monthly exchange-ratevalues within each year to proxy risk, in order to capture two types of risk: that basedon recent changes and that based on trend. Abrams formulates export value as afunction of the importing and exporting countries’ GDPs, the distance between eachpair of countries, the percentage difference in each pair’s real per capita incomes, anddummies for membership in the European Economic Community. Annual data overthe period 1973-1976 show that uncertainty had a significantly negative effect for thepooled sample for equations using both proxies for volatility.

A different gravity specification is used by Thursby and Thursby (1987), who studythe export values of 17 countries using annual data over the period 1974-1982[3]. In thismodel, the value of trade flows is broken into a price component and a quantitycomponent. The determinants of trade flows are both countries’ CPIs and GDPs; avariable that captures consumer tastes; relative export and import prices, transportcosts; tariff rates (proxied by dummies for membership in trade blocs; the nominalexchange rate, and hedging opportunities. The estimate of volatility used is thestandard deviation of the spot rate around a predicted trend; the estimation techniqueis OLS with lagged variables. The authors find that in the majority (10 of 17) of cases,uncertainty depressed trade flows. In addition, Thursby and Thursby test the “LinderHypothesis,” which states that, because producers design their goods to matchdomestic tastes, they export those goods mainly to countries that have similar income– which corresponds to similar tastes. They find strong support for this hypothesis aswell.

Additional papers have incorporated gravity models in their bilateral analyses.Brada and Mendez (1988) test the export values of 30 developed and less-developedcountries as a function of foreign income, population, distance, and the existence ofpreferential trade agreements between each pair of nations. In order to avoid a relianceon a specific measure of volatility, simple dummy variables are applied to representfixed and floating exchange-rate regimes between each pair of countries. Many of thecountries evaluated in the study were members of cooperative agreements such as theEuropean Monetary system; many of the countries’ currencies were pegged to the US

JES34,3

232

dollar, and other currencies were allowed to float to some degree. Using OLS for annualdata over the time span 1973-1977, which includes both “tranquil” and volatile periods,the authors’ results confirm the results of past research: that volatility reduces trade.Nevertheless, they conclude that this reduction is not as bad as the reduction of tradebrought on by the restrictive trade policies of countries that maintain fixed exchangerates.

Dell’Ariccia (1999) applies pooled OLS as well as fixed- and random effects paneldata and two-stage least squares models to study the 15 countries of the EuropeanUnion, plus Swizterland, over the annual period from 1975-1994. He tests the followinggravity model:

logðTRADEijtÞ ¼ gt þ aij þ b1 logðGDPitGDPjtÞ þ b2 logðDISTijÞ

þ b3 logð popitpopjtÞ þ b4BORDijt þ b5EUijt þ b1LANGijt

þ b7nijt þ 1ijt

ð11Þ

where between every pair of countries i and j, the total trade (bilateral exports plusimports, TRADE) is a function of the product of the countries’ incomes, the distancebetween the two countries (DIST), the product of the countries’ populations, anddummies to signify the presence of a common border (BORD), a common language(LANG), and membership in the European Union (EU). The variable aij is a shiftdummy to capture individual effects for each pair of countries; nijt representsexchange-rate volatility, proxied four different ways as the standard deviations of thefirst differences of the log nominal and real exchange rates; the sum of squares of theforward errors in the exchange rate; and the percentage difference between themaximum and minimum of the nominal spot rate; all of the measures use monthlyobservations within each year. For every measure of volatility employed, and for everymodel specification, the effect of uncertainty is consistently significantly negative. Theother deterministic variables are significant as well, and show the expected signs.Dell’Ariccia suggests that future studies use more disaggregated data. In addition, hedraws attention to the Exchange Rate Mechanism (ERM), which was designed toreduce uncertainty between the European currencies, and thus promote increasedtrade. When the volatility variables were included in the model, the ERM coefficientwas significant – but had a negative sign. While the author offers a number of possibleexplanations, this outcome remains a mystery.

Tenreyro (2004) addresses a number of the problems associated with the gravitymodel of bilateral trade, including properties of the error term, and potentialendogeneity – volatility may be partially determined by the level of trade. Using apseudo-maximum likelihood procedure, she corrects for the relevant biases. Likewise,an instrumental variable method – modeled as a logit procedure – is used to eliminateendogeneity. Volatility is modeled as the standard deviation using the movingstandard deviation method on the nominal exchange rate, and a gravity modelexamines exports as a function of distance, per capita GDP, population, area, anddummies for free-trade agreements, contiguity, common language, and colonialheritage. Analyzing 104 countries over the period from 1970-1997 using this nonlinearmethod, Tenreyro finds that nominal exchange-rate volatility has no effect on trade.


233

Studies using growth rates of variablesA few early papers realized that trend may play a role in time-series models, possiblyproducing misleading results. To de-trend the data, these studies employ the growth oftrade rather than at the level or log level of these flows. Thursby and Thursby (1985),applying pooled time series OLS for the years 1973-1977, examine the followingequation:

Xij ¼ a1 þ a2ADGij þ a3Vij þ a4MEij ð12Þ

where Xij, the rate of change of bilateral exports, is expressed as a function of ADGij,the absolute difference between the two countries’ GDPs growth rate, volatility(standard deviation of absolute percentage changes in the monthly nominal and realexchange rate), and MEij, the mean percentage change in the monthly exchange rate.The sample, which consists of the countries of the G-7 as well as Austria, Belgium,Denmark, Finland, Greece, The Netherlands, Norway, Portugal, South Africa, Spain,Sweden, Switzerland, and Turkey, shows an insignificant result at the aggregate level,but produced significant results – both positive and negative – at the bilateral level.Thus, exchange-rate volatility is shown not to reduce the level of trade, but rather toaffect the pattern of trade.

De Grauwe (1987) and De Grauwe and de Bellefroid (1987) find that the measure ofvolatility is important when assessing its impact on trade flows. The basic methodcommon to both papers is a regression of the average yearly growth rate of bilateralexports on a measure of trade integration, the growth rate of world income, the rate ofchange of the real bilateral exchange rate, and a volatility term. De Grauwe and deBellefroid (1987) pointed out that most previous studies evaluated the effects ofshort-run rather than long-run exchange-rate volatility by focusing on weekly ormonthly fluctuations. For this reason, they measure volatility by the standarddeviation of the yearly growth rates of the nominal and real exchange rates around themean. De Grauwe (1987) performs a Seemingly Unrelated Regressions (SUR) analysisfor the G-7 countries plus Belgium, The Netherlands, and Switzerland over the periods1960-1969 (fixed exchange-rate system) and 1973-1984 (floating period). The result:volatility has led to a slowdown in the growth of trade, but the coefficient wassignificant only if real, not nominal volatility is used. Other factors may be moreimportant in contributing to the slowdown in world trade, however, including theslowdown in trade integration during the 1970s and the decline in the growth rate inworld output. De Grauwe and de Bellefroid (1987) employ the same specification for thesame sample of countries over the same period, and add as an additional term thevariable of each country’s effective exchange rate. The coefficients are significant inreducing trade flows, especially when the real effective exchange rate is used; at thesame time, the effects of exchange-rate volatility are reduced when the new variable isadded. Thus, introducing third countries to the analysis gives significant results.

Third-country effects are included in Kumar and Dhawan (1991), who study theless-developed country of Pakistan using a bilateral specification similar to equation(5), in which export volume can be expressed as a function of importer’s income,relative prices, the nominal exchange rate, and uncertainty. The authors also payattention to the exchange rate employed in their volatility measure, which is proxied bythe within-period standard deviation, the coefficient of variation and Gini’s meandifference of the nominal and real exchange rates; as well as measures of third-country

JES34,3

234

effects (including other countries’ volatilities). The authors find that volatility in thenominal, but not the real, exchange rate is significantly negative; that a linear, ratherthan a log-linear, specification provides significant results; and that third-country riskis important in explaining the reduction in trade flows.

Models using income and substitution effectsMost bilateral studies, however, use the simple income/relative price specification thatis common in the more recent aggregate studies surveyed earlier. This can begeneralized as:

Xt ¼ b0 þ b1Yt þ b2RPt þ b3VOLt þ 1t; ð13Þ

but it often includes trend terms, dummies, and other additions. The relative price maybe either a ratio of price levels or the real exchange rate. This is a reduced-formequation that captures supply and demand effects.

The earliest studies employed Ordinary Least Squares as the estimation technique.Chan and Wong (1985) use the four-quarter moving average of the percentage changein the real bilateral exchange rate as a proxy for volatility, performing an OLSregression of the volume of Hong Kong’s exports to the USA, the UK, and WestGermany. The determining variables are foreign income, the real exchange rate,seasonal dummies, and a trend term, including lags of all variables over the period1977-1984. The authors find that volatility had no effect on these trade flows.Extending the model to use the first differences, rather than the levels of the variables,did not change the results.

Other papers that follow this general specification have found that volatility hashad a significant effect. Peree and Steinherr (1989), creating a specific measure of“medium-term” exchange-rate volatility (see Table I), perform a bilateral study as wellas an aggregate one, and find that volatility had reduced trade flows. Bleaney (1992)analyzes German exports to Japan, Switzerland, UK, the USSR, Italy, The Netherlands,Belgium, and France using quarterly data from 1979-1990 and a standarddeviation-based volatility measure; volatility was shown to have had a significantlynegative effect. Bleaney notes the difference between volatility and “misalignment,”where a real exchange rate deviates from its equilibrium value. Pozo (1992) examinesbilateral exports from the UK to the USA from 1900-1940 using both GARCH- andstandard-deviation-based volatility proxies. The results support the adverse impact ofvolatility on trade flows. However, hedging opportunities may have alleviated thiseffect over certain sub-periods.

Studying US imports from Canada from 1974 to 1992, Caporale and Doroodian(1994) model uncertainty with a GARCH process, and find that these trade flows werealso hurt by volatility. These studies did not always find adverse effect on trade flowsof exchange rate volatility. McKenzie and Brooks (1997), using ARCH and analyzingUS-German trade from 1973:4-1992:9, arrive at significantly positive effects, regardlessof whether they used the real or the nominal exchange rate. Thus, the authors come to adifferent conclusion than those previous studies that had not captured any effect due toexchange rate volatility. McKenzie and Brooks also attain the opposite outcome asKumar and Dhawan (1991) – again, the literature had not arrived at even a universallyvalid choice of nominal versus real exchange rates.


235

As time-series methods were refined, bilateral studies began to apply them just asaggregate ones had. Various macroeconomic variables have been incorporated intomodels of trade and volatility. Koray and Lastrapes (1989) apply a VAR of the bilateralimports of the UK, France, Germany, Japan, and Canada from 1959-1985. The otherseven variables in the 8 £ 8 VAR are the interest rate, the money supply, prices,income, the nominal exchange rate, and a moving standard deviation-based volatilityproxy. The authors reveal a weak relationship, but concluded that, permanentvolatility shocks depress imports.

In more recent studies, co-integration and error-correction models have been appliedto bilateral studies of the effects of exchange-rate volatility. These methods have alsoarrived at mixed conclusions. Aristotelous (2001) employs the Engle-Granger methodof co-integration to evaluate British exports to the USA over the period 1889-1999,using annual data. His model, called a gravity model, uses income and substitutionvariables that are similar to those used in the other papers of this sub-section. Proxyingvolatility with dummies for the fixed and floating periods, as well as the standarddeviation of growth in the REER, Aristotelous models exports as a function of bothcountries’ total and per capita income, relative prices, and a war dummy. He finds thatneither volatility nor regime shifts had any effect on export flows. Aristotelous (2002)finds a different result in his co-integration analysis of US exports to Canada, Japan,Germany an the UK over the period 1959:1-1997:4. He estimates a model similar toequation (13) with an additional dummy for the post-1972 floating period, and uses themoving standard deviation proxy for risk. He finds that volatility has significant short-and long-run effects, and that the shift dummy was significantly negative in the case ofUS exports to Japan and Germany. Thus, the floating exchange-rate regime is shown tohave had a detrimental effect on US trade. Vergil (2002), also using the Engle-Grangermethod, analyzes Turkish exports to the USA, Germany, France, and Italy, using thesimple price-income specification. The effect of volatility is significant in the long runto all countries but Italy. One advantage of the ECM methodology is that it reveals theshort run-effects: Vergil finds a significantly negative short-run effect for Germany.

The volatility measure in most models (aggregate or bilateral) is based on standarddeviation of the real exchange rate, which is suspected to be a stationary variable.Since other variables in trade models are non-stationary, this introduces a problem inapplying Engle-Granger or Johansen’s co-integration technique. These techniquesrequire that all variables to be non-stationary; but the linear combination proxied bythe residuals of the trade model must be stationary. The introduction of theAutoregressive Distributed Lag (ARDL) or “bounds testing” approach toco-integration by Pesaran et al. (2001) has solved this problem. In this relatively newapproach, the variables in any time-series model could be I(1) or I(0). Anotheradvantage of the approach is that one could infer the short-run as well as the long-runeffects of one variable on the other at the same time. Indeed, De Vita and Abbott(2004b) employ the bounds testing approach in their study of US exports. They modelexport volume (ex) as a function of relative prices ( p), income (ic) and volatility (V) inthis popular long-run equation, which is the log version of (13):

ext ¼ b0 þ b1pt þ b2ict þ b3Vt þ ut ð14Þ

where V is the moving-average standard deviation of the log level of the real-exchangerate, for US exports to Canada, Mexico, Germany, Japan, and the UK over the period

JES34,3

236

1987:I-2001:II. This equation is then extended to incorporate short-run and long-runeffects of the left-hand side variables (labeled in the paper as y) and the right-hand sidevariables (labeled as x):

Dyt ¼ c0t þ pyyt21 þ pxxt21 þXp21

i¼1

wtDyt2i þXq21

j¼1

d 0tDxt2j þ g 0Dxt þ 1t ð15Þ

Modeling the differences and the level variables together allows an analysis of bothdisequilibrium (short-run) and equilibrium (long-run) relationships. In addition, the testfor co-integration is conducted with an F-test of the joint significance of the coefficientspy and px in which the critical values were tabulated by Pesaran et al. If the F-valuefalls above the upper bound, the variables are shown to be co-integrated; if the statisticfalls below the lower bound, they are shown not to be co-integrated; and if the valuefalls in between, the test is inconclusive. The results are mixed in De Vita and Abbott’sstudy: The authors find significantly negative effects of volatility for Germany, the UK,and Mexico; and significantly positive results for Japan. As newer papers adopt thismethod, they offer the opportunity to assess short- and long-run effectssimultaneously, while allowing the presence of co-integration to be tested moreeasily than can be done by older methods. Table III provides a summary and mainfeatures of all studies in this group.

VI. Empirical studies that use sectoral dataSince this disaggregation of total trade data by trading partners resulted in no changein the mixed conclusion produced by aggregate studies, some authors havedisaggregated bilateral trade data between pairs of countries by either sector or bycommodity with a hope that some significant results could be discovered. In doing so,these studies have relied on recent advances in time-series analysis. Because differentsectors face different levels of risk, this disaggregation helps isolate specific effects onspecific goods. These studies can be broken down into two groups: Those thatdisaggregate trade into as many diverse sectors as possible, and those that focus on asingle sector and investigate its specific properties.

Multi-sector studiesWhereas the first aggregate and bilateral studies concentrated on the exports ofdeveloped countries, this is not the case for sectoral analyses. Coes (1981) performedone of the first sectoral studies, examining the effects of uncertainty in Brazil.Analyzing a wide range of manufactured goods (13 in all, including mineral products,rubber products, transportation equipment, and textiles), as well as nine primaryproducts, he formulates the following equation, which shows export volume as a linearfunction of level and lagged uncertainty, relative prices and foreign income as in (15):

ðX=QÞ ¼ b0 þX3

j¼0

b1jð1=U Þ þX3

j¼0

b2jP þ b3Y ð15Þ

The polynomial lag has a maximum of three years; uncertainty is proxied using ameasure of variance of the monthly real exchange rate; and the model is estimated withOLS. Estimating over the period 1957-1974, Coes finds that all manufactured goods


237

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Table III.


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Table III.


241

registered significant effects of exchange rate volatility, most of which are positive.Only the volatility coefficients for Beverages and Rubber Products are negative.Agricultural goods, which had showed signs of autocorrelation and thus are correctedfor serial correlation with the Cochrane-Orcutt procedure, have been affected less: Sixhave significantly positive coefficients at the 10 percent level, four of which are so atthe 5 percent level – and none are significantly negative. Thus, although Coes admitspotential data and specification errors, he shows that agricultural and manufacturedgoods face different effects due to exchange-rate uncertainty.

Developed countries are not neglected, however. Maskus (1986) models the volumeof US bilateral exports to Japan, the UK, Germany, and Canada as a function of foreignincome, labor costs, and capacity utilization; home labor costs; a measure of risk thatincludes both inflation and nominal exchange rate uncertainty; and the “sectoral realexchange rate” in a simplified version of (9). Using OLS and quarterly data from theperiod 1974-1984, Maskus finds that 58 of the 64 sectoral flows studied were negativelyaffected; for 26 of these, the impact was significant. Germany’s trade has been mostaffected. The volume of trade has been reduced in machinery, transport, chemicals, andmiscellaneous manufactures; the sector most negatively affected by exchange-ratevolatility is that of agriculture. The author speculates that this might be because of thelevel of concentration or internationalization of the industry, or the length of contracts.Nonetheless, this provides an important benchmark for future disaggregatedstudies[4].

Klein (1990) provides another sectoral study that finds mixed results. Examiningthe value of US bilateral exports to the G-7 as a function of foreign income and the realexchange rate, he arrives at positive as well as negative results. His model applies OLSto a regression of exports on current and lagged income, real exchange rate, andvolatility – as well as five country-specific dummy variables, and interaction variablesbetween the dummies and volatility. Evaluating six bilateral trade flows for nine1-digit SITC categories over the annual period 1978-1986, Klein’s study generated 54regressions – of which three are significantly negative and seven are significantlypositive. Most flows are thus shown to be unaffected by real exchange-rate volatility.Two of the three sectors that are hurt by uncertainty were to France (food andmanufactured goods), and four of the positively affected industry trade flows were toCanada; perhaps there is a country-specific effect at work.

Belanger et al. (1992) look further into the US relationship with Canada, this timeevaluating US imports from its main trading partner. Utilizing a novel nonparametricapproach to measure volatility, the authors examine the import volume of five sectors(food, industrial supplies, capital goods, automotive goods, and consumer goods) as afunction of a substitution effect (relative prices), a trade composition effect, a scalevariable (output), and the volatility proxy. OLS (with lags and seasonal dummies) isapplied to quarterly data over the period 1974-1983. The authors do not find anysignificant effect with the possible exception of capital goods.

Grobar (1993) applies a pooled and fixed-effects panel approach to the sectors ofchemicals, machinery and transport equipment, manufactures, and miscellaneousmanufactures (SITC categories 5-8) for ten LDCs. Using four proxies for uncertainty,Grobar models exports over the period 1963-1985 as a function of uncertainty, the realexchange rate, manufacturing’s proportion of the country’s GDP, and a premium thatis based on the level of misalignment of the official and black market exchange rate.

JES34,3

242

Chemicals and manufactures are negatively affected by volatility across all fourproxies; miscellaneous manufactures have been depressed by three of the four (all butthe one modeled from the trend equation); and machinery has not been significantlyaffected.

Stokman (1995) extends previous studies of European trade to account for sectoraltrade, using OLS. Stokman applies OLS to a simple income/relative price/volatilitymodel with a seasonal dummy over the period 1980:IV-1990:IV, measuring volatility asthe standard deviation of weekly percentage changes in the NEER. The authoranalyzes the export volume of Germany, France, Italy, Belgium, and The Netherlandsto the European Community, for the grouped SITC categories 0 and 1, 2 and 4, 5, 6, and7. Stokman concludes that because exchange-rate volatility depresses trade acrosscountries and sectors, the countries of the EC have benefited by the reduction of riskbrought on by economic integration. He further concludes that disaggregating the dataat the sectoral level proved to be a fruitful exercise.

Many of the sectoral studies use co-integration methods for their analysis. Theseanalyses often model exports as a simple linear function of income, relative prices, anduncertainty. Rapp and Reddy (2000) apply the Johansen procedure to a study ofmonthly US export value to its G-7 partners from 1978-1995, (eight sectors, 1-digitSITC groupings), with risk proxied by the moving standard deviation of the rate ofchange of the real exchange rate. Their estimated equation is as follows:

DXt ¼ c0 þ c1jt21 þX

diDXt2i þX

f iDYt2i þX

giDPt2i þX

kiDVt2i

þ et ð16Þ

where c1jt21 is the lagged error correction term (ECM). This analysis produces mixedresults. Most Canadian trade showed no evidence of co-integration among thevariables; most US trade with other countries did show evidence. Of the 39co-integrating vectors that resulted, 18 have significantly negative volatilitycoefficients, and 14 have significantly positive ones. These effects vary acrosscountries and sectors. French trade appears to be positively influenced by uncertainty;Japanese trade shows negative effects across sectors. Across countries, the food sectorseems to be positively affected in the long run – contradicting the conclusion ofMaskus (1986) in sign – but the machinery, crude, and chemical sectors showednegative effects.

Two papers, applying the Engle-Granger method of co-integration, focusedspecifically on Irish exports and find positive results across sectors. Doyle (2001), usinga GARCH-based proxy for nominal and real exchange-rate volatility, examines Irishexports to the UK using monthly sectoral data at the SITC 2-digit level from 1979-1992.The volatility variable is found to be I(0) for seven sectors and I(1) for the rest; it is alsoshown to carry significantly positive coefficient not only for the aggregate data, butalso for most sectors. This effect is present for both nominal and real exchange ratebased uncertainty measure. Doyle surmises that as a small, open economy, Irishtraders have no choice but to accept exchange-rate risk. Multinational corporations,however, may be able to diversify away from risk, thus reducing the impact ofuncertainty. Bredin et al. (2003) examine Irish sectoral exports to the European Union,over the period 1978:III-1998:IV. A moving standard deviation volatility proxy is used;the authors analyze the pooled 1-digit SITC commodity groupings 0-4, 5-8, and 0-8. The


243

authors note that multinational corporations (MNCs) dominate sectors 5-8, but that thesectors where locally based firms are more prominent show similar effects due touncertainty. Again, these results are positive, perhaps because volatility increases afirm’s expected profits.

The papers described above make use of the Engle-Granger and Johansen methodsof co-integration analysis, but the bounds testing approach is also applied to sectoraldata. One of the first studies to use this approach was Chou (2000), who applies theapproach as well as the Johansen error-correction method to study Chinese exportsfrom 1981-1986. These two approaches produce different results for the four sectors offoodstuffs, fuels, industrial materials, and manufactured goods (SITC categories 0 and1, 2 and 4, 3, and 5-9, respectively). While the error-correction method signifies thatexchange-rate volatility had a negative impact on all sectors except foodstuffs, thebounds testing approach registered a positive effect for industrial goods.

De Vita and Abbott (2004a) also achieve fewer significant results in their boundstesting analysis of the volume of UK exports to other European Union countries.Testing four uncertainty proxies (the sample and moving standard deviations ofweekly percentage changes in the log nominal exchange rate, as well as ARCH usingthe nominal and real exchange rates), the authors evaluate the four sectors ofmanufactures, food, basic materials, and services. The authors compare their volatilityproxies with a “tournament” in which the two standard-deviation proxies “competed”against each other, with the winner chosen via the log-maximizing Akaike InformationCriterion (AIC) and Schwarz Bayesian Criterion (SBC). The two ARCH proxies face offas well; the winners of the two semifinals are then compared against each other. Theauthors find that while the best risk proxy varied by the market of destination, theMoving Average Standard Deviation proxy dominated for sectoral trade. Thevolatility term itself has varying results at these levels of aggregation. At theaggregate level, the volatility coefficient was significantly positive only for exportflows to Germany, Denmark, and Sweden. At the sectoral level, pooled acrossimporting countries, only the service sector registers any significant effect. Theauthors speculate that exporters in different industries have different levels of riskaversion.

Just as was the case for aggregate and bilateral studies, analyses of sectoral tradeare not confined to time-series analysis. Panel data approaches are also fairly common,and often arrive at similar results. Peridy (2003) chooses a fixed-effects model, as wellas SUR and GMM models, to assess the export demand and supply of each G-7country’s exports to their main partners[5]. The author notes that such characteristicsas competition, pricing strategy, and costs are specific to each industry; therefore, eachindustry must have a different reaction to exchange-rate volatility. Peridy capturesthese factors by using a number of explanatory variables: employment, productivity,returns to scale, foreign income, product differentiation, and lagged exports. While theresults are significantly negative for all countries, they vary across sectors andgeographic areas, especially when a GARCH-based proxy is used for exchange-ratevolatility. Peridy thus calls attention to an “aggregation bias,” both sectorally andgeographically. While some fuels, natural products, and textiles are strongly affectedby risk, such manufactured goods as machinery are not. Peridy suggests that productdifferentiation might cushion these goods against swings in prices. Nonetheless, somecountries show positive effects for certain industries. France, for example, registers a

JES34,3

244

significantly positive volatility effect in such industries as electrical machinery,communication equipment, pharmaceuticals, and medical equipment; this seems toconcur with the results of Rapp and Reddy (2000). Other countries, such as Japan, showsignificantly positive coefficients as well. Peridy also examines the geographicdestination of trade and concludes that developed countries’ exports to LDCs arenegatively affected; but their exports to other DCs are either positively affected or notaffected at all. Thus, aggregate data – both across sectors and across countries – maygive misleading results.

Specific sectorsBesides the studies reviewed above, a few studies have focused specifically on a singlegood or sector. Usman and Savvides (1994) examine the export and import volume ofcocoa and coffee for the CFA franc zone in Africa. The goods are treated asdifferentiated according to their market of destination. Using OLS (Parks method),Usman and Savvides model trade volume as a function of relative prices, the totalcoffee or cocoa import volume, a dummy for membership in the CFA, and volatility,which is proxied as the 12-year standard deviation of the percentage change in the realexchange rate. Using annual data from the period 1973-1984, they show a significantlynegative impact for most countries. In addition, the authors find that for two countries,membership in the CFA had a positive effect on their coffee and cocoa exports, eventhough the rigid peg to the French franc has exposed these countries to volatility.

While studies that evaluate the performance of manufactured goods often can becategorized with bilateral or aggregate analyses, it is also possible to treat them asstudies of a specific sector. Holly (1995) incorporates both supply and demand effects,rather than the more simple reduced form that has dominated empirical studies onexchange-rate volatility. Making use of the Johansen method of co-integration analysis,and using a GARCH approximation of volatility, his study find that volatility has had anegative effect on the supply, but not the demand, of UK manufacturing exports overthe period from 1980-1996.

Many of these papers used different empirical methods. Lee (1999) focuses on thevalue of US imports of durable goods from its G-7 partners, Belgium, Sweden, andSwitzerland over the period 1973-1992. Employing a vector autoregression thatincludes durable-goods prices, the real exchange rate, US income, and a GARCH-basedmeasure of volatility, Lee finds that uncertainty has had no discernible impact onimports.

Agriculture is another sector that is worthy of analysis. Cho et al. (2002) choose afixed-effects gravity model to study the total agricultural trade (exports plus imports)of the G-7 countries plus Belgium, The Netherlands, and Switzerland. Using not only astandard-deviation based volatility proxy, but also the Peree-Steinherr method, theauthors estimate the specification used by Dell’Ariccia (1999) and find that agriculturehas been hurt more by exchange-rate volatility than other sectors had been.

Giorgioni and Thompson (2002) examine an agricultural good as well, noting thatwheat requires high storage costs. They note however, that there are other types ofvolatility besides that of the exchange rate. Using pooled, fixed, and random-effectspanel data models, the authors evaluate the value of US wheat exports to Egypt, Israel,Italy, Japan, Korea, Morocco, Pakistan, the Philippines, and Venezuela. Income,substitution, and volatility effects are captured for each bilateral relationship with the


245

following deterministic variables: the total wheat imports of the importing country; thereal bilateral exchange rate; the REER of each of the USA’s main competitors in thewheat market; exchange-rate volatility, proxied by the 12-month moving standarddeviation of the percentage changes in the real exchange rate; and the volatility of theimporting country’s imports, measured by a three-year moving standard deviation.The authors find that it is the volatility of total imports, not of the real exchange rate,that depresses exports. Once again, Table IV collects all relevant information for eachpaper in this section in one place.

VII. New directions and conclusionsAn argument put forward by the opponents of the floating exchange rates is that suchrates introduce uncertainty into the foreign exchange market, which could deter tradeflows. However, a theoretical argument is put forward by some to show thatuncertainty could also boost trade flows if traders increase their trade volume to offsetany decrease in future revenue due to exchange rate volatility. The empirical literaturereviewed in this paper supports both views. In this study, we have classified theempirical studies into three categories. The first includes studies that used aggregatetrade data between one country and the rest of the world. The second category includesstudies that used disaggregate data at the bilateral level, i.e. trade flows between twocountries. Finally, the third category includes those studies that disaggregated thetrade data further by commodities or by sectors between two countries. For each groupa table is provided which summarizes each paper by its main features. For futureresearch the following recommendations are in order.

First, while the general trend in evaluating the effects of exchange-rate volatility hasbeen toward disaggregated and even sectoral data, no clear consensus has beenreached regarding a single measure of uncertainty. Coric and Pugh (2006) areperforming a meta-regression analysis, currently as a working paper, to determine thegeneral characteristics of this large body of literature. While statistical analyses havebeen performed to determine the stochastic properties of various volatility measures,their conclusion – such as that the higher-order properties of the volatility variable(skewness and kurtosis), have not been integrated into the body of literature. Whilesome early studies tested multiple proxies and then selected the one that best fits acertain criterion, this practice seems to be less common in more recent papers. For now,it seems that there is no optimal measure of uncertainty, and that authors are morecomfortable choosing one or two and concentrating on the results they provide. Futureresearch that concentrates on introducing new measures of volatility or refining theexisting measures, are recommended.

Second, certain modifications of the determinants of trade flows, such asincorporating third-country effects, also have not been incorporated in the majority ofstudies. Thus, simplified models are most common, often using income, relative price,and exchange rate volatility as determinants. Rather than focusing on specification,authors have recently used these specifications either to investigate countries that hadnot been studied, or to disaggregate the data so that specific effects may be uncovered.Future studies could advance our knowledge if they include third-country effects.

Third, because of foreign exchange controls in many less-developed countries, thereis a black market for foreign currencies in these countries. Not much attention is paid tothe effects of black-market exchange rate volatility on trade in developing countries,

JES34,3

246

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effects of exchange-ratevolatility


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Table IV.


249

nor on the impact of the black-market premium as a measure of exchange ratemisalignment.

Finally, it is shown that while all variables in a given trade flow model arenon-stationary, most measures of exchange rate volatility are stationary. The onlyco-integration and error-correction method that allows some of the variables to benon-stationary and some to be stationary is the bounds testing approach by Pesaranet al. (2001). It is highly recommended that future studies rely on this method. It shouldbe indicated that the other advantage of the bounds testing approach is that theshort-run and the long-run effects of exchange rate volatility on trade flows could beassessed simultaneously, using one model specification[6].

Notes

1. If a time-series variable is stationary at its level, it is said to be an I(0) variable. However, if itachieves stationarity after being differenced once, then it is said to integrated of order one,denoted by I (1).

2. Ecuador, Indonesia, Korea, Malaysia, Malawi, Mauritius, Mexico, Morocco, Philippines, SriLanka, Taiwan, Thailand, and Tunisia.

3. Austria, Belgium, Denmark, Finland, France, Germany, Greece, Italy, Japan, TheNetherlands, Norway, Sweden, Switzerland, Canada, South Africa, the UK, and the USA.

4. One recommendation for future study is to repeat Maskus’ analysis for disaggregated datausing cointegration and error-correction modeling, since he did not engage in unit-roottesting or co-integration analysis.

5. Belgium, Luxembourg, Switzerland, Denmark, Spain, The Netherlands, Mexico, China, HongKong, Korea, Malaysia, Singapore, Thailand, Taiwan, Australia, and Brazil.

6. For more on the bounds testing approach see Bahmani-Oskooee and Goswami (2004) orBahmani-Oskooee and Ardalani (2006).

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Further reading

Cushman, D.O. (1992), “The effects of real exchange rate risk on international trade”, Journal ofInternational Economics, Vol. 15, pp. 45-63.

Goldstein, M. and Khan, M.S. (1985), “Income and price effects in foreign trade”, in Kenen, P.B.and Jones, R.W. (Eds), Handbook of International Economics,Vol. II, North Holland,Amsterdam.

Corresponding authorMohsen Bahmani-Oskooee can be contacted at: [email protected]


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