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The Exchange-Rate Exposure of U.S. Multinationals Author(s): Philippe Jorion Source: The Journal of Business, Vol. 63, No. 3 (Jul., 1990), pp. 331-345 Published by: The University of Chicago Press Stable URL: http://www.jstor.org/stable/2353153 Accessed: 26/05/2009 21:02 Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at http://www.jstor.org/action/showPublisher?publisherCode=ucpress. Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission. JSTOR is a not-for-profit organization founded in 1995 to build trusted digital archives for scholarship. We work with the scholarly community to preserve their work and the materials they rely upon, and to build a common research platform that promotes the discovery and use of these resources. For more information about JSTOR, please contact support@jstor.org.

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Philippe Jorion ColumbiaUniversity

The Exchange-Rate Exposure of U.S. Multinationals*

It is widely believed that exchange rates affect the value of the firm. Exchange rates are a major source of uncertainty for multinationals,being typically four times as volatile as interest rates and 10 times as volatile as inflation.1While the relationship between inflation rates or interest rates and the value of the firm has been been extensively analyzed,2 it is astonishing that the association between exchange rates and the value of the firm has not been subject to much empiricalresearch. The purpose of this paper is to analyze the foreign exchange exposure of U.S. multinationals. Withoutpresumingany causal link, exposure representsthe sensitivity of the value of the firm to exchange rate randomness and can be measured by the regression coefficient of the change in the value of the firm on the change in the exchange rate. To date, a number of theoretical papers have investigated the possible sources of exchange* Thanks are due to Michael Adler, MauriceLevi, Rex Thompson, Arthur Warga, and an anonymous referee for helpfulcomments. 1. Overthe period 1971-87, the annualizedvolatilityof the dollar/markexchange-ratechangewas 12%,againsta volatility of 3%for the U.S. Treasurybill rateand 1.3%for the U.S. inflation. 2. See, e.g., French, Ruback, and Schwert (1983), Flannery and James (1984), Bernard (1986), and Sweeney and Warga(1986). (Journal of Business, 1990,vol. 63, no. 3) ? 1990by The Universityof Chicago.All rightsreserved.

.50 0021-9398/90/6303-0004$01 331

This article examines the exposure of U.S. multinationalsto foreign currencyrisk. Evidence is presentedthat the relationshipbetween stock returns and exchange rates differs systematically across multinationals. Given these results, the study focuses on the determinantsof exchange-rateexposure. The comovementbetween stock returns and the value of the dollaris found to be positively relatedto the percentageof foreign operationsof U.S. multinationals.


332

Journal of Business

rate exposure.3 However, no study has yet addressedthe problemof empiricallymeasuringthe determinantsof foreign currencyexposure. This could be due to the generalpaucityof data sources in international financebut also to the fact that the absolute size of currencyexposure is generally small relative to measurementerror.This study, however, reports significantcross-sectional differences in the exposure of U.S. multinationals.Conversely, firms with no foreign operations, which could conceivably be exposed to exchange-raterisk, exhibitin practice little measurabledifference in exchange-rateexposure. Given these results, the sources of these differentialeffects are examined next. The exchange-rateexposure is related to the fraction of total sales made overseas by U.S. multinationals,which is derived from accounting data.4In spite of potentialproblemsof measurement error and instability, exposure is found to be significantlypositively correlatedwith the foreign sales measure. The article is organized as follows. Section I defines exchange-rate exposure and analyzes the sources of economic exposure for firms. Section II presents empiricalevidence on the distributionof exposure coefficientsfor a sampleof U.S. multinationals.The methodologyused to relate exposure to a foreign operationsmeasureis describedin Section III. Section IV contains some concludingremarks. I. ForeignCurrencyExposure Dumas (1978), Adler and Dumas (1980), and Hodder (1982) can be interpretedas definingeconomic exposure to exchange-ratemovement as the regression coefficient of the real value of the firm on the exchange rate across states of nature. Taking the viewpoint of a U.S. investor who assumes that domestic inflationis nonrandom,exposure is measuredby the slope coefficient of a linearregressionof the dollar value of the firm on the exchange rate. As Adler and Dumas (1984) point out, the concept of exposure is arbitraryin the sense that stock prices and exchange rates are determined jointly. Decomposing the value of the firm into a component 3. The effect of exchange risk on the firm has been studied by Heckerman(1972), Shapiro(1977), Adler and Dumas (1980), Wihlborg(1980), and Hodder (1982), among others. 4. While the accountinginformationon foreign operationshas been availablefor a numberof years, the data have not previouslybeen used to investigatethe relationship between exchange rates and the value of the firm. Typically, previous studies have focused on the effect of foreign operationson the systematic risk of multinationalsto derive implicationsfor the benefit of indirectinternationaldiversification.See Agmon and Lessard (1977), Jacquillatand Solnik (1978), Senchakand Beedles (1980). Errunza and Senbet (1981, 1984)used similardata to analyze the effects of internationaloperations on the value of the firm. 5. Assumingmultivariatenormalitybetween the value of the firmand the exchange rate.


Exchange-Rate Exposure

333

perfectly correlatedwith the exchange rate and an orthogonalcomponent does not imply a causal relationshipbetween exchange rates and stock prices. This is simply a statistical decompositioncomparableto others used to study the relationshipbetween the value of an asset and inflationrates, interest rates, and, for that matter, marketmovements. Clearly, the degree of association between endogenous variables such as stock prices and exchange rates depends on the natureof the shocks affecting the economy. Blanchard and Summers (1984), for example, present a good summary of how various disturbancesare likely to affect these asset prices. Thus, exposure mayjust reveal, say, the simultaneous impact of monetary factors on exchange rates and stock prices. To the extent that monetaryshocks affect firmsdifferentially, cross-sectional variation could appear in the association between stock prices and exchange rates, even for purely "domestic" firms. Some empiricalevidence on this issue is presented in the next section. Foreign exchange exposure can generally be decomposed into the effect of exchange-raterandomness on (i) the value of net monetary assets with fixed nominal payoffs6 and on (ii) the value of real assets held by the firm. Abstracting from inflation uncertainty, short-term foreignmonetaryassets are, in general,fully exposed to exchangerisk, whereas domestic monetary assets are not. This is usually called "translationexposure." Real assets, however, will be affectedin value by exchange-rate movements, whatever their location. Thus purely domestic firms, like utilities, may be affected by exchange-ratemovements through effects on aggregatedemand or on the cost of traded inputs; domestic firmsthat sell goods competingwith importswill also be exposed to exchange-ratemovements. United States multinationals that rely heavily on exports should see the value of their U.S. real assets unfavorablyaffected by an appreciationof the U.S. dollar;conversely, foreign real assets producing goods that are ultimately importedinto the United States shouldbenefitfrom an appreciationof the dollar. In addition, Dumas (1978) emphasized that exposure contains an "operational" element that accounts for the firm's responsiveness to exchange-rate changes. It could be argued, for instance, that the unique ability of multinationalsto shift productionfrom one countryto another actually lessens their exchange-rateexposure. Finally, multinationals may actively adjust their transactionor balance sheet exposure by means of various covering instruments;these hedging activities, if known and impounded in stock prices, will reduce the correlationbetween stock prices and exchange rates. What emerges from the above is that the determinantsof exposure are quite complex, and that exposure may be difficultto identify. In 6. This class also includes contractualcash flows in the foreigncurrency.


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334

spite of this, U.S. multinationalsexhibit significantcross-sectionaldifferences in their association with exchange rates, as will be shown below. This article presents a first attemptat analyzingthe sources of these differences in exposure. For example, one could analyze exposure due to monetaryassets with balance sheet informationconcerning foreign versus domestic nominal assets, as French, Ruback, and Schwert (1983) did when studyingexposure to inflationrisk. Unfortunately, such detailed accounting data on internationaloperations are not readily available, to my knowledge. Informationon foreign and domestic sales, however, is availablefor a numberof U.S. multinationals.These foreign sales figures combine exports from the United States, both direct and intragroup,and sales by foreign subsidiaries.To see how exposure can be relatedto foreign operations, consider, first, a monopolistic trading firm whose costs are incurredin dollarsbut whose sales receipts are determinedby foreign prices. All else equal, an unanticipatedforeign currencyappreciation should increase profits and the value of the firm.7More precisely, Levi (1983) shows that, with fixed marginal costs, the change in exports dollar profits af due to exchange rate changes can be written as dwrfIdS= -qwflS, where -q is the elasticity of foreign demand, whichmustbe greaterthanone to ensurepositive earnings.Assume now that the value of the firm V can be writtenas the discountedvalue of a stream of constant total profits, which are the sum of foreign profits and domestic profits rr=

1rrf + lrd.

Focusing only on the exposure of

foreign operations, the exchange-rateexposure of the firm is (dVIV)I (dSIS) = -qlTw/l. So, using the ratio of foreign sales to total sales as a proxy for the ratio of foreign profits to total profits, the exposure should increase as the proportionof sales exported increases. Consider next a foreign subsidiarywith revenues and costs principally denominatedin the foreigncurrency.The dollarvalue of this subsidiaryshould (ceteris paribus)increase as the foreign currencyappreciates because of a translation effect. Using the same notation as above, the exchange-rateexposure can be writtenas (dVIV)I(dSIS) = 1flw. Again, this suggests a positive cross-sectional relationshipbetween currency exposure and the degree of foreign operations.

7. A caveat is in orderhere. Manyauthors,such as Shapiro(1975)andCornell(1980), have suggested that exchange risk is really measuredby deviations from purchasing powerparity.If the law of one priceholds, barringrelativepricechanges,thenexchangeratemovementsare exactly offset by pricemovements,andexchangeriskdisappears.In reality,however, largeandpersistentdeviationsfrompurchasingpowerparityhave been documented.The monthly volatility of relative changes in exchange rates is about 10 times the volatility in inflationrates, so that most of the movementin exchange rates cannotbe accountedfor by inflationrates. Therefore,the correlationbetweenmonth-tomonth changes in real exchange rates is extremely high, and similarresults will be obtainedusing nominalor real exchange rates.


335

Exchange-Rate Exposure

II.

The Exposure of U.S. Multinationals

Estimates of the exposure coefficient can be obtained from the timeseries regression,

t = 1, ... ., T. 1 where Ritis the rate of returnon the ith company's common stock and Rstis the rate of changein a trade-weightedexchangerate, measuredas the dollar price of the foreign currency. Thus a positive value for Rst indicates a dollar depreciation. This specificationis appropriateif changes in stock prices and exchange rates are essentially unanticipated. If, for example, the expected rate of return on the common stock and the expected rate of change in the exchange rate are constant over time, then the intercept 13oiwill reflect these expected values, and the slope coefficient will correctly measure the effect of unanticipatedchanges in exchange rates on stock returns. Another possibility would be to take the forward premiumon the exchange rate as the expected rate of change in the exchange rate. However, a growing number of empirical studies indicate that the forward rate is a biased predictorof the future spot rate and does not even outperformthe contemporaneousspot rate.8 Furthermore,since the percentage of actual variationin the spot rate explainedby the forwardpremiumis quite small, about 5%in sample, it can safely be concluded that most of the actual change in the spot rate is unanticipated. The trade-weightedexchange rate is derived from the weights in the Multilateral Exchange Rate Model (MERM) computed by the International Monetary Fund. These weights are based on 1977 trade flows and price elasticities and are reported in the Appendix.9 End-of-periodexchange rates were constructedfrom MERMweights and end-of-periodbilateralnominalrates. Collapsingall exchangerates into one multilateralexchange rate results in a parsimoniousrepresentation that is convenient to use. In addition, it avoids the problem of multicollinearitythat arises because many cross-exchange rates are fixed relative to each other, or nearly so.10 Changes in the value of the firm, Rit, are measured by the rate of returnon the companies' common stocks as provided by the University of Chicago Center for Research in Security Prices (CRSP) data base. The sampleperiod startsin January1971,which is the year when exchange rates started to float, and ends in December 1987, which Rit = hoi + PliRst

+

Eit,

8. See, e.g., Bilson (1981). 9. The procedurefor computingthe MERMweightsis detailedin Artusand McGuirk (1981). 10. For example, the correlation between movements in dollar/markand dollar/ Frenchfranc exchange rates was 0.88 from 1971to 1987.


Journal of Business

336

correspondsto the latest CRSP data. Only firms with no missing data over the period 1971-87 are selected. Three subperiods, 1971-75, 1976-80, 1981-87, are also considered. Because the next section attempts to explain exposure in terms of the degree of foreign involvement, the sampleis restrictedto firmsthat reportforeign operations in the Value Line database. Since the publication of Financial Accounting Standards (FAS) no. 14 in December 1976, U.S. companies have been requiredto disclose a geographical analysis of foreign operations to the extent that foreign operations account for more than 10% of total operations. A large number of companies also report segmental geographicalinformationeven with limitedforeign operations. It should be emphasized,however, that the distinctionbetween domestic andforeignoperationsis not always clear cut because of problems such as transferpricing and cost allocation. Recognizing these issues, the Financial Accounting StandardsBoard has left managementwide latitude for interpretation.As a result, the definition of foreign sales may differ across companies, which will create measurementerrors.11 The Value Line data base contains dataon foreignsales for about900 firms from 1977to 1984. The degree of foreign involvementwas measured as the sum of all reported foreign sales divided by the sum of total sales over the same years. After eliminatingforeign firms, the samplewas reducedto 673 firmsincludedon the monthlyCRSPfile, of which only 305 had nonmissing price data from 1971 to 1987. For reasons explained below, companies in the petroleum industry were also excluded from the sample, leaving 287 companies. This is the basic sample of U.S. multinationals,which, it should be noted, also includes many companieswith zero or low reportedforeignoperations. An alternativespecificationto (1), which explicitly controlsfor market movements, is Rit =

POi + P2iRst + P3iRmt + 'nit,

t =

1, .

. .,

T,

(2)

where Rmtis the rate of return on the CRSP value-weightedmarket index. In practice, the coefficients of exchange-rate exposure estimated by (1) and (2) are highly correlated, with a rank correlationof .968for the whole sample, so that the subsequentanalysiswill focus on the market-adjustedbetas. Table 1 reportsthe distributionof exposure coefficientsfor these 287 U.S. companies. Five firms were selected with representativevalues 11. All these foreignsales dataare collectedfromcompanyreports.Besides problems of noncomparability,the ValueLine database containsmistakes.A few companies,e.g., had morethan 100%of sales abroad.These companieswere eliminatedfromthe sample. Thus the results may be obscuredby measurementerror, which typicallybiases slope coefficientstowardzero.


337

Exchange-Rate Exposure TABLE 1

Distribution of Exposure Coefficients 082of U.S. Multinationals Rit=

Ii +

02iRst

+ P3Rmt + nit

Period (Monthly Returns) 1971-87 Minimum First quartile Median Third quartile Maximum Cross-sectional mean Cross-sectional SD Significance, no. of firms with significant exposure at 5% level Stability, no. of firms with same sign for exposure: 1971-75 and 1976-80 1976-80 and 1981-87 All three subperiods

1971-'75

1976-80

1981-87

- 1.45* (-3.10) -.27 (-1.17) -.07 (- .25) .12 (.49) .56* (1.99) -.093 .307

- 2.93* (-2.07) -.61 (-.7Q) -.17 (.17) .22 (.18) 1.72* (2.35) -.234 .712

- 1.83* (-2.59) -.38 (-.73) .05 (- .18) .27 (.84) .52* (3.02) - .079 .493

- 1.94* (-2.63) -.25 (-.93) -.06 (-.20) .13 (.50) 1.17* (2.56) -.078 .353

15/287

5/287

16/287

15/287

... ... ...

190/287 ... 109/287

190/287 159/287 109/287

... 159/287 109/287

0/287

2/287

5/287

0/287

Autocorrelation,no. of firmswith significant Durbin-Watson statistic at 5% level Value-weighted market exposure

Equal-weightedmarketexposure

.158 (1.01)

.058 (.30)

.712* (1.98)

.493 (.96)

.084 (.30)

.081 (.23)

.022 (.10)

-.101 (-.43)

NOTE.-t-statisticsare in parentheses.The exposure coefficientis computedby regressingthe stockmonthlyreturnsRi,on the rateof changein a trade-weightedexchangerateRs,andon the stock marketreturnRing. The marketexposureis derivedfroma univariateregressionon Rs,. Total sample consists of 287 nonoil companies. * Significant at the 5% level.

for the cross-sectional distribution.Table 1 provides a partialexplanation for the rarity of studies on the relationbetween the stock market and the foreign exchange market.Most exposure coefficientsare small relative to their standarderror, except in a few cases. Of course, this does not necessarily mean that the true exposure coefficients are all zero but ratherthat exposure is imprecisely estimated. The coefficients also appear to change over time. Only 190 firms have an exposure with the same sign in the firstand second subperiods, and the number drops to 159 when comparingthe second and third subperiods. The bottom part of the table reports the exposure (PI) of the stock market. From one subperiodto another, the exposure of the value-weightedmarketchanges from 0.71 to 0.08 to 0.02, with the first coefficient significant. This is consistent with the results of Obstfeld


338

Journal of Business

(1985), who finds evidence that monetary developments have dominated the behavior of the dollar from 1975 to 1981, while goods-market developments have gained in importancesince. Monetaryexpansion, for instance, raises the nominal prices of stocks and currencies simultaneously, which translates into a positive exposure coefficient.12 But given the large standarderrors, tests of stability cannot reject the hypothesis of constant coefficients. These conclusions are reinforced when standarderrorsare correctedfor heteroscedasticitysince the correction tends to increase standard errors.13 Otherwise these regressions are well specified in terms of serially uncorrelatedresiduals: a very small proportionof the regressionsyields significantDurbin-Watsonstatistics. In view of the previous results, it seems importantto test whether these exposure coefficients are all equal or even zero. Equal coefficients could also reveal the endogenousnatureof the relationshipbetween stock prices and exchange rates, which, in this case, would be unrelatedto foreign operations. To perform such a test, equation (2) should be run jointly for all firms in the sample, accounting for the contemporaneouscorrelations in the error terms. The model should thereforebe estimated by generalizedleast squares (GLS). Since this procedurerequiresthat the numberof firmsbe smallerthanthe number of monthly observations, 40 firms were selected at a time. Thus, 40 multinationalswere chosen so as to maximize the dispersion in the percentage of foreign operations measure. The available sample of 287 firmswas classified in orderof increasingforeign operations, and 40 firmswere chosen by samplingat regularintervals.Table 2 reports strong rejections of the hypotheses that the exposure coefficients of these multinationalsare all equal or all zero. These rejections can be traced to the use of a system of equations, which yields more powerfultests thanthe previousunivariateregressions,by pooling informationacross series. Additionally, the exposure coefficients are also found to be significantlyunstable across the three subperiods. To confirm these findings, the analysis is repeated with 40 portfolios, sorted by foreign operations. Sortinginto portfolios should yield less variablereturns,and thus more precise parameterestimates. The drawbackof the sortingprocedureis that informationis lost by averaging out the exposure coefficients within portfolios. In any event, table 2 shows that similarresults are achieved with the portfolios of multinationals. Then, in orderto distinguishthe extent to which comovementsunre12. Huizingaand Mishkin(1986)also find markeddifferencesin the behaviorof real interestrates before and after 1980. 13. For instance, the t-statisticfor the exposure of the value-weightedmarketin the first subperiodsdrops from 1.98 to 1.43 when correctingfor heteroscedasticity.


Exchange-Rate Exposure TABLE 2

339

Hypothesis Tests on Exposure Coefficients 082, January 1971-December 1987

F-Test (P-Value) Sample

Average No. Significant Hypothesis Exposure at 5% Level Equal 132 Zero 132 Stable 132

40 multinationalswith most dispersionin foreignoperations

-.049

3/40

1.8599** 1.8997** (.0009) (.0005)

40 portfolioswith most dispersionin foreignoperations

-.088

6/40

1.7834** 1.7454** 1.6254** (.002) (.003) (.0004)

40 firmswith lowest percentageof foreign operations

-.120

0/40

1.1816 (.203)

1.2910 (.104)

1.3086 (.034)

40 largestdomestic firmswith no reportedforeign operations

- .151

2/40

1.2676

1.3286

1.2101

(.123)

(.081)

(.099)

1.5892** (.0007)

40 firmswith highest percentageof foreign operations

.073

2/40

1.5787 (.012)

1.7400** (.003)

1.2395 (.073)

14 foreignfirms

.563

9/14

2.7818** 5.6477** (.0006) (.0001)

1.6408 (.018)

NOTE.-Systemestimatedby generalizedleast squares.The sampleconsists of 287 nonoilfirms, with foreign operationsreportedin Value Line, except for the foreignfirms and the "domestic" firms,whichare the largestFortune500 firmswith no reportedforeignoperations.The stabilitytest jointly tests the equalityof exposurecoefficientsover the subperiods1971-75, 1976-80, 1981-87. ** Significantat the 1%level.

lated to exchange-rate exposure may give nonzero coefficients, the tests were repeated for firms with little or no foreign involvement. Forty companieswere selected from the sampleof 287 with the lowest reportedforeign operations, rangingfrom 0 to 6% of total sales. As is apparent from table 2, the cross-sectional variability in exposure coefficients is much lower than before: the hypothesis of equal coefficients now cannot be rejected at the 5% confidencelevel. Given that multinationalsare among the largest U.S. firms, an alternative experiment was designed to control for firm size. A sample was collected with the 40 largest companies among the Fortune 500 without any reportedforeign operations. These domestic firmsbest match the Value Line sample of multinationalsin terms of size and also cover many different industries. As explained above, differing exposure coefficients may conceivably arise because of differingcross-sectional


Journal of Business

340

effects of economywide disturbances.But it appearsfrom table 2 that the dispersion in the exposure coefficients of domestic firms is quite low, and that their exposure is not significantlydifferentfrom zero. Finally, table 2 reports tests on the exposure of the 40 firms in the Value Line sample with the highest percentage of foreign operations, as well as 14 foreignfirmslisted on the New York Stock Exchange. As expected, these firms display a high and significantexposure to exchange-ratemovements, with the results particularlystrongfor foreign firms. This section thereforehas shown that cross-sectionalvariations in exposure coefficients are identifiableand quite markedacross multinationalfirms, which indicates that an analysis of the determinantsof exchange-rateexposure is warranted. III. The Determinantsof Exchange-RateExposure The purpose of this section is to determine whether exchange-rate exposure is related to the degree of foreign involvement. The hypothesis can be cast in terms of the cross-sectional regression, Eli = yo + yFi + ui, i = 1, ... ., Ng

(3)

where Fi is the ratio of foreignto total sales. The value of Yomay differ from zero if the returnon the purely domestic componentof the U.S. stock marketis correlatedwith changes in exchange rates. Therefore, the constant incorporatesthe effect of the correlationbetween market movements and the exchange rate. A major difficulty arises in this two-step estimation procedure if the errorsin (1) are correlatedacross companies, as is likely to be the case. In that situation,ordinaryleast squares(OLS) estimationof (1) is fully efficient but the first-step estimates P'li are not independent across equations. Generally,their covariancematrixcan be writtenas l[lt(Rst

-

Rs)2]-1, where fl is the covariance matrix of the contem-

poraneouserrors t. As Thompson(1985)points out, the problemarises because all the betas are estimated over the same sampleperiod. This correlationacross betas impartsnonzero correlationsacross the error terms in (3), which violates the classical OLS assumptions.The standarderrorof i' in (3) is then biased and tests of significanceare difficult to interpret. To illustrate, figure 1 plots the exposure coefficients againstthe percentageof foreign operationsfor the sampleof multinationals. Whereas the slope coefficient is clearly positive, statistical significanceis difficultto assess. This problemis too often overlooked in empirical studies.14 14. For instance, the high t-statisticsfoundby Agmonand Lessard(1977)and Fatemi (1984)are probablyseriously biased upward.These authorsregress the estimatedsystematic risk coefficients against a degree of internationaloperationsmeasure without recognizingthat the betas are correlatedacross firms.


Exchange-Rate Exposure

341

0.8 0.6 U

-0.4 8 0. X

0.2 *

0

~~~~

-

CL~~~~

*

.2

1

an87.

-mu~~O

1

-~U~

I

~Foeg toOM

0.

I~ Sale

.4

.8

I.

l

l Toa

ae

U ai

tinas Janar 191-ecmer197 0

0

40

.6

80

Onesolutionutothis psoblem y the istoemodelSlesdetl stuctaofte error terms. A straightforwardprocedure is to use market-adjusted FhiG.1.exposredandafryeign operatios, 28 inU.S.alze mltia-t as inexane-riated that the errors due betas, (2). Such a model-indicating (1) are to marketmovements-eliminates most of the correlationsin the error (3) becomes isthe modeldimesrietlthegsructureof thean terms. ntEquation matrisolution to tis paeroblm which can be estimated by ordinaryleast squares. A generalizedleast squares procedure can also be implemented, where the covariance matrixof the 1o2r is taken from the joint time-seriesregression(2) and used to transformthe 1s2t into independentN(0, 1) variables. in (2) by (gyo+ yFi) and directly Alternatively, one can replace of2i estimate the parametersby generalizedleast squares: Rt= POi +

(yo + y1Fi)Rst + I33iRmt +

Titg

(5)

Both procedureswere implemented,with similarresults, which will be reportedonly for the one-step approach.As this requiresthe inversion of a covariance matrix estimated in some cases over only 5 years of monthly data, the number of firms was reduced by groupinginto 40 portfolios, classified accordingto the foreign sales operationmeasure. 15. For this data set, the averagecorrelationcoefficienttypicallydropsfrom 0.37 for the oi's in (1) to 0.04 for the 'qi,'sin (2).


342 TABLE 3

Journal of Business Exchange Rate Exposure and Foreign Operations (40 Portfolios Sorted by Foreign Operations)

Rit = ri + r2iRst+ r3iRmt+ qits i = 1. where 12i= Yo + YiFi Yo Estimation period, 1971-87 Subperiods: 1971-75 1976-80 1981-87

40,

Y1

-.105 (- 1.86)

.30* (2.33)

-.213* (-1.96) .014 (.26) -.191** (-4.65)

.57* (2.22) - .01 (-.48) .41** (2.87)

NOTE.-t-statisticsfrom generalizedleast squaresestimationare in parentheses;R.,, R,,, Rmt are definedas the returnon portfolioi, the rate of changein the exchangerate, and the returnon the market,respectively;Fi is the ratioof foreignsales to total sales. The 40 portfoliosare sortedin order of foreignoperations,from a sampleof 287 nonoil firms. * Significantat the 5%level. ** Significantat the 1%level.

For reasons noted below, these portfoliosexclude oil firms. WithGLS estimation,the cross-sectional standarderrorsare asymptoticallyconsistent, even if there remains substantialcorrelationacross portfolios after adjustingfor the market. The drawbackof this method is that grouping will reduce the variability in the independent variable and thus lead to less powerful tests. In addition, since the matrix ft is unknown and has to be estimated, the improvementdepends on the trade-off between estimation error and the departurefrom the OLS assumption. Regression results for equation (5) are presented in table 3. The estimated slope coefficient is positive and significant in two subperiods, and for the whole period 1971-87. Thus, in spite of potential measurementand instabilityproblems,this evidence is consistent with the hypothesis that exchange-rateexposure is positively relatedto the foreign sales variable. The previous results were achieved with 40 portfolios of 287 nonoil firms. Oil companies were specificallyexcluded from the above analysis because it appearedthat the multinationalswith the most foreign operations belonged to the oil industry, where output prices are commonly set in dollars all over the world: foreign sales account for over 53%of the sales of the U.S. oil industry,while the computerindustry ranks second, with a 41%foreign sales ratio. It is thereforereasonable to expect that U.S. oil companiesare not so sensitive to fluctuationsin the value of the dollar, in which case the structuralrelationship(4) could yield different coefficients for oil and nonoil firms.


Exchange-Rate Exposure TABLE 4

343

Exchange Rate Exposure and Foreign Operations: Oil versus Nonoil Firms, January 1971-December 1987

+ 58, where 02i = _Yw Rit = 30i+ r2iRst + 3iRmt+ 1itsi =1. y1N Fi for 40 nonoil portfolios, 2i = 'Y + 'y Fi for 18 oil firms Firms

Yo

40 nonoil portfolios 18 oil firms

Y1i

-.146** (-3.11) .082 (.70)

.33** (3.00) .03 (.21)

Joint test of equal coefficients: X2 = 5.99*

P-value =

.05

NOTE.-t-statistics from generalized least squares estimation are in parentheses. The parameters -Yo,-Yiare different across the 40 nonoil portfolios and the 18 oil firms and are estimated jointly across the 58 assets. The chi-square statistic tests the hypothesis that the coefficients (yo, -yl) are equal for nonoil portfolios and oil firms. * Significant at the 5% level. ** Significant at the 1% level.

This hypothesis was tested by estimating (5) jointly for 40 nonoil portfolios and 18 oil firms, with results reported in table 4. The slope coefficients are clearly different: 0.33 for nonoil portfolios, and 0.03 for oil firms, the latter not significant. This indicates that the exchange-rate exposure of oil firms is only weakly related to foreign operations. In addition, the joint hypothesis of equal coefficients (yo,yl) across oil and nonoil firms is rejected at the 5% level. These results justify excluding oil firms from the sample of U.S. multinationals for the analysis of the determinants of exchange-rate exposure. IV.

Conclusions

This article has identified significant cross-sectional differences in the relationship between the value of U.S. multinationals and the exchange rate. This association, called exposure, was found to be positively and reliably correlated with the degree of foreign involvement. Conversely, exposure without foreign operations does not appear to differ across domestic firms. These results have direct implications for asset-pricing tests. Given that the value of the dollar appears to be one factor that differentially affects U.S. stocks, exchange-rate exposure could theoretically be priced in an arbitrage pricing theory framework. If so, firms could affect their cost of capital by currency hedging. However, there is some preliminary evidence, presented in Jorion (1988), that exchangerate risk appears to be diversifiable.


344

Journal of Business

Appendix Currency Weights TABLE Al

Currency Weights in the Effective Dollar Exchange Rate Based on the IMF's Multilateral Exchange Rate Model

Country

Percentage Weight

Australia Austria Belgium Canada Denmark France Germany Italy Japan Netherlands Norway Spain Sweden Switzerland United Kingdom

5.0 1.1 2.4 20.7 1.4 10.3 13.2 7.6 21.7 3.3 1.2 2.4 2.7 1.7 5.2

Total

100.0

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MichaelAdler,MauriceLevi,RexThompson,ArthurWarga,andananonymousrefereeforhelpfulcomments.1.Overthepe  

* Thanks are due to Michael Adler, Maurice Levi, Rex Thompson, Arthur Warga, and an anonymous referee for helpful comments. 1. Over the peri...

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