Abstract
Exchange Rate Volatility and Trade Flows--Some New Evidence
Preface
This paper examines the effect of exchange rate volatility on trade, prepared in response to a request from the Director General of the World Trade Organization to the IMF. The IMF produced a study in 1984 for the General Agreement on Tariffs and Trade (GATT) on this subject. Since then, there have been major developments in the world economy, some perhaps having exacerbated fluctuations in exchange rates whereas others perhaps having reduced the impact of volatility on trade. It is therefore appropriate to revisit the issue some 20 years later.
This paper was prepared by a team led by Peter Clark and Shang-Jin Wei and consisted of Natalia Tamirisa, Azim Sadikov (summer intern), and Li Zeng (research assistant). It has benefited from comments from Mary Amiti, Giovanni Dell’Ariccia, Raghuram Rajan, Stephen Tokarick, the Management and various departments of the IMF as well as from Marc Auboin, Richard Eglin, and other staff of the WTO. The views expressed are those of the authors and do not necessarily reflect the views of national authorities or IMF Executive Directors.
Raghuram Rajan
Economic Counsellor and Director
IMF Research Department
Executive Summary
In 1984 the IMF produced a study for the General Agreement on Tariffs and Trade (GATT) on the impact of exchange rate volatility on world trade. As there have been major developments in the world economy since then, it is appropriate to revisit the issue some 20 years later.
Some of the developments would appear to have exacerbated fluctuations in exchange rates. The liberalization of capital flows in the last two decades and the enormous increase in the scale of cross-border financial transactions have increased exchange rate movements. Currency crises in emerging market economies are special examples of high exchange rate volatility. In addition, the transition to a market-based system in Central and Eastern Europe often involves major adjustments in the international value of these economies’ currencies.
Other changes in the world economy may have reduced the impact of exchange rate volatility. The proliferation of financial hedging instruments over the last 20 years could reduce firms’ vulnerability to risks arising from volatile currency movements. In addition, for multinational firms, fluctuations in different exchange rates may have offsetting effects on their profitability. As a growing fraction of international transactions is undertaken by these multinational firms, exchange rate volatility may have a declining impact on world trade.
As these different developments in the world economy may have opposing effects in altering the impact of exchange rate fluctuations on trade over the last two decades, it is not clear what the net effect is without undertaking a careful empirical study.
The review of the theoretical literature on this topic indicates that there is no clear-cut relationship between exchange rate volatility and trade flows. The presumption that trade is adversely affected by exchange rate volatility depends on a number of specific assumptions and does not necessarily hold in all cases, especially in a general equilibrium setting where other variables change along with exchange rates. The ambiguity of the theoretical predictions reinforces the importance of investigating the issue empirically.
The empirical research in this study differs from the earlier one (IMF, 1984) in a number of dimensions. First, the country coverage is considerably broader. While the earlier study focused exclusively on the G-7 countries, this study covers all Fund members for which data are available. Second, this study explores a range of different exchange rate volatility measures. Third, in addition to examining aggregate trade, the study also divides all products into two groups—differentiated and homogeneous products—and tests whether volatility has a differential effect on them. Fourth, the estimation techniques are also quite different, as recent theoretical advances in the specification of a gravity equation are incorporated to assess the impact of exchange rate volatility on trade.
The main empirical findings of the study can be summarized as follows. First, while exchange rate fluctuations have increased in times of currency and balance of payments crises during the 1980s and 1990s, there has not been any increase, on average, in such volatility between the 1970s and the 1990s. It is also noteworthy that an exchange rate regime that is classified as “pegged ” does not necessarily have lower overall exchange rate volatility than an arrangement that permits some degree of exchange rate flexibility. Pegging to an anchor currency still leaves a country exposed to fluctuations in the anchor against other currencies, and a peg that becomes misaligned can subsequently generate exchange market pressures and large, discrete changes in currency values, and hence volatility.
Second, a negative relationship between exchange rate volatility and trade is borne out by some of the empirical evidence in this study. However, such a negative relationship is not robust to reasonable perturbations of the specification linking bilateral trade to its determinants. Overall, if exchange rate volatility has a negative effect on trade, this effect would appear to be fairly small and is by no means a robust, universal finding.
These results suggest that, from the perspective of enhancing trade, exchange rate volatility is probably not a major policy concern. This does not necessarily rule out the possibility that a large exchange rate volatility could affect an economy through other channels.
I. Introduction and Overview
In 1984 the IMF (1984) produced a study for the General Agreement on Tariffs and Trade (GATT) on the impact of exchange rate volatility on world trade. That study was motivated by an increase in protectionist pressures, large exchange rate movements among the major currencies, and a significant slowdown in world trade. Some of these developments have reappeared. For example, the growth in world exports of goods and services declined sharply in 2001 and 2002 from the double-digit pace in 2000, and the exchange value of the U.S. dollar has fluctuated fairly sharply in the last year. The 1984 study also reflected a desire to take stock of the implications for currency volatility and trade of the shift from the largely fixed rates among the major currencies to floating after the breakdown of the Bretton Woods system in 1971–1973. As there have been other major developments in the international monetary system since then, it is appropriate to revisit the issues addressed in that study some 20 years later.
Some of these developments would appear to have exacerbated fluctuations in exchange rates. The liberalization of capital flows in the last 30 years and the enormous increase in the scale and variety of cross-border financial transactions have clearly increased the magnitude of exchange rate movements in those countries with underdeveloped capital markets and where there is not yet a track record of consistently stable economic policies.1 Currency crises in emerging markets, which have become more frequent in the last two decades, are especially notable cases of large exchange rate volatility.2 This has been of particular concern to developing countries and emerging market economies. In addition, the transition to a market-based system in Central and Eastern Europe often involves major adjustments in the international value of these economies’ currencies.
Other changes in the world economy may have reduced the impact of exchange rate volatility. The proliferation of financial hedging instruments over the last 20 years could reduce firms’ vulnerability to the risks arising from volatile currency movements. In addition, for multinational firms, fluctuations in different exchange rates may have offsetting effects on their profitability. As a growing fraction of international transactions is undertaken by these multinational firms, exchange rate volatility may have a declining impact on world trade.
On balance, it is not clear whether the major changes in the world economy over the past two decades have operated to reduce or increase the extent to which international trade is adversely affected by fluctuations in exchange rates. One aspect of this issue is the extent to which such volatility itself has changed, and another is the degree to which firms are sensitive to exchange rate risk and can take steps to mitigate it at low cost. It is therefore necessary to examine new empirical evidence at this issue.
There are a number of differences between the current study and the earlier one. Most importantly, the country coverage is considerably broader. In IMF (1984) the analysis was almost exclusively focused on the G-7 countries. This reflected the view that the fluctuations in the major currencies were the most important factor for the “environment” within which other countries have to plan their policies.3 While these currencies are the most important for the functioning of the international monetary system, fluctuations in other exchange rates are now also relevant for systemic reasons as well as for their implications for other countries themselves. Therefore this study takes a more comprehensive view of the subject and covers the exchange rates of all Fund members for which data are available.
This study also explores a range of different exchange rate volatility measures. Moreover, aside from examining aggregate trade, the study divides all products into two groups—differentiated and homogeneous products—and tests whether volatility has a differential effect on them.
Given the large number of countries in the data set, it is possible to estimate the degree to which volatility has a differential effect depending on whether the country is advanced or developing. The estimation techniques are also quite different, as recent theoretical advances in gravity-equation specification are employed to assess the impact of exchange rate volatility on trade.
Finally, following the work of Rose (2000), the study looks at the effect of common currency arrangements on trade. This is a related yet distinct issue from the impact of exchange rate volatility, as a currency union is more than just an elimination of exchange rate volatility among members. It reduces other transactions costs relevant for trade and provides a commitment device for macroeconomic policies.
Anticipating some of the findings below, this study shows that while exchange rate fluctuations have increased in times of currency and balance of payments crises during the 1980s and 1990s, there does not appear to have been any increase, on average, in such volatility between the 1970s and the 1990s. It is also noteworthy that an exchange rate regime that is classified as “pegged” does not necessarily have lower overall exchange rate volatility than an arrangement that permits some degree of rate flexibility. Pegging to an anchor currency still leaves a country exposed to fluctuations in the anchor against other currencies, and a peg that becomes misaligned can subsequently generate exchange market pressures and large, discrete changes in currency values, and hence volatility.
The review of the theoretical literature since the 1984 study has, if anything, reinforced the conclusion there that there is no unambiguous relationship between exchange rate volatility and trade flows. The general presumption that trade is adversely affected by an increase in the exchange rate fluctuations depends on a number of specific assumptions and does not necessarily hold in all cases, especially in general equilibrium models where other variables change along with exchange rates. These models show that exchange rate volatility is the
result of the volatility in underlying shocks to the economy and the policy regime which determines how the shocks are reflected in exchange rates and other variables.
For the world as a whole, there is no obvious association between periods of low exchange rate volatility and periods of fast growth in trade. In other words, at an aggregate level, there is no evidence of a negative effect of exchange rate on world trade. Once one goes to trade and exchange rate volatility at a bilateral level, a negative relationship between the two is borne out by some of the empirical evidence in this study. However, this negative relationship is not robust to a more general specification of the equation linking bilateral trade to its determinants that embodies the recent theoretical advances in a gravity model. Thus, if there is a negative impact of exchange rate volatility on trade, it is not likely to be quantitatively large and the effect is not robust.
These findings suggest that, from the perspective of world trade, exchange rate volatility is probably not a major policy concern. Note that this does not imply necessarily that exchange rate fluctuations should be viewed with equanimity. For example, currency crises – special cases of exchange rate volatility - have required painful adjustments in output and consumption. However, in this case, what is important is not that measures need to be taken to moderate currency fluctuations directly, but that appropriate policies need to be pursued in order to avoid the underlying causes of large, unpredictable and damaging movements in exchange rates.
There are a number of aspects related to exchange rate volatility that are not covered in this study. First, it does not deal with the determination of the level of exchange rates. Second, it does not deal with the optimal choice of exchange rate arrangement, e.g., fixed versus floating.4
Part II of the paper reviews the relevant theoretical and empirical literature over the last two decades. Part III describes the recent history of exchange rate volatility in different parts of the world. Part IV presents some new evidence on the effect of exchange rate volatility on trade. Finally, Part V offers concluding remarks.
II. Brief Review of the Theoretical and Empirical Literature
Since the appearance of the IMF (1984) study of the effects of exchange rate volatility on trade, two survey papers of the literature on the topic have appeared: Cote (1994) and McKenzie (1999). In addition, the U.K. Treasury (2003) recently commissioned a number of studies, and invited submissions from numerous academics, to inform their assessment of the desirability of joining EMU. Therefore it is not necessary to present a comprehensive discussion of the many contributions to the field. Rather, the focus here will be on certain key issues which highlight why it has been difficult to reach clear-cut conclusions on the impact of exchange rate variability on trade flows, as well as on some of the more recent work in the area. These two surveys conclude that from a theoretical perspective there is no unambiguous response in the level of trade to an increase in exchange rate volatility, as differing results can arise from plausible alternative assumptions and modeling strategies. The same ambiguity pervades much of the empirical literature, which may reflect the lack of clear-cut theoretical results as well as the difficulty in arriving at an appropriate proxy for exchange rate risk. Nonetheless, some recent studies as well as some of the evidence presented here appear to suggest that a negative relationship has some support from the data.
A. Theoretical Aspects of the Relationship Between Exchange Rate Volatility and Trade
It is useful to begin with the example of a rudimentary exporting firm to illustrate how (real) exchange rate volatility can affect the level of the firm’s exports. The simplest case described by Clark (1973), for example, considers a competitive firm with no market power producing only one commodity which is sold entirely to one foreign market and does not import any intermediate inputs. The firm is paid in foreign currency and converts the proceeds of its exports at the current exchange rate, which varies in an unpredictable fashion, as there are assumed to be no hedging possibilities, such as through forward sales of the foreign currency export sales. Moreover, because of costs in adjusting the scale of production, the firm makes its production decision in advance of the realization of the exchange rate and therefore cannot alter its output in response to favorable or unfavorable shifts in the profitability of its exports arising from movements in the exchange rate. In this situation the variability in the firm’s profits arises solely from the exchange rate, and where the managers of the firm are adversely affected by risk, greater volatility in the exchange rate – with no change in its average level - leads to a reduction in output, and hence in exports, in order to reduce the exposure to risk. This basic model has been elaborated by a number of authors, e.g., Hooper and Kohlhagen (1978), who reach the same conclusion of a clear negative relationship between exchange rate volatility and the level of trade.
However, this strong conclusion rests on a number of simplifying assumptions. First, it is assumed that there are no hedging possibilities either through the forward exchange market or through offsetting transactions. For advanced economies where there are well developed forward markets, specific transactions can be easily hedged, thus reducing exposure to unforeseen movements in exchange rates.5 But it needs to be recognized that such markets do not exist for the currencies of most developing countries. Moreover, even in advanced economies the decision to continue to export or import would appear to reflect a series of transactions over time where both the amount of foreign currency receipts and payments, as well are the forward rate, are not known with certainty.
Moreover, there are numerous possibilities for reducing exposure to the risk of adverse exchange rate fluctuations other than forward currency markets. The key point is that for a multinational firm engaged in a wide variety of trade and financial transactions across a large number of countries, there are manifold opportunities to exploit offsetting movements in currencies and other variables. For example, there is a clear tendency for exchange rates to adjust to differences in inflation rates, and recent evidence suggests that such adjustment may be quicker than indicated by earlier studies. Thus, if exports are priced in a foreign currency that is depreciating, the loss to the exporter from the declining exchange rate is at least partly offset by the higher foreign-currency export price (Cushman, 1983 and 1986). In a similar vein, as noted by Clark (1973), to the extent that an exporter imports intermediate inputs from a country whose currency is depreciating, there will be some offset to declining export revenue in the form of lower input costs. In addition, when a firm trades with a large number of countries, the tendency for some exchange rates to move in offsetting directions will provide a degree of protection to its overall exposure to currency risk. Finally, as analyzed by Makin (1978), a finance perspective suggests that there are many possibilities for a multinational corporation to hedge foreign currency risks arising from exports and imports by holding a portfolio of assets and liabilities in different currencies.
One reason why trade may be adversely affected by exchange rate volatility stems from the assumption that the firm cannot alter factor inputs in order to adjust optimally to take account of movements in exchange rates. When this assumption is relaxed and firms can adjust one or more factors of production in response to movements in exchange rates, increased variability can in fact create profit opportunities. This situation has been analyzed by Canzoneri, et al. (1984), De Grauwe (1992), and Gros (1987), for example. The effect of such volatility depends on the interaction of two forces at work. On the one hand, if the firm can adjust inputs to both high and low prices, its expected or average profits will be larger with greater exchange rate variability, as it will sell more when the price is high, and vice versa. On the other hand, to the extent that there is risk aversion, the higher variance of profits has an adverse effect on the firm and constitutes a disincentive to produce and to export. If risk aversion is relatively low, the positive effect of greater price variability on expected profits outweighs the negative impact of the higher variability of profits, and the firm will raise the average capital stock and the level of output and exports. In a more general setting analyzing the behavior of a firm under uncertainty, Pindyck (1982) has also shown that under certain conditions, increased price variability can result in increased average investment and output as the firm adjusts to take advantage of high prices and to minimize the impact of low prices.
One aspect of the relationship between trade and exchange rate volatility that needs to be mentioned is the role of “sunk costs.” Much of international trade consists of differentiated manufactured goods that typically require significant investment by firms to adapt their products to foreign markets, to set up marketing and distribution networks, and to set up production facilities specifically designed for export markets. These sunk costs would tend to make firms less responsive to short-run movements in the exchange rate, as they would tend to adopt a “wait and see” approach and stay in the export market as long as they can recover their variable costs and wait for a turnaround in the exchange rate to recoup their sunk costs. Following the finance literature on real options (e.g., McDonald and Segel, 1986), Dixit (1989) and Krugman (1989) have explored the implications of sunk costs in the context of an “options” approach, which has been applied by Franke (1991) and Sercu and Vanhulle (1992). The key idea is that an exporting firm can be viewed as owning an option to leave the export market, and a firm not currently exporting can be regarded as owning an option to enter the foreign market in the future. The decision to enter or exit the export market involves considering explicit fixed and variable costs, but also the cost of exercising the option to enter or leave the market. The greater the volatility in exchange rates, the greater the value of keeping the option, and hence the greater the range of exchange rates within which the firm stays in the export market, or stays out if it has not yet entered. This suggests that increased exchange rate volatility would increase the inertia in entry and exit decisions.
It is useful to note that in most theoretical models, what is being studied is the volatility of the real exchange rate as opposed to the nominal exchange rate. The two are distinct conceptually but do not differ much in reality: prices of goods tend to be “sticky” in local currency in the short-to-medium run. In this case, real and nominal exchange rate volatilities are virtually the same for practical purposes. For this reason, after reviewing the literature on the effect of real exchange rate volatility, we do not present a separate discussion on the effect of nominal exchange rate volatility. The exceptions are episodes of high inflation when nominal exchange rate volatility tends to be bigger than real exchange rate volatility. For this reason, in the empirical analysis that will be presented later, we examine explicitly whether real versus nominal exchange rate volatilities have different effects on trade or not.
Up to this point the discussion of the impact of volatility on trade has been within a partial equilibrium framework, i.e., the only variable that changes is some measure of the variability of the exchange rate, and all other factors that may have an influence on the level of trade are assumed to remain unchanged. However, those developments that are generating the exchange rate movements are likely to affect other aspects of the economic environment which will in turn have an effect on trade flows. Thus it is important to take account in a general equilibrium framework the interaction of all the major macroeconomic variables to get a more complete picture of the relationship between exchange rate variability and trade.
Such an analysis has recently been provided by Bacchetta and Van Wincoop (2000). They develop a simple, two-country, general equilibrium model where uncertainty arises from monetary, fiscal, and technology shocks, and they compare the level of trade and welfare for fixed and floating exchange rate arrangements. They reach two main conclusions. First, there is no clear relationship between the level of trade and the type of exchange rate arrangement. Depending on the preferences of consumers regarding the tradeoff between consumption and leisure, as well as the monetary policy rules followed in each system, trade can be higher or lower under either exchange rate arrangement. As an example of the ambiguity of the relationship between volatility and trade in a general equilibrium environment, a monetary expansion in the foreign country would depreciate its exchange rate, causing it to reduce its imports, but the increased demand generated by the monetary expansion could offset part or all of the exchange rate effect. Thus the nature of the shock that causes the exchange rate change can lead to changes in other macroeconomic variables that offset the impact of the movement in the exchange rate. Second, the level of trade does not provide a good index of the level of welfare in a country, and thus there is no one-to-one relationship between levels of trade and welfare in comparing exchange rate systems. In their model, trade is determined by the certainty equivalent of a firm’s revenue and costs in the home market relative to the foreign market, whereas the welfare of the country is determined by the volatility of consumption and leisure.
Obstfeld and Rogoff (1998) also provide an analysis of the welfare costs of exchange rate volatility. They extend the “new open economy macroeconomic model” to an explicitly stochastic environment where risk has an impact on the price-setting decisions of firms, and hence on output and international trade flows. They provide an illustrative example whereby reducing the variance of the exchange rate to zero by pegging the exchange rate could result in a welfare gain of up to one percent of GDP. Bergin and Tchakarov (2003) provide an extension of this type of model to more realistic situations involving incomplete asset markets and investment by firms. They are able to calculate the effects of exchange rate uncertainty for a wide range of cases and find that the welfare costs are generally quite small, on the order of one tenth of one percent of consumption. However, they explore the implications of two cases where risk does matter quantitatively, on the order of the effect in the example cited above by Obstfeld and Rogoff (1998): first, where consumers exhibit considerable persistence in their pattern of consumption, such that welfare is adversely affected by sudden changes in consumption, and second, where asset markets are asymmetric in that there is only one international bond, such that the country without its own bond is adversely affected.
Finally, Koren and Szeidl (2003) develop a model which brings out clearly the interactions among macroeconomic variables. They show that what matters is not the unconditional volatility of the exchange rate as a proxy for risk, as used in many empirical papers in the literature, but rather that exchange rate uncertainty should influence trade volumes and prices through the covariances of the exchange rate with the other key variables in the model. In this general equilibrium context, they stress that it is not uncertainty per se in the exchange rate that matters, but rather whether this uncertainty magnifies or reduces the firm’s other risks on the cost and demand side, and ultimately whether it exacerbates or moderates the risk faced by consumers. In addition, they analyze the extent to which local currency vs. producer currency pricing by exporters affects the risks facing the firm; their empirical evidence suggests that risk is higher with the former pricing rule.
B. Empirical Results on the Relationship Between Exchange Rate Volatility and Trade
The early empirical work on the effect of exchange rate variability and trade surveyed in the IMF (1984) study did not yield consistent results, with many studies yielding little or no support for a negative effect. For example, the early work by Hooper and Kohlhagen (1978) utilized the model of Ethier (1973) for traded goods and derived equations for export prices and quantities in terms of the costs of production reflecting both domestic and imported inputs, other domestic prices, domestic income, and capacity utilization. Exchange rate risk was measured by the average absolute difference between the current period spot exchange rate and the forward rate last period, as well as the variance of the nominal spot rate and the current forward rate. They examined the impact of exchange rate volatility on aggregate and bilateral trade flow data for all G-7 countries except Italy. In terms of the effect of volatility on trade flows, they found essentially no evidence of any negative effect. Cushman (1983) used a model similar to that of Hooper and Kohlhagen (1978) but extended the sample size and used real as opposed to nominal exchange rates. Of fourteen sets of bilateral trade flows between industrial countries, he found a negative and significant effect of volatility for six cases. Finally, the IMF (1984) used a simplified version of Cushman’s model to estimate bilateral exports between the G-7 countries from the first quarter of 1969 to the fourth quarter of 1982, with real GNP, the real bilateral exchange rate, relative capacity utilization, and variability measured as the standard deviation of the percentage changes in the exchange rate over the preceding five quarters. In only two cases did variability have a significantly negative coefficient, while positive coefficients were significant in several cases.
A number of factors may have contributed to the lack of robust findings in this early work. First, as noted above, theoretical considerations do not provide clear support for the conventional assumption that exchange rate volatility has a negative impact on the level of trade. Second, the sample period over which exchange rates showed significant variation was relatively short. Finally, the specification of the estimating equations was typically rather crude, consisting of a few macro variables from standard trade equations in use at the time.
McKenzie (1999) surveys a large number of empirical papers on the topic, most of which appeared after the IMF study. He stresses the point made above that at a theoretical level, models have been constructed which lead to negative or positive effects of variability on trade, and that a priori there is no clear case that one model is superior to another. His survey of the empirical work leads to the same mixed picture of results, with many studies finding no significant effect, or where significant, no systematic effect in one direction or the other. He finds, however, that the most recent contributions to the literature have been more successful in obtaining a statistically significant relationship between volatility and trade, which he attributes to more careful attention to the specification of the estimation technique and the measure of volatility used. Similarly, the U.K. Treasury (2003) cites (Box 4.1, p. 29) a number of recent studies (De Grauwe (1987), Rose (2000), Dell’Ariccia (1999), Anderton and Skudelny (2001), Arize (1998) and Fountas and Aristotelous (1999)) which find a negative link, but these effects are not very large: complete elimination of volatility would raise trade by a maximum of 15 percent, compared to the consensus estimate of the effect as typically less than ten percent.
Recent work on this topic employing the gravity model has found some significant evidence of a negative relationship between exchange rate variability and trade.6 The gravity equation has been widely used in empirical work in international economics and has been highly successful in explaining trade flows.7 In its basic form, the gravity model explains bilateral trade flows between countries as depending positively on the product of their GDPs and negatively on their geographical distance from each other. Countries with larger economies tend to trade more in absolute terms, while distance can be viewed as a proxy for transportation costs which act as an impediment to trade. In addition, population is often included as an explanatory variable as an additional measure of country size. In many applications a host of dummy variables are added to account for shared characteristics which would increase the likelihood of trade between two countries, such as common borders, common language, and a membership in a free trade association. To this basic equation researchers add some measure of exchange rate variability to see if this proxy for exchange rate risk has a separate, identifiable effect on trade flows after all other major factors have been taken into account.
The work by Dell’Ariccia (1999) provides a systematic analysis of exchange rate volatility on the bilateral trade of the 15 EU members and Switzerland over the 20 years from 1975 to 1994, using four different measures of exchange rate uncertainty: the standard deviation of the first difference of the logarithm of the monthly bilateral nominal and real (CPI) exchange rate, the sum of the squares of the forward errors, and the percentage difference between the maximum and the minimum of the nominal spot rate. In the basic regressions, exchange rate volatility has a small but significantly negative impact on trade: eliminating volatility to zero in 1994 would have increased trade by an amount ranging from 10 to 13 percent, depending on the particular measure of variability.8 The results for both nominal and real variability are very close, which is not surprising, given that in the sample the two exchange rate measures are highly correlated.
Dell’Ariccia then goes on to take account of the simultaneity bias that can result from central banks trying to stabilize their exchange rates with their main trading partners. If they were successful, there would be a negative association between exchange rate variability and the level of trade, but it would not reflect causation from the former to the latter. He first uses an instrument (the sum of squares of the three-month logarithmic forward error) for the measures of exchange rate volatility to account for possible endogeneity in this variable. The results confirm the negative relationship between volatility and trade, with the magnitude of the effect about the same as before. In addition, he uses both fixed effects and random effects estimation methods to account for the simultaneity bias. In this case the effect is still significant, but the magnitude is much smaller: total elimination of exchange rate volatility in 1994 would have increased trade by only 3 – 4 percent.
Rose (2000) also employs the gravity approach and uses a very large data set involving 186 countries for the five years 1970, 1975, 1980, 1985, and 1990. His main objective in the paper is to measure the effect of currency unions on members’ trade – an issue which is dealt with at length below – but he also uses his model to test for the effects of exchange rate volatility on trade. His primary measure of volatility is the standard deviation of the first difference of the monthly logarithm of the bilateral nominal exchange rate, which is computed over the five years preceding the year of estimation. In his benchmark results using the pooled data, he finds a small but significant negative effect: reducing volatility by one standard deviation (7 percent) around the mean (5 percent) would increase bilateral trade by about 13 percent, which is similar to the finding of Dell’Ariccia described above.9 This result is robust when using three alternative measures of volatility, but not when the standard deviation over the previous five years of the level of the exchange rate is used. However, when random effects are incorporated in the estimation, the magnitude of the effect of volatility on trade is reduced to about a third of the benchmark estimate, or roughly 4 percent. Thus the estimation results of Rose and Dell’Ariccia appear to be quite consistent.
However, a recent paper by Tenreyro (2003) casts some doubt on the robustness of these results. She utilizes a gravity equation similar to that of Rose for a broad sample of countries using annual data from 1970 to 1997. The measure of volatility is the same as that employed by Rose, except that the standard deviation of the log change in monthly exchange rates is measured only over the current year. Her main objective is to address several estimation problems in previous studies of the effect of volatility on trade. When these problems are not addressed and ordinary least squares is used, she finds a small effect: reducing volatility from its sample mean of about 5 percent to zero results in an increase in trade of only 2 percent. When the more appropriate method is used, but without taking account of endogeneity, eliminating exchange rate uncertainty lead to an estimated 4 percent increase in trade. However, when endogeneity is taken into account through the use of instruments, volatility has an insignificant effect on trade, a result that is robust on the choice of instruments.
Finally, it should be noted that there has been some recent work looking at the effects of exchange rate volatility on disaggregated trade flows. Broda and Romalis (2003) find that volatility decreases trade in differentiated products relative to trade in commodities, although the effect is rather small: eliminating all real exchange rate volatility would increase trade in manufactures by less than 5 percent and total trade by less than 3 percent. They note, however, that some countries with particularly volatile exchange rates, especially developing countries, would experience a more pronounced increase in trade. Koren and Szeidl (2003) also use disaggregated data and find small effects: eliminating exchange rate variability would result in a change in export prices of only a few percentage points.
III. Recent History and Geography of Exchange Rate Volatility
A. Measuring Exchange Rate Volatility
In the voluminous literature on exchange rate volatility and trade, there is no consensus on the appropriate method for measuring such volatility. This lack of agreement reflects a number of factors. As noted in the section below, there is no generally accepted model of firm behavior subject to risk arising from fluctuations in exchange rates and other variables. Consequently theory cannot provide definitive guidance as to which measure is most suitable. Moreover, the scope of the analysis will to some extent dictate the type of measure used. If the focus is on advanced countries, then one could take into account forward markets for the assessment of exchange rate volatility on trade, whereas this would not be possible if the analysis extended to a large number of developing countries. In addition, one needs to consider the time horizon over which variability is to be measured, as well as whether it is unconditional volatility or the unexpected movement in the exchange rate relative to its predicted value, that is the relevant measure. Finally, the level of aggregation of trade flows being considered will also play a role in determining the appropriate measure of the exchange rate to be used.
This study provides a comprehensive picture of volatility in exchange rates across the entire Fund membership for which data are available. In the empirical analysis, the paper starts with an examination of the relationship between aggregate exchange rate volatility and aggregate trade. Recognizing the limitations of looking at the aggregate data, the paper then turns to analyzing the effect of exchange rate volatility on trade across different country pairs and over time. Methodologically, the switch to bilateral trade and volatility allows one to better control a variety of other factors that could affect trade other than volatility. As a consequence, the chance to detect an effect of exchange rate volatility on trade improves. Given this methodological approach, the basic building block in the analysis is the volatility in the exchange rate between the currencies of each pair of countries in the sample. For the descriptive part of the study below, which looks at the exchange rate volatility facing a country as a whole, it is necessary to aggregate the bilateral volatilities using trade shares as weights to obtain what is referred to as the “effective volatility” of a country’s exchange rates. This ensures that the measures of volatility in the descriptive and econometric parts of the study are fully consistent.
Such a measure of “effective volatility” presupposes that the exchange rate uncertainty facing an individual firm is an average of the variability of individual bilateral exchange rates (Lanyi and Suss, 1982). However, if a trading firm engages in international transactions with a wide range of countries, any tendency for exchange rates to move in offsetting directions would reduce the overall exposure of the firm to exchange rate risk. This would argue for using the volatility of a country’s effective exchange rate as the measure of the exchange rate uncertainty facing a country. This would seem particularly appropriate for advanced economies where much trade is undertaken by diversified multinational corporations. This was the approach taken in the original IMF (1984) study, which focused almost exclusively on the G-7 countries. However, the present study covers nearly all developing countries, where the role of diversified firms is less pronounced. For this reason, as well as to have consistency with the econometric analysis below, effective volatility is used in the descriptive part of the study.
It is important to realize that the degree of exchange rate variability a country is exposed to is not necessarily closely related to the type of exchange rate regime it has adopted. A country may peg its currency to an anchor currency, but it will float against all other currencies if the anchor does as well. Thus, as with effective exchange rates, effective volatility is a multi-dimensional concept (Polak, 1988). Pegging can reduce nominal exchange rate volatility visà-vis one trading partner, but it can by no means eliminate overall exchange rate variability. This is shown below, where measured volatility is related to two different classifications of a country’s exchange rate arrangement.
The choice between using nominal and real exchange rates depends in part on the time dimension that is relevant for the economic decision being taken. In the short- run where costs of production are known and export and import prices have been determined, the exchange rate exposure of a firm is a function of the nominal exchange rate. However, the decision to engage in international transactions stretches over a longer period of time during which production costs and export and import prices in foreign currency will vary. From this perspective, exchange rates measured in real terms are appropriate. Nonetheless, as nominal and real exchange rates tend to move closely together, given the stickiness of domestic prices, the choice of which one to use is not likely to affect significantly measured volatility or the econometric results. Nonetheless, real rates are preferable on theoretical grounds and are used in the benchmark measures of volatility below. Consumer prices are used to construct the real rates, as they are the most widely available measures of domestic prices. As a robustness check, results using nominal exchange rates are also reported.
While exchange rates are often highly volatile, the extent to which they are a source of uncertainty and risk depends on the degree to which exchange rate movements are foreseen. When hedging instruments are available, the predicted part of exchange rate volatility can be hedged away and hence may not have much effect on trade. This suggests that the appropriate measure of risk should be related to deviations between actual and predicted exchange rates. One possibility along these lines would be to use the forward rate as a prediction of the future spot rate, and to use the difference between the current spot rate and the previous period forward rate as an indicator of exchange rate risk. One problem with this approach is that the forward rate is not a good predictor of future exchange rates. In addition, quotations are available only for the major currencies. More generally, there are a wide variety of methods—ranging from structural models to time series equations using ARCH/GARCH approaches, for example—that could be used to generate predicted values of exchange rates (McKenzie, 1999). However, as pointed out by Meese and Rogoff (1983), there are inherent difficulties in predicting exchange rates. Therefore this study adopts the approach followed in much of the work on the topic and uses a measure of the observed variability of exchange rates as the benchmark. GARCH estimates are included as an alternative measure of volatility.
The most widely used measure of exchange rate volatility is the standard deviation of the first difference of logarithms of the exchange rate.10 This measure has the property that it will equal zero if the exchange rate follows a constant trend, which presumably could be anticipated and therefore would not be a source of uncertainty. Following the practice in most other studies, the change in the exchange rate is computed over one month, using end-of-month data. The standard deviation is calculated over a one-year period, as an indicator of short-run volatility, as well as over a five-year period to capture long-run variability.
Finally, it is useful to take note of the role of currency invoicing here. Very often trade between a pair of countries, especially between two developing countries, is not invoiced in the currency of either country. Instead, a major currency, especially the U.S. dollar, is often used as the invoicing currency. It might appear to be the case that the volatility of the exchange rate between the currencies of the two trading partners is not the relevant volatility to consider. For example, if Chinese exports to India are invoiced in U.S. dollars, it might seem that the Chinese exporters would only care about the fluctuations between the U.S. dollar and the Chinese yuan, but not between the Indian rupee and the Chinese yuan. However, this view is not correct. Any fluctuation between the Chinese yuan and the Indian rupee holding constant the Chinese yuan/U.S. dollar rate, must reflect fluctuations in the Indian rupee/U.S. dollar rate. As the latter could affect the Indian demand for Chinese exports, fluctuations in the Chinese yuan/Indian rupee exchange rate would also affect the Chinese exports to India even if the trade is invoiced in the U.S. dollar. Generally speaking, the choice of invoicing currency does not alter the effect of exchange rate volatility on trade.
B. Comparisons Using the Benchmark Measure of Volatility
It is useful to begin the analysis of exchange rate volatility over time and across countries by examining the evolution of fluctuations in exchange rates for broad groups of countries shown in Figure 3.1.11 This shows the short-run effective volatility since 1970 of exchange rates reported in the IMF’s International Financial Statistics (IFS), converted to real terms using consumer prices, for advanced, transition, emerging market, and developing economies.12 As noted in the Introduction, there were several developments in the international monetary system over this period, including crises in emerging market economies, capital account liberalization, and the breakup of the former Soviet Union, all of which tended to be associated with an increase in exchange rate volatility.
Short-Run Effective Volatility of the Real Exchange Rate by Country Groups, 1970-2002
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Short-Run Effective Volatility of the Real Exchange Rate by Country Groups, 1970-2002
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Short-Run Effective Volatility of the Real Exchange Rate by Country Groups, 1970-2002
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
First, looking at the how variability has changed over the sample period, it is noteworthy that there is no obvious trend increase over time. In the first three years of the sample period, 1970–1972, lower-than-average effective volatility is evident for the advanced economies, which reflects the fixed-rate system of most of these countries. Since then, the exchange rates of these countries have exhibited greater volatility, but not markedly so. In fact, the average effective volatility from 1991–2002 is about the same as in 1970–1980. There is also no clear upward trend in exchange rate volatility in emerging market economies and developing countries over the entire period. While transition economy exchange rates exhibited much greater variability, on average, in the 1990–2002 period, this reflects the very large change in exchange rates associated with the breakup of the former Soviet Union and the shift to market economies from 1989 to 1993. The unprecedented high level of volatility during these years was a reflection by and large of adjustments in real exchange rates that were needed to accommodate the structural transformation of these economies. These adjustments now appear to be essentially complete and in recent years (1999–2002) the effective volatility in their real exchange rates has been less than that of emerging and developing countries.
Second, looking across the major country groupings, it is not surprising that measured volatility is lowest for the advanced economies. This reflects both that these countries trade relatively more with each other and that their bilateral exchange rates with each other tend to exhibit smaller fluctuations than with other countries. (See the discussion below.) The lower volatility within the group presumably arises from the greater stability in economic policies in the advanced economies, as well as their ability to adjust relatively smoothly to shocks. In addition, the foreign exchange markets in which these currencies are traded are very large and liquid, with instruments available to hedge volatility, which enables these markets to clear quickly, dampening potentially large fluctuations in exchange rates.
Figure 3.2 shows the same measure of volatility for the G-7 countries individually, as well as for the group as a whole. While variability is, on average, very similar to that for advanced countries as a whole, there are notable differences. The high average volatility for Japan, at 3.50 percent, is double that of Canada, at 1.75 percent; this low figure would appear to reflect the close integration of the Canadian and U.S. economies, as well the strategy of the Canadian authorities to avoid large swings in the Canadian-U.S. dollar exchange rate over part of the sample period. Also noteworthy is the increased volatility for France, Germany, and Italy surrounding the ERM turmoil in 1991–1993 (which also affected the United Kingdom in 1992), as well as noticeable reduction in effective volatility in the exchange rates of these three countries with the introduction of the euro in 1999.
Short-Run Effective Volatility of the Real Exchange Rate for the G-7 Countries
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Short-Run Effective Volatility of the Real Exchange Rate for the G-7 Countries
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Short-Run Effective Volatility of the Real Exchange Rate for the G-7 Countries
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
To illustrate the reasons for the relatively low effective volatility of the advanced economies, it is useful to decompose the variability in their exchange rates into the contributions of each of the major country groups. This is done in Table 3.1 for the G-7 countries. Part A of Table 3.1 decomposes the effective volatility of each of the G-7 into the share of volatility from each group for each year 1970, 1980, 1990, and 2000, so that the row sum equals the overall effective volatility for that country shown in the last row. It is clear that with two exceptions (Japan and the United States in 1970) the largest component of volatility is that arising from the exchange rates of the other advanced economies. This reflects, in part, the fact that the trade weights of the industrial countries are very high as well as the lower volatility of the individual bilateral exchange rates among the advanced countries. This is shown in Part B of Table 3.1, which gives the volatility of the exchange rates of the G-7 with each of the major country groupings, computed with the trade weights summing to unity for each group. It shows that with only a few exceptions, the volatility of the exchange rates of the G-7 with other advanced economies was less than that of their exchange rates with the other country groups.
Short-Run Effective Volatility of Real I.F.S. Exchange Rates in G-7 Countries by Major Country Groups: Decomposition of Volatility
Short-Run Effective Volatility of Real I.F.S. Exchange Rates in G-7 Countries by Major Country Groups: Decomposition of Volatility
| Country | Year | Advanced Economies | Transition Economies | Emerging Economies | Rest Countries of the World | Total Effective Volatility |
|---|---|---|---|---|---|---|
| United States | 1970 | 0.616 | 0.005 | 1.808 | 0.101 | 2.529 |
| United States | 1980 | 1.751 | 0.021 | 0.364 | 0.321 | 2.457 |
| United States | 1990 | 1.397 | 0.045 | 0.648 | 0.283 | 2.372 |
| United States | 2000 | 1.385 | 0.028 | 0.811 | 0.111 | 2.335 |
| United Kingdom | 1970 | 0.602 | 0.006 | 0.321 | 0.176 | 1.105 |
| United Kingdom | 1980 | 1.957 | 0.029 | 0.251 | 0.415 | 2.651 |
| United Kingdom | 1990 | 1.827 | 0.061 | 0.301 | 0.208 | 2.396 |
| United Kingdom | 2000 | 2.569 | 0.085 | 0.334 | 0.132 | 3.119 |
| France | 1970 | 0.427 | 0.008 | 0.263 | 0.200 | 0.899 |
| France | 1980 | 1.213 | 0.049 | 0.239 | 0.520 | 2.020 |
| France | 1990 | 0.951 | 0.109 | 0.254 | 0.244 | 1.558 |
| France | 2000 | 0.998 | 0.090 | 0.302 | 0.224 | 1.614 |
| Germany | 1970 | 0.523 | 0.022 | 0.360 | 0.115 | 1.019 |
| Germany | 1980 | 1.353 | 0.143 | 0.347 | 0.365 | 2.209 |
| Germany | 1990 | 0.964 | 0.283 | 0.347 | 0.130 | 1.724 |
| Germany | 2000 | 1.102 | 0.228 | 0.401 | 0.117 | 1.847 |
| Italy | 1970 | 0.624 | 0.044 | 0.407 | 0.180 | 1.255 |
| Italy | 1980 | 1.355 | 0.136 | 0.355 | 0.575 | 2.421 |
| Italy | 1990 | 0.950 | 0.258 | 0.361 | 0.334 | 1.903 |
| Italy | 2000 | 0.908 | 0.218 | 0.386 | 0.260 | 1.772 |
| Canada | 1970 | 1.144 | 0.002 | 0.261 | 0.041 | 1.448 |
| Canada | 1980 | 1.706 | 0.006 | 0.140 | 0.090 | 1.943 |
| Canada | 1990 | 1.384 | 0.022 | 0.199 | 0.054 | 1.660 |
| Canada | 2000 | 1.443 | 0.008 | 0.197 | 0.039 | 1.687 |
| Japan | 1970 | 0.663 | 0.002 | 1.315 | 0.170 | 2.150 |
| Japan | 1980 | 1.885 | 0.009 | 1.173 | 0.898 | 3.966 |
| Japan | 1990 | 2.354 | 0.029 | 1.343 | 0.351 | 4.077 |
| Japan | 2000 | 1.789 | 0.036 | 1.465 | 0.229 | 3.519 |
Short-Run Effective Volatility of Real I.F.S. Exchange Rates in G-7 Countries by Major Country Groups: Decomposition of Volatility
| Country | Year | Advanced Economies | Transition Economies | Emerging Economies | Rest Countries of the World | Total Effective Volatility |
|---|---|---|---|---|---|---|
| United States | 1970 | 0.616 | 0.005 | 1.808 | 0.101 | 2.529 |
| United States | 1980 | 1.751 | 0.021 | 0.364 | 0.321 | 2.457 |
| United States | 1990 | 1.397 | 0.045 | 0.648 | 0.283 | 2.372 |
| United States | 2000 | 1.385 | 0.028 | 0.811 | 0.111 | 2.335 |
| United Kingdom | 1970 | 0.602 | 0.006 | 0.321 | 0.176 | 1.105 |
| United Kingdom | 1980 | 1.957 | 0.029 | 0.251 | 0.415 | 2.651 |
| United Kingdom | 1990 | 1.827 | 0.061 | 0.301 | 0.208 | 2.396 |
| United Kingdom | 2000 | 2.569 | 0.085 | 0.334 | 0.132 | 3.119 |
| France | 1970 | 0.427 | 0.008 | 0.263 | 0.200 | 0.899 |
| France | 1980 | 1.213 | 0.049 | 0.239 | 0.520 | 2.020 |
| France | 1990 | 0.951 | 0.109 | 0.254 | 0.244 | 1.558 |
| France | 2000 | 0.998 | 0.090 | 0.302 | 0.224 | 1.614 |
| Germany | 1970 | 0.523 | 0.022 | 0.360 | 0.115 | 1.019 |
| Germany | 1980 | 1.353 | 0.143 | 0.347 | 0.365 | 2.209 |
| Germany | 1990 | 0.964 | 0.283 | 0.347 | 0.130 | 1.724 |
| Germany | 2000 | 1.102 | 0.228 | 0.401 | 0.117 | 1.847 |
| Italy | 1970 | 0.624 | 0.044 | 0.407 | 0.180 | 1.255 |
| Italy | 1980 | 1.355 | 0.136 | 0.355 | 0.575 | 2.421 |
| Italy | 1990 | 0.950 | 0.258 | 0.361 | 0.334 | 1.903 |
| Italy | 2000 | 0.908 | 0.218 | 0.386 | 0.260 | 1.772 |
| Canada | 1970 | 1.144 | 0.002 | 0.261 | 0.041 | 1.448 |
| Canada | 1980 | 1.706 | 0.006 | 0.140 | 0.090 | 1.943 |
| Canada | 1990 | 1.384 | 0.022 | 0.199 | 0.054 | 1.660 |
| Canada | 2000 | 1.443 | 0.008 | 0.197 | 0.039 | 1.687 |
| Japan | 1970 | 0.663 | 0.002 | 1.315 | 0.170 | 2.150 |
| Japan | 1980 | 1.885 | 0.009 | 1.173 | 0.898 | 3.966 |
| Japan | 1990 | 2.354 | 0.029 | 1.343 | 0.351 | 4.077 |
| Japan | 2000 | 1.789 | 0.036 | 1.465 | 0.229 | 3.519 |
As noted above, of the major country groups, the transition economies have had the highest level of exchange rate variability, which was associated with the breakup of the former Soviet Union. Data for this group are shown only starting in 1988, as most countries did not exist in the 1970s and 1980s. Only starting in 1995 are data available for all 22 transition countries, and over the period 1995–2002 the exchange rate volatility of this group was comparable to that of emerging market economies and developing countries. Volatility in these two groups, while on average not quite double that of the advanced countries for the period as a whole, nonetheless declined between the 1980s and 1990s, especially for the emerging market economies.
Some additional detail is shown in Figure 3.3, which gives a geographic breakdown of developing countries (WEO classification), and in Figure 3.4 for two analytical groups (fuel exporters and exporters of non-fuel primary products.)13 Among the geographic regions, sub-Saharan Africa (excluding South Africa and Nigeria) shows the highest average level of volatility of real exchange rates over the sample period, although this may reflect the unusually large 14.5 percent figure in 1994, which is related in large part to the dramatic devaluation of the CFA franc that year. By contrast, the developing countries in Asia have fairly consistently exhibited below-average volatility, especially if one excludes the exceptionally high degree of variability associated with the Asian crisis in 1997–1998. For the developing countries in the Western Hemisphere, exchange rate fluctuations have been below-average, except for the turbulence associated with the “lost decade” of the 1980s. Regarding the analytic groupings shown in Figure 3.4, fuel exporters have experienced increasing exchange rate volatility over the sample period, and exporters of non-fuel primary products have had the highest average level of real exchange volatility over the entire period, which likely reflects the effects of movements in the terms of trade of these countries.
Short-Run Effective Volatility of the Real Exchange Rat in Developing Countries Grouped by Geographic Region
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Short-Run Effective Volatility of the Real Exchange Rat in Developing Countries Grouped by Geographic Region
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Short-Run Effective Volatility of the Real Exchange Rat in Developing Countries Grouped by Geographic Region
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Short-Run Effective Volatility of the Real Exchange Rat in Two Developing Country Groups by Source of Export Earnings
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Short-Run Effective Volatility of the Real Exchange Rat in Two Developing Country Groups by Source of Export Earnings
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Short-Run Effective Volatility of the Real Exchange Rat in Two Developing Country Groups by Source of Export Earnings
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
The average figures for the country groups embody wide variation in the level of exchange rate volatility of the individual countries in each group. It is therefore useful to examine the variation across the members in each group. This is done in Table 3.2, which presents figures for the average effective volatility of real exchange rates over the entire sample period 1970–2002 for the five countries with the highest and lowest volatilities.14 As expected, the dispersion of exchange rate volatility across the advanced economies is quite low, compared with the other groups. It is noteworthy, however, that Japan has the highest measured volatility in this group, with another G-7 country, the United Kingdom, ranking fifth. The dispersion is much higher within the other country groups.
Average Effective Volatility Ranking (1970-2002)
Average Effective Volatility Ranking (1970-2002)
| Advanced (Avg =2.42) | Emerging (Avg =4.43) | Other (Avg = 4.59) | |||
|---|---|---|---|---|---|
| Top 5 (Avg = 3.21) | Top 5 (Avg = 6.70) | Top 5 (Avg = 16.05) | |||
| Japan | 3.50 | Argentina | 9.36 | Angola | 27.32 |
| Israel | 3.48 | Chile | 6.52 | Zambia | 15.59 |
| Australia | 3.23 | Mexico | 5.92 | Nicaragua | 13.51 |
| New Zealand | 3.03 | Peru | 5.89 | Congo, Dem. Rep. of | 13.07 |
| United Kingdom | 2.81 | Uruguay | 5.80 | Uganda | 10.77 |
| Bottom 5 (Avg = 1.78) | Bottom 5 (Avg = 2.33) | Bottom 5 (Avg = 1.30) | |||
| Austria | 1.64 | Panama | 1.89 | Réunion | 1.08 |
| Canada | 1.76 | Singapore | 2.17 | Martinique | 1.20 |
| Bel_Lux | 1.77 | China,P.R.:Hong Kong | 2.38 | Aruba | 1.33 |
| Netherlands | 1.81 | Malaysia | 2.40 | Guiana, French | 1.34 |
| Denmark | 1.91 | Thailand | 2.80 | China,P.R.:Macao | 1.55 |
| Sub-Sahara (Avg = 5.89) | Developing Asia (Avg = 3.66) | Middle-East andTurkey (Avg = 4.28) | Western Hemisphere (Avg = 4.54) | Fuel Export (Avg = 6.18) | Nonfuel Export (Avg = 6.15) | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Top 5 (Avg = 15.46) | Top 5 (Avg = 5.44) | Top 5 (Avg = 6.65) | Top 5 (Avg = 9.55) | Top 5 (Avg = 11.25) | Top 5 (Avg = 11.65) | ||||||
| Angola | 27.32 | Afghanistan, I.S. of | 6.82 | Iran, I.R. of | 8.39 | Nicaragua | 13.51 | Angola | 27.32 | Zambia | 15.59 |
| Zambia | 15.59 | Maldives | 5.73 | Lebanon | 8.27 | Bolivia | 10.26 | Iran, I.R. of | 8.39 | Congo, Dem. Rep. of | 13.07 |
| Congo, Dem. Rep. o | 13.07 | Lao People's Dem.Rep | 5.43 | Yemen, Republic of | 6.07 | Argentina | 9.36 | Equatorial Guinea | 7.86 | Uganda | 10.77 |
| Uganda | 10.77 | Indonesia | 4.87 | Syrian Arab Republic | 5.48 | Suriname | 8.11 | Nigeria | 6.61 | Bolivia | 10.26 |
| Sudan | 10.53 | Sri Lanka | 4.37 | Turkey | 5.04 | Chile | 6.52 | Yemen, Republic of | 6.07 | Ghana | 8.56 |
| Bottom 5 (Avg = 2.60) | Bottom 5 (Avg = 2.43) | Bottom 5 (Avg = 2.46) | Bottom 5 (Avg = 2.11) | Bottom 5 (Avg = 2.67) | Bottom 5 (Avg = 2.90) | ||||||
| Mali | 2.16 | Tonga | 2.19 | Malta | 2.15 | Panama | 1.89 | Bahrain, Kingdom of | 2.22 | Mali | 2.16 |
| Liberia | 2.63 | Bangladesh | 2.23 | Bahrain, Kingdom of | 2.22 | Netherlands Antilles | 2.13 | Kuwait | 2.51 | Liberia | 2.63 |
| Cape Verde | 2.72 | Malaysia | 2.40 | Kuwait | 2.51 | Bahamas, The | 2.14 | Saudi Arabia | 2.58 | Solomon Islands | 3.10 |
| Mauritius | 2.72 | Fiji | 2.55 | Saudi Arabia | 2.58 | Barbados | 2.14 | Oman | 2.93 | Guyana | 3.30 |
| Cameroon | 2.75 | Thailand | 2.80 | Yemen, P.D. Rep. | 2.85 | Dominica | 2.24 | Gabon | 3.13 | Côte d'Ivoire | 3.31 |
Average Effective Volatility Ranking (1970-2002)
| Advanced (Avg =2.42) | Emerging (Avg =4.43) | Other (Avg = 4.59) | |||
|---|---|---|---|---|---|
| Top 5 (Avg = 3.21) | Top 5 (Avg = 6.70) | Top 5 (Avg = 16.05) | |||
| Japan | 3.50 | Argentina | 9.36 | Angola | 27.32 |
| Israel | 3.48 | Chile | 6.52 | Zambia | 15.59 |
| Australia | 3.23 | Mexico | 5.92 | Nicaragua | 13.51 |
| New Zealand | 3.03 | Peru | 5.89 | Congo, Dem. Rep. of | 13.07 |
| United Kingdom | 2.81 | Uruguay | 5.80 | Uganda | 10.77 |
| Bottom 5 (Avg = 1.78) | Bottom 5 (Avg = 2.33) | Bottom 5 (Avg = 1.30) | |||
| Austria | 1.64 | Panama | 1.89 | Réunion | 1.08 |
| Canada | 1.76 | Singapore | 2.17 | Martinique | 1.20 |
| Bel_Lux | 1.77 | China,P.R.:Hong Kong | 2.38 | Aruba | 1.33 |
| Netherlands | 1.81 | Malaysia | 2.40 | Guiana, French | 1.34 |
| Denmark | 1.91 | Thailand | 2.80 | China,P.R.:Macao | 1.55 |
| Sub-Sahara (Avg = 5.89) | Developing Asia (Avg = 3.66) | Middle-East andTurkey (Avg = 4.28) | Western Hemisphere (Avg = 4.54) | Fuel Export (Avg = 6.18) | Nonfuel Export (Avg = 6.15) | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Top 5 (Avg = 15.46) | Top 5 (Avg = 5.44) | Top 5 (Avg = 6.65) | Top 5 (Avg = 9.55) | Top 5 (Avg = 11.25) | Top 5 (Avg = 11.65) | ||||||
| Angola | 27.32 | Afghanistan, I.S. of | 6.82 | Iran, I.R. of | 8.39 | Nicaragua | 13.51 | Angola | 27.32 | Zambia | 15.59 |
| Zambia | 15.59 | Maldives | 5.73 | Lebanon | 8.27 | Bolivia | 10.26 | Iran, I.R. of | 8.39 | Congo, Dem. Rep. of | 13.07 |
| Congo, Dem. Rep. o | 13.07 | Lao People's Dem.Rep | 5.43 | Yemen, Republic of | 6.07 | Argentina | 9.36 | Equatorial Guinea | 7.86 | Uganda | 10.77 |
| Uganda | 10.77 | Indonesia | 4.87 | Syrian Arab Republic | 5.48 | Suriname | 8.11 | Nigeria | 6.61 | Bolivia | 10.26 |
| Sudan | 10.53 | Sri Lanka | 4.37 | Turkey | 5.04 | Chile | 6.52 | Yemen, Republic of | 6.07 | Ghana | 8.56 |
| Bottom 5 (Avg = 2.60) | Bottom 5 (Avg = 2.43) | Bottom 5 (Avg = 2.46) | Bottom 5 (Avg = 2.11) | Bottom 5 (Avg = 2.67) | Bottom 5 (Avg = 2.90) | ||||||
| Mali | 2.16 | Tonga | 2.19 | Malta | 2.15 | Panama | 1.89 | Bahrain, Kingdom of | 2.22 | Mali | 2.16 |
| Liberia | 2.63 | Bangladesh | 2.23 | Bahrain, Kingdom of | 2.22 | Netherlands Antilles | 2.13 | Kuwait | 2.51 | Liberia | 2.63 |
| Cape Verde | 2.72 | Malaysia | 2.40 | Kuwait | 2.51 | Bahamas, The | 2.14 | Saudi Arabia | 2.58 | Solomon Islands | 3.10 |
| Mauritius | 2.72 | Fiji | 2.55 | Saudi Arabia | 2.58 | Barbados | 2.14 | Oman | 2.93 | Guyana | 3.30 |
| Cameroon | 2.75 | Thailand | 2.80 | Yemen, P.D. Rep. | 2.85 | Dominica | 2.24 | Gabon | 3.13 | Côte d'Ivoire | 3.31 |
Table 3.3 provides information on the frequency (number of years) each country appeared in the top five or bottom five in terms of effective real exchange rate volatility. This indicates which countries exhibited persistently high or low variability over the sample period. The results are often similar to what is shown in Table 3.2; for example, Japan is in the top five advanced economies in 30 out of the 33 years in the sample. Similarly, in the emerging markets group, Argentina is in the top five in 20 of the 33 years.15
Frequency Counting (f) for Top 5 (High Vol) and Bottom 5 (Low Vol) Lists (1970-2002)
Frequency Counting (f) for Top 5 (High Vol) and Bottom 5 (Low Vol) Lists (1970-2002)
| Advanced | Emerging | Developing Countries | |||
|---|---|---|---|---|---|
| Frequency in Top 5 | Frequency in Top 5 | Frequency in Top 5 | |||
| Japan | 28 | Argentina | 21 | Congo, Dem. Rep. of | 17 |
| Australia | 21 | Uruguay | 21 | Sudan | 13 |
| Israel | 18 | Turkey | 14 | Angola | 9 |
| New Zealand | 16 | Chile | 14 | Bolivia | 9 |
| United Kingdom | 15 | Indonesia | 13 | Ghana | 8 |
| Frequency in Bottom 5 | Frequency in Bottom 5 | Frequency in Bottom 5 | |||
| Austria | 28 | Panama | 22 | Martinique | 25 |
| Bel_Lux | 28 | Singapore | 21 | Guiana, French | 22 |
| Canada | 19 | Malaysia | 18 | Réunion | 20 |
| Netherlands | 17 | Venezuela, Rep. Bol. | 18 | Netherlands Antilles | 14 |
| Denmark | 13 | Mexico | 17 | Bahamas, The | 9 |
| Sub-Sahara | Developing Asia | Middle East and Turkey | Western Hemisphere | Fuel-Export | Nonfuel-Export | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Frequency in Top 5 | Frequency in Top 5 | Frequency in Top 5 | Frequency in Top 5 | Frequency in Top 5 | Frequency in Top 5 | ||||||
| Congo, Dem. Rep. of | 23 | Sri Lanka | 22 | Turkey | 30 | Argentina | 19 | Nigeria | 25 | Congo, Dem. Rep. of | 24 |
| Sudan | 21 | Myanmar | 21 | Syrian Arab Republic | 29 | Paraguay | 15 | Iran, I.R. of | 20 | Bolivia | 12 |
| Ghana | 11 | Pakistan | 19 | Egypt | 24 | Chile | 14 | Algeria | 19 | Ghana | 12 |
| Angola | 10 | Samoa | 17 | Iran, I.R. of | 19 | Haiti | 11 | Venezuela, Rep. Bol. | 16 | Sierra Leone | 11 |
| Uganda | 9 | Indonesia | 16 | Jordan | 17 | Bolivia | 11 | Angola | 12 | Burkina Faso | 11 |
| Frequency in Bottom 5 | Frequency in Bottom 5 | Frequency in Bottom 5 | Frequency in Bottom 5 | Frequency in Bottom 5 | Frequency in Bottom 5 | ||||||
| Cameroon | 24 | Malaysia | 28 | Malta | 32 | Netherlands Antilles | 20 | Kuwait | 25 | Côte d'Ivoire | 19 |
| Gabon | 16 | Thailand | 23 | Bahrain, Kingdom of | 26 | Panama | 16 | Bahrain, Kingdom of | 24 | Rwanda | 17 |
| Côte d'Ivoire | 13 | Fiji | 22 | Kuwait | 23 | Bahamas, The | 13 | Gabon | 23 | Togo | 15 |
| Madagascar | 12 | Philippines | 13 | Saudi Arabia | 21 | Mexico | 13 | Venezuela, Rep. Bol. | 22 | Liberia | 15 |
| Mauritius | 12 | Samoa | 12 | Egypt | 12 | Trinidad and Tobago | 12 | Saudi Arabia | 21 | Bolivia | |
Frequency Counting (f) for Top 5 (High Vol) and Bottom 5 (Low Vol) Lists (1970-2002)
| Advanced | Emerging | Developing Countries | |||
|---|---|---|---|---|---|
| Frequency in Top 5 | Frequency in Top 5 | Frequency in Top 5 | |||
| Japan | 28 | Argentina | 21 | Congo, Dem. Rep. of | 17 |
| Australia | 21 | Uruguay | 21 | Sudan | 13 |
| Israel | 18 | Turkey | 14 | Angola | 9 |
| New Zealand | 16 | Chile | 14 | Bolivia | 9 |
| United Kingdom | 15 | Indonesia | 13 | Ghana | 8 |
| Frequency in Bottom 5 | Frequency in Bottom 5 | Frequency in Bottom 5 | |||
| Austria | 28 | Panama | 22 | Martinique | 25 |
| Bel_Lux | 28 | Singapore | 21 | Guiana, French | 22 |
| Canada | 19 | Malaysia | 18 | Réunion | 20 |
| Netherlands | 17 | Venezuela, Rep. Bol. | 18 | Netherlands Antilles | 14 |
| Denmark | 13 | Mexico | 17 | Bahamas, The | 9 |
| Sub-Sahara | Developing Asia | Middle East and Turkey | Western Hemisphere | Fuel-Export | Nonfuel-Export | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Frequency in Top 5 | Frequency in Top 5 | Frequency in Top 5 | Frequency in Top 5 | Frequency in Top 5 | Frequency in Top 5 | ||||||
| Congo, Dem. Rep. of | 23 | Sri Lanka | 22 | Turkey | 30 | Argentina | 19 | Nigeria | 25 | Congo, Dem. Rep. of | 24 |
| Sudan | 21 | Myanmar | 21 | Syrian Arab Republic | 29 | Paraguay | 15 | Iran, I.R. of | 20 | Bolivia | 12 |
| Ghana | 11 | Pakistan | 19 | Egypt | 24 | Chile | 14 | Algeria | 19 | Ghana | 12 |
| Angola | 10 | Samoa | 17 | Iran, I.R. of | 19 | Haiti | 11 | Venezuela, Rep. Bol. | 16 | Sierra Leone | 11 |
| Uganda | 9 | Indonesia | 16 | Jordan | 17 | Bolivia | 11 | Angola | 12 | Burkina Faso | 11 |
| Frequency in Bottom 5 | Frequency in Bottom 5 | Frequency in Bottom 5 | Frequency in Bottom 5 | Frequency in Bottom 5 | Frequency in Bottom 5 | ||||||
| Cameroon | 24 | Malaysia | 28 | Malta | 32 | Netherlands Antilles | 20 | Kuwait | 25 | Côte d'Ivoire | 19 |
| Gabon | 16 | Thailand | 23 | Bahrain, Kingdom of | 26 | Panama | 16 | Bahrain, Kingdom of | 24 | Rwanda | 17 |
| Côte d'Ivoire | 13 | Fiji | 22 | Kuwait | 23 | Bahamas, The | 13 | Gabon | 23 | Togo | 15 |
| Madagascar | 12 | Philippines | 13 | Saudi Arabia | 21 | Mexico | 13 | Venezuela, Rep. Bol. | 22 | Liberia | 15 |
| Mauritius | 12 | Samoa | 12 | Egypt | 12 | Trinidad and Tobago | 12 | Saudi Arabia | 21 | Bolivia | |
C. Alternative Measures of Volatility
It is useful to compare the benchmark measure of volatility with a number of alternatives. Figure 3.5 provides figures for the short-run effective volatility of the nominal official exchange rate. A comparison with Figure 3.1 shows that while there are no major differences between these two measures, it is generally the case that real exchange rate volatility is somewhat higher than nominal volatility. This is particularly the case in 1970, when fixed nominal rates were more widespread and inflation differentials generated larger movements in real rates.16 Lower volatility in nominal exchange rates is also more pronounced among developing countries over the entire sample period, which would appear to reflect the “fear of floating” described by Reinhart and Rogoff (2002).
Short-Run Effective Volatility of the Nominal Exchange Rate by Major Country Groups 1970-2002
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Short-Run Effective Volatility of the Nominal Exchange Rate by Major Country Groups 1970-2002
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Short-Run Effective Volatility of the Nominal Exchange Rate by Major Country Groups 1970-2002
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Figure 3.6 shows the longer-run measure of exchange rate volatility, namely, the standard deviation of monthly log differences in exchange rates calculated over the five years preceding the year in question. As one would expect, the measured volatility is larger than the average of the short-run volatilities over the same years. Figure 3.7 shows a measure of conditional volatility, namely, that estimated for each currency assuming it follows a GARCH process. The underlying idea is that part of the volatility can be forecasted, based on past values of the exchange rate, and firms engaged in trade would naturally make an effort to develop such a forecast. Figure 3.7 plots the conditional—or forecasted—exchange rate volatility (for a description of this methodology, see the Appendix). A comparison with Figure 3.6 shows that this measure is in general somewhat lower than the standard measure, particularly the case for the transition economies in 1995. Figure 3.8 gives the long-run volatility for the G-7 countries.
Long-Run Effective Volatility of the Real Exchange Rate by Major Country Groups
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Long-Run Effective Volatility of the Real Exchange Rate by Major Country Groups
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Long-Run Effective Volatility of the Real Exchange Rate by Major Country Groups
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Long-Run Effective Conditional Volatility of the Real Exchange Rate by Major Country Groups
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Long-Run Effective Conditional Volatility of the Real Exchange Rate by Major Country Groups
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Long-Run Effective Conditional Volatility of the Real Exchange Rate by Major Country Groups
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Long-Run Effective Volatility of the Real Exchange Rate in the G-7 Countries
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Long-Run Effective Volatility of the Real Exchange Rate in the G-7 Countries
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Long-Run Effective Volatility of the Real Exchange Rate in the G-7 Countries
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Up to this point, exchange rates in IFS, i.e., those compiled and reported by the authorities to the IMF, have been used in the analysis. Recently, however, attention has focused on the classification of exchange rate regimes and the appropriateness of using these exchange rates as the basis for such a classification. In particular, Reinhart and Rogoff (2002) have put together an extensive dataset for 153 countries of monthly market-determined parallel exchange rates going back to 1946. They find striking and widespread differences between the official de jure regime, as reported in the IMF’s Annual Report on Exchange Rate Arrangements and Exchange Restrictions (AREAER), and that implied by the information they gathered on actual de facto exchange rate practices.17 As the exchange rates reported by Reinhart and Rogoff may be more representative of the price of foreign exchange at which international trade transactions were conducted, it would also appear worthwhile to calculate exchange rate volatility using these market-determined rates.
In order to compare the volatility implied by both IFS and market-determined rates, it is necessary to use the same set of countries. As the usable data for real market-determined rates is significantly smaller (107 countries) than what is available for real IFS rates (172), the benchmark measure of volatility for the latter had to be recomputed.18 This is shown in Appendix Table III.4, where the sample period extends only through 1998 because of data limitations. Comparing the benchmark measure of exchange rate volatility with the same measure, but using the larger sample of countries, the evolution of exchange rate volatility over time and between major country groupings is quite similar. The difference in measured volatility for the same country group reflects only the difference in the sample of countries and the fact that the variability of the currencies of the countries included in the larger sample is not the same as that of the currencies in the smaller sample.
Short-Run Effective Volatility of Real I.F.S Exchange Rate: Smaller Sample of Major Country Groups
Short-Run Effective Volatility of Real I.F.S Exchange Rate: Smaller Sample of Major Country Groups
| Year | G-7 | Advanced Economies | Transition Economies | Emerging Market Economies | Rest Countries of the World |
|---|---|---|---|---|---|
| 1970 | 1.39 | 1.17 | 1.56 | 6.15 | 1.82 |
| 1971 | 1.75 | 1.93 | 6.34 | 1.99 | 2.33 |
| 1972 | 1.76 | 1.63 | 2.41 | 4.17 | 3.68 |
| 1973 | 2.84 | 2.73 | 3.26 | 6.82 | 4.17 |
| 1974 | 2.26 | 2.35 | 3.05 | 3.89 | 3.08 |
| 1975 | 1.71 | 1.77 | 1.94 | 4.89 | 3.21 |
| 1976 | 2.38 | 2.25 | 2.42 | 4.70 | 2.86 |
| 1977 | 1.98 | 2.40 | 1.86 | 4.97 | 2.74 |
| 1978 | 2.41 | 2.20 | 3.21 | 2.81 | 3.47 |
| 1979 | 1.94 | 1.78 | 2.31 | 2.18 | 4.12 |
| 1980 | 2.01 | 1.84 | 5.83 | 2.71 | 2.73 |
| 1981 | 2.28 | 2.23 | 3.89 | 3.01 | 5.47 |
| 1982 | 2.67 | 2.61 | 4.33 | 6.46 | 4.04 |
| 1983 | 1.97 | 2.05 | 3.34 | 3.65 | 5.68 |
| 1984 | 1.79 | 1.96 | 3.61 | 3.84 | 4.16 |
| 1985 | 2.47 | 2.38 | 4.23 | 4.24 | 7.52 |
| 1986 | 2.49 | 2.39 | 4.48 | 4.49 | 6.61 |
| 1987 | 1.96 | 1.90 | 6.49 | 3.03 | 5.75 |
| 1988 | 1.96 | 1.90 | 4.60 | 5.27 | 8.56 |
| 1989 | 2.12 | 2.19 | 11.71 | 7.37 | 5.18 |
| 1990 | 2.10 | 1.81 | 13.55 | 4.68 | 5.04 |
| 1991 | 2.03 | 2.03 | 22.04 | 3.45 | 4.89 |
| 1992 | 3.02 | 2.84 | 27.35 | 2.78 | 5.17 |
| 1993 | 2.49 | 2.41 | 6.52 | 2.48 | 6.29 |
| 1994 | 1.73 | 1.62 | 7.23 | 3.60 | 7.22 |
| 1995 | 2.66 | 2.23 | 4.53 | 3.54 | 3.15 |
| 1996 | 1.33 | 1.33 | 3.56 | 1.87 | 2.25 |
| 1997 | 2.16 | 2.00 | 5.78 | 3.55 | 3.19 |
| 1998 | 2.55 | 2.29 | 8.43 | 5.02 | 3.74 |
| 1970-1980 | 2.04 | 2.01 | 3.11 | 4.11 | 3.11 |
| 1981-1990 | 2.18 | 2.14 | 6.83 | 4.62 | 5.87 |
| 1991-1998 | 2.25 | 2.09 | 8.83 | 3.29 | 4.47 |
| 1970-1998 | 2.14 | 2.08 | 7.46 | 4.04 | 4.55 |
Short-Run Effective Volatility of Real I.F.S Exchange Rate: Smaller Sample of Major Country Groups
| Year | G-7 | Advanced Economies | Transition Economies | Emerging Market Economies | Rest Countries of the World |
|---|---|---|---|---|---|
| 1970 | 1.39 | 1.17 | 1.56 | 6.15 | 1.82 |
| 1971 | 1.75 | 1.93 | 6.34 | 1.99 | 2.33 |
| 1972 | 1.76 | 1.63 | 2.41 | 4.17 | 3.68 |
| 1973 | 2.84 | 2.73 | 3.26 | 6.82 | 4.17 |
| 1974 | 2.26 | 2.35 | 3.05 | 3.89 | 3.08 |
| 1975 | 1.71 | 1.77 | 1.94 | 4.89 | 3.21 |
| 1976 | 2.38 | 2.25 | 2.42 | 4.70 | 2.86 |
| 1977 | 1.98 | 2.40 | 1.86 | 4.97 | 2.74 |
| 1978 | 2.41 | 2.20 | 3.21 | 2.81 | 3.47 |
| 1979 | 1.94 | 1.78 | 2.31 | 2.18 | 4.12 |
| 1980 | 2.01 | 1.84 | 5.83 | 2.71 | 2.73 |
| 1981 | 2.28 | 2.23 | 3.89 | 3.01 | 5.47 |
| 1982 | 2.67 | 2.61 | 4.33 | 6.46 | 4.04 |
| 1983 | 1.97 | 2.05 | 3.34 | 3.65 | 5.68 |
| 1984 | 1.79 | 1.96 | 3.61 | 3.84 | 4.16 |
| 1985 | 2.47 | 2.38 | 4.23 | 4.24 | 7.52 |
| 1986 | 2.49 | 2.39 | 4.48 | 4.49 | 6.61 |
| 1987 | 1.96 | 1.90 | 6.49 | 3.03 | 5.75 |
| 1988 | 1.96 | 1.90 | 4.60 | 5.27 | 8.56 |
| 1989 | 2.12 | 2.19 | 11.71 | 7.37 | 5.18 |
| 1990 | 2.10 | 1.81 | 13.55 | 4.68 | 5.04 |
| 1991 | 2.03 | 2.03 | 22.04 | 3.45 | 4.89 |
| 1992 | 3.02 | 2.84 | 27.35 | 2.78 | 5.17 |
| 1993 | 2.49 | 2.41 | 6.52 | 2.48 | 6.29 |
| 1994 | 1.73 | 1.62 | 7.23 | 3.60 | 7.22 |
| 1995 | 2.66 | 2.23 | 4.53 | 3.54 | 3.15 |
| 1996 | 1.33 | 1.33 | 3.56 | 1.87 | 2.25 |
| 1997 | 2.16 | 2.00 | 5.78 | 3.55 | 3.19 |
| 1998 | 2.55 | 2.29 | 8.43 | 5.02 | 3.74 |
| 1970-1980 | 2.04 | 2.01 | 3.11 | 4.11 | 3.11 |
| 1981-1990 | 2.18 | 2.14 | 6.83 | 4.62 | 5.87 |
| 1991-1998 | 2.25 | 2.09 | 8.83 | 3.29 | 4.47 |
| 1970-1998 | 2.14 | 2.08 | 7.46 | 4.04 | 4.55 |
Appendix Table III.5 shows the benchmark measure of volatility using parallel market exchange rates, which can be compared directly with Appendix Table III.4, as both utilize the same list of countries. It is immediately clear that, in almost all cases, the volatility of parallel market rates is larger than that of IFS rates.19 This is true for advanced countries as well. Even though there are unlikely to be significant differences between IFS and market quotations for the bilateral rates between advanced countries, there would tend to be much larger differences for the bilateral rates between the advanced economies and countries in other groups. The only exceptions occur in 1991, 1992, 1997 and 1998 for transition economies, when movements in IFS rates exceeded those in parallel market rates. However, it should be noted that the difference between the two measures of volatility declined from the 1970s to the 1990s for all the country groups except emerging markets, where there was a slight increase. This largely reflects the fact that, except for transition economies, the effective volatility of the market exchange rate declined between the 1970s and the 1990s, whereas the volatility of the IFS rate increased for transition and developing economies, was about unchanged for advanced countries, but decreased for emerging market economies.
Short-Run Effective Volatility of Real Parallel Exchange Rate: Smaller Sample of Major Country Groups
Short-Run Effective Volatility of Real Parallel Exchange Rate: Smaller Sample of Major Country Groups
| Year | G-7 | Advanced Economies | Transition Economies | Emerging Market Economies | Rest Countries of the World |
|---|---|---|---|---|---|
| 1970 | 2.27 | 1.88 | 4.87 | 8.47 | 4.15 |
| 1971 | 2.01 | 2.43 | 5.58 | 4.75 | 4.93 |
| 1972 | 2.32 | 2.27 | 3.97 | 4.98 | 5.57 |
| 1973 | 4.02 | 4.06 | 4.48 | 7.51 | 6.95 |
| 1974 | 6.00 | 6.18 | 6.71 | 5.74 | 8.10 |
| 1975 | 2.78 | 4.14 | 4.81 | 4.87 | 6.57 |
| 1976 | 3.95 | 4.26 | 5.58 | 7.15 | 6.01 |
| 1977 | 2.90 | 3.83 | 6.83 | 6.31 | 6.49 |
| 1978 | 4.29 | 4.58 | 7.88 | 4.54 | 6.44 |
| 1979 | 3.34 | 3.59 | 9.47 | 3.17 | 6.96 |
| 1980 | 3.37 | 3.55 | 9.86 | 4.14 | 8.50 |
| 1981 | 4.39 | 4.83 | 7.32 | 6.28 | 10.01 |
| 1982 | 4.24 | 4.67 | 7.13 | 9.61 | 9.65 |
| 1983 | 3.71 | 4.70 | 6.59 | 9.42 | 7.87 |
| 1984 | 3.88 | 4.49 | 7.82 | 6.60 | 7.67 |
| 1985 | 3.58 | 3.69 | 8.30 | 8.14 | 10.15 |
| 1986 | 3.53 | 3.72 | 10.67 | 6.60 | 8.52 |
| 1987 | 2.89 | 2.94 | 7.53 | 7.52 | 9.09 |
| 1988 | 2.78 | 2.79 | 8.48 | 7.27 | 12.42 |
| 1989 | 3.17 | 3.35 | 16.16 | 9.64 | 8.47 |
| 1990 | 2.83 | 2.71 | 13.76 | 10.88 | 6.32 |
| 1991 | 2.71 | 2.91 | 10.16 | 4.92 | 6.71 |
| 1992 | 3.64 | 3.66 | 15.18 | 4.84 | 6.40 |
| 1993 | 3.71 | 4.65 | 8.15 | 3.94 | 6.80 |
| 1994 | 2.09 | 1.89 | 6.04 | 4.43 | 6.33 |
| 1995 | 3.13 | 2.74 | 6.23 | 3.57 | 4.49 |
| 1996 | 1.89 | 1.83 | 4.57 | 2.50 | 3.58 |
| 1997 | 2.63 | 2.51 | 8.57 | 3.87 | 4.79 |
| 1998 | 3.03 | 2.70 | 6.96 | 5.26 | 3.98 |
| 1970-1980 | 3.39 | 3.71 | 6.85 | 5.59 | 6.50 |
| 1981-1990 | 3.50 | 3.79 | 9.99 | 8.22 | 9.02 |
| 1991-1998 | 2.86 | 2.86 | 7.86 | 4.17 | 5.37 |
| 1970-1998 | 3.28 | 3.49 | 8.23 | 6.08 | 7.02 |
Short-Run Effective Volatility of Real Parallel Exchange Rate: Smaller Sample of Major Country Groups
| Year | G-7 | Advanced Economies | Transition Economies | Emerging Market Economies | Rest Countries of the World |
|---|---|---|---|---|---|
| 1970 | 2.27 | 1.88 | 4.87 | 8.47 | 4.15 |
| 1971 | 2.01 | 2.43 | 5.58 | 4.75 | 4.93 |
| 1972 | 2.32 | 2.27 | 3.97 | 4.98 | 5.57 |
| 1973 | 4.02 | 4.06 | 4.48 | 7.51 | 6.95 |
| 1974 | 6.00 | 6.18 | 6.71 | 5.74 | 8.10 |
| 1975 | 2.78 | 4.14 | 4.81 | 4.87 | 6.57 |
| 1976 | 3.95 | 4.26 | 5.58 | 7.15 | 6.01 |
| 1977 | 2.90 | 3.83 | 6.83 | 6.31 | 6.49 |
| 1978 | 4.29 | 4.58 | 7.88 | 4.54 | 6.44 |
| 1979 | 3.34 | 3.59 | 9.47 | 3.17 | 6.96 |
| 1980 | 3.37 | 3.55 | 9.86 | 4.14 | 8.50 |
| 1981 | 4.39 | 4.83 | 7.32 | 6.28 | 10.01 |
| 1982 | 4.24 | 4.67 | 7.13 | 9.61 | 9.65 |
| 1983 | 3.71 | 4.70 | 6.59 | 9.42 | 7.87 |
| 1984 | 3.88 | 4.49 | 7.82 | 6.60 | 7.67 |
| 1985 | 3.58 | 3.69 | 8.30 | 8.14 | 10.15 |
| 1986 | 3.53 | 3.72 | 10.67 | 6.60 | 8.52 |
| 1987 | 2.89 | 2.94 | 7.53 | 7.52 | 9.09 |
| 1988 | 2.78 | 2.79 | 8.48 | 7.27 | 12.42 |
| 1989 | 3.17 | 3.35 | 16.16 | 9.64 | 8.47 |
| 1990 | 2.83 | 2.71 | 13.76 | 10.88 | 6.32 |
| 1991 | 2.71 | 2.91 | 10.16 | 4.92 | 6.71 |
| 1992 | 3.64 | 3.66 | 15.18 | 4.84 | 6.40 |
| 1993 | 3.71 | 4.65 | 8.15 | 3.94 | 6.80 |
| 1994 | 2.09 | 1.89 | 6.04 | 4.43 | 6.33 |
| 1995 | 3.13 | 2.74 | 6.23 | 3.57 | 4.49 |
| 1996 | 1.89 | 1.83 | 4.57 | 2.50 | 3.58 |
| 1997 | 2.63 | 2.51 | 8.57 | 3.87 | 4.79 |
| 1998 | 3.03 | 2.70 | 6.96 | 5.26 | 3.98 |
| 1970-1980 | 3.39 | 3.71 | 6.85 | 5.59 | 6.50 |
| 1981-1990 | 3.50 | 3.79 | 9.99 | 8.22 | 9.02 |
| 1991-1998 | 2.86 | 2.86 | 7.86 | 4.17 | 5.37 |
| 1970-1998 | 3.28 | 3.49 | 8.23 | 6.08 | 7.02 |
In comparing the volatility of currencies across countries, it is relevant to consider the type of exchange rate regime, as this would likely have a bearing on the degree of variability of a country’s currency against other currencies. This is done in Table 3.4, which shows the real effective exchange rate volatility across country groupings in terms of both the official IMF exchange rate classification as well as the Reinhart-Rogoff Natural classification. It is noteworthy that a currency that is classified as “pegged” is by no means insulated from exchange rate fluctuations. Indeed, the average effective volatility of “freely floating” advanced countries (2.94 percent with the IMF classification and 3.09 percent with the Natural classification, respectively) is less than the average volatility of “pegged” currencies of other country groups, except for the emerging market countries in the Natural classification. Also, looking across types of currency regimes within country groupings, “limited flexibility” confers less exchange rate volatility than “pegged,” except for the advanced countries under the Natural classification; and “managed floating” is not associated with a great deal more volatility than “pegged” regimes. Only “freely floating” and “freely falling” regimes have distinctly greater average volatility; the latter category in the Natural classification includes those countries that had annual inflation rates exceeding 30 percent, which not surprisingly caused considerable exchange rate volatility.
Real Effective Volatility Across Country Groups by Type of Exchange Rate Regime
Real Effective Volatility Across Country Groups by Type of Exchange Rate Regime
| OFFICIAL IMF CLASSIFICATION /1 | ||||
|---|---|---|---|---|
| Country Groups | Peg | Limited Flexibility | Managed Floating | Freely Floating |
| Advanced | 2.14 | 2.07 | 2.81 | 2.94 |
| Emerging | 3.74 | 2.28 | 4.30 | 6.90 |
| Transition | 5.73 | 4.56 | 6.37 | |
| Developing | 4.35 | 2.94 | 4.95 | 6.47 |
Real Effective Volatility Across Country Groups by Type of Exchange Rate Regime
| OFFICIAL IMF CLASSIFICATION /1 | ||||
|---|---|---|---|---|
| Country Groups | Peg | Limited Flexibility | Managed Floating | Freely Floating |
| Advanced | 2.14 | 2.07 | 2.81 | 2.94 |
| Emerging | 3.74 | 2.28 | 4.30 | 6.90 |
| Transition | 5.73 | 4.56 | 6.37 | |
| Developing | 4.35 | 2.94 | 4.95 | 6.47 |
Based on the IMF's annual publication Exchange Arrangements and Exchange Restrictions
Based on Reinhart and Rogoff (2003)
| NATURAL CLASSIFICATION /2 | |||||
|---|---|---|---|---|---|
| Country Groups | Peg | Limited Flexibility | Managed Floating | Freely Floating | Freely Falling |
| Advanced | 1.81 | 2.37 | 2.81 | 3.09 | 4.76 |
| Emerging | 2.98 | 2.81 | 4.02 | 4.66 | 8.31 |
| Transition | 3.75 | 3.11 | 3.48 | 11.15 | 9.95 |
| Developing | 3.28 | 3.16 | 4.53 | 5.26 | 13.47 |
Based on the IMF's annual publication Exchange Arrangements and Exchange Restrictions
Based on Reinhart and Rogoff (2003)
| NATURAL CLASSIFICATION /2 | |||||
|---|---|---|---|---|---|
| Country Groups | Peg | Limited Flexibility | Managed Floating | Freely Floating | Freely Falling |
| Advanced | 1.81 | 2.37 | 2.81 | 3.09 | 4.76 |
| Emerging | 2.98 | 2.81 | 4.02 | 4.66 | 8.31 |
| Transition | 3.75 | 3.11 | 3.48 | 11.15 | 9.95 |
| Developing | 3.28 | 3.16 | 4.53 | 5.26 | 13.47 |
Based on the IMF's annual publication Exchange Arrangements and Exchange Restrictions
Based on Reinhart and Rogoff (2003)
Table 3.5 shows how effective volatility has varied over time by exchange rate regime. Again, “limited flexibility” is associated with less variability than a “pegged” regime. If one ignores the 1970s, when the major industrial countries were pegged early in the decade, volatility declined from between the 1980s to the 1990s, except in the category “freely floating” in the Natural classification.
Real Effective Volatility Across Regimes and Time
Real Effective Volatility Across Regimes and Time
| OFFICIAL IMF CLASSIFICATION /1 | ||||
|---|---|---|---|---|
| 1970-1980 | 1981-1990 | 1991-2001 | 1970-2001 | |
| Peg | 3.12 | 4.96 | 4.11 | 3.99 |
| Limited Flexibility | 2.13 | 2.20 | 2.13 | 2.15 |
| Managed Floating | 4.93 | 4.75 | 4.18 | 4.43 |
| Freely Floating | 3.05 | 6.95 | 5.01 | 5.22 |
Real Effective Volatility Across Regimes and Time
| OFFICIAL IMF CLASSIFICATION /1 | ||||
|---|---|---|---|---|
| 1970-1980 | 1981-1990 | 1991-2001 | 1970-2001 | |
| Peg | 3.12 | 4.96 | 4.11 | 3.99 |
| Limited Flexibility | 2.13 | 2.20 | 2.13 | 2.15 |
| Managed Floating | 4.93 | 4.75 | 4.18 | 4.43 |
| Freely Floating | 3.05 | 6.95 | 5.01 | 5.22 |
Based on the IMF's annual publication Exchange Arrangements and Exchange Restrictions
Based on Reinhart and Rogoff (2003)
| NATURAL CLASSIFICATION /2 | ||||
|---|---|---|---|---|
| 1970-1980 | 1981-1990 | 1991-2001 | 1970-2001 | |
| Peg | 2.80 | 3.17 | 3.03 | 2.98 |
| Limited Flexibility | 2.58 | 2.97 | 2.88 | 2.83 |
| Managed Floating | 3.48 | 4.27 | 4.16 | 4.02 |
| Freely Floating | 3.32 | 4.11 | 4.64 | 4.26 |
| Freely Falling | 7.99 | 13.04 | 9.31 | 10.56 |
Based on the IMF's annual publication Exchange Arrangements and Exchange Restrictions
Based on Reinhart and Rogoff (2003)
| NATURAL CLASSIFICATION /2 | ||||
|---|---|---|---|---|
| 1970-1980 | 1981-1990 | 1991-2001 | 1970-2001 | |
| Peg | 2.80 | 3.17 | 3.03 | 2.98 |
| Limited Flexibility | 2.58 | 2.97 | 2.88 | 2.83 |
| Managed Floating | 3.48 | 4.27 | 4.16 | 4.02 |
| Freely Floating | 3.32 | 4.11 | 4.64 | 4.26 |
| Freely Falling | 7.99 | 13.04 | 9.31 | 10.56 |
Based on the IMF's annual publication Exchange Arrangements and Exchange Restrictions
Based on Reinhart and Rogoff (2003)
IV. New Evidence on the Effect of Exchange Rate Volatility on Trade
As discussed in Part II, theoretical models do not point unambiguously to a negative effect of exchange rate volatility on trade. Moreover, empirical analysis in the existing literature has not uncovered a strong, causal, and consistently negative impact. In the empirical analysis we report in this section, there is no obvious negative relationship between aggregate exchange rate volatility and aggregate trade. When we turn to bilateral trade, we do find some evidence that exchange rate volatility tends to reduce trade. However, this negative effect is not robust to alternative ways of controlling for factors that could affect trade. The key findings of our empirical analysis are summarized below, and an Appendix describes the statistical results in more detail.
The objective of our empirical analysis is to examine the role of exchange rate volatility in trade in a comprehensive manner. Compared to the existing academic literature and the Fund (1984) paper on the topic, the contribution of our analysis lies in exploring the effect of exchange rate volatility on trade along several dimensions:
By the type of exchange rate volatility: We examine a range of different exchange rate volatility measures – short- and long-run, real and nominal, official, IFS-based and parallel market-based, and conditional and unconditional.
By country group: We test if the impact of exchange rate volatility differs across country groupings, including industrial and developing countries.
By the type of trade: We examine the impact not only on aggregate but also on sectoral trade, which allows us to test if the effect of exchange rate volatility varies in direction and magnitude across different types of goods. The role of exchange rate volatility has not yet been explored extensively using disaggregated trade data.
In addition to the disaggregation of the volatility effect, we test its robustness to alternative ways of controlling for joint causality between trade and exchange rates and for trade-related factors other than exchange rate volatility. Finally, while our focus is on exchange rate volatility, we take this opportunity to revisit a related topic – the role of a common currency in enhancing trade flows – and explore the robustness of the finding by Rose (2002) that this positive effect is very large.
A. Aggregate Volatility and Aggregate Trade – A First Look
It is instructive to look at the time paths of world trade and exchange rate volatility, and examine if there is any obvious negative association between the two. Figure 4.1 shows the evolution of world trade since 1970 together with the average real effective volatility for all countries in the sample. There is a clear bulge in exchange rate volatility from 1989 to 1993, which reflects the large fluctuations in the currencies of a number of transition economies during this period in the aftermath of the breakup of the Soviet Union.20 If one excludes transition economies from the measure of world currency volatility, the bulge disappears. What one then sees is an upward trend in average volatility from the early 1970s through the end of the 1980s, but a general moderation in the overall level of currency volatility since then.
Effective Volatility of the Real Exchange Rate and World Trade
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Note: World trade is measured as the average of the volume of world exports and imports in billions of 1995 US dollars (right) scale. Volatility is measures as the unweighted average of the volatility of the real exchange rate of the countries in the sample. The dashed line above includes the volatility of the transition economies starting in1988.
Effective Volatility of the Real Exchange Rate and World Trade
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Note: World trade is measured as the average of the volume of world exports and imports in billions of 1995 US dollars (right) scale. Volatility is measures as the unweighted average of the volatility of the real exchange rate of the countries in the sample. The dashed line above includes the volatility of the transition economies starting in1988.Effective Volatility of the Real Exchange Rate and World Trade
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Note: World trade is measured as the average of the volume of world exports and imports in billions of 1995 US dollars (right) scale. Volatility is measures as the unweighted average of the volatility of the real exchange rate of the countries in the sample. The dashed line above includes the volatility of the transition economies starting in1988.In comparison, the world trade has increased steadily since 1970 and the growth rate is much more smooth than that of the exchange rate volatility. Looking at the movement of world trade and aggregate volatility over time, there does not appear to be any clear relationship between them. Therefore, at the aggregate level, there is no evidence of a negative effect of real exchange rate volatility on trade.
It may be useful to examine the relationship between the two by breaking down the sample by major country groups (Figure 4.2) and developing countries by geographic region (Figure 4.3) and by type of export (Figure 4.4). In some of the sub-samples and for some of the years, there appears to be a negative association between exchange rate volatility and the level of trade in certain country groupings. This is most evident in the case of transition economies in 1990-1994 (lower left graph of Figure 4.2), the Asian crisis in 1997-1998 (upper right graph of Figure 4.3), and non-fuel primary product exporting economies in the early 1980s (lower graph of Figure 4.4). However, this negative association may not reflect a causal relationship, but rather is a manifestation of the effects of a common set of factors that both raises currency volatility and reduces trade. For example, the Asian crisis led to a large decline in the imports of the affected countries and major movements in their exchange rates, but the fall in domestic demand was the most important factor reducing import volumes, not currency volatility. Similarly, the breakup of the Soviet Union caused widespread dislocations in many transition economies, resulting in substantial falls in output and trade, and huge changes in many exchange rates that were part and parcel of the transition process.
Effective Volatility of the Real Exchange Rate and Trade: Major Country Groups
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Effective Volatility of the Real Exchange Rate and Trade: Major Country Groups
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Effective Volatility of the Real Exchange Rate and Trade: Major Country Groups
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Effective Volatility of the Real Exchange Rate and Trade: Developing Countries by Region
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Effective Volatility of the Real Exchange Rate and Trade: Developing Countries by Region
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Effective Volatility of the Real Exchange Rate and Trade: Developing Countries by Region
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Effective Volatility of the Real Exchange Rate and Trade: Developing Countries by Type of Export
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Effective Volatility of the Real Exchange Rate and Trade: Developing Countries by Type of Export
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
Effective Volatility of the Real Exchange Rate and Trade: Developing Countries by Type of Export
Citation: Policy Papers 2004, 023; 10.5089/9781498330282.007.A001
In order to estimate the specific impact of exchange rate volatility on trade flows, it is necessary to take account of the separate effects of the myriad of factors that determine the level of exports and imports. In what follows, we move away from aggregate trade and discuss a methodology that exploits the much richer variations in the data on bilateral trade and bilateral exchange rates that permits identifying the distinct contribution of volatility on trade.
B. The Conceptual Framework for the Analysis of the Volatility Effect in Trade
To investigate the effect of exchange rate volatility on trade, there are several “building blocks” to consider. First, there are factors other than exchange rate volatility that affect trade and it is important to account for them in a way that is consistent with economic theory. Otherwise, one runs the risk of mis-attributing the effect of these other factors to exchange rate volatility. Second, the measure of exchange rate volatility should be conceptually reasonable. Third, it may be useful to allow exchange rate volatility to have different effects on different types of trade or trade in different country groupings. We explain these building blocks in turn.
As part of the first building block, we seek to account for the determinants of trade patterns other than exchange rate volatility in a modified gravity model. This model relates trade between a given pair of countries to characteristics of each of these two countries and the characteristics of their relationship. The characteristics that are most important—which the model owes its name to—are the economic mass (i.e., GDP) and the distance between the countries. In addition, the empirical specifications of the gravity model typically control for other factors augmenting or reducing trade, such as land areas, cultural similarity, geographical position, historical links, and preferential trading arrangements, all of which tend to affect the transaction costs relevant for bilateral trade and have been found to be statistically significant determinants of trade in various empirical applications. The model also typically controls for the level of economic development, which is expected to have a positive effect on trade, as more developed countries tend to specialize and trade more (e.g., Frankel and Wei, 1993). The gravity model has been empirically successful in terms of its ability to explain a large part of the variations in the observed trade patterns. It also has the merit of being grounded in international trade theories, ranging from those based on country differences in factor endowments or technology to models of increasing returns to scale and monopolistic competition.
A relatively recent development in the theoretical foundation of the gravity model emphasizes “remoteness” or “multilateral resistance” effects. These effects were proposed by Anderson and van Wincoop (2003) and are defined as a function of unobservable equilibrium price indices, which depend on bilateral trade barriers and income shares of all the trading partners. In other words, the “multilateral resistance” effects are catch-all expressions that summarize the effects on a given bilateral trade from differential, possibly unobserved, trade costs between this country pair and all other trading partners. The gravity equation can then be interpreted as indicating that bilateral trade depends on the bilateral trade barrier between the two countries in question, relative to the two countries’ multilateral resistance indices: for a given bilateral trade barrier between the two countries, higher barriers between them and their other trading partners would reduce the relative price of goods traded between them, raising bilateral trade. In empirical applications, the multilateral resistance indices can be conveniently proxied by country effects (fixed or time varying). We also include time effects in the model to control for time-specific factors such as world business cycles, global shocks, etc.
The second building block is the measure of exchange rate volatility. In the benchmark model we focus on the long-run measure of IFS-based real exchange rate volatility. Its value in any given year t is calculated as the standard deviation of the first-difference of the monthly natural logarithm of the bilateral real exchange rate in the five years preceding year t, which is a conventional measure most commonly used in the current literature on the subject. To check the robustness of results, we examine alternative, yet analogously calculated measures of exchange rate volatility: long-run IFS-based nominal exchange rate volatility, short-run, contemporaneous IFS-based real and nominal exchange rate volatility, and the short- and long-run volatility of real parallel market rates using data from Reinhart and Rogoff (2002). As additional robustness analysis, we also consider the conditional volatilities of real exchange rates estimated using a GARCH (1, 1) model. To ensure the stationarity of the GARCH model, we exclude countries with hyper-inflation episodes, extreme exchange rate fluctuations and/or incomplete data, focusing our estimation on 124 industrial, developing, emerging and transition economies.
The third building block for the model is the consideration of different country groups and different types of trade. We analyze the exchange rate volatility effect separately for industrial countries and developing countries. We also examine how the exchange rate volatility effect depends on the type of product trade – differentiated or homogeneous. In classifying products into differentiated and homogenous varieties, we follow the strategy in Rauch (1999). Conceptually, Rauch first identifies two types of homogenous products: those traded on an organized exchange (“commodities”), and those whose prices are reported regularly in a professional trade publication (“referenced price products”). All other products are then defined as differentiated products.
C. What Do the Data Tell Us?
The gravity model performs well empirically, yielding precise and generally reasonable estimates. The coefficient on distance is negative and statistically significant, while the coefficient on the economic mass is positive and statistically significant. Most other control variables are also mostly significant and have the expected signs.
Does exchange rate volatility hamper trade? As a benchmark specification using country and time fixed effects, we find that the long-run real exchange rate volatility has a statistically significant negative effect on trade (Table 4.1, Column 1 Row 1). If exchange rate volatility were to rise by one standard deviation (from 0.12 to 0.15, for example, in our sample), trade would fall by about 7 percent (Table 4.1, Column 2 Row 1).21 This effect is comparable to the estimates found by previous studies, e.g., Rose (2000) and Tenreyro (2003).
Effect of Long-Run Real Exchange Rate Volatility on Aggregate Trade 1/
Standard errors are in parentheses. An asterisk denotes significance at the 90 percent level or higher. For details, see Appendix Tables IV.2 and IV.6A.
Effect of Long-Run Real Exchange Rate Volatility on Aggregate Trade 1/
| Model Specification | Not Controlling for Joint Causality between Trade and Exchange Rates | Implied Pecentage Change in Trade by One Standard Deviation Increase in Volatility | Controlling for Joint Causality between Trade and Exchange Rates | Implied Pecentage Change in Trade by One Standard Deviation Increase in Volatility |
|---|---|---|---|---|
| (1) | (2) | (3) | (4) | |
| With Country and Time Fixed Effects | -2.37* | -6.64% | -22.64* | -63.39% |
| (0.67) | (12.50) | |||
| With Country Pair and Time Fixed Effects | -2.40* | -6.72% | -6.49 | -18.17% |
| (0.47) | (6.24) | |||
| With Time-Varying Country Effects | 2.89* | 8.09% | -23.82 | -66.70% |
| (1.78) | (28.87) | |||
| With Country and Time Fixed Effects on the Full Sample | -1.16* | -8.82% | ... | ... |
| (0.22) |
Standard errors are in parentheses. An asterisk denotes significance at the 90 percent level or higher. For details, see Appendix Tables IV.2 and IV.6A.
Effect of Long-Run Real Exchange Rate Volatility on Aggregate Trade 1/
| Model Specification | Not Controlling for Joint Causality between Trade and Exchange Rates | Implied Pecentage Change in Trade by One Standard Deviation Increase in Volatility | Controlling for Joint Causality between Trade and Exchange Rates | Implied Pecentage Change in Trade by One Standard Deviation Increase in Volatility |
|---|---|---|---|---|
| (1) | (2) | (3) | (4) | |
| With Country and Time Fixed Effects | -2.37* | -6.64% | -22.64* | -63.39% |
| (0.67) | (12.50) | |||
| With Country Pair and Time Fixed Effects | -2.40* | -6.72% | -6.49 | -18.17% |
| (0.47) | (6.24) | |||
| With Time-Varying Country Effects | 2.89* | 8.09% | -23.82 | -66.70% |
| (1.78) | (28.87) | |||
| With Country and Time Fixed Effects on the Full Sample | -1.16* | -8.82% | ... | ... |
| (0.22) |
Standard errors are in parentheses. An asterisk denotes significance at the 90 percent level or higher. For details, see Appendix Tables IV.2 and IV.6A.
Is the negative effect on trade robust to alternative ways of controlling for factors other than bilateral exchange rate volatility? The answer is “No.” On the one hand, a negative effect is still observed when we control for unobservable cultural, economic, historical, geographical and other factors specific to a given pair of countries rather than individual countries (Table 4.1, Column 1 Row 2). On the other hand, no negative effect is found when we allow country-specific effects to vary over time, as appears justified theoretically, given the dynamic nature of “multilateral resistance.” Indeed, this specification could result in some cases in even a positive coefficient (e.g., Table 4.1, Column 1 Row 3). While this does not necessarily imply that volatility promotes trade, it suggests that the finding of a negative effect of exchange rate volatility on trade is not robust.
A note of caution is in order here. Recent developments in the theoretical foundation of a gravity specification suggest that it is important to include time-varying country fixed effects in order to fully absorb the “multilateral resistance” effects. Otherwise, one might mis-attribute to exchange rate volatility those effects on bilateral trade that should have been attributed to other factors. At the same time, we note that part of the forces underlining bilateral exchange rate volatility is time-varying and country-specific. The inclusion of the time-varying country fixed effects could also “overcorrect.” For example, an unexpected increase in one country's money supply could raise all the bilateral exchange rate volatility involving that country. Even if this increase in volatility depresses all bilateral trade involving that country, a specification that controls for that country's time-varying fixed effects would not be able to reveal a negative effect of exchange rate volatility on trade. We have to keep this qualification in mind in interpreting the result.
Sorting out causality. To the extent that countries implement policies aimed at lowering exchange rate volatility in order to increase bilateral trade, the model considered so far would suffer from an endogeneity bias. We control for this possibility using two instrumental variable approaches: (i) that proposed by Frankel and Wei (1993), whereby the volatility in the relative quantity of money is used as an instrumental variable for exchange rate volatility, and (ii) that implemented by Tenreyro (2003) which relates the exchange rate volatility to the propensity of countries to adopt a common currency anchor. Neither of these approaches is perfect and each has its advantages: the Frankel-Wei approach appeals to the monetary theory of exchange rate determination, and is simple and easy to implement, while the Tenreyro approach appeals to the optimal currency framework as described in Alesina, Barro, and Tenreyro (2002). There is no significant effect of exchange rate volatility on trade in the models with country-pair and time-varying country effects (Table 4.1, Column 3 Row 2 and Column 3 Row 3). However, the negative volatility effect found in the model with constant country effects survives (Table 4.1, Column 3 Row 1).
Would the conclusion change when one employs alternative measures of volatility? The short answer is “no.” Table 4.2 reports results from the same regression which includes our standard long-run measure together with all three alternative measures of exchange rate volatility (as differences from the long-run real IFS-based measure). The short-run volatility in the real exchange rate appears to discourage trade, albeit to a smaller extent than the long-run volatility. The volatility in the parallel market exchange rates has a similar effect on trade as the volatility in the IFS-reported exchange rates, but only in the long run. As shown in Appendix Tables IV.3 and IV.4 the volatilities of the nominal and real exchange rates are highly correlated and thus have similar effects on trade. In addition, conditioning the measure of exchange rate volatility on historical information using the GARCH approach, instead of using the simple statistical measure of volatility, also preserves the negative relationship with trade. As in Table 4.1, when time-varying country fixed effects are controlled for, there is no longer evidence of a negative and significant association between volatility and trade.
Alternative Measures of Exchange Rate Volatility 1/
Standard errors are indicated in parentheses. An asterisk denotes significance at the 90 percent level or higher. For details, refer to Appendix Tables IV.3., IV.4, and IV.5.
In excess of long-run real official exchange rate volatility.
Alternative Measures of Exchange Rate Volatility 1/
| Model Specification | Long-Run Real Exchange Rate Volatility | Short-Run Real Exchange Rate Volatility 2/ | Long-Run Real Parallel Market Exchange Rate Volatility 2/ | Short-Run Real Parallel Market Exchange Rate Volatility 2/ |
|---|---|---|---|---|
| (1) | (2) | (3) | (4) | |
| With Country and Time Fixed Effects | -3.92* | -2.72* | -1.20* | -0.55 |
| (1.3) | (1.04) | (0.63) | (0.73) | |
| Implied Pecentage Change in Trade by One Standard Deviation Increase in Volatility | -10.98% | -6.80% | -6.48% | -2.15% |
| With Country Pair and Time Fixed Effects | -4.72* | -4.15* | -0.42 | -1.14* |
| (0.76) | (0.55) | (0.34) | (0.41) | |
| Implied Pecentage Change in Trade by One Standard Deviation Increase in Volatility | -13.22% | -10.38% | -2.27% | -4.45% |
| With Time-Varying Country Effects | 7.52* | 6.70* | -2.20 | -1.55 |
| (3.89) | (3.24) | (2.71) | (2.8) | |
| Implied Pecentage Change in Trade by One Standard Deviation Increase in Volatility | 21.06% | 16.75% | -11.88% | -6.05% |
Standard errors are indicated in parentheses. An asterisk denotes significance at the 90 percent level or higher. For details, refer to Appendix Tables IV.3., IV.4, and IV.5.
In excess of long-run real official exchange rate volatility.
Alternative Measures of Exchange Rate Volatility 1/
| Model Specification | Long-Run Real Exchange Rate Volatility | Short-Run Real Exchange Rate Volatility 2/ | Long-Run Real Parallel Market Exchange Rate Volatility 2/ | Short-Run Real Parallel Market Exchange Rate Volatility 2/ |
|---|---|---|---|---|
| (1) | (2) | (3) | (4) | |
| With Country and Time Fixed Effects | -3.92* | -2.72* | -1.20* | -0.55 |
| (1.3) | (1.04) | (0.63) | (0.73) | |
| Implied Pecentage Change in Trade by One Standard Deviation Increase in Volatility | -10.98% | -6.80% | -6.48% | -2.15% |
| With Country Pair and Time Fixed Effects | -4.72* | -4.15* | -0.42 | -1.14* |
| (0.76) | (0.55) | (0.34) | (0.41) | |
| Implied Pecentage Change in Trade by One Standard Deviation Increase in Volatility | -13.22% | -10.38% | -2.27% | -4.45% |
| With Time-Varying Country Effects | 7.52* | 6.70* | -2.20 | -1.55 |
| (3.89) | (3.24) | (2.71) | (2.8) | |
| Implied Pecentage Change in Trade by One Standard Deviation Increase in Volatility | 21.06% | 16.75% | -11.88% | -6.05% |
Standard errors are indicated in parentheses. An asterisk denotes significance at the 90 percent level or higher. For details, refer to Appendix Tables IV.3., IV.4, and IV.5.
In excess of long-run real official exchange rate volatility.
Alternative Measures of Volatility: Short-Run, Parallel Market, Nominal and Conditional
Alternative Measures of Volatility: Short-Run, Parallel Market, Nominal and Conditional
| Variable | LR & SR, Official, Real | LR, Official & Parallel, Real | LR & SR, Official & Parallel, Real | LR, Official, Nominal | Conditional, Official, Real |
|---|---|---|---|---|---|
| (1) | (2) | (3) | (4) | (5) | |
| L-R Official Real Vol | -3.86 | -1.86 | -3.92 | ||
| (0.88) | (0.98) | (1.30) | |||
| (S-R Official Real Vol) - | -1.87 | -2.72 | |||
| (L-R Official Real Vol) | (0.71) | (1.04) | |||
| (L-R Parallel Real Vol) - | -1.55 | -1.20 | |||
| (L-R Official Real Vol) | (0.60) | (0.63) | |||
| (S-R Parallel Real Vol) - | -0.55 | ||||
| (S-R Official Real Vol) | (0.73) | ||||
| L-R Official Nominal Vol | -2.60 | ||||
| (0.60) | |||||
| Volatility Conditional upon Historical Information | -2.20 | ||||
| (0.92) | |||||
| Dummy for Common Currency Union | 1.37 | 1.30 | 1.41 | ||
| (0.14) | (0.14) | (0.14) | |||
| Log of Real GDP Product | 0.18 | 0.21 | 0.21 | 0.22 | 0.18 |
| (0.13) | (0.19) | (0.19) | (0.13) | (0.13) | |
| Log of Real Per Capita GDP Product | -0.05 | 1.04 | 1.03 | -0.09 | -0.04 |
| (0.12) | (0.21) | (0.21) | (0.12) | (0.12) | |
| Log of Distance | -1.51 | -1.40 | -1.39 | -1.51 | -1.52 |
| (0.02) | (0.03) | (0.03) | (0.02) | (0.02) | |
| Common Language Dummy | 0.25 | 0.30 | 0.30 | 0.25 | 0.25 |
| (0.05) | (0.06) | (0.06) | (0.05) | (0.05) | |
| Common Border Dummy | -0.12 | -0.26 | -0.22 | -0.12 | -0.12 |
| (0.09) | (0.12) | (0.12) | (0.10) | (0.10) | |
| Number of Landlocked Countries in the Country Pair | -3.12 | -3.06 | -3.33 | -0.01 | 0.10 |
| (0.56) | (0.69) | (0.55) | (0.50) | (0.50) | |
| Number of Island Countries in the Country Pair | 0.56 | -3.08 | -5.11 | 2.51 | 2.58 |
| (0.54) | (0.72) | (1.11) | (0.36) | (0.36) | |
| Log of Area Product | 0.53 | 0.27 | 0.23 | 0.50 | 0.52 |
| (0.09) | (0.10) | (0.12) | (0.08) | (0.08) | |
| Dummy for Same Colonizer after 1945 | 0.68 | 0.70 | 0.70 | 0.70 | 0.70 |
| (0.07) | (0.11) | (0.11) | (0.07) | (0.07) | |
| Dummy for Both Currently Being Colonies | 0.31 | -0.81 | -0.82 | 0.28 | 0.29 |
| (0.48) | (0.16) | (0.16) | (0.46) | (0.47) | |
| Dummy for Common Nation | 0.80 | 0.91 | 0.84 | ||
| (0.58) | (0.57) | (0.57) | |||
| Dummy for Being Colonizer and Colony to Each Other | 1.44 | 1.01 | 1.00 | 1.45 | 1.46 |
| (0.07) | (0.09) | (0.09) | (0.07) | (0.07) | |
| Dummy for Common FTA Membership | 0.27 | -0.49 | -0.50 | 0.28 | 0.28 |
| (0.08) | (0.11) | (0.11) | (0.08) | (0.08) | |
| Dummy for One in WTO | 0.27 | -0.14 | -0.18 | 0.26 | 0.29 |
| (0.12) | (0.18) | (0.18) | (0.12) | (0.12) | |
| Dummy for Both in WTO | 0.43 | -0.15 | -0.18 | 0.43 | 0.49 |
| (0.14) | (0.22) | (0.22) | (0.14) | (0.14) | |
| Dummy for GSP | 0.63 | 0.59 | 0.60 | 0.63 | 0.63 |
| (0.03) | (0.04) | (0.04) | (0.03) | (0.03) | |
| Time Fixed Effects | Yes | Yes | Yes | Yes | Yes |
| Country Fixed Feffects | Yes | Yes | Yes | Yes | Yes |
| Number of Observations | 16085 | 6988 | 6949 | 16238 | 16238 |
| R-squared | 0.79 | 0.82 | 0.83 | 0.79 | 0.79 |
| RMSE | 1.64 | 1.44 | 1.44 | 1.64 | 1.65 |
Alternative Measures of Volatility: Short-Run, Parallel Market, Nominal and Conditional
| Variable | LR & SR, Official, Real | LR, Official & Parallel, Real | LR & SR, Official & Parallel, Real | LR, Official, Nominal | Conditional, Official, Real |
|---|---|---|---|---|---|
| (1) | (2) | (3) | (4) | (5) | |
| L-R Official Real Vol | -3.86 | -1.86 | -3.92 | ||
| (0.88) | (0.98) | (1.30) | |||
| (S-R Official Real Vol) - | -1.87 | -2.72 | |||
| (L-R Official Real Vol) | (0.71) | (1.04) | |||
| (L-R Parallel Real Vol) - | -1.55 | -1.20 | |||
| (L-R Official Real Vol) | (0.60) | (0.63) | |||
| (S-R Parallel Real Vol) - | -0.55 | ||||
| (S-R Official Real Vol) | (0.73) | ||||
| L-R Official Nominal Vol | -2.60 | ||||
| (0.60) | |||||
| Volatility Conditional upon Historical Information | -2.20 | ||||
| (0.92) | |||||
| Dummy for Common Currency Union | 1.37 | 1.30 | 1.41 | ||
| (0.14) | (0.14) | (0.14) | |||
| Log of Real GDP Product | 0.18 | 0.21 | 0.21 | 0.22 | 0.18 |
| (0.13) | (0.19) | (0.19) | (0.13) | (0.13) | |
| Log of Real Per Capita GDP Product | -0.05 | 1.04 | 1.03 | -0.09 | -0.04 |
| (0.12) | (0.21) | (0.21) | (0.12) | (0.12) | |
| Log of Distance | -1.51 | -1.40 | -1.39 | -1.51 | -1.52 |
| (0.02) | (0.03) | (0.03) | (0.02) | (0.02) | |
| Common Language Dummy | 0.25 | 0.30 | 0.30 | 0.25 | 0.25 |
| (0.05) | (0.06) | (0.06) | (0.05) | (0.05) | |
| Common Border Dummy | -0.12 | -0.26 | -0.22 | -0.12 | -0.12 |
| (0.09) | (0.12) | (0.12) | (0.10) | (0.10) | |
| Number of Landlocked Countries in the Country Pair | -3.12 | -3.06 | -3.33 | -0.01 | 0.10 |
| (0.56) | (0.69) | (0.55) | (0.50) | (0.50) | |
| Number of Island Countries in the Country Pair | 0.56 | -3.08 | -5.11 | 2.51 | 2.58 |
| (0.54) | (0.72) | (1.11) | (0.36) | (0.36) | |
| Log of Area Product | 0.53 | 0.27 | 0.23 | 0.50 | 0.52 |
| (0.09) | (0.10) | (0.12) | (0.08) | (0.08) | |
| Dummy for Same Colonizer after 1945 | 0.68 | 0.70 | 0.70 | 0.70 | 0.70 |
| (0.07) | (0.11) | (0.11) | (0.07) | (0.07) | |
| Dummy for Both Currently Being Colonies | 0.31 | -0.81 | -0.82 | 0.28 | 0.29 |
| (0.48) | (0.16) | (0.16) | (0.46) | (0.47) | |
| Dummy for Common Nation | 0.80 | 0.91 | 0.84 | ||
| (0.58) | (0.57) | (0.57) | |||
| Dummy for Being Colonizer and Colony to Each Other | 1.44 | 1.01 | 1.00 | 1.45 | 1.46 |
| (0.07) | (0.09) | (0.09) | (0.07) | (0.07) | |
| Dummy for Common FTA Membership | 0.27 | -0.49 | -0.50 | 0.28 | 0.28 |
| (0.08) | (0.11) | (0.11) | (0.08) | (0.08) | |
| Dummy for One in WTO | 0.27 | -0.14 | -0.18 | 0.26 | 0.29 |
| (0.12) | (0.18) | (0.18) | (0.12) | (0.12) | |
| Dummy for Both in WTO | 0.43 | -0.15 | -0.18 | 0.43 | 0.49 |
| (0.14) | (0.22) | (0.22) | (0.14) | (0.14) | |
| Dummy for GSP | 0.63 | 0.59 | 0.60 | 0.63 | 0.63 |
| (0.03) | (0.04) | (0.04) | (0.03) | (0.03) | |
| Time Fixed Effects | Yes | Yes | Yes | Yes | Yes |
| Country Fixed Feffects | Yes | Yes | Yes | Yes | Yes |
| Number of Observations | 16085 | 6988 | 6949 | 16238 | 16238 |
| R-squared | 0.79 | 0.82 | 0.83 | 0.79 | 0.79 |
| RMSE | 1.64 | 1.44 | 1.44 | 1.64 | 1.65 |
Country-Pair Fixed Effects
Country-Pair Fixed Effects
| Variable | LR & SR, Official, Real | LR, Official & Parallel, Real | LR & SR, Official & Parallel, Real | LR, Official, Nominal | Conditional, Official, Real |
|---|---|---|---|---|---|
| (1) | (2) | (3) | (4) | (5) | |
| L-R Official Real Vol | -4.84 | -1.43 | -4.72 | ||
| (0.61) | (0.60) | (0.76) | |||
| (S-R Official Real Vol) - | -2.97 | -4.15 | |||
| (L-R Official Real Vol) | (0.44) | (0.55) | |||
| (L-R Parallel Real Vol) - | -0.94 | -0.42 | |||
| (L-R Official Real Vol) | (0.34) | (0.34) | |||
| (S-R Parallel Real Vol) - | -1.14 | ||||
| (S-R Official Real Vol) | (0.41) | ||||
| L-R Official Nominal Vol | -2.51 | ||||
| (0.43) | |||||
| Volatility Conditional upon Historical Information | -4.12 | ||||
| (0.70) | |||||
| Dummy for Common Currency Union | 0.22 | 0.20 | 0.28 | ||
| (0.38) | (0.38) | (0.38) | |||
| Log of Real GDP Product | 0.50 | 0.61 | 0.65 | 0.51 | 0.50 |
| (0.10) | (0.13) | (0.13) | (0.10) | (0.10) | |
| Log of Real Per Capita GDP Product | 0.52 | 0.47 | -0.27 | -0.24 | |
| (0.09) | (0.14) | (0.13) | (0.09) | (0.09) | |
| Dummy for Both Currently Being Colonies | 0.17 | 0.15 | 0.16 | ||
| (0.98) | (0.98) | (0.98) | |||
| Dummy for Common FTA Membership | 0.25 | 0.29 | 0.33 | 0.26 | 0.24 |
| (0.10) | (0.13) | (0.13) | (0.10) | (0.10) | |
| Dummy for One in WTO | 0.10 | -0.24 | -0.25 | 0.10 | 0.13 |
| (0.10) | (0.13) | (0.13) | (0.10) | (0.10) | |
| Dummy for Both in WTO | 0.24 | -0.20 | -0.20 | 0.22 | 0.29 |
| (0.10) | (0.14) | (0.14) | (0.10) | (0.10) | |
| Dummy for GSP | 0.43 | 0.03 | 0.00 | 0.45 | 0.46 |
| (0.10) | (0.14) | (0.14) | (0.09) | (0.09) | |
| Time Fixed Effects | Yes | Yes | Yes | Yes | Yes |
| Country Pair Fixed Effects | Yes | Yes | Yes | Yes | Yes |
| Number of Observations | 16085 | 6988 | 6949 | 16238 | 16238 |
| R-squared | 0.49 | 0.69 | 0.69 | 0.48 | 0.49 |
Country-Pair Fixed Effects
| Variable | LR & SR, Official, Real | LR, Official & Parallel, Real | LR & SR, Official & Parallel, Real | LR, Official, Nominal | Conditional, Official, Real |
|---|---|---|---|---|---|
| (1) | (2) | (3) | (4) | (5) | |
| L-R Official Real Vol | -4.84 | -1.43 | -4.72 | ||
| (0.61) | (0.60) | (0.76) | |||
| (S-R Official Real Vol) - | -2.97 | -4.15 | |||
| (L-R Official Real Vol) | (0.44) | (0.55) | |||
| (L-R Parallel Real Vol) - | -0.94 | -0.42 | |||
| (L-R Official Real Vol) | (0.34) | (0.34) | |||
| (S-R Parallel Real Vol) - | -1.14 | ||||
| (S-R Official Real Vol) | (0.41) | ||||
| L-R Official Nominal Vol | -2.51 | ||||
| (0.43) | |||||
| Volatility Conditional upon Historical Information | -4.12 | ||||
| (0.70) | |||||
| Dummy for Common Currency Union | 0.22 | 0.20 | 0.28 | ||
| (0.38) | (0.38) | (0.38) | |||
| Log of Real GDP Product | 0.50 | 0.61 | 0.65 | 0.51 | 0.50 |
| (0.10) | (0.13) | (0.13) | (0.10) | (0.10) | |
| Log of Real Per Capita GDP Product | 0.52 | 0.47 | -0.27 | -0.24 | |
| (0.09) | (0.14) | (0.13) | (0.09) | (0.09) | |
| Dummy for Both Currently Being Colonies | 0.17 | 0.15 | 0.16 | ||
| (0.98) | (0.98) | (0.98) | |||
| Dummy for Common FTA Membership | 0.25 | 0.29 | 0.33 | 0.26 | 0.24 |
| (0.10) | (0.13) | (0.13) | (0.10) | (0.10) | |
| Dummy for One in WTO | 0.10 | -0.24 | -0.25 | 0.10 | 0.13 |
| (0.10) | (0.13) | (0.13) | (0.10) | (0.10) | |
| Dummy for Both in WTO | 0.24 | -0.20 | -0.20 | 0.22 | 0.29 |
| (0.10) | (0.14) | (0.14) | (0.10) | (0.10) | |
| Dummy for GSP | 0.43 | 0.03 | 0.00 | 0.45 | 0.46 |
| (0.10) | (0.14) | (0.14) | (0.09) | (0.09) | |
| Time Fixed Effects | Yes | Yes | Yes | Yes | Yes |
| Country Pair Fixed Effects | Yes | Yes | Yes | Yes | Yes |
| Number of Observations | 16085 | 6988 | 6949 | 16238 | 16238 |
| R-squared | 0.49 | 0.69 | 0.69 | 0.48 | 0.49 |
Does exchange rate volatility have a different effect on trade in differentiated or homogeneous products? Recent developments in the economics of trade suggest that a given increase in transaction costs (of which exchange rate volatility is a component) could have a larger, negative effect on trade in differentiated products than on trade in homogenous products. But, as with aggregate trade, the estimation results show that this theoretical prior is not robust. When we control for country and time effects separately, exchange rate volatility indeed has a negative effect on trade in differentiated products, but not on trade in homogenous products (Table 4.3, Column 1). However, when we include time-varying country fixed effects (Table 4.3, Column 3), the conclusion is overturned, as in the aggregate trade model.
Effect of Exchange Rate Volatility on Trade in Different Types of Products 1/
Standard errors are indicated in parentheses. An asterisk denotes significance at the 90 percent level or higher. For details, refer to Appendix Table IV.8.
Effect of Exchange Rate Volatility on Trade in Different Types of Products 1/
| Model Specification | With Country and Time Effects | Implied Pecentage Change in Trade by One Standard Deviation Increase in Volatility | With Time-Varying Country Effects | Implied Pecentage Change in Trade by One Standard Deviation Increase in Volatility |
|---|---|---|---|---|
| (1) | (2) | (3) | (4) | |
| Trade in Homogeneous Products | -0.59 | -1.65% | -2.97 | -8.32% |
| (2.12) | (4.39) | |||
| Trade in Differentiated Products | -2.89* | -8.09% | 0.98 | 2.74% |
| (1.66) | (3.06) |
Standard errors are indicated in parentheses. An asterisk denotes significance at the 90 percent level or higher. For details, refer to Appendix Table IV.8.
Effect of Exchange Rate Volatility on Trade in Different Types of Products 1/
| Model Specification | With Country and Time Effects | Implied Pecentage Change in Trade by One Standard Deviation Increase in Volatility | With Time-Varying Country Effects | Implied Pecentage Change in Trade by One Standard Deviation Increase in Volatility |
|---|---|---|---|---|
| (1) | (2) | (3) | (4) | |
| Trade in Homogeneous Products | -0.59 | -1.65% | -2.97 | -8.32% |
| (2.12) | (4.39) | |||
| Trade in Differentiated Products | -2.89* | -8.09% | 0.98 | 2.74% |
| (1.66) | (3.06) |
Standard errors are indicated in parentheses. An asterisk denotes significance at the 90 percent level or higher. For details, refer to Appendix Table IV.8.
Do members in currency unions trade more? Our core results confirm Rose’s (2000) finding that common currency arrangements triple trade. The trade-enhancing benefits of currency unions apparently by far exceed gains from a reduction in exchange rate volatility and are preserved over time (Appendix Tables IV.2 and IV.3). They are also robust to controlling for time-varying country effects, but break down in a model with country-pair fixed effects (Appendix Table IV.4). This suggests that currency union membership may be correlated with other country-pair characteristics. Once these characteristics are controlled for by the inclusion of country-pair fixed effects, there is no additional trade-promoting effect from currency unions.
Does the volatility effect differ across country groups? In principle, the effects could be different. Foreign exchange markets are typically less developed and less liquid in developing countries, limiting firms’ opportunities for hedging foreign exchange risk. Indeed, we do find that volatile exchange rates are more likely to be associated with smaller trade of developing countries than for trade among advanced economies in the specification with country fixed effects. However, the negative effect disappears for both country groups when county effects are time varying (Appendix Table IV.7). As hedging instruments are more readily available for the currencies of industrial countries, one might expect that their trade would be less affected by exchange rate volatility. However, Wei (1999) finds little support for the hypothesis that the growing availability of hedging instruments is responsible for the small impact of volatility on trade.
Differentiation by Country Type 1/
The estimated coefficients for standard control dummies in the baseline specification are not reported.
Differentiation by Country Type 1/
| Variable | Developed & Developing Countries | |
|---|---|---|
| (1) | (2) | |
| L-R Official Real Vol | 43.47 | 47.90 |
| (3.54) | (3.85) | |
| (L-R Official Real Vol) * (NS Dummy) | -45.70 | -47.59 |
| (3.60) | (3.90) | |
| (L-R Official Real Vol) * (SS Dummy) | -46.69 | -47.92 |
| (3.64) | (4.00) | |
| Dummy for Common Currency Union | -0.62 | -0.60 |
| (0.23) | (0.21) | |
| (Currency Union Dummy) * (SS Dummy) | 2.11 | 2.12 |
| (0.27) | (0.25) | |
| Dummy for North-South Country Pair | -0.49 | 1.11 |
| (0.28) | (0.45) | |
| Dummy for South-South Country Pair | -2.49 | 0.56 |
| (0.52) | (0.87) | |
| Time Fixed Effects | Yes | No |
| Country Fixed Effects | Yes | No |
| Time-Varying Country Effects | No | Yes |
| Number of Observations | 16238 | 16238 |
| R-squared | 0.79 | 0.81 |
| RMSE | 1.64 | 1.59 |
The estimated coefficients for standard control dummies in the baseline specification are not reported.
Differentiation by Country Type 1/
| Variable | Developed & Developing Countries | |
|---|---|---|
| (1) | (2) | |
| L-R Official Real Vol | 43.47 | 47.90 |
| (3.54) | (3.85) | |
| (L-R Official Real Vol) * (NS Dummy) | -45.70 | -47.59 |
| (3.60) | (3.90) | |
| (L-R Official Real Vol) * (SS Dummy) | -46.69 | -47.92 |
| (3.64) | (4.00) | |
| Dummy for Common Currency Union | -0.62 | -0.60 |
| (0.23) | (0.21) | |
| (Currency Union Dummy) * (SS Dummy) | 2.11 | 2.12 |
| (0.27) | (0.25) | |
| Dummy for North-South Country Pair | -0.49 | 1.11 |
| (0.28) | (0.45) | |
| Dummy for South-South Country Pair | -2.49 | 0.56 |
| (0.52) | (0.87) | |
| Time Fixed Effects | Yes | No |
| Country Fixed Effects | Yes | No |
| Time-Varying Country Effects | No | Yes |
| Number of Observations | 16238 | 16238 |
| R-squared | 0.79 | 0.81 |
| RMSE | 1.64 | 1.59 |
The estimated coefficients for standard control dummies in the baseline specification are not reported.
On balance, for both aggregate and disaggregated trade, there is empirical evidence pointing to a generally small negative effect of exchange rate volatility on trade. But this evidence is not overwhelming and not robust across different empirical specifications.
V. Summary and Concluding Remarks
This study has provided a much more comprehensive analysis of exchange rate volatility and trade than the previous IMF (1984) analysis. It has examined exchange rate variability over the past thirty years for all countries for which data are available and has employed state-of the-art statistical techniques to test the natural presumption that volatility in exchange rates reduces the level of international trade.
In terms of observed variability, the analysis here shows that while exchange rate fluctuations have increased in times of currency and balance of payments crises, there has been no clear increase in exchange rate volatility, on average, between the 1970s and the 1990s. It is not surprising that the currencies of the advanced economies have had lower average volatility than other country groups. Nonetheless, many transition, emerging market, and developing countries have recently exhibited exchange rate variability on a par, or close to that of many advanced economies.
In terms of the impact of exchange rate volatility on trade flows, the current study does not find a robustly negative effect. To be more precise, the study reports some evidence that is consistent with a negative effect of volatility on trade. However, such a relationship is not robust to certain reasonable perturbation of the specification. Specifically, when time-varying country fixed effects are allowed, which are suggested by recent theoretical work on the gravity model specification, the analysis does not reveal a negative association between volatility and trade.
The lack of a robustly negative impact of exchange rate volatility on trade may well reflect the ambiguity of the theoretical results in the general equilibrium models. These models show that exchange rate variability is the result of the volatility of the underlying shocks to technology, preferences, and policies, for example, as well as the overall policy regime. Changes in the volatility of the exchange rate may reflect changes in the volatility of the underlying shocks and/or changes in the policy regime. For example, trade liberalization undertaken together with a move to greater exchange rate flexibility could well be associated with increased trade flows as well as increased exchange rate volatility. This possibility is a reason for the ambiguity of the theoretical results as well as the difficulty in finding consistent and robust empirical results regarding the impact of volatility on trade. An additional implication is that the empirical results do not provide clear policy guidance. Even if it were the case that such volatility is associated with reduced trade flows, this does not necessarily mean that trade would expand if the authorities stabilized the exchange rate in the face of shocks that occur.
These considerations suggest that there do not appear to be strong grounds to take measures to reduce exchange rate movements from the perspective of promoting trade flows. Note that this does not rule out the possibility that exchange rate fluctuations can affect an economy through other channels. For example, currency crises – special cases of exchange rate volatility - have required painful adjustments in output and consumption. However, in this case, appropriate policies are those that help to avoid the underlying causes of large and unpredictable movements in exchange rates, rather than measures to moderate currency fluctuations directly for the purpose of enhancing trade.
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