Drawing further on information in the *Survey on Foreign Exchange Market Organization*, this chapter examines factors affecting exchange rate volatility. In addition to indicators of macroeconomic performance and the choice of exchange rate regime, these include in particular various (micro) structural features of the foreign exchange market. The results presented in this chapter offer a number of new insights into the role that structural factors may play in the choice and implementation of exchange rate policy.1

The determinants of exchange rate volatility are of interest because of its linkages to other economic variables. A common supposition is that volatile exchange rates depress international trade. The empirical evidence on this issue is mixed, but several more recent studies have found significant adverse effects on trade.2 Some studies have also found a relationship between exchange rate volatility and real output growth. One major study found that exchange rate flexibility has tended to be associated with lower output volatility (see Ghosh and others, 1995). Reinhart and Rogoff (2002) note in addition that in countries with extremely high rates of depreciation, growth was negative on average. By contrast, countries with floating exchange rate regimes and low inflation have exhibited higher GDP growth than other country groups. Other studies, however, have found that investment and profitability have been adversely affected by exchange rate volatility, at least in some developing countries (Bleaney and Greenaway, 2001).

The results obtained in the present study may help guide the design of technical assistance on foreign exchange issues by focusing attention on factors that may be more likely than others to affect exchange rate volatility. For example, a key finding is that decentralized dealer markets are associated with lower volatility. Another finding is that regulations on the use of domestic currency by nonresidents may reduce exchange rate volatility.

## Earlier Work on the Determinants of Exchange Rate Volatility

There is no consensus in the economic literature on the factors affecting exchange rates and their volatility. This absence of agreement reflects basic difficulties in modeling and predicting exchange rates. Much of the existing work focuses on the levels of exchange rates (in statistical terms, the mean or first moment), but also has implications for exchange rate volatility (the standard deviation or second moment). In the literature, three principal views have emerged:

The first view is that, at least over short time horizons and for countries without high inflation, exchange rate models that include macroeconomic fundamentals do not perform better than a random walk in out-of-sample forecasting.3 Exchange rate volatility is simply the standard deviation of the error term.

A second view is that macroeconomic fundamentals play an important role in explaining the behavior of exchange rates. Some authors hold that these fundamentals are important only in the long run but have little to offer in explaining short-run movements, while others believe that macroeconomic fundamentals have explanatory power both in the long run and the short run.4

A third school of thought holds that neither macroeconomic fundamentals nor the random walk model adequately account for exchange rate behavior at short horizons. Rather, short-run exchange rate movements are attributed to market microstructure factors, including inventory management and information aggregation by foreign exchange dealers. Specifically, the microstructure approach suggests that non-dealers learn about fundamentals affecting the exchange rate, and this knowledge is reflected in the orders they place with dealers. Dealers in turn learn about fundamentals from order flow. The outcome of this two-stage learning process results in the formation of a price (see Lyons, 2001).

## Design of the Study

The analysis of the factors affecting exchange rate volatility is based on a broad cross section of 85 developing and transition economies in 2001. Volatility in the cross section is related in the first instance to macroeconomic fundamentals—most notably inflation, real GDP growth, the fiscal deficit (in percent of GDP), and the openness of the economy (measured by the sum of exports and imports relative to GDP).5, 6 Controlling for the effect of these macroeconomic variables, a wide range of structural factors is then examined one by one. These factors include, among many others, the prevailing exchange rate regime; the status with respect to the acceptance of obligations of Article VIII, Sections 2, 3, and 4 of the IMF’s Articles of Agreement; and features of the foreign exchange market structure and regulation drawn from the *Survey*, discussed in Chapter IV.7

This approach complements the microstructure approach to foreign exchange markets. It differs from much of the existing microstructure literature, which uses data on order flows as indicators of buying or selling pressures in the domestic foreign exchange market, but does not seek to identify the ultimate factors affecting order flows.8 This chapter, however, estimates directly the effect of macroeconomic and structural factors on exchange rate volatility. Future research could examine how the macroeconomic and structural fundamentals influence the more technical factors, such as order flows and bid-ask spreads, emphasized in the microstructure literature.

Particular attention was given to the robustness of the results. To this end, the regressions reported below were reestimated using a large number of random subsamples of countries. This procedure, known as resampling, provides information on whether the results hold only for the particular sample of countries chosen, or whether they also hold for other samples of countries. The resampling strongly confirmed the validity of the main results. Moreover, the results were not substantially affected when exchange rate volatility was calculated at weekly and monthly horizons, in addition to the results using volatility estimated from daily data presented below.

The measure of volatility used is based on the nominal effective exchange rate (NEER), rather than on exchange rates with a single major currency used as an anchor, like the U.S. dollar. The objective is to capture the effect of cross-currency changes on the value of the domestic currency.9 Moreover, the NEER expresses the value of the domestic currency in terms of the currencies of the main trading partners. The use of NEER volatility is appropriate when the sample includes countries that peg to or closely follow different international currencies. A country pegging to the U.S. dollar but trading mainly with countries in the euro area (for example, Egypt until mid-2000) would still be subject to significant nominal effective exchange rate volatility. NEER volatility is computed as the standard deviation in 2001 of the logarithm of the daily exchange rate (also known as the daily return).10, 11

## Principal Results of the Cross-Sectional Analysis

NEER volatility is related in the expected fashion to key domestic macroeconomic variables. While exchange rate volatility may also depend on external developments, the cross-sectional analysis reveals that a large fraction of the disparities between volatilities across countries can be explained by domestic developments (Table 5.1). Nominal variables play an especially important role, which is not surprising given that nominal exchange rate volatility is the variable to be explained.12 NEER volatility is higher in countries with higher inflation and higher fiscal deficits, and lower in countries with faster real GDP growth and more open economies. These results were highly robust. As noted previously, these macroeconomic variables are included as controls in examining the effect on NEER volatility of various structural factors and thus allow for an estimation of the marginal effect of each structural factor on exchange rate volatility. Other macroeconomic variables—notably the current account deficit, private capital flows relative to GDP, and the volatility of the terms of trade—were not found to be significantly correlated with NEER volatility.

Table 5.1.

**Exchange Rate Volatility and Main Characteristics of Foreign Exchange Markets in Developing and Transition Economies, 20011**

Source: IMF staff estimates.

^{1}The cross-section regressions are estimated by ordinary least squares, controlling for macroeconomic variables. The dependent variable is NEER volatility measured as the standard deviation of the log of daily NEER returns in 2001. Most variables are dummy variables so that a significant positive variable would mean a higher mean volatility of the group after controlling for macroeconomic variables. Significance at the 1,5, and 10 percent level are expressed as three, two, and one asterisks, respectively.

^{2}A total of 85 countries were included in the regression.

^{3}To test the robustness of the results, a bootstrap analysis was conducted by which 100 regressions were run on randomly selected subsamples representing 90 percent of the number of observations in the full sample.

^{4}Percent of regressions with the corresponding sign.

^{5}Percent of regressions in which the variable was statistically significant at the 10 percent significance level.

^{6}The control variables were chosen by a model selection algorithm among a list of 20 candidate variables. Among the variables that were not significant were the current account deficit, net private sector capital flows, and different measures of reserve adequacy.

^{7}Including de facto peg arrangements under managed floating.

^{8}With no preannounced path for the exchange rate.

^{9}Excludes countries where banks cannot hold net open positions or conduct foreign exchange operations on their own behalf.

^{10}Includes net open position limits expressed in percent of capital or as a fixed nominal amount.

Table 5.1.

**Exchange Rate Volatility and Main Characteristics of Foreign Exchange Markets in Developing and Transition Economies, 20011**

Robustness Analysis3 | |||||||
---|---|---|---|---|---|---|---|

Full Sample2 | Percent | Percent | |||||

Sign | Significance | Sign | sign4 | significant5 | |||

Macroeconomic control variables6 | |||||||

Consumer Price Inflation | positive | ^{***} | positive | 100 | 99 | ||

GDP growth | negative | ^{***} | negative | 100 | 99 | ||

Fiscal deficit/GDP | positive | ^{*} | positive | 92 | 78 | ||

External Trade/GDP | negative | negative | 100 | 100 | |||

Exchange rate regimes | |||||||

Hard pegs | positive | positive | 94 | 0 | |||

No separate legal tender | negative | negative | 98 | 2 | |||

Currency board arrangements | positive | positive | 95 | 11 | |||

Intermediate regimes | negative | ^{**} | negative | 100 | 85 | ||

Other conventional fixed peg arrangements7 | negative | negative | 65 | 0 | |||

Against a single currency | positive | positive | 71 | 0 | |||

Against a composite | negative | negative | 96 | 0 | |||

IMF-supported or other monetary program | positive | positive | 96 | 0 | |||

Crawling pegs | negative | negative | 100 | 0 | |||

Exchange rates within crawling bands | negative | ^{***} | negative | 100 | 98 | ||

Floating regimes | positive | ^{*} | positive | 100 | 32 | ||

Managed floating8 | positive | positive | 53 | 0 | |||

Independently floating | positive | ^{**} | positive | 100 | 85 | ||

IMF jurisdiction | |||||||

Article VIII status | negative | ^{**} | negative | 100 | 88 | ||

With exchange restrictions and multiple currency practices | negative | negative | 100 | 0 | |||

Article XIV status | positive | ^{**} | positive | 100 | 88 | ||

With exchange restrictions and multiple currency practices | positive | ^{**} | positive | 100 | 83 | ||

Article XIV restrictions | positive | ^{*} | positive | 100 | 64 | ||

Article VIII restrictions | positive | ^{**} | positive | 100 | 87 | ||

Without exchange restrictions and multiple currency practices | positive | ^{**} | positive | 100 | 88 | ||

Foreign exchange market structure | |||||||

Dealer markets9 | |||||||

Decentralized9 | negative | ^{**} | negative | 100 | 83 | ||

With electronic trading platforms | negative | ^{*} | negative | 100 | 72 | ||

Auction markets | negative | negative | 72 | 0 | |||

Periodic | positive | positive | 98 | 0 | |||

Continuous | negative | negative | 93 | 0 | |||

With Reuters brokered systems | negative | positive | 52 | 0 | |||

Other selected factors | |||||||

Restrictions on monetary use of domestic currency by nonresidents | |||||||

Holding domestic notes and coins. | negative | ^{*} | negative | 100 | 81 | ||

Denominating nonfinancial contracts in domestic currency | negative | ^{**} | negative | 100 | 99 | ||

Net foreign exchange open position limits10 | negative | ^{**} | negative | 100 | 84 | ||

Existence of a foreign exchange dealers’ association | negative | ^{**} | negative | 100 | 89 | ||

Emerging markets | negative | ^{*} | negative | 100 | 72 | ||

Forward markets | negative | negative | 99 | 9 |

Source: IMF staff estimates.

^{1}The cross-section regressions are estimated by ordinary least squares, controlling for macroeconomic variables. The dependent variable is NEER volatility measured as the standard deviation of the log of daily NEER returns in 2001. Most variables are dummy variables so that a significant positive variable would mean a higher mean volatility of the group after controlling for macroeconomic variables. Significance at the 1,5, and 10 percent level are expressed as three, two, and one asterisks, respectively.

^{2}A total of 85 countries were included in the regression.

^{3}To test the robustness of the results, a bootstrap analysis was conducted by which 100 regressions were run on randomly selected subsamples representing 90 percent of the number of observations in the full sample.

^{4}Percent of regressions with the corresponding sign.

^{5}Percent of regressions in which the variable was statistically significant at the 10 percent significance level.

^{6}The control variables were chosen by a model selection algorithm among a list of 20 candidate variables. Among the variables that were not significant were the current account deficit, net private sector capital flows, and different measures of reserve adequacy.

^{7}Including de facto peg arrangements under managed floating.

^{8}With no preannounced path for the exchange rate.

^{9}Excludes countries where banks cannot hold net open positions or conduct foreign exchange operations on their own behalf.

^{10}Includes net open position limits expressed in percent of capital or as a fixed nominal amount.

Table 5.1.

**Exchange Rate Volatility and Main Characteristics of Foreign Exchange Markets in Developing and Transition Economies, 20011**

Robustness Analysis3 | |||||||
---|---|---|---|---|---|---|---|

Full Sample2 | Percent | Percent | |||||

Sign | Significance | Sign | sign4 | significant5 | |||

Macroeconomic control variables6 | |||||||

Consumer Price Inflation | positive | ^{***} | positive | 100 | 99 | ||

GDP growth | negative | ^{***} | negative | 100 | 99 | ||

Fiscal deficit/GDP | positive | ^{*} | positive | 92 | 78 | ||

External Trade/GDP | negative | negative | 100 | 100 | |||

Exchange rate regimes | |||||||

Hard pegs | positive | positive | 94 | 0 | |||

No separate legal tender | negative | negative | 98 | 2 | |||

Currency board arrangements | positive | positive | 95 | 11 | |||

Intermediate regimes | negative | ^{**} | negative | 100 | 85 | ||

Other conventional fixed peg arrangements7 | negative | negative | 65 | 0 | |||

Against a single currency | positive | positive | 71 | 0 | |||

Against a composite | negative | negative | 96 | 0 | |||

IMF-supported or other monetary program | positive | positive | 96 | 0 | |||

Crawling pegs | negative | negative | 100 | 0 | |||

Exchange rates within crawling bands | negative | ^{***} | negative | 100 | 98 | ||

Floating regimes | positive | ^{*} | positive | 100 | 32 | ||

Managed floating8 | positive | positive | 53 | 0 | |||

Independently floating | positive | ^{**} | positive | 100 | 85 | ||

IMF jurisdiction | |||||||

Article VIII status | negative | ^{**} | negative | 100 | 88 | ||

With exchange restrictions and multiple currency practices | negative | negative | 100 | 0 | |||

Article XIV status | positive | ^{**} | positive | 100 | 88 | ||

With exchange restrictions and multiple currency practices | positive | ^{**} | positive | 100 | 83 | ||

Article XIV restrictions | positive | ^{*} | positive | 100 | 64 | ||

Article VIII restrictions | positive | ^{**} | positive | 100 | 87 | ||

Without exchange restrictions and multiple currency practices | positive | ^{**} | positive | 100 | 88 | ||

Foreign exchange market structure | |||||||

Dealer markets9 | |||||||

Decentralized9 | negative | ^{**} | negative | 100 | 83 | ||

With electronic trading platforms | negative | ^{*} | negative | 100 | 72 | ||

Auction markets | negative | negative | 72 | 0 | |||

Periodic | positive | positive | 98 | 0 | |||

Continuous | negative | negative | 93 | 0 | |||

With Reuters brokered systems | negative | positive | 52 | 0 | |||

Other selected factors | |||||||

Restrictions on monetary use of domestic currency by nonresidents | |||||||

Holding domestic notes and coins. | negative | ^{*} | negative | 100 | 81 | ||

Denominating nonfinancial contracts in domestic currency | negative | ^{**} | negative | 100 | 99 | ||

Net foreign exchange open position limits10 | negative | ^{**} | negative | 100 | 84 | ||

Existence of a foreign exchange dealers’ association | negative | ^{**} | negative | 100 | 89 | ||

Emerging markets | negative | ^{*} | negative | 100 | 72 | ||

Forward markets | negative | negative | 99 | 9 |

Source: IMF staff estimates.

^{1}The cross-section regressions are estimated by ordinary least squares, controlling for macroeconomic variables. The dependent variable is NEER volatility measured as the standard deviation of the log of daily NEER returns in 2001. Most variables are dummy variables so that a significant positive variable would mean a higher mean volatility of the group after controlling for macroeconomic variables. Significance at the 1,5, and 10 percent level are expressed as three, two, and one asterisks, respectively.

^{2}A total of 85 countries were included in the regression.

^{3}To test the robustness of the results, a bootstrap analysis was conducted by which 100 regressions were run on randomly selected subsamples representing 90 percent of the number of observations in the full sample.

^{4}Percent of regressions with the corresponding sign.

^{5}Percent of regressions in which the variable was statistically significant at the 10 percent significance level.

^{6}The control variables were chosen by a model selection algorithm among a list of 20 candidate variables. Among the variables that were not significant were the current account deficit, net private sector capital flows, and different measures of reserve adequacy.

^{7}Including de facto peg arrangements under managed floating.

^{8}With no preannounced path for the exchange rate.

^{9}Excludes countries where banks cannot hold net open positions or conduct foreign exchange operations on their own behalf.

^{10}Includes net open position limits expressed in percent of capital or as a fixed nominal amount.

Surprisingly, measures of the adequacy of foreign exchange reserves were not strongly correlated with NEER volatility. Reserves were not found to be statistically significant, whether measured relative to the money base, short-term debt owed to the countries reporting to BIS, imports of goods, or GDP; however, the coefficients had the correct sign, with higher reserves negatively correlated with NEER volatility, except when reserves were measured relative to short-term debt. Countries satisfying the “currency board criteria,” with international reserves exceeding the money base at the prevailing exchange rate, did not have a statistically significant lower level of NEER volatility.

The exchange rate regime may also have an effect on NEER volatility. Several authors have argued that flexible exchange rate regimes have higher nominal and real exchange rate volatility than fixed regimes.13 A visual inspection of the average NEER volatility across regimes suggests that volatility is higher for independent floating but otherwise not significantly related to the degree of flexibility of the exchange rate regimes (Figure 5.1). Statistical analysis confirms that countries following an independently floating regime exhibit significantly higher volatility (Table 5.1).14 Also, countries with a crawling band regime appear to have been successful in lowering NEER volatility below the level that would correspond to their macroeconomic developments and degree of openness. Related arguments are presented in Williamson (2000). Although less flexible exchange rate regimes do not markedly reduce NEER volatility, such regimes do reduce volatility vis-à-vis the anchor currency or basket of currencies. A key purpose and benefit of exchange rate arrangements, such as a conventional fixed peg, a currency board, or dollarization, may be the establishing of a more credible nominal anchor for monetary policy and the improving of prospects for achieving lower inflation.

The acceptance of Article VIII obligations is also related to NEER volatility.15 Volatility was significantly lower for the group of countries that have accepted the obligations of Article VIII. Conversely, it was significantly higher for countries that maintain Article XIV status. It is difficult to determine whether Article XIV status is a cause or a symptom of exchange rate volatility. It is possible that the policies followed by Article XIV countries, including the use of exchange restrictions, limit the development and depth of the foreign exchange market and thus raise daily NEER volatility. On the other hand, it is also conceivable that countries experiencing higher exchange rate volatility, possibly for reasons beyond their control, have been more reluctant than others to accept the obligations of Article VII I, Sections 2, 3, and 4.

Some structural features of the foreign exchange market are also correlated with NEER volatility. Notably, countries in which foreign exchange transactions are carried out by dealers exhibit lower volatility. This result may reflect the greater liquidity typically associated with these types of foreign exchange market structures. Countries with a foreign exchange dealers association also tended to exhibit lower volatility.

Countries restricting the use of domestic currency by nonresidents had lower NEER volatility. In particular, controls on the use of domestic currency in the denomination of nonfinancial contracts and controls on nonresidents’ holdings of domestic notes and coins seemed to be associated with lower volatility.

Limits on banks’ foreign exchange positions also tended to lower NEER volatility. Specifically, countries adopting limits on the net open foreign exchange position had lower volatility. This result may reflect the constraints that these prudential rules place on speculative position taking. It is conceivable, however, that in some instances limits on foreign exchange positions could result in higher volatility, as dealers seek to lay off unwanted exposures. This effect, which is known as “hot potato” trading, is discussed in Lyons (1997) and Lyons (1995).

A broad range of other variables were also examined, but were not found to be strongly associated with NEER volatility. These included:

restrictions on the domestic monetary use of domestic and/or foreign currencies

the presence or absence of forward foreign exchange markets16

country size, whether measured by surface area, population, or GDP in U.S. dollars

type of legal code and most other sociocultural factors

country classification used in the IMF’s

*World Economic Outlook or International Financial Statistics*. Exceptions were countries in the Western Hemisphere, which had lower volatility and Africa, which had higher volatility.

The findings presented in this chapter provide a starting point for additional investigation. An eventual update of the *Survey on Foreign Exchange Market Organization* would be most useful, as this would permit a more thorough check of the robustness of the findings. It would also allow for an inter temporal study of the factors associated with exchange rate volatility, which is likely to provide significant information above and beyond the cross-sectional analysis reported here. It could also be used to examine the relationship between structural features of the foreign exchange market and exchange regime transitions.

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1

For a more detailed discussion of the issues raised in this chapter, see Canales-Kriljenko and Habermeier (forthcoming), which also provides a full treatment of the statistical issues.

2

Much of the earlier literature, summarized for example in IMF (1984), focused on individual countries or small groups of mainly advanced countries. More recent studies, which have either included a wider range of both advanced and developing countries or have approached the issue with greater statistical sophistication, have tended to find adverse effects of exchange rate volatility on trade, mainly in developing countries but also in advanced countries. Examples of such studies include Sauer and Bohara (2001), Dell’ Ariccia (1999), and Chowdhury (1993).

3

See Meese and Rogoff (1983). The authoritative survey of the literature on the random walk hypothesis in Frankel and Rose (1995) concludes that attempts to overturn the results of Meese and Rogoff have failed. Further support for the random walk hypothesis is provided in Rogoff (1999). Here Rogoff concludes that, at least for the major currencies and more generally for countries with low inflation, the random walk model has not been overturned by more recent empirical work. He also argues that the difficulties in relating financial variables to fundamentals is a more general problem and not one confined exclusively to exchange rates.

4

McDonald (1999) notes that there is by now considerable empirical work favoring the view that models of the exchange rate that include fundamentals can out perform the random walk even at short time horizons.

5

It has long been argued that the more closed economies require a larger change in the exchange rate to bring about a given adjustment in the balance of payments, relative to GDP.

6

These variables were selected from a larger set of potential macroeconomic controls using a model selection algorithm. The variables identified by the algorithm are also ones that would normally suggest themselves on theoretical grounds.

7

The structural characteristics are measured using dummy variables, which divide countries into two groups: those that possess a particular characteristic and those that do not.

8

Order flow is transaction volume that is signed. The sign is positive if the initiator of the deal wants to buy and negative if he wants to sell.

9

Very few studies have focused on the volatility of the nominal effective exchange rate, partly because of limitations in data availability. The IMF’s Information Notice System database computes monthly values for the NEER, but the frequency of the resulting time series is too low to allow the use of econometric techniques for analyzing exchange rate volatility. Accordingly, daily values of the NEER for 85 countries were computed for this study. The indices are based on data from Datastream and Bloomberg on exchange rates to the U.S. dollar or the pound sterling and have been computed using the trade weights and methodology of the IMF’s Information Notice System.

10

That is, log(e_{t})-log(e_{t-1}), where e stands for the nominal effective exchange rate.

11

Canales-Kriljenko and Habermeier (forthcoming) also consider alternative measures of volatility based on the steady-state variance of a GARCH model of the daily returns. The GARCH model seeks to capture persistence over time in the standard deviation of the daily returns (Bollerslev, 1986). Another issue examined in that paper is whether the underlying NEER processes are integrated which, if true, could result in significant distortions in simple measures of volatility in a time series or panel data context.

12

Simple regressions (not presented) indicate that individual nominal variables explain up to 70 percent of the variance of NEER volatility. Money market interest rates showed a particularly strong correlation with NEER volatility, but data were only available for 21 countries.

13

Examples of this view include Mussa (1986) and Flood and Rose (1999). Other authors have provided a theoretical explanation for higher volatility in flexible regimes in terms of the effect of the choice of regime on the evolution and information contact of order flows, within the framework of the market microstructure literature (see Killeen, Lyons, and Moore, 2001).

14

The result is essentially the same when the regression controls for inflation only, suggesting that countries following independently floating regimes have higher nominal and real exchange rate volatility.

15

These obligations are to avoid multiple currency practices and restrictions on international current payments and transfers.

16

The data did not permit testing for the effect of other types of derivatives on NEER volatility.