Estimating Exchange Rate Exposures:

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1 Estimting Exchnge Rte Exposures: Issues in Model Structure * Gordon M. Bodnr ** Pul H. Nitze School of Advnced Interntionl Studies, The Johns Hopkins University 1740 Msschusetts Avenue NW Wshington, DC nd M. H. Frnco Wong Grdute School of Business University of Chicgo 1101 E. 58th Street Chicgo, IL October 2002 First Drft : November 1999 * The uthors grtefully cknowledge finncil support from the Weiss Center for Interntionl Finncil Reserch t the Whrton School nd the Center for Finncil Reporting nd Mngement t the Hs School of Business. The uthors lso thnk seminr prticipnts t Americn University, DePul University, Hrvrd Business School, the University of Illinois, the University of Minnesot, the 1999 Interntionl Finnce Conference (Georgi Tech), nd the 4 th Annul Wshington Are Finnce Assocition Conference for their helpful comments nd suggestions. ** Contct uthor.

2 Estimting Exchnge Rte Exposures: Issues in Model Structure Abstrct We show tht both return mesurement horizon nd model specifiction hve noticeble impcts on estimtes of exposure from equity prices for U.S. firms. Although incresing the return horizon increses mrginlly the precision of the estimtes, the inclusion of mrket return vrible hs significnt impct on the distribution of the exposure estimtes. We demonstrte tht the construction of the mrket vrible drmticlly influences the sign nd size of the exposures due to strong reltion between firm size nd exposure for U.S. firms. We propose using CRSP cp-bsed portfolios s the control for mrket fctors nd show tht this produces exposures with stronger reltion to foreign csh flows nd correltions with firm size. JEL Clssifiction: F3 Keywords: Exchnge Rte Exposure, Mrket Portfolio

3 1 I. Introduction For the pst decde, reserchers hve been empiriclly investigting the exchnge rte exposure of firms. Much of this reserch mesures the exposure s the elsticity between chnges in firm vlue nd exchnge rte mesures. Empiriclly, the exposure elsticity is obtined from regression of stock returns on n exchnge rte chnge, often with dditionl control vribles such s mrket portfolio return. The resulting exposure estimtes for firms (or industries) tend to be very noisy nd suffer from much lower levels of sttisticl significnce thn suggested by reserchers priors. It hs been the cse, however, tht further tests hve uncovered ptterns of crosssectionl vrition in the exposure estimtes tht is brodly consistent with the predictions of csh flow models of firms with prticulr chrcteristics (see, e.g., Bodnr nd Gentry, 1993; Jorion, 1990). The difficulty in obtining sttisticlly significnt nd economiclly meningful point estimtes of exchnge rte exposure csts some doubt on the usefulness of these mrket-bsed estimtes s mesures of the exchnge rte exposure desired by prticipnts in the firm. Users of exchnge rte exposure estimtes, whether investors looking to hedge their portfolios or mngers ttempting to mke corporte risk mngement decisions, re understndbly put off by the lck of sttisticl significnce nd the questionble economic interprettion of these estimtes. Moreover, from n cdemic perspective, this problem drws into question the bsic premise of how significntly nd in wht fshion exchnge rte chnges impct firm performnce nd vlue. The mjority of exposure studies on U.S. firms shre some common methodologicl chrcteristics. Generlly, they estimte n empiricl specifiction tht includes mrket portfolio return s control vrible, nd in keeping with the stndrd prctice in the sset pricing literture, they typiclly use one-month horizon for mesuring returns. The purpose of this pper is to investigte the importnce of these fetures of model structure on the resulting estimtes of exchnge rte exposure using lrge smple of U.S. firms over the period We show tht the incorportion of mrket portfolio return vrible in the exposure model plys n importnt role in ensuring tht the estimted exposures re not unduly influenced by correlted mcroeconomic events, especilly over short horizons. However, the definition of the mrket portfolio vrible hs significnt influence on the results, mking the estimtes of the exposures model dependent nd difficult to interpret s csh flow sensitivities for corporte risk mngement decisions. This influence rises from n nomlous reltion between exchnge rte sensitivity nd firm size for U.S. firms. As for the length of the return mesurement horizon, we find, consistent with recent evidence (see, e.g., Chow, Lee, nd Solt, 1997, 1997b), tht exchnge rte exposure

4 2 estimtes re more sttisticlly significnt t longer horizons. However, lengthening the horizon beyond one month does not reduce the model sensitivity of the exposure estimtes rising from the reltion between exposure nd firm size. In response to the question of how to control for confounding mcroeconomic influences without imprting undue influence of firm size on the exposure estimtes, we propose n empiricl pproch tht uses returns to mrket-cpitliztion-bsed portfolios s the control for mcroeconomic fctors. The resulting cp-bsed exposure estimtes re distributed slightly negtively, so they re more consistent with ggregte corporte csh flow exposures. Tests lso indicte tht unlike stndrd exposure estimtes, the cp-bsed exposures show significnt correltion with firms foreign sles nd virtully no correltion with firm size. As result, we rgue tht these cpbsed exposure estimtes re better cndidtes for interprettion s mesure of firms underlying csh flow exposure to the exchnge rte. The pper is orgnized s follows: Section II discusses the bckground of exposure estimtes. Section III exmines the methodologicl issues fcing the resercher in the estimtion of exchnge rte exposure nd describes the dt. Section IV presents results of the impcts of these methodologicl choices on the estimtes of exposure nd demonstrtes the resulting reltion between stndrd exposure estimtes nd firm size. Section V proposes our lterntive pproch to estimte exposures to reduce the exposure-size reltion. Section VI summrizes nd discusses the impliction of the pper s findings for exchnge rte exposure reserch. II. Exchnge Rte Exposure A. Mesurement of Exchnge Rte Exposure The estimtion of exchnge rte exposure is reltively recent re of reserch in interntionl finnce. 1 In response to the onset of fluctuting exchnge rtes in 1973, mngers becme concerned bout the impct of exchnge rte fluctutions on firms. The erly ppers discussing exchnge rte exposures (e.g., Flood nd Lessrd, 1986; Hekmn, 1985; Hodder, 1982; Levi, 1993; Shpiro, 1974) exmine the impct of the exchnge rte on firms by modeling its impct on csh flows. From this work cme the predictions tht the csh flow sensitivity of firm to the exchnge rte should depend on the nture of the firm s ctivities, such s the extent to which it exports nd imports, its involvement in foreign opertions, the currency denomintion of its competition, nd the

5 3 competitiveness of its input nd output mrkets. 2 Most theoreticl models of exchnge rte exposures, such s Mrston (2000), suggest tht the firm s exchnge rte exposure is function of its net foreign currency revenues. 3 This theoreticl exmintion of exposure coincides with the interest of firm mngers in understnding how their csh flows re ffected by exchnge rte chnges nd how best to mnge those effects. Most of the theoreticl justifictions for firm mnging its currency risk come directly from csh flow voltility rguments (see, e.g., Froot, Schrfstein, nd Stein, 1993; Smith nd Stulz, 1985; Stulz, 1984). Thus, for the purposes of mking optiml risk mngement decisions, mngers re interested in n exposure mesure tht guges their firm s csh flow sensitivity to exchnge rte chnges. 4 This suggests mesuring exchnge rte exposure by modeling the ctul csh flows of the firm. Lewent nd Kerney (1990) demonstrte this pproch using the phrmceuticl firm Merck. From this model, the impct of exchnge rte chnges on the firm cn be simulted nd hedging decisions mde. Such method, however, suffers from the difficulty of incorporting other complexities into the model, such s competitive rections nd impcts of mrket prmeters nd structure. For exmple, Mrston (2000) shows the complexity of determining the demnd nd cost derivtives necessry for estimting the exct exposure for the simple cse of Cournot duopoly with constnt elsticity demnd functions. In generl, these pproches require significnt mounts of firm-specific nd competitor-specific informtion tht is vilble, if t ll, only to those inside the firm. Consequently, this cshflow bsed method of determining exposure, while useful in identifying the determinnts of exposure, is good only for specific situtions nd not esily pplicble to multifirm studies or lrge-scle cross-firm comprisons of exchnge rte exposures. For these sorts of studies, methodology tht uses esily ccessible informtion is needed. Adler nd Dums (1984) suggested n lterntive to the csh flow modeling pproch. They utilized the fct tht the mrket vlue of the firm is by definition the present vlue of ll future csh flows. Under this ssumption, the exposure cn be determined from the elsticity of firm vlue with respect to the exchnge rte, which in turn cn be obtined from simple regression. This pproch, which only requires the resercher to obtin 1 See Stulz nd Willimson (1997) for discussion of the vrious wys in which exchnge rte exposure cn be mesured. 2 Of course, the exposure of the firm lso depends on the extent to which the firm offsets the remining risk from these ctivities through finncil hedging. 3 For exmple, Mrston demonstrtes tht the exposure of n exporting monopolist is exctly its net foreign currency revenues. Even under other competition structures, the exposure is generlly proportionl to the net foreign currency revenue position. 4 Such view is consistent with the results of survey dt (e.g., Bodnr, Hyt, nd Mrston, 1996, 1998; Bodnr, Hyt, Mrston, nd Smithson, 1995), which overwhelmingly suggest tht mngers primry gol of hedging is to reduce voltility in csh flows, nd tht the gol of reducing voltility in firm vlue is much less importnt.

6 4 mrket dt, gretly simplifies the estimtion of exchnge rte exposures nd gives rise to the possibility of lrgescle empiricl studies on exchnge rte exposure. B. Regression Models for Exposure Estimtion Adler nd Dums (1984) define the exposure elsticity s the chnge in the mrket vlue of the firm resulting from unit chnge in the exchnge rte. This is the definition of exposure tht n investor is interested in, nd it cn lso be the definition of exposure tht the risk mnger of the firm would be interested in if the chnge in the vlue of the firm is directly relted to the chnge in the firm s expected csh flows. The beuty of the Adler- Dums pproch is tht the exposure elsticity of the firm cn be obtined from the coefficient on the exchnge rte vrible in the following regression: (1) R j = α j + δ j XR + ε j, where R j is the stock return for firm j, XR is the percentge chnge in n exchnge rte vrible, defined s the home currency price of foreign currency (HC/FC), nd δ j is the elsticity of firm vlue to the exchnge rte chnge. This elsticity indictes the firm s verge exposure over the estimtion period, in home currency units, s percentge of the firm s mrket vlue. 5 As they point out, this definition of exchnge rte exposure is simply vrince decomposition of firm s returns into component tht ws correlted with the exchnge rte chnge nd component tht ws orthogonl to exchnge rte chnges. We refer to δ j s the totl exposure elsticity of firm j. This totl exposure of firm comprises two effects. One effect is the verge chnge in the present vlue of csh flow cused by unit exchnge rte movement. This is the exposure predicted by corporte finnce/industril orgniztion optimizing models of the firm. The other effect is the nonexchnge-rte relted phenomen tht ffect vlutions nd re spuriously correlted with the exchnge rte vrible over the smple period. While some portion of this ltter effect is idiosyncrtic, portion of it includes mcroeconomic effects tht influence the vlution of ll firms, such s chnges in the risk-free rte, the mrket risk premium, nd investor sentiment tht hppen to be correlted with the exchnge rte. If these vlue-relevnt influences hve nonzero correltion with the exchnge rte 5 In mthemticl terms, the exposure of the firm s defined by Adler nd Dums is the derivtive of firm vlue with respect to the exchnge rte, dv/ds. The regression coefficient, s n elsticity then becomes dv/ds(s/v). To obtin the ctul exposure, the elsticity estimte from the regression must be multiplied by V nd converted into foreign currency by dividing by S.

7 5 over the estimtion period, they influence the estimte of totl exposure nd confound the interprettion of the estimted exposure s the csh flow effect predicted by optimizing models of firm behvior. 6 If the correltion of these mcroeconomic effects with exchnge rtes could be modeled, it would be possible to djust the totl exposure estimtes to remove this impct. However, previous reserch hs limited success in identifying consistent reltion between exchnge rtes nd observed proxies for these mcroeconomic fctors. 7 To control for other mcroeconomic influences on relized returns, most empiricl studies include return to mrket portfolio in the empiricl model. This mrket portfolio return not only controls for mcroeconomic influences but lso drmticlly reduces the residul vrince of the regression compred with eqution (1). This improves (somewht) the precision of the exposure estimtes, which hs been serious concern to prior reserchers. Thus, the commonly estimted exposure model looks like (2) R j = α j + γ j XR + β j R M + ε j, where R j is the stock return for firm j, XR is the percentge chnge in n exchnge rte vrible, R M is the return on the domestic mrket portfolio, γ j is the exchnge rte exposure elsticity of firm j, β j is the bet of the firm with respect to the mrket portfolio. Eqution (2) is generlly preferred by reserchers (see, e.g., Allynnis, 1997, 1997b; Allynnis nd Ofek, 2001; Bodnr nd Gentry,1993; Choi nd Prsd, 1995; Jorion, 1990, 1991; Willimson, 2001; Wong, 2000). 8 It is importnt to note, nd often overlooked or undermentioned in the empiricl literture, tht the definition of the exposure coefficient from eqution (2) is now different from before. The new exposure coefficient, γ j, mesures the exchnge rte exposure elsticity of the firm s the difference between the firm s totl exposure elsticity nd the mrket s exposure elsticity djusted by the firm s mrket bet. 9 Therefore, we refer to γ j s the 6 This problem does not ffect cross-sectionl evlution of the exposure estimtes, becuse the effect is common to ll firms nd fll out when considering the reltive exposures s opposed to the bsolute exposure. 7 Most studies trying to consistently link exchnge rte chnges with other mcroeconomic vribles hve limited success. This is consistent with the common view tht exchnge rtes evolve s rndom wlks. The literture tht investigtes the pricing of exchnge rte risk suggests tht ny risk premium of exchnge rtes is significntly time vrying nd difficult to predict. 8 An ltertive is to control for confounding mcroeconomic events using predetermined vribles. Chow et l. (1997) pply the three Fm- French fctors in their studies. However, it should be noted tht this pproch only controls for the expected prt of future confounding mcroeconomic events. Nevertheless, our results to be discussed in Section IV remin qulittively similr when this lterntive pproch is dopted. 9 This cn be shown s follows: Using mtrix nottion, where F is T by 2 mtrix of the constnt term nd the exchnge rte chnge vrible nd R j is T by 1 vector of stock returns for firm j, the sttisticl definition of the coefficients from eqution (1) is

8 6 residul exposure elsticity of the firm. The reson for this is tht the incorportion of the mrket return in the model lso controls for the mrket portfolio s own exchnge rte exposure. The estimted residul exposure elsticity differs from the totl exposure elsticity whenever the mrket portfolio hs nonzero exposure to the exchnge rte. In such cse, the distribution of residul exposure elsticities is shifted reltive to the totl exposure elsticities. The exposure of the mrket portfolio consists of two fctors. One is the vlue impct of mrketwide mcroeconomic fctors or other non-csh-flow-relted vlue impcts common cross ll firms tht hppen to be correlted with the exchnge rte over the smple period. The other fctor is the weighted-verge vlue impct of the exchnge rte chnges on the firms csh flows. Thus, the residul exposure estimtes re mesured reltive to both the common mcroeconomic influence on vlue tht hppen to be correlted with the exchnge rte nd the chnges to the weighted-verge mrket csh flow rising from the exchnge rte movement. Becuse of this mesurement of the firm exposure estimte, if the mrket portfolio used s control for mcroeconomic fctors hs nonzero exposure, the interprettion of firm hving zero residul exposure does not men the sme thing s hving zero totl exposure. This is significnt point becuse the empiricl result of hving zero exposure is often given the economic mening in nlysis tht the firm tht is not ffected by exchnge rte chnges. However, zero residul exposure implies firm hs the sme exposure s the mrket portfolio. Since it is unlikely tht the exposure of the mrket portfolio used to control for mcroeconomic effect will be zero, the choice of mrket portfolio in the exposure regression directly impcts the size nd the interprettion of the resulting exposure estimtes. This mens tht the choice of the mrket portfolio in the specifiction of eqution (2) is significnt decision for the resulting firm-specific exposure estimtes. We might expect this to be problem. The common prctice of using vlue-weighted mrket portfolio s the mcroeconomic control in eqution (2) gives more importnce to lrge firms. Becuse these firms re more likely to be multintionl nd export oriented (net sellers in foreign currency), they should see their csh flows (E1) δ j = (F`F) -1 F`R j, where δ j is the vector coefficient estimtes of the intercept term (α j ) nd the totl exchnge rte exposure elsticity (δ j ). With the inclusion of the mrket return s T by 1 vector M in eqution (2), the sttisticl definition of the exposure coefficient (including the constnt term) is (E2) γ j = (F`F) -1 F`R j (F`F) -1 F`M β j. See prtitioned regression in Greene (1990), mong others. The first term of γ j is the sme s the δ j from (E1), but this is reduced by the second term. Since the term (F`F) -1 F`M is simply the coefficients from the regression of the mrket return on constnt nd the exchnge rte chnge (i.e., the exchnge rte exposure elsticity of the mrket portfolio), if we define these coefficients s δ M, we cn rewrite (E2) s (E3) γ j = δ j δ M β j.

9 7 increse when the home currency deprecites, thereby generting more negtive mrket portfolio exposure. 10 Alterntively, more eqully weighted mrket portfolio gives more importnce to smll firms. As these firms re more likely to be import oriented or non-trded-goods producers, they should see their csh flows rise when the home currency pprecites, thereby generting more positive mrket portfolio exposure. This suggests tht we should expect differences in the exposures of vlue-weighted versus equl-weighted mrket portfolios. 11 If these differences in exposure re significnt, then the choice of different mrket portfolios in the empiricl model would led to differences in the distributions of the residul exposures. This would led to different interprettions bout the impct of exchnge rtes on firms csh flows conditionl on the construction of the mrket portfolio used to control for mcroeconomic events. In Section IV, we investigte the importnce of this issue long with other issues previously discussed in the pper. III. Dt nd Reserch Design Our study covers the 20-yer period from 1977 to We choose not to include the most recent yers for severl resons. First, in 1997 nd 1998, severl mjor currency crises in the emerging mrkets generted significnt economic turmoil tht disproportiontely ffected certin U.S. firms. Second, 1999 sw the introduction of the euro s currency with the locking of most Europen bilterl rtes, which led to the discontinution of the clssic benchmrk G-10 exchnge rte index. Third, lte 1999 nd 2000 sw stock price bubble in the technology, medi, nd telecommunictions sectors in the United Sttes tht mkes meningful exchnge rte exposure estimtion difficult for these firms. Finlly, given the drmtic rise in merger ctivity in the lte 1990s, the smple of firms with continuous history over the period drops off drmticlly. Consequently, we select the firms for our study from the 1996 CRSP NYSE/AMEX monthly stock file. To be included in the finl smple, firm must hve monthly stock prices/returns covering the period Jnury 1977 through December This selection criterion results in 910 firms. Monthly return dt on individul stock, vlue- nd equl-weighted NYSE/AMEX mrket portfolios, nd It is pprent tht this modified exposure coefficient differs from the clssic definition of exposure (Adler nd Dums, 1984) by the product of two esily identifible terms: the exposure elsticity of the mrket nd the mrket bet of the firm. 10 This is true whether the mrket portfolio is domestic or globl, lthough some lrge globl firms my increse in dollr vlue when the U.S. dollr pprecites, so the impct might not be s significnt. 11 In support of this clim, Chow, Lee, nd Solt (1997) show tht the CRSP vlue- nd equl-weighted mrket portfolios exhibit different exposures to exchnge rte movements, nd Chow, Lee, nd Solt (1997b) show tht exposures of U.S. multintionl firms flip signs s the horizon increses beyond 12 months. However, in both cses, they use n empiricl specifiction tht does not use mrket portfolio return or ny other vrible s control for contemporneous mcroeconomic events.

10 8 NYSE/AMEX size portfolios re retrieved from the CRSP files. The exchnge rte chnge vrible, XR, is computed s the return on the Federl Reserve s U.S. dollr trde-weighted index (Federl Reserve System, 1978). Figure 1 plots the exchnge rte index over the smple period. By construction, n increse in the currency index corresponds to rel pprecition of the U.S. dollr. All nominl return dt re converted to rel mesures using the monthly U.S. nd G-10 foreign consumer price indexes tken from the Interntionl Finncil Sttistics Dtbse of the Interntionl Monetry Fund. 12 The estimtion is undertken for the full 20-yer period nd four 5-yer subperiods. The four sub-periods re 77/01 81/12, 82/01 86/12, 87/01 91/12, nd 92/01 96/12. For ech of these smple periods, we estimte currency exposures over multiple return horizons. We begin with the stndrd empiricl finnce return horizon of one month, but we exmine exposure estimtes over longer return horizons becuse it is possible tht exposures my be more ccurtely estimted over longer horizons due to the complexity of fctors influencing exposures nd the noise in high-frequency observtions of exchnge rtes reltive to the persistence of low-frequency movements. 13 The mjority of firm s csh flow exposure rises from chnges in future s opposed to current csh flows in response to n exchnge rte chnge, since these djustments tke time to become fully pprent; therefore, longer horizons should provide better estimtes of exposure. For horizon of one month, the estimtion is bsed on nonoverlpping monthly observtions. Long-horizon returns re continuously compounded over the corresponding intervl, nd the estimtion is bsed on overlpping monthly observtions. The use of overlpping observtions is common in long-horizon regressions in which the vribles of interests re generted by compounding the more finely smpled dt to investigte the long-term reltion mong the vribles. Efficiency is improved becuse overlpping observtions llow the time-series properties of the finely smpled dt to be incorported into the estimtion (Richrdson nd Smith, 1991). We correct for the seril correltion induced by the use of overlpping observtions using the method of Newey nd West (1987). Moreover, we conduct ll significnce tests t the 5% level for ech til, with the degree of freedom equl to the number of nonoverlpping observtions (rther thn the 12 We run our tests on smple period from 1997 to 2000; however, we hve fewer firms (668 out of 910) in the smple due to mergers nd cquisitions, nd we re forced to use n lterntive exchnge rte mesure becuse the Federl Reserve Bord discontinued the clssic G-10 index in 1999 due to EU monetry integrtion. Consequently, results from this period re not directly comprble to those in the pper. However, the sme bsic ptterns for the totl nd residul exposure reported in the pper re found with the exposures in this subsmple. Interestingly, the rolling mrket exposures for the vlue-weighted, equl-weighted, nd globl mrket portfolios continue the decline reltive to those reported for the end of our smple period. This suggests tht fctors relted to incresed stock mrket vlution were incresingly negtively correlted with the vlue of the U.S. dollr over the period. 13 In support of longer-horizon view, evidence by Chow, Lee, nd Solt (1997b) suggests tht the exposure of U.S. firms becomes much more detectble when the return horizon is extended beyond 12 months. Their pper looks t mesure of totl exposure for smll set of U.S. multintionl firms (N=213). We consider the impct of horizon issues in vriety of model specifictions s well s for much lrger set of firms (N=910).

11 9 ctul degree of freedom). This conservtive pproch mkes it more difficult to reject the null hypothesis of no exposure to exchnge rte fluctutions nd is dopted to ensure tht our findings re not driven by limited number of nonoverlpping observtions in long-horizon regressions, especilly over the four 5-yer subperiods. IV. Empiricl Findings In this section, we exmine the effect of possible differences in methodology (outlined in Section II.B) on the estimtion of exposure elsticities. In doing so, we consider summry sttistics for the distribution of exchnge rte exposures t the vrious horizons s well s the percentge of firms with sttisticlly significnt exposure to exchnge rtes nd the proportion of these firms tht gin versus lose from pprecition of the U.S. dollr. In ddition, we consider the stbility of the exposure estimtes cross multiple subperiods. A. Firm-Level Totl Exchnge Rte Exposures We begin by considering the originl pproch suggested by Adler nd Dums (1984), in which mrket portfolio return is not included in the model nd the exposure estimted is the totl exposure elsticity. Pnel A of Tble I nd the top plot in Figure 2 show tht the men nd medin totl exposure estimtes re positive for the 1- month-return horizon nd increse through the 6-month horizon, only to fll nd become negtive for horizons of 15 to 24 months, nd finlly switching bck to positive t the 36-month horizon nd beyond. This shifting of the distribution of exposures over different return horizons is troubling becuse it suggests tht the sign of the verge totl exposure elsticities is not independent of the return horizon. In ddition, Pnel A of Tble I indictes tht the percentge of firms with sttisticlly positive nd negtive exposures vries noticebly over different horizons. For short horizons, the percentge of firms with significnt positive exposures drmticlly outnumbers the firms with significnt negtive exposures. Beyond 12-month-return horizons, we find more significnt negtive exposures thn positive exposures, only to see them equl out beyond 24-month-return horizons. As for the overll percentge of significnt exposures, we find only bout 15% of firms with significnt exposure elsticity t 1-month through 18- month horizons, with the percentge of significnt exposures incresing from 20% to 50% s horizons increse from 24 to 60 months. Another troubling finding on totl exposure elsticities is the time vrition in the estimtes cross subperiods. The lower plot of Figure 2 shows tht the mens re highly voltile cross the four subperiods. The third

12 10 subperiod, especilly, produces positive exposure estimtes tht defy belief in terms of csh flow sensitivities. Results (not tbulted) indicte tht the men exposure elsticities re generlly significntly negtive in the first two periods but sttisticlly positive in the lst two periods. Moreover, for horizons of three months nd longer, twothirds of the 910 firms hd significntly positive exposures for the third subperiod, while less thn 10% of these firms hd positive exposures during the previous five-yer period. The theoreticl corporte finnce models require us to explin this shift in terms of the interntionl ctivities of the firms or chnges in the competitive structure of their mrkets. However, this shift ppers too lrge, too sudden, nd too widespred to be explined by csh-flowrelted chnges in firm structures or chnges in the competitive environment. This result highlights drwbck of the totl exposure estimtes, s discussed in Section II.B. In prticulr, totl exposures cpture not only the csh flow-relted exchnge rte exposure but lso the reltion between exchnge rte chnges nd other mcroeconomic fctors tht influence the mrket vlue of the firm. B. Firm-Level Residul Exchnge Rte Exposures In prctice, reserchers hve tended to estimte exposure elsticities by including mrket portfolio return in the model specifiction. This modifiction to the bsic Adler nd Dums (1984) specifiction hs two beneficil effects. First, including the mrket return reduces the residul vrince of the regression, thereby improving the precision of the exposure elsticity estimtes. Second, nd more importntly, the mrket return implicitly controls for the vlue-relevnt mcroeconomic fctors tht re correlted with the exchnge rte. This improves our bility to interpret the resulting exposure elsticities in terms of the corporte-finnce-bsed models s csh flow sensitivities importnt for risk mngement decisions. However, s discussed in Section II.B, the inclusion of the mrket portfolio chnges the interprettion of the estimted exposure elsticity to residul exposure. A zero exposure no longer implies tht the firm s vlue is independent of exchnge rtes; rther, it implies tht the firm vlue is ffected to the sme degree s the mrket portfolio. A similr logic pplies when interpreting positive or negtive residul exposure estimtes. Pnel B of Tble I nd Figure 3 show the results for the distributions of the residul exposure estimtes for model including the return to the CRSP U.S. vlue-weighted index s the mrket portfolio. 14 We see severl 14 Results, not tbulted, indicte tht the verge mrket bet of the smple firms is close to one nd the mjority of the firms hve significntly positive bet s expected. However, for horizons of 36 months or longer, smll number of the firms re found to hve sttisticlly negtive bet.

13 11 different fetures compred with the totl exposure elsticities discussed erlier. First, the tble indictes tht the men nd medin exposures re positive t ll horizons nd increse monotoniclly with the return horizon. Figure 3 illustrtes tht both the medin nd the spred of the distribution increse stedily with horizon; the sme pttern holds in the subperiods. Second, the significnt jump in the distribution of totl exposures for subperiod three, seen in Figure 2, is not s pprent in Figure 3. This demonstrtes tht the residul exposure estimtes re more stble thn the totl exposure estimtes over time, which is consistent with the mrket portfolio, removing much of the influence of common mcroeconomic vlue shocks correlted with the exchnge rte. Third, the totl percentge of firms with significnt exposure estimtes is higher thn tht for totl exposure estimtes reported in Pnel A of Tble I. The totl percentge is round 20% to 25% for horizons of 1 to 21 months but increses up to 60% for the 60-month horizon. This is consistent with it tking long time for the mrket to fully incorporte exchnge rte chnges into firm vlue. The finding of better exposure identifiction t long horizons is lso consistent with the finding of Chow, Lee, nd Solt (1997b) tht much greter numbers of significnt exposures occur over longer return horizons. 15 A noticeble difference from Tble I is tht firms with significnt positive exposures outnumber firms with significnt negtive exposures by more thn two to one. 16 This finding is economiclly troubling becuse it implies tht most U.S. firms experience gins (reltive to the mrket) when the U.S. dollr pprecites. As mesure of the csh flow exposure of these firms to the exchnge rte, this interprettion is t odds with the results of studies on the reltion of reported profits of U.S. corportions with the exchnge rte. For exmple, Clrid (1997) demonstrtes tht the U.S. dollr rise in the erly 1980s reduced US mnufcturing firms profits by 25% while the subsequent fll boosted profits by 30%. Along these sme lines, Hung (1992) reports tht the upwrd swing in the dollr during the 1980s resulted in profit loss to U.S. mnufcturing firms of $23 billion per yer. Finlly, Uctum (1996) estimtes tht 1% deprecition of the U.S. dollr leds to nerly 1% increse in the dollr vlue of overses profits of U.S. corportions. Tken together, these findings would led one to expect tht if stock prices were directly relted to 15 It is lso interesting to note tht the findings of positive men exposure nd 20 25% of firms with significnt exposure estimtes for the United Sttes re consistent with the results documented in previous studies tht employed the sme model specifiction nd monthly returns (see, e.g., Allynnis, 1997; Bodnr nd Gentry, 1993; Jorion, 1990; Wong, 2000). 16 Results, not tbulted, show tht the men nd medin exposures for the subperiods re similr to those for the full period. Except for few cses, the men nd medin estimtes re positive in ll periods nd ll horizons. In generl, more firms re found to be significntly positively exposed to dollr pprecitions thn re found to be significntly negtively exposed. However, in subperiod 1 (Jnury 1977 through December 1981), the percentge of firms with negtive currency exposure elsticities is higher thn tht with positive exposures for the longer horizons. The subperiods do seem to improve the bility to estimte sttisticlly significnt exposures s, for horizons longer thn three months, the percentges of firms with significnt exposure estimte (positive or negtive) in the subperiods re higher thn the corresponding numbers in the full period.

14 12 csh flows, the exchnge rte exposures estimted from equity prices should lso revel this negtive reltion, on verge. We ddress this counterintuitive finding through the mesurement of the mrket portfolio. Since most studies look t the verge of the residul exposures, giving ech estimte equl weight, it might mke more sense to control for the verge mcroeconomic effect by giving ech firm equl weight in the mrket portfolio included in regression model. The use of n eqully weighted mrket portfolio trets ech firm s exposure eqully in terms of determining the mrket exposure. This choice lso leds to the convenient fct tht the residul exposures cross ll the firms in the mrket portfolio must sum to zero (s the verge of the exposures in the mrket cptured in the equl-weighted mrket exposure). Pnel B of Tble I reports summry results for the estimtes of the U.S. equl-weighted (EW) mrket bsed exposures longside the comprble results from the U.S. vlue-weighted (VW) mrket exposure model. First, both the men nd medin exposure estimtes re now negtive, nd they become more negtive s the return horizon increses. Figure 4 depicts this finding grphiclly. Compring Figure 4 with Figure 3, one cn observe tht, except for subperiod 1, the distributions re shifted downwrd nd the medin exposures re negtive. Second, firms with significnt negtive exposures outnumber positive exposures by nerly two to one. These two findings pper more consistent with the results of forementioned studies tht relte profits nd exchnge rte chnges. However, the use of the EW mrket does not improve the totl number of firms with sttisticlly significnt exposures. As before, for return horizons out to 21 months, we find between 20% nd 25% of firms with sttisticlly significnt exposure elsticities. Agin, t 24 months nd beyond, we see greter bility to detect significnt exchnge rte exposures. 17 Given tht we re exmining n exposure with interntionl dimensions, n rgument could be mde tht we should control for mcroeconomic fctors using world mrket portfolio return. This cretes nother possible empiricl specifiction for the exposure regression. Pnel B of Tble 1 reports the exposure elsticity estimtes for our smple of U.S. firms using the U.S. dollr return to the Morgn Stnley Cpitl Interntionl (MSCI) world mrket portfolio return s the control for mcroeconomic effects. As this is vlue-weighted index of lrge firms in the world s mjor equity mrkets (including predomintely U.S. firms), we would expect to see results similr to those for the VW U.S. mrket portfolio. In fct, we see tht the distribution of firm-level exposures with this specifiction is shifted noticebly in the positive direction. The mens nd medins for the exposure elsticities re 17 The subperiods tell similr story. The men nd medin exposures for most subperiods re negtive, especilly t short return horizons. As the return horizons grow, the mens (nd occsionlly the medins) for the first subperiod become sttisticlly positive. In ll subperiods but the

15 13 in the 0.40 to 0.60 rnge for ll horizons. Not surprisingly, the percentge of significnt exposure is tilted strongly towrd the positive side. Figure 5 displys the distributions of the firm-level exposures for the full period s well s the four subperiods. In ll subperiods, we see noticeble positive shift in the distributions reltive to either the totl exposures or the reltive exposures controlling for mcroeconomic fctors with either of the U.S. mrket indices. These comprisons, from Pnel B of Tble I nd Figures 3, 4, nd 5, re striking. We obtin completely different economic interprettion regrding the exposures of the smple firms bsed on the choice of the mrket portfolio included in the empiricl model. Clerly, the choice of the mrket portfolio does mke different in one s inference. While the estimtion horizon lso plys role in the estimtion of the exposure elsticity, the impct of the choice of including mrket portfolio ppers to hve much more substntive impct. In the next subsection, we exmine the reson why the choice of mrket portfolio to include in n exposure regression hs such significnt effect on the resulting exposure elsticity estimtes. C. Portfolio-Level Exchnge Rte Exposures As we mention in Section II.B, firm s residul exposure is the difference between its totl exposure nd the product of the mrket s exposure to the exchnge rte nd the firm s mrket bet. Thus, the difference in the sets of exposure estimtes must rise from differences in the three mrket portfolios exposure to the exchnge rte index. Given tht the entire distribution of firm-level exposure elsticities using the U.S. VW mrket nd MSCI world mrket portfolio returns s control vribles re more positive thn re those using the U.S. EW mrket return, it must be the cse tht the U.S. EW mrket itself hs more positive exposure t ll horizons thn the two VW mrket portfolios. In Pnel A of Tble II, we report the estimtes of the currency exposures of ll three mrket portfolios used previously over the full smple period. As we nticipted, the exposures of the mrkets portfolios re drmticlly different. The exposure of the U.S. EW mrket portfolio is everywhere positive, the exposure of the MSCI world mrket portfolio is everywhere negtive, nd the U.S. VW mrket portfolio strts out smll positive but drifts negtive s the horizon increses. The positive exposure for the U.S. EW mrket portfolio is consistent with the greter weight given to smller firms tht re more likely to be in nontrded sectors or be net importers nd thereby gin when the U.S. dollr pprecites. The lrge negtive exposures for the MSCI world mrket portfolio is first, the percentge of firms with sttisticlly significnt negtive exposure to dollr pprecitions is greter thn tht of those with significnt

16 14 consistent with the greter weight plced on lrge firms with firms locted in other countries whose U.S. dollr vlue rises purely s result of the trnsltion effect when the U.S. dollr deprecites. The exposure of the U.S. VW mrket portfolio is not surprisingly in between these, becuse it only contins U.S. firm but plces most of its weight on the lrgest firms, which re lso some of the U.S. firms contined in the MSCI world mrket portfolio. Figure 6 plots five-yer rolling estimtes of the exposures of these three mrkets t horizons of 1, 3, 6, nd 12 months. From the plots, it is pprent tht the exposures of these mrkets re not very constnt over time; insted, ll disply similr time-vrying pttern. The exposure elsticities of the mrkets re negtive in the erly prt of the smple period nd become positive in the mid- to lte 1980s nd decrese gin into the 1990s. These chnges in mrket exposure re quite drmtic, from n exposure elsticity of pproximtely 1 to +1. It would pper tht these chnges re too lrge nd too sudden to be explined by chnges in the foreign currency csh flow position of the firms. These chnges in exposure elsticity suggest tht within decde, the net foreign currency csh flow position of the firms in the U.S. mrket chnged from being net long foreign currency in n mount equl to totl mrket cpitliztion (circ 1980) to being net short foreign currency in n mount equl to totl mrket cpitliztion (circ 1990). 18 Interestingly, roughly the sme chnge in exposure is pprent for the firms in the MSCI world mrket portfolio. This similr pttern for the world mrket supports the view tht in ddition to mesuring the csh flow sensitivity of firms to exchnge rtes, these mrket exposures re cpturing time-vrying correltions between the chnges in the exchnge rte index nd other determinnts of mrket vlue, such s required rtes of return nd expected growth rtes, or other common influences on firm csh flows tht re spuriously correlted with exchnge rte chnges. Attempting to provide n economic explntion for these common ptterns of mrket exposures is n interesting question but not the focus of this investigtion. The importnt point to glen here is tht controlling for these lrge chnges in mrket exposure, which re not relted to csh flow impcts of exchnge rte, will be n importnt issue when estimting exposure elsticities of firms over longer smple periods. Despite the common time vrition, the figures reinforce the fct tht the mrket exposures t ny point in time hve different levels of exposure. An obvious explntion for the difference in the exchnge rte exposures of the different mrket portfolios is systemtic difference in the exposure of firms bsed on size. This explntion does hve some intuitive ppel becuse of generl differences in the operting structures of lrge nd smll firms. positive exposure to dollr pprecitions.

17 15 In prticulr, lrge firms re more likely to be multintionls or lrge exporters, hve net foreign currency revenues (long foreign currency position), nd gin when the U.S. dollr deprecites. Moreover, foreign firms re mechniclly likely to see their U.S. dollr vlues increse when the U.S. dollr deprecites (unless their vlues re significntly negtively ffected by the rel dollr deprecition). On the other hnd, smll firms re more likely to be non-trded-goods producers nd therefore potentil net importers (short foreign currency position) who gin when the U.S. dollr pprecites. To explore this size explntion more generlly, we estimte the totl exchnge-rte exposure elsticities of the CRSP U.S. cpitl-bsed decile portfolios. Summry sttistics of the estimted exposure elsticities for the Size 1 (lrgest), Size 4, Size 7, nd Size 10 (smllest) decile portfolios re reported in Pnel B of Tble II. Figure 7 displys the results in visul form. The portfolio exposures re monotonic in terms of the mrket cpitliztion t ll horizons. The Size 1 (lrgest) portfolio strts with n exposure elsticity of zero nd becomes progressively more negtive, becoming significntly negtive t the 21-month horizon. In contrst, the Size 10 portfolio strts off with significntly positive exposure elsticity, which decreses slightly, becomes insignificnt through 21 months, nd then increses rpidly, becoming significnt gin t 24 months onwrd. 19 Pnel B lso reports tests on the significnce of the difference between the exposure of ech of the smller portfolios nd tht of the Size 1 portfolio. Generlly, the difference between the exposures of the Size 1 nd Size 10 portfolios re sttisticlly significnt. To investigte whether this size-exposure reltion is due to systemtic differences in foreign csh flow positions of different-sized firms rising from opertionl differences, we crete portfolios with our own sizes from the subset of firms tht pper on the Compustt geogrphic segment dtbse. This smple is only subset of the CRSP decile portfolios previously studied, becuse not ll firms re reported on the Compustt geogrphic segment dtbse nd its dt only begin in Nonetheless, we construct ten mrket cpitliztion portfolios from these firms bsed on their mrket cpitliztion in the beginning of the yer. For ech smple yer nd decile size, we further divide the firms into three subportfolios bsed on their reported foreign-nd-export sles rtio. We denote these subportfolios s high, low, or zero foreign sles. We compute the foreign-nd-export sles rtio s the sum of 18 This results from the fct tht the ctul csh exposure is equl to the exposure elsticity times the mrket vlue of the portfolio. 19 The impressive monotonicity of the exposures cross size breks down somewht when we look t subperiods. In the first subperiod, cross return horizons of 9 to 18 months, we ctully see the pttern reversed. Typiclly, the lrgest firms hve the most positive exposures nd the smllest firms hve the most negtive exposures (t lest for return horizons beyond 6 months). One explntion for this reversl of the reltion between size nd exposure is tht the first subperiod ( ) ws the noted smll-cp mrket boom in the U.S. During this period, smll-cp stocks significntly outperformed lrge-cp stocks. Moreover, during this period, the U.S. dollr deprecited on verge, leding to the observed stronger negtive exposure for the smll-cp stocks. For the second subperiod, the Size 4 portfolio hs the most negtive exposure elsticities.

18 16 foreign sles nd export sles scled by totl firm sles. We use this foreign-nd-export sles rtio s proxy for the underlying csh flow exposure of the firm. The portfolios monthly returns re clculted s the vlue-weighted verge of the returns for the firms in the subportfolios. Tble III reports selected sttistics on 12 of these subportfolios; the full set of high, low, nd zero foreign sles for groups of selected sizes: 1 (lrgest), 4, 7, nd 10 (smllest). Results not tbulted show tht the numbers of firms in ech decile portfolios rnge from 103 firms in 1979 to 220 firms in Tble III indictes tht the (timeseries) verge number of firms without foreign ctivities (0% FGN sles) increses s we move from the lrge-cp (Size1) to the smll-cp (size 10) group. For the high (or low) foreign-nd-export sles portfolios, the vlueweighted verge of foreign-nd-export sles rtio decreses from 50.4% (14.8%) in the size 1 portfolio to 31.5% (1.0%) in the size 10 portfolio. Tken together, these sttistics re consistent with our erlier conjecture bout the chrcteristics of the U.S. CRSP cpitl-bsed decile portfolios in tht size is correlted with the extent of multintionlity. Tble IV reports nd Figure 8 displys the totl exposure estimtes for these 12 portfolios of different size nd foreign-nd-export-sles rtio. The csh flow model of exposure would suggest negtive exposures mong portfolios with high foreign sles nd positive exposures mong portfolios with zero foreign sles, regrdless of firm size. However, the results show tht the lrgest firms (Size 1) exhibit mostly negtive exposure estimtes, while the smller firms (Size 7 nd Size10) lwys exhibit positive exposure estimtes regrdless of the degree of foreign opertions. The fnning of exposure estimtes cross size portfolios, shown erlier in Figure 7, still exists for ll conditionl foreign sles groupings shown in Figure 8. As simple indiction of the importnce of firm size reltive to the foreign sles rtio for exposure, notice tht the lrge firm (Size 1) zero foreign sles portfolio exposures re everywhere more negtive thn the exposure elsticities of the smller (Sizes 4, 7, nd 10) high foreign sles portfolios, despite ech of these subportfolios hving verge foreign sles rtios of more thn 30%. Finlly, s simple test of whether firm size mtters for the exposure once one controls for csh flow effect (using the foreignnd-export sles rtio), the Diff. columns in Tble IV report the significnce level of the test of ech exposure reltive to tht of the corresponding size 1 portfolio. It is cler tht, especilly for the Size 10 portfolio, mny of the exposure differences cross firm sizes re sttisticlly significnt, even fter controlling for the csh flow effect. Finlly, the monotonicity of the full smple pttern reppers in subperiods 3 nd 4. However, in these subperiods, the exposures of portfolios of

19 17 V. Size Effect in Exchnge Rte Exposures A. Size Effect in Firm-Level Exchnge Rte Exposures To exmine whether the portfolio-level size effect previously documented crries to the firm-level exposure estimtes, we run cross-sectionl regression of our smple firms exchnge rte exposure estimtes on the firms foreign-nd-export sles rtio nd mrket vlue. Becuse the exposure estimtes re estimted over certin period, we use the verge foreign-nd-export sles rtio nd verge mrket vlue over the sme period. The foreign-ndexport sles rtio is n ccounting proxy for the firms csh flow exposure. While csh flow models of exposure suggest tht the exposure should be relted to net foreign currency position (see, e.g., Mrston, 2000) becuse firms only report foreign currency revenues nd not costs, we hve no choice except to use this crude proxy for the underlying determinnt of exposure. 20 The results of this cross-sectionl regression for the full period re displyed in Tble V. It is immeditely pprent tht t ll horizons, both the foreign-nd-export sles rtio (% FGN sles) nd firm size (MVE) re importnt for explining cross-sectionl differences in exposures. The foreign-nd-export sles rtio is everywhere significntly negtively relted to exposure. The negtive reltion implies tht firms experience higher returns when the dollr deprecites, which is consistent with economic intuition becuse dollr deprecitions increse the vlue of foreign csh flow strems. On the other hnd, the significntly negtive coefficient on MVE indictes tht lrger firms hve exposures tht re more negtive, independent of their foreign sles rtio, just s we sw in the previous portfolio-level nlysis. In ll cses, however, these two vribles only explin smll percentge of the totl crosssectionl vribility of exposure estimtes. The djusted R 2 is only bout 5% to 6%. 21 B. Controlling for the Size Effect in Firm-Level Exchnge Rte Exposures Hving demonstrted size effect in exposures tht complictes their interprettion s mesures of exchnge-rte-relted csh flow impct, we suggest method of estimting exposures tht mitigtes this problem. ll sizes re everywhere positive. 20 Foreign-nd-export sles rtios hve been identified s n importnt determinnt of exposures in previous cross-sectionl tests (see, e.g., Jorion, 1990). 21 The cross-sectionl regression results should be interpreted with cution. First, we ssume tht the foreign sles rtio dequtely cptures firms underlying csh flow exposures, n imperfect ssumption without the ddition of unreported foreign currency cost informtion. Second, we do not control for finncil hedging becuse dt on corporte use of derivtives re not vilble during most of the smple period (Informtion bout the notionl mount of outstnding finncil derivtives ws first vilble in 1991, while improved disclosures were required in However, evidence in Wong, 2000, suggests tht the usefulness of these disclosures is limited.) However, the finding of negtive coefficient on MVE cnnot be due to the filure to control for hedging becuse it should hve positive effect on currency exposure.

20 18 Our pproch is to use n pproprite size of portfolio s the control for the mcroeconomic fctors discussed erlier. Doing so llows us to control for both the mcroeconomic fctors tht influence firm vlue nd the size effect in currency exposure. For prcticl purposes, we use the CRSP U.S. cpitl-bsed decile portfolios s the size of portfolios. Thus, insted of using common mrket portfolio return in the exposure regressions, we use mtching CRSP cp-bsed portfolio return s the mcroeconomic control for ech firm. Tble VI shows summry sttistics of the distributions of the exposure elsticities for our originl smple of 910 firms, controlling for the mtching CRSP cp-bsed portfolio. The distributions of exposures re similr, lbeit shifted slightly more to the negtive side, compred with those reported in Pnel B of Tble I using the U.S. CRSP EW mrket portfolio. The men nd medin cp-bsed exposures re modertely negtive t most horizons nd lwys sttisticlly significnt. Moreover, the percentge of firms with significnt negtive exposures is lrger thn tht of firms with significnt positive exposures. Given tht our smple selection criterion bises us towrd selecting mture firms more likely to be interntionl, we expect our smple firms, on verge, to disply slightly negtive residul exposure reltive to their size-mtched peers. This view is confirmed by the fct tht regrdless of horizon, the verge exposure (bet) of our smple firms to their mtching CRSP cp-bsed portfolios is slightly less thn one. Further, most of the firms re significntly positively exposed to the size-mtched portfolio, especilly t short horizons. 22 To demonstrte tht the pproch of using size-bsed mrket control portfolio reduces the reltion between the estimted exchnge rte exposures nd firm size, we cross-sectionlly regress the exposures on foreign sles nd firm size s previously. The results of this regression re shown in Tble VII. From the tble, one cn immeditely see tht with the exception of the 1-, 3-, nd 60- month horizons, MVE is no longer significntly relted to the exchnge rte exposure estimte. In ll cses, the foreign sles rtio remins significntly negtively relted to exposure, s previously. However, in compring the results here with those of Tble V, the foreign sles rtio is more strongly relted to the exposures controlled by decile size thn those controlled by the VW mrket. 23 In sum, Tbles VI nd VII show tht our proposed pproch genertes exchnge rte exposure estimtes tht exhibit two ppeling properties. First, they re less ffected by the size effect tht influences the trditionl exposure estimtes so tht economiclly meningful cross-sectionl nlysis cn be crried out on them. Second, 22 The subperiod results re similr to those for the full period. The men nd medin exchnge rte exposure estimtes re lwys negtive in ll periods nd ll horizons, except for the fourth subperiod. With few exceptions, the percentge of firms with sttisticlly significnt negtive exposures to dollr pprecitions is greter thn the percentge of those with significnt positive exposures.

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