The Latin American exchange exposure of U.S. multinationals

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J. of Multi. Fin. Manag. 18 (2008) 112–130

Available online at www.sciencedirect.com

The Latin American exchange exposure ofU.S. multinationals

A. Muller a,b, Willem F.C. Verschoor a,∗a Nijmegen School of Management (NSM), Radboud University Nijmegen, Department of Economics,

P.O. Box 9108, 6500 HK Nijmegen, The Netherlandsb HEC Business School of the University of Liege, Department of Finance, 4000 Liege, Belgium

Received 6 April 2005; accepted 18 March 2007Available online 27 March 2007

Abstract

This article examines whether there exists any relationship between individual U.S. multinationals’ stockreturns and fluctuations in Latin American exchange rates. By using a disaggregated dataset of weeklystock returns and real exchange rate movements, it appears that the apparent lack of currency expo-sure previously documented is mainly due to both the use of too aggregated economic variables and theignorance of the intervaling effect. We find that about 4% (12%) of the firms experienced economicallysignificant positive (negative) exposure effects for the period of January 1970 to December 2001. Whilethere is time-variation in significant exposure effects, the overall extent of exposure is not sample depen-dent – a depreciating (appreciating) dollar against Latin American currencies has a net positive (negative)impact on U.S. multinationals. Individual firms in industry groups show high positive as well as negativeexposure suggesting that exposure is not necessarily economically significant in the aggregate. The extentto which firms are exposed to Latin American currency fluctuations varies with return horizons; short-term exposure seems to be relatively well hedged, where considerable evidence of long-term exposure isfound.© 2007 Elsevier B.V. All rights reserved.

JEL classification: F3; G12

Keywords: Exchange rate risk; Geographic diversification; Long-term exposure; Multinational firms; Stock returnvolatility

∗ Corresponding author. Tel.: +31 24 361 54 54.E-mail address: [email protected] (W.F.C. Verschoor).

1042-444X/$ – see front matter © 2007 Elsevier B.V. All rights reserved.doi:10.1016/j.mulfin.2007.03.003


A. Muller, W.F.C. Verschoor / J. of Multi. Fin. Manag. 18 (2008) 112–130 113

1. Introduction

The debate regarding the relationship between foreign exchange rate movements and the valueof the firm is a long-standing one and continues to be an issue of central concern in the internationalfinancial economics literature – see Jorion (1990), Amihud (1994), Bartov and Bodnar (1994), Heand Ng (1998), and Griffin and Stulz (2001) for instance. Probably the most important factor thathas generated this considerable interest is the fact that the economic importance of exchange rateshocks has had a direct impact not only on economic science, but also on the world of businessand international finance.

As is well documented for many industrial economies, empirical tests of foreign exchange expo-sure demonstrated several empirical contradictions of the economic importance of exchange rateshocks; studies have so far documented weak contemporaneous relationships between exchangerates and stock returns of U.S. multinational firms. Jorion (1990) finds that only 15 of 287 U.S.Multinationals exhibit significant exchange rate exposure and that these exposures are increas-ing in the percentage of foreign operations. These findings are consistent with Amihud (1994)who reports no significant contemporaneous exposure for the largest 32 U.S. exporting firms.Similarly, Bartov and Bodnar (1994) observe insignificant relationships between exchange ratechanges and stock returns of 208 firms with foreign operations. Interestingly, the internationalexposure evidence differs substantially from the U.S. experience. Bodnar and Gentry (1993) findmore significant exchange rate exposure for Canada and Japan using industry returns. Similarly,He and Ng (1998) document significant positive exchange rate exposure in Japanese multina-tionals. Recently, in contrast to those studies, Griffin and Stulz (2001) suggest that exchange rateshocks have almost a negligible impact on the value of industries around the world.1

Overall, the findings overwhelmingly favor the conclusion that contemporaneous exchange ratefluctuations affect – to a certain extent – shareholder wealth observed in international stocks. Theparadoxes revealed in the data have in turn influenced the developments of both new theoreticalmodels and comparative research across a wider range of financial markets. For instance, theexistence of weak evidence of systematic exchange rate exposure may be attributable to thedifficulty in obtaining stable measures of exchange exposures – as suggested by Levi (1994); orto restrictions imposed on the sample of data and the use of too aggregated economic measures inprevious studies – as suggested by and Choi and Prasad (1995), Dahlquist and Robertsson (2001),and Dominguez and Tesar (2001); or to the ignorance of the intervaling effect – as suggested byChow et al. (1997a,b); or to systematically mispricing caused by investors’ errors in estimatingthe linkage between exchange rate changes and stock returns – as suggested by Bartov and Bodnar(1994). Such errors are commonly interpreted as evidence of either market inefficiencies or timevariation in expected stock returns.

The lack of convincing empirical U.S. evidence warrants further investigation that is basedon individual firms that actively engage in international trade. The foreign exchange exposure ofU.S. multinationals with significant foreign activities in the Latin American emerging markets isof interest for several reasons. First, the foreign exchange exposure literature pays voluminousattention to exposure effects of the most actively traded currencies vis-a-vis the U.S. dollar, and,to a much lesser extent, to the relationship between Latin American exchange rate movements andU.S. shareholder wealth. Second, the growing significance of the Latin American share of U.S.

1 For a detailed discussion on previously reported evidence on foreign exchange risk exposure please refer to Mullerand Verschoor (2006b).


114 A. Muller, W.F.C. Verschoor / J. of Multi. Fin. Manag. 18 (2008) 112–130

trade over the past few decades has positioned Latin-America among the most leading economictrade partners of the U.S. economy. Third, the rapid growth in market capitalization and tradingvalue of Latin American markets, its increasing significance in the international financial arena,and the significant different economic episodes – sustained economic growth, the internationalfinancial crises, and the current economic recovery – permit an additional test of the robustnessof previous reported results, which could be more powerful than a marginally more sophisticatedtest of foreign exchange exposure to a broad sample of currencies already thoroughly studied.Finally, the focus on this particular currency area allows us to explain previously reported weakempirical results by comparing U.S. multinationals with geographical diversification outside ofLatin America with U.S. companies whose foreign operations are exclusively concentrated inLatin America – whose exposure to Latin American currencies should be stronger by definition.

We investigate whether the value of individual U.S. multinational firms with foreign activitiesin four Latin American markets – Argentina, Brazil, Chili, and Mexico – are affected by LatinAmerican exchange rate fluctuations, whether Latin American exchange risk exposure patternsare industry-specific, and whether firms’ exchange exposure is more evident across increasingtime horizons.2 The analysis of the latter is motivated by Chow et al. (1997a,b) and Di Iorioand Faff’s (2001) findings that foreign exchange exposure of individual firms increases withlengthening shareholder return horizons; short-horizon returns contains errors made by investorsin forecasting the long-term effects of current exchange rate changes. We test the impact ofintervaling by estimating the Latin American exchange exposure of U.S. multinational stockreturns at the 1, 4, 12 and 54-week horizon. We furthermore investigate the relationship betweenthe breadth of the international network structure of our sample firms and their sensitivity to LatinAmerican currency shocks.

Our results are easily summarized. Using a sample of 1,075 U.S. multinationals, we find thatabout 4% of the firms experienced economically significant positive exposure effects and about12% significant negative effects for the whole period of January 1970 to December 2001. Theresult is also robust across the three sub-periods, indicating that a depreciating dollar againstLatin American currencies has a (net) positive impact on stock returns of U.S. multinationals.Individual firms in U.S. industry groups show high positive as well as negative exposure suggestingthat exposure is not necessarily economically significant in the aggregate. Multinationals withinsignificant exposure effects are concentrated only in a small number of industries. The extent towhich firms are exposed to Latin American exchange rate fluctuations varies with return horizons;short-term exposure seems to be relatively well hedged, where considerable evidence of long-term exposure is found. Finally, building on previous studies showing that U.S. multinationalswith highly diversified network structures are better equipped to implement operational hedgingstrategies (Pantzalis et al., 2001), our results reveal that U.S. multinationals whose foreign tradingrelationships are solely concentrated in Latin America suffer more from adverse Latin Americancurrency shocks than U.S. multinationals whose foreign operations are not exclusively located inLatin America but are as well distributed in the rest of the world.

The paper is organized as follow. The next section discusses the relationship between LatinAmerican exchange rate fluctuations and individual U.S. multinationals’ stock returns. The con-struction of the sample selection and summary statistics describing the exchange rate data are

2 Note that the frequent use of industry portfolio returns to analyze exposure may not reveal the true exchange risksensitivity of firm value; it seems likely that positive and negative exposures are simply averaged out. We, therefore, arguethat a firm-value analysis is warranted to understand whether, and why, individual firm value display varying sensitivityto exchange risk.



contained in Section 3. Section 4 presents the main empirical results of our analysis. Section 5investigates the link between U.S. multinationals’ exposure and the geographical dispersion oftheir foreign operations. Section 6 concludes the paper.

2. Measuring exchange rate exposure

Adler and Dumas (1984) and Jorion (1990) define the firm-specific exchange rate sensitivity,called firm-specific exposure, as the effect of exchange rate changes on the value of a firm inexcess of the global market’s reaction to foreign exchange rate movements. Thus, the exchangeexposure of a firm can be measured by the following augmented market model:

Rit = αi + βiRmt + γiXt + εit (1)

where Rit designates the total return of firm i in period t, Rmt the overall stock market return inperiod t, βi firm i’s return sensitivity to market fluctuations, Xt the rate of return on a trade-weightedexchange rate index, measured as the Latin American exchange price of the U.S. dollar, γ i firm i’sexposure to exchange rate changes independent of the effect these variations have on the overallmarket, and εit denotes the white noise error term. Hence, γ i is the exchange rate exposure measurebecause it describes the sensitivity of stock returns to unanticipated changes in exchange rates.An appreciation of the U.S. dollar makes exporting goods more expensive in terms of the LatinAmerican currencies, and this may lead to a fall in foreign demand and foreign sales revenue.Consequently, the importing firm will benefit from an appreciation of the U.S. dollar, as theirimports becomes cheaper in terms of the U.S. dollar. Thus, the γ i coefficient should be positivefor net-importers and negative for net-exporters.3 Similarly, U.S. multinationals with net exposedliabilities in Latin-America will gain with a strengthening U.S. dollar, while multinationals withnet exposed assets lose.

One of the most important features of financial weekly time series is the presence ofheteroskedasticity. Indeed, the hypothesis of constant variance, we implicitly make in the above-mentioned model (1), is in most cases rejected for common financial weekly time series – likeexchange rate and stock returns series.4 As the presence of heteroskedasticity invalidates thetest statistics, we suggest adding a GARCH(1,1) specification to the basic regression model. Thechoice of a GARCH(1,1) specification is supported by many empirical studies which show that theGARCH(1,1) specification is valuable for modeling the variance generating process of financialtime series. Thus, the regression model we use in this study can be described as:

Rit + αi + βiRmt + γiXt + εit εit = μit ∗ (hit)1/2, (2)

hit = δi + τ ε2it−1 + νhit−1 (3)

where hit denotes the conditional variance of the residuals and μit the white noise error term. Inorder to investigate the nature of exchange rate exposure across increasing return horizons we

3 Note that the sign of the exchange rate exposure coefficient becomes less distinct for a company that imports as wellas exports. See, for example Adler and Dumas (1984) and He and Ng (1998) for their suggestion that the sensitivity ofthe firm value to exchange rate fluctuations depends on the elasticity of the firm’s demand for foreign goods relative tothe elasticity of the foreign market’s demand for the firm’s goods.

4 See, for example Bollerslev et al. (1992), and Nieuwland et al. (1994).



moreover define the above-mentioned returns on horizons from t to t + T, where T equals 1, 4, 12and 54 weeks:

Rit,t+T = αi + βi Rmt,t+T + γiXt,t+T + εit,t+T (4)

In order to fully exploit the information contained in the data, overlapping return observa-tions are used in this analysis for return horizons greater than one week. This overlapping ofobservations creates a moving average error term μit – this term will be autocorrelated of orderT − 1. On the other hand, the estimation of Eq. (2) for one week return horizons reveals thatthe assumption of conditional normality for the standardized innovations μit is also not strictlyverified. As shown in Weiss (1986), maximization of the Gaussian log likelihood function stillprovides consistent estimates of the parameters (αi, βi, γ i, } δi, τi, νi) even if the distributionof μit is non-Gaussian. However, the estimated variance–covariance matrix is inconsistent andinvalidates the hypothesis tests. We, therefore, use an approximate variance–covariance matrixthat is robust to misspecifications of the probability density of μit:

V = A−10 B0A

−10 (5)

where the A0 matrix is consistently estimated by the sample counterpart of the inverse of theinformation matrix evaluated at the true parameter vector (αi, βi, γ i, δi, τi, νi) and B0 is similarlymeasured by the sample counterpart of the expected value of the outer product of the gradientsevaluated at the true parameter vector (αi, βi, γ i, δi, τi, νi). This variance–covariance matrixhas been proposed by White (1982), who described this approach as quasi-maximum likelihoodestimation.5

3. Sample selection of U.S. Multinationals, economic factors, and summary statistics

3.1. U.S. Multinational corporations with foreign involvement in Latin America

The selection procedure for our sample of U.S. multinational firms active in Latin Americaconsists of three steps. We first select multinational firms based on information about their foreignactivities in Latin America as reported in the 1969, 1979, 1987 and 1999 versions of the Directoryof American Firms Operating in Foreign Countries. A total of 2146 firms are identified as U.S.firms with real operations either in Mexico, Brazil, Chile or Argentina between 1969 and 1999.These firms are then checked for their weekly stock market return availability in the Universityof Chicago Center for Research in Security Prices (CRSP) database. A total of 1116 firms areidentified. As we include only firms that have more than 1 year of consecutive weekly returnsbetween January 1970 and December 2001, we exclude 41 firms. Consecutively, our sampleconsists of 1075 U.S. multinationals that actively engage in Latin American trade. All these firmsare sorted into 20 industry groups according to their first four standard industrial classification(SIC) numbers.

The data employed are continuously compounded weekly total returns. The sample period startson January, 2nd 1970 and ends on December, 31st 2001. In order to test whether the results arerobust over sub-samples and unveil any structural change in the U.S. multinational corporations’exposure to Latin American exchange fluctuations – and whether specific sub-samples drive the

5 See, for example, Bollerslev and Wooldridge (1992) for a formal discussion on the test statistics based on this robustvariance-covariance matrix.



Table 1Summary statistics of the continuously compounded weekly real exchange rate series (Argentine peso, Brazilian real,Chilean peso and Mexican peso/U.S. dollar) for the entire sample period January 2, 1970 to December 28, 2001

Argentine peso/US$ Brazilian real/US$ Chilean peso/US$ Mexican peso/US$

Mean −0.0086 −0.0156 −0.0137 −0.0137Median 0.0000 0.0000 −0.0063 −0.0091Maximum 0.6891 0.2831 0.9345 0.4622Minimum −1.0962 −0.8452 −0.6909 −0.3942S.D. 0.1048 −0.0761 0.0625 0.0361Skewness −4.1852 −3.3702 0.6766 2.4534Kurtosis 40.7982 23.6429 99.4928 50.4287Nber of observations 1670 1670 1670 1670

full sample results – the sample is also divided into three approximately equal sub-periods: January1970 to December 1979, January 1980 to December 1989, and January 1990 to December 2001.Following the usual conventions, these periods were selected so as to cover ten years each exceptfor the latter, which corresponds to the remaining 12 years.

3.2. Economic factors

We use two economic factors in this study: the market risk factor and the exchange rate riskfactor. The proxy for the market portfolio used is the value-weighted stock market return asprovided by the University of Chicago CRSP database. The exchange rate risk factor is measuredas the continuously compounded rate of change in a real trade-weighted exchange rate index.Following Jorion (1990) and Dominguez and Tesar (2001), an exchange rate index is indeeda parsimonious representation of the effect of multiple exchange rate changes.6 As a changein the nominal exchange rate index may not give a complete picture of how much the U.S.competitiveness is changing towards Latin American countries, we decide to use a real multilateralcurrency index.7 The index is computed as a weighted average of the four real bilateral exchangerates of interest, defined as the Mexican peso, Brazilian real, Chilean peso and Argentinean pesoprice of one U.S. dollar (as delivered by Global Financial Data) adjusted by the relative inflationrates. The weights of the index, updated monthly, are based on each country’s proportion of thefour countries’ total import and export flows with the U.S. as reported by the Foreign TradeDivision of the U.S. Census Bureau.

3.3. Summary statistics

Table 1 provides summary statistics on weekly log price changes of the Latin American realexchange rates underlying the trade-weighted currency index and reveals a few notable facts.

For the period analyzed (January 2, 1970 through December 31, 2001) all mean weekly logprice changes are negative, indicating that the U.S. dollar depreciated against the Latin Americancurrencies in real terms. It is interesting to note that the standard deviations of the Brazilian real

6 Any test that restricts the measurement of exposure to one exchange rate – whether it is a trade-weighted rate or abilateral rate – is likely to be biased downwards.

7 We performed the same empirical analysis with a nominal trade-weighted exchange rate index. The results areconsistent and are communicated by the authors on request.



Table 2Cross-sectional distribution of Latin American exchange exposure coefficients across sub-periods

Sample period N Cross-sectional summary statistics N*(−) N*(+)

Mean Median Variance Minimum Maximum

02/01/1970–28/12/2001 1075 −0.0804* −0.0702* 0.0865 −3.0161 2.2307 131 39−9.257 −6.446

02/01/1970–28/12/1979 909 −0.1302* −0.1277* 0.0546 −1.8899 0.4574 109 22−16.791 −13.144

04/01/1980–29/12/1989 920 −0.0957* −0.0933* 0.0865 −2.9053 0.9549 117 27−9.872 −7.678

05/01/1990–28/12/2001 796 −0.0788* −0.0883* 0.0688 −1.2801 1.0921 60 25−8.481 −7.578

The table reports summary statistics for γ i for the entire sample consisting of 1075 U.S. multinational firms operating inLatin America:

Rit = αi + βiRmt + γiXt + εit

where Rit the total return of firm i in week t, Rmt designates the overall stock market return in week t and Xt is the changein the trade-weighted Latin American exchange rate index, and εit denotes the white noise error term.Note: t-statistics are in italics. N*(−) reports the number of firms with negative γ i coefficients significant at the 5% level,and N*(+) reports the number of firms with positive γ i coefficients significant at the 5% level. Asterisk symbol (*) denotessignificance at the 5% level.

and Chilean peso relative to the U.S. dollar are twice as large as the standard deviation of theMexican peso relative to the U.S. dollar. The standard deviation of the Argentine peso relative tothe U.S. dollar is still larger. Furthermore, the distributions of the Argentine peso and Brazilianreal/U.S. dollar weekly real exchange rate returns are skewed to the left, whereas the distributionsof the Chilean peso and Mexican peso/U.S. dollar weekly real exchange rate returns are skewedto the right. These features may be a result of asymmetric movements in the exchange rates parityadjustments. The kurtosis measure is higher than the value associated with the normal distribution,indicating that the distributions have fat tails. Overall, the statistics suggest that the weekly ratesof return for Latin American real exchange rates are likely not drawn from normal distributions.

4. The Latin American exposure of U.S. multinationals

Table 2 provides the cross-sectional distribution of the 1075 U.S. multinationals’ estimatedexposure coefficients, γ i, as defined in Eq. (2), for the full sample period and the three sub-sampleperiods. It reports the mean, median, variance, minimum and maximum values of γ i together withthe number of significant positive and negative coefficients obtained.

Overall, the evidence presented suggests that for the full sample period U.S. multinationalsexhibit significant Latin American exchange rate exposure; about 4% of the 1075 firms yieldsignificant positive exposure coefficients and about 12% yield negative coefficients. The extentof exchange rate exposure is remarkably high, differs substantially from the U.S. experienceand is clearly above the ratios one would expect to see in a sample of firms that engage ininternational trade.8 The negative γ i coefficient suggests that a depreciation of the U.S. dollar

8 Indeed, one might argue that these firms would be the least likely to be exposed since they are the most likely to haveaccess to both financial and operational hedging strategies.



against the Latin American currencies has a positive impact on U.S. multinationals’ stock returns(net-exporters), whereas the positive γ i coefficient obtained suggests that these multinationalsexperience an adverse valuation effect when the U.S. dollar depreciates and benefit when the U.S.dollar appreciates (net-importers). We find that U.S. multinationals’ exposure to exchange ratechanges in Latin-America is predominately negative, indicating that the value of U.S. firm sharevalues benefits (is hurt) when the U.S. dollar depreciates (appreciates) in real terms.

In addition, the evidence suggests time-variation in exposure at the firm level; the number ofsignificant γ i coefficients increases from 131 in the first sub-sample period to 144 in the secondsub-period, and subsequently decreases to 85 in the third sub-period. It seems likely that theincrease in exchange rate fluctuations during the period 1980–1989 generally has led to a higherpercentage of firms with significant exchange risk exposure.9 However, the overall extent ofexposure is not sample dependent; the majority of firms with significant exchange risk sensitivity(60–64%) gain from a depreciation in the value of the U.S. dollar.

Since our data cover individual firms, we are able to analyze the exposure of individual firmswithin different U.S. industries, as opposed to average industry exposure. This approach allowsus to determine the extent to which individual firms with significant exposure are concentratedin particular U.S. industries and also to ascertain the type of industry that is more exposed toLatin American influences. Previous empirical research on how exposure varies across industriessuffers from aggregation problems10: for instance, Bodnar and Gentry (1993) find that only a fewindustries in Canada, Japan, and only 9 of 39 industries in the U.S. exhibit significant foreignexchange exposure during the period 1979 to 1988. On the contrary, Allayannis (1995) finds strongevidence of significant industry exposure of U.S. manufacturing industries at the four-digit (SIC)level. His study points out that significant exposure is often masked at a coarser classification ofindustries and the two-digit industry grouping may result in averaging out the exposure effect,hence rendering the underlying exposure undetected. In order to avoid this effect, we also lookat the four-digit SIC code – a finer classification of U.S. industries – to determine the extent towhich some types of industries are (more) exposed to Latin American currencies influences. InTable 3 we report the distribution of significant exposure coefficients from Table 2 by 20 selectedU.S. industry groups defined according to their first four SIC numbers.11

Overall, the evidence suggests that the magnitude of industry exposure is relatively large; 15U.S. industries report statistically significant – positive and negative – exposure coefficients atthe four-digit level. Consistent with the results of Table 2, all but three industries experience apredominantly negative exposure effect; a depreciation of the dollar against the Latin Americancurrencies has a positive impact on stock returns of U.S. multinational firms in these industries.However, three industries – food, beverages and tobacco, petroleum refining and related industriesand utilities – experience an adverse valuation effect when the dollar depreciates and benefit whenthe dollar appreciates. Furthermore, firms with insignificant exposure effects are concentratedonly in three industries, namely stone, clay and glass, transport and communication, and utilities.Clearly, these firms are relatively less exposed to Latin American movements than the others. In a

9 Bartov et al. (1996) show that there is an increase in the variability of equity returns following the period ofincreased exchange rate variability; the increase in exchange rate fluctuations suggests an increase in the riskiness ofthe multinationals’ cashflows.10 Aggregating across firms with positive and negative exchange risk exposures will result in finding an insignificant

exposure coefficient for the industry group.11 Note that the results displayed in Table 3 are generated using individual firms’ information and show high positive as

well as negative exposure, suggesting that exposure is not necessarily economically significant in the aggregate.


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Table 3Cross-sectional distribution of Latin American exchange rate exposure coefficients by industries

No. Industry SIC codes N Summary statistics (Entire sampleperiod: 02/01/1970–28/12/2001)

N*(−) N*(+)

Mean Median Variance

1 Mining, construction & agriculture 0–1799 48 −0.0241 −0.0551** 0.0199 3 2−1.2326 −2.2446

2 Food, beverages & tobacco 2011–2141 51 0.0756* 0.0366 0.0850 4 21.9222 0.7422

3 Textile & apparel 2211–2399 25 −0.0953 −0.0833 0.1520 3 1−1.2467 −0.8699

4 Paper, publishing & printing 2611–2796 43 −0.1167** −0.0901** 0.0142 6 0−6.6501 −4.0981

5 Chemicals & allied products 2812–2899 117 −0.0672** −0.0628** 0.0114 14 7−6.8708 −5.127

6 Petroleum refining & related industries 2911–2999 19 0.0199 0.0328 0.0065 1 21.0728 1.4148

7 Rubber & plastics products 3011–3089 19 −0.0673** −0.0557** 0.0043 4 0−4.4717 −2.9548

8 Stone, clay & glass 3211–3299 19 −0.0754** −0.0638 0.0011 1 0−10.0348 −6.7808

9 Primary metals 3312–3399 31 −0.1221** −0.0921** 0.0343 3 1−3.7295 −2.2434

10 Fabricated metals 3411–3499 43 −0.0511** −0.0665** 0.0128 8 1−3.0635 −3.1853

11 Machinery 3511–3599 123 −0.1047** −0.0792** 0.0412 18 2−5.8998 −3.5621

12 Electrical equipment 3612–3699 97 −0.1438** −0.1052 0.3139 19 1−2.6667 −1.5565

13 Transport equipment 3711–3799 81 −0.0620** −0.0543** 0.0352 8 1−3.0486 −2.1301

14 Miscellaneous manufacturing industries 3812–3999 77 −0.00425** −0.0688** 0.0167 9 2−2.9443 −3.8086

15 Transport & communcation 4011–4899 32 −0.0550** −0.0740** 0.0075 1 0−3.7654 −4.048

16 Utilities 4911–4971 11 0.0723** 0.0501** 0.0029 0 3


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Table 3 (Continued )

No. Industry SIC codes N Summary statistics (Entire sampleperiod: 02/01/1970–28/12/2001)

N*(−) N*(+)

Mean Median Variance

4.6283 −2.558317 Wholesale & retail trade 5012–5999 33 −0.0401 50.0355 0.0355 3 2

−1.3464 −1.271318 Finance, insurance & real estate 6021–6799 75 −0.0769** −0.0439 0.0444 9 3

−3.2236 −1.469419 Personal and business services 7011–8999 95 −0.0986** −0.0781** 0.0981 12 5

−3.1649 −2.000220 Diversified and other industrials 36 −0.1145* −0.0858 0.1346 5 4

−1.9486 −1.1647Across all industries 1075 −0.0804** −0.0702** 0.08650 131 39

−9.2566 −6.4461

The table reports summary statistics for γ i, by twenty industry groups, from the following regression model:


where Rit the total return of firm i in week t, Rmt designates the overall stock market return in week t and Xt is the change in the trade-weighted Latin American exchange rateindex, and εit denotes the white noise error term. The sample consists of 1075 U.S. multinational firms operating in Latin America.Note: t-statistics are in italics. N*(−) reports the number of firms with negative γ i coefficients significant at the 5% level, and N*(+) reports the number of firms with positive γ i

coefficients significant at the 5% level. Asterisk symbols (*, **) denote significance at the 10 and 5% level, respectively.



world of market imperfections, extensive use of derivatives should diminish a firm’s exposure.12

It might, therefore, be the case that corporations such as in these industries use foreign currencyderivatives and other financial instruments to shield themselves from Latin American exchangerate movements.

Table 4 reports the distribution of significant exposure coefficients for 1, 4, 12, and 54 weekreturn measurements intervals.

Overall, the findings suggest that there is a general increase in the number of significant –positive and negative – exchange rate exposure coefficients from the 1 week horizon to the longerterm 54 week horizon. Correspondingly, the number of firms who yield significant exposurecoefficients increases from 170 for the 1 week horizon return to 448 for the 4 week horizon return,to 701 for the 12 week horizon, and to 861 for the “long-term” 54 week horizon. Moreover, wefind that the firm’s exchange rate exposure coefficient becomes larger as the horizon lengthens.Thus, it appears that Latin American exchange exposure becomes more evident as the returnhorizon lengthens. Interestingly, these figures are considerably higher than those reported inprevious empirical research on long-term foreign exchange exposure effects on U.S. stock andbond returns – see Chow et al. (1997a,b), and Australian stock returns – see Di Iorio and Faff(2001). These studies, however, focus exclusively on the long-term exposure of industry portfolios,thereby ignoring the aggregation problems. Another plausible reason for the higher incidence of“long-run” exposure is the greater volatility attached to the Latin American exchange rates, as itis likely to take several days for random fluctuations to net out.

We find moreover a strong positive correlation between the estimated exchange exposuresacross different return horizons.13 Interestingly, the correlation between the 1 week exchange rateexposure and other horizons declines as the horizon length increases. These results are consistentwith those of Chow et al. (1997a) and Di Iorio and Faff (2001) who also find strong, positive cor-relations between the estimated foreign exchange exposures across horizons and that correlationdiminishes as the difference between two return horizons increases.

5. Geographical diversification and the Latin American exchange rate exposure of U.S.multinationals

As shown in Tables 2 and 3, the U.S. multinationals of our sample are very diversely affectedby Latin American currency fluctuations. The large variety of empirically observed exchange rateexposures is undoubtedly related to the fact that all these firms have very different import andexport relationships with Latin America. Moreover they are not capable of managing currency riskexposure with the same degree of effectiveness. Firms that have all their foreign operations con-centrated in Latin America are for instance more strongly affected by Latin American exchangerate shocks than firms that have trading relationships all around the world. First, the latter have thepossibility to counterbalance adverse Latin American fluctuations with simultaneous favorablemovements of other currencies included in their operation portfolio – and vice versa. Second,they have a larger set of opportunities to design effective operational hedges. It is indeed easierand less costly for these companies to adjust their pricing and market strategies as well as to

12 See, for example, Allayannis and Ofek (2001) and Geczy et al. (1997) for the use of foreign currency derivatives andtheir impact on foreign exchange rate risk.13 The correlation coefficients between the exchange rate exposure estimates of different return horizons are available

from the authors upon request.


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Table 4Exchange rate exposure coefficients γ i of U.S. multinational firms by industries: intervaling results using overlapping observations

Exchange rate exposure coefficients γ i

Sample 1 week 4 weeks 12 weeks 54 weeks

No. Industry N Cross-sectionalmean

N* Cross-sectionalmean



N*

1 Mining, construction & agriculture 48 0.1206 2 0.1099 3 0.1564 6 0.2049 170.0806 0.0686 0.1849 0.1649

−0.1146 3 −0.1147 18 −0.1598 24 −0.2547 190.0787 0.0768 0.1442 0.3964

2 Food, beverages & tobacco 51 0.1862 2 0.0631 8 0.1522 17 0.1094 100.3536 0.0447 0.3172 0.1277

−0.0719 4 −0.1033 9 −0.0880 10 −0.1377 240.0200 0.1609 0.0490 0.1587

3 Textile & apparel 25 0.3252 1 0.2177 1 0.1428 5 0.3019 70.3247 0.2356 0.0943 0.2602

−0.2155 3 −0.1839 7 −0.2299 13 −0.3528 140.3306 0.2659 0.2027 0.6371

4 Paper, publishing & printing 43 0.0854 0 0.0553 2 0.0366 2 0.3417 110.0300 0.0332 0.0245 0.4126

−0.1302 6 −0.1012 19 −0.1418 26 −0.1316 280.1100 0.0755 0.1345 0.1233

5 Chemicals & allied products 117 0.0524 7 0.0564 12 0.0972 24 0.1676 270.0141 0.0361 0.1158 0.1977

−0.1071 14 −0.1193 36 −0.0149 55 −0.2607 580.0938 0.1162 0.2193 0.4959

6 Petroleum refining & related industries 19 0.0690 2 0.0776 3 0.0396 4 0.0877 90.0412 0.0762 0.0224 0.0917

−0.0785 1 −0.0458 3 −0.0449 6 −0.1225 40.0200 0.0200 0.0245 0.1334

7 Rubber & plastics products 19 0.0629 0 0.0733 0 0.1407 5 0.0418 10.0200 0.0387 0.1364 0.0224

−0.0844 4 −0.1200 9 −0.1331 10 −0.1896 150.0539 0.0825 0.0922 0.1131


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Table 4 (Continued )

Exchange rate exposure coefficients γ i

Sample 1 week 4 weeks 12 weeks 54 weeks

No. Industry N Cross-sectionalmean




N*

8 Stone, clay & glass 19 – 0 0.0628 2 0.1245 2 0.1864 5– 0.0361 0.1480 0.1723

−0.0754 1 −0.1054 4 −0.1256 11 −0.2159 130.0332 0.0831 0.1058 0.1838

9 Primary metals 31 0.0449 1 0.0350 0 0.2797 3 0.1586 110.0173 0.0548 0.4409 0.1918

−0.1890 3 −0.1190 14 −0.1196 20 −0.2610 130.1786 0.1393 0.0806 0.2385

10 Fabricated metals 43 0.1518 1 0.1540 2 0.0722 7 0.0907 160.1572 0.2054 0.0600 0.0557

−0.0891 8 −0.1348 21 −0.1575 26 −0.2880 240.0480 0.1265 0.2985 0.5650

11 Machinery 123 0.1959 2 0.0903 4 0.1878 16 0.1628 410.1517 0.0557 0.2304 0.1931

−0.1333 18 −0.1368 49 −0.1454 66 −0.3525 570.1841 0.1170 0.1095 0.4256

12 Electrical equipment 97 0.0399 1 0.1659 6 0.1514 16 0.2052 330.0458 0.2883 0.2369 0.3061

−0.2397 19 −0.2242 41 −0.2232 58 −0.4248 480.3655 0.3412 0.2893 0.6033

13 Transport equipment 81 0.1041 1 0.2975 2 0.2022 12 0.1927 330.1789 0.1694 0.2494 0.3734

−0.1358 8 −0.0856 36 −0.1299 51 −0.2471 390.1407 0.0616 0.1661 0.3646

14 Miscellaneous manufacturing industries 77 0.1192 2 0.1109 7 0.1200 16 0.2516 290.0616 0.0995 0.1187 0.3150

−0.1191 9 −0.1252 27 −0.1554 35 −0.2465 330.0632 0.0922 0.1360 0.2737

15 Transport & communcation 32 0.0850 0 0.1577 3 0.1157 10 0.2009 200.0300 0.2406 0.1273 0.2252

−0.1037 1 −0.1080 5 −0.2495 9 −0.4052 100.0412 0.0872 0.3257 0.5618


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16 Utilities 11 0.0724 3 0.0774 3 0.0490 4 0.1170 40.0539 0.0529 0.0100 0.0700– 0 −0.0253 0 −0.0510 0 −0.0352 2– 0.0632 0.0100 0.0141

17 Wholesale & retail trade 33 0.1375 2 0.1644 4 0.1971 10 0.1895 130.1523 0.1960 0.4693 0.3333

−0.1388 3 −0.1026 11 −0.0765 10 −0.2265 170.1241 0.0975 0.0656 0.2245

18 Finance, insurance & real estate 75 0.0604 3 0.0965 11 0.1860 17 0.2008 330.0300 0.1196 0.3984 0.3097

−0.1496 9 −0.1342 12 −0.2095 17 −0.3988 190.2296 0.1664 0.2415 0.5450

19 Personal and business services 95 0.2134 5 0.2857 6 0.2843 15 0.3640 280.2052 0.3878 0.3999 0.7242

−0.1877 12 −0.1714 28 −0.2208 42 −0.2860 500.2818 0.2090 0.3360 0.3931

20 Diversified and other industrials 36 0.1698 4 0.1183 4 0.2961 6 0.4178 100.1323 0.2152 0.5511 0.9296

−0.1003 5 −0.1572 16 −0.2497 15 −0.3422 160.0583 0.1153 0.3412 0.4260

Across all industries 1075 0.1407 39 0.1457 83 0.1934 197 0.2172 3580.2663 0.2798 0.3738 0.3818

−0.1436 131 −0.1405 365 −0.1683 504 −0.3084 5030.2022 0.1942 0.2245 0.4898

The table reports cross-sectional mean values and standard deviations for γ i, by twenty industry groups, estimated by the following regression model:

Rit,t+T = αi + βiRmt,t+T + γiXt,t+T + εit,t+T

where Rit,t+T reports the total return of firm i from week t to t + T, Rmt,t+T designates the overall stock market return from week t to t + T and Xt,t+T is the change in thetrade-weighted Latin American exchange rate index from week t to t + T, where T equals 1, 4, 12 and 54 weeks, and εit,t+T denotes the white noise error term. The sampleconsists of 1075 U.S. multinational firms operating in Latin America.Note: Cross-sectional standard deviations are in italics. N indicates the number of firms included in the industry sample. N* reports the number of firms with respectively positive,and negative, γ i coefficients that are significant at the 5% level.



Table 5Cross-sectional distribution of Latin American exchange exposure coefficients across different sub-samples

Sample N Cross-sectional summary statistics N*(−) N*(+)

Mean Median Variance Minimum Maximum

Entire sample 1075 −0.0804* −0.0702* 0.0865 −3.0161 2.2307 131 39−9.257 −6.446 12.2% 3.6%

Highly diversifiedmultinationals†

91 −0.0708* −0.0612* 0.0774 −2.1160 2.2307 7 2

−7.349 −6.871 7.7% 2.2%Weakly diversified

multinationals‡45 −0.1064* −0.0832* 0.0531 −1.0223 0.9463 21 8

−12.661 −9.573 46.7% 17.8%

The table reports, across different firm samples, summary statistics for the Latin American currency exposure coefficientsγ i estimated at the 1-week horizon using the following model:


where Rit the total return of firm i in week t, Rmt designates the overall stock market return in week t and Xt is the changein the trade-weighted Latin American exchange rate index, and εit denotes the white noise error term.The Latin American currency exposure coefficients γ i are estimated for the entire sample consisting of 1075 U.S. multi-nationals, for a sub-sample of 91 U.S. multinationals that are highly geographically diversified and for a sub-sample of45 U.S. multinationals whose foreign operations are exclusively concentrated in Latin America.Note: t-statistics are in italics. N*(−) reports the number of firms with negative γ i coefficients significant at the 5% level,and N*(+) reports the number of firms with positive γ i coefficients significant at the 5% level.

* Denotes significance at the 5% level.† Highly diversified multinationals are U.S. multinationals with foreign operations not only in Latin America but also

in Europe, Asia, Australia and Africa.‡ Weakly diversified multinationals are U.S. multinationals whose foreign operations are exclusively located in Latin

America.

adapt the combination and location of their production, input and output markets in response tocurrency movements. Since they have the possibility to implement operational hedging strategiesat a lower cost, we assume hence that U.S. multinationals whose foreign operations are highlygeographically diversified hedge more than U.S. multinationals whose operations are exclusivelyfocused in Latin America. As optimal hedging theories postulate that firms that hedge more oughtto be less exposed to foreign exchange rate fluctuations than non-hedging firms, we expect thatU.S. multinationals with a strongly diversified international operating network are less sensi-tive to Latin American fluctuations than U.S. companies that are exclusively trading with LatinAmerica.

To verify this assumption we first compare the cross-sectional distribution of Latin Ameri-can currency exposures between U.S. multinationals whose foreign operating relationships areconcentrated in Latin America and U.S. multinationals that are actively trading all around theworld. In Table 5 we observe that less than 10% of U.S. firms with operations in Latin America,Europe, Asia, Australia and Africa is significantly affected by Latin American currency shockswhile the shareholder value of more than 60% of firms whose network is exclusively focused onLatin America is significantly sensitive to Latin American currency fluctuations. These resultslend strong empirical support to our prediction that exposure should be negatively related to thebreath of the network.

We now empirically test the above hypothesis by estimating the relationship between afirm’s Latin American exposure and its international breadth of operations – while controlling



Table 6Latin American exchange exposure coefficients and multinationality

Dependent variable OLS regression WLS regression

absolute value of γ i Coeff. t stat. Coeff. t stat.

Constant 0.0813** 3.5621 0.1254** 6.6173ln(size†) 0.0021 1.1210 0.0062* 1.8471Multinationality‡ −0.0762** −2.0589 −0.1599** −3.7785Number of observations 1075 1075Adjusted R2 12.23% 26.44%

The table reports estimates of the relationship between the absolute value of the Latin American exposure coefficients γ i

(estimated at the 1-week horizon) and two factors: the natural logarithm of the firm’s market value of equity (size) andthe firm’s degree of geographical diversification (multinationality). The empirical relationship is described below:

|γi| = a0 + a1 ln(sizei) + a2 multinationalityi + υi,T

where |γ i| is the absolute value of the exposure coefficient and υi,T denotes the white noise error term. The regression isestimated with ordinary least squares and weighted least squares (in which the independent variables are weighted by thereciprocal of the squared standard error of the exposure coefficient γ i).Note: t-statistics are in italics.

* Denotes significance at the 10% level.** Denotes significance at the 5% level.† Size is measured by the firm’s market value of equity.‡ The firm’s degree of multinationality is estimated by an index measuring the dispersion of the firm’s foreign operations

across different geographic regions. This index is calculated as the ratio of the number of regions in which the firm hasforeign operations by the total number of reported regions (as we consider Latin America, Europe, Asia, Australia andAfrica, this number is five). Hence, a higher value for the index indicates a more strongly geographically diversifiednetwork structure.

for its size:

|γi| = a0 + a1 ln(sizei) + a2 multinationalityi + υi,T (6)

where the subscript i indicates firm i in the cross-sectional regression. |γ i| is the absolute value offirm i’s Latin American foreign exchange risk exposure and υi the standard error term. Firm i’ssize (sizei) is measured by the market value of firm i’s equity. The breadth of firm i’s internationalnetwork (multinationalityi) is measured by the ratio of the number of regions in which firm ihas foreign operations by the total number of reported regions.14 A higher value for the index isassociated with a more strongly geographically diversified network structure and hence expectedto be related to a lower sensitivity to Latin American currency shocks. The results are displayedin Table 6.

The estimation of Eq. (6) is first conducted using ordinary least squares. In the first two columnsof Table 6 we observe that whereas a firm’s size has a weak positive influence on its sensitivity toLatin American currency fluctuations, its degree of geographical diversification has a statisticallysignificant negative impact on its Latin American exposure coefficient. In line with Pantzalis etal. (2001), it appears that U.S. multinationals with larger breadth of operations are better armed toeffectively design and implement operational hedging strategies in order to reduce their exposureto Latin American currency shocks.

To verify the robustness of our results we estimate now Eq. (6) using weighted least squares. Inorder to give more weight to the exposure coefficients γ i that have been estimated more precisely,

14 As we consider the regions Latin America, Europe, Asia, Australia and Africa, the total number of regions is five.



the independent variables are weighted by the inverse of the estimated squared standard errorof γ i. Overall the last two columns of Table 6 confirm the results described above. In contrastwith Nance et al. (1993) who predict that larger firms with economies of scale in hedging shouldbe more likely to hedge than smaller firms, we find that larger firms seem to hedge less as theyare more exposed to Latin American currency movements.15 One potential explanation for thisempirical finding is that larger firms face lower bankruptcy costs than smaller firms – since theirprobability of distress is lower – and are hence hedging less. On the other hand, we observethat U.S. multinationals with a large international network – thus able to effectively reduce theirexposure through operational hedges – are less sensitive to these fluctuations. We confirm thusour prediction that a firm whose foreign operations are primarily concentrated in Latin Americais less able to adapt its pricing policies as well as shift its input and output markets in case of anadverse currency shock in Latin America than a highly geographically diversified company.

6. Concluding remarks

This paper examines whether there exists any relationship between individual U.S. multina-tionals’ stock returns and fluctuations in Latin American exchange rates. Our results highlightthe importance of movements in Latin American real exchange rates to the U.S. shareholder anddiffer substantially from previous U.S. experience. By using a disaggregated dataset of weeklystock returns and real exchange rate movements, it seems likely that the apparent lack of exchangerate exposure previously documented is mainly due to both the use of too aggregated economicvariables and the ignorance of the intervaling effect.

Using a sample of 1075 U.S. multinationals, we find that about 4% of the firms experiencedeconomically significant positive exposure effects and about 12% significant negative effects forthe whole period of January 1970 to December 2001. In spite of the growing significance ofthe Latin American share of U.S. trade over the past few decades, U.S. multinationals are – tosome extent – exposed to fluctuations in the Latin American foreign exchange. It would seemthat either U.S. financial managers ignore their shareholders’ exposure to the most dominantcurrencies in the Latin American region in their risk-management decision-making process orthat it represents an economic exposure that is unrelated to known transactions, and is hencevery difficult to manage. While there is time-variation in contemporaneous exposure effects atthe individual firm level, the overall extent of exposure is not sample dependent; the majority offirms with significant exchange risk sensitivity (60–64%) suffer from an appreciation in the valueof the Latin American currencies.

We also study the exposure of individual firms within different U.S. industries, as opposedto average industry exposure. Importantly, individual firms in these industry groups show highpositive as well as negative exposure, suggesting that exposure is not necessarily economicallysignificant in the aggregate. Furthermore, multinationals with insignificant exposure effects areconcentrated only in three industries, namely stone, clay and glass, transport and communica-tion, and utilities. The investigation of the nature of exchange exposure across increasing returnmeasurement intervals suggests that the extent to which U.S. multinationals are exposed to LatinAmerican exchange rate fluctuations varies with return horizons. Short-term exposure seems tobe relatively well hedged, where considerable evidence of long-term exposure is found. Some ofour results appear to be sensitive to the exchange rate volatility.

15 These empirical findings are however in line with previously reported results (see, for instance, Muller and Verschoor,2006a).



Finally, the extent to which U.S. multinationals are exposed to Latin American currency fluc-tuations depends on the geographic diversification of their operations outside Latin America.Our results reveal that while more than 60% of U.S. multinationals without geographic diversi-fication outside Latin America is significantly exposed to Latin American currency movements,whereas less than 10% of companies with a highly diversified international network is stati-cally significantly affected. Overall, our results are consistently showing that U.S. multinationalswhose foreign operations are distributed all around the world – and are hence assumed to be betterequipped to implement appropriate operational hedging strategies – suffer less from adverse LatinAmerican currency shocks than U.S. multinationals whose network structures are concentratedin Latin America.

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The Latin American exchange exposure of U.S. multinationals

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