Foriegn Exchange Exposure Evidence from France

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Foriegn Exchange Exposure Evidence from France

Category : Articles

ASYMMETRIC FOREIGN CURRENCY EXPOSURES AND DERIVATIVES USE: EVIDENCE FROM FRANCE Ephraim Clarka, Salma Meftehb?
a

Middlesex Business School, Lille Graduate School of Management Research Center

b

ESSCA Business School, 1 rue Lakanal, 49003, Angers and Paris-Dauphine University, 75775 Paris, FRANCE.

?

Corresponding author. Tel: +33 (0)2 41 73 47 39; Fax: +33 (0)2 41 73 47 45; E-mail: [email protected]

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ABSTRACT This paper provides evidence on the asymmetric sensitivity of stock returns of French firms to exchange rate risk and the effect of FC derivative use in alleviating this risk. The results show that exposure is frequently asymmetric and asymmetric exposure is sensitive to different currency exposures. Cross sectional analysis provides evidence that FC derivatives use has a significant effect on reducing FC exposure levels. This effect is sensitive to the hedging strategy and it varies with respect to different currencies.

Keywords: Foreign currency exposure, Foreign currency hedging, Derivatives, Firm value. JEL Classification: F31; G32.

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1. INTRODUCTION Corporate use of derivatives to hedge foreign currency exposure has become standard practice for firms with foreign operations or commercial interests.1 The conception and implementation of a FC hedging strategy requires a commitment of financial, physical and human resources that can represent significant costs for the firm. According to the positive theory of corporate hedging developed by Smith and Stulz (1985), these costs can be justified only if imperfect capital markets create conditions where corporate hedging reduces exposure and adds value to the firm. The key question for shareholders, then, is whether hedging does, in fact, reduce exposure and add value to the firm. Given the complex relationships between exchange rates and other economic factors, such as relative prices, income, expenditure, interest rates, supply and demand, to mention only a few, designing and implementing an effective FC hedging strategy is difficult, at best. This brings up the importance of understanding the nature of FC exposure and identifying the strategies that are most effective in reducing it.

Exchange rate fluctuations have long been recognized as an important source of macroeconomic uncertainty that can have a significant impact on firm value.2 There is also a substantial literature on the foundations of currency risk exposure analyzing the parameters and transmission mechanisms that determine a firm??™s sensitivity to exchange rate

This is well documented in the corporate hedging literature. For US firms, there are studies such as Wysocki (1995), Geczy et al. (1997), Goldberg et al. (1998), Howton and Perfect (1998), Graham and Rogers (2000), Allayannis and Ofek (2001) and Bartram et al. (2006). Studies of non-US firms include Berkman and Bradbury (1996) on New Zealand firms, Hagelin (2003) on Swedish firms and Pramborg (2005) on Swedish and Korean firms, Nguyen and Faff (2003) on Australian firms, Bartram et al. (2006) on firms of 48 different countries, and Heaney and Winata (2005) on Australian firms. The International Swaps and Derivatives Association (ISDA) 2003 derivative usage survey reports that today 92% of the world??™s 500 largest companies representing a wide range of geographic regions and industry sectors use derivatives for risk management on a regular basis (http://www.isda.org/statistics/surveynewsrelease030903v2.html).
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Exchange rate fluctuations and the balance of payments figured prominently in the international economics literature of the 1950s and 60s. For some of the original work see: Meade (1951), Alexander (1952 and 1959), Pearce (1961), Tsiang (1961), Gerakis (1964) and Caves and Johnson (1968).

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movements.3 However, notwithstanding a few exceptions, such as Kiymaz (2003),4 the existing empirical results suggest that firms generally do not have significant exchange rate exposures. For example, Jorion (1990), Bodnar and Gentry (1993), Amihud (1994), Choi and Prasad (1995), He and Ng (1998), Miller and Reuer (1998), Hagelin and Prambourg (2004), to mention only a few,5 find that only a small percentage of their sample firms show significant exchange rate exposure. Although this could be interpreted as preliminary evidence that hedging may be neutralizing exposure, this evidence is weakened by the fact that there doesn??™t seem to be much difference in significant exposure rates between hedgers and non-hedgers. Furthermore, Allayannis and Ofek (2001) and Hagelin and Prambourg (2004) suggest that FC hedging, although often negative and significant, has only a marginal effect on FC exposure.6 Given the contemporary empirical status quo, this paper revisits the world of exchange rate exposure and FC derivatives use in the context of potential asymmetries in reactions of stock returns to currency appreciations and depreciations. There is a theoretical literature on firm behaviour – hysterisis (e.g. Ljungqvist, 1994; Christophe, 1997) and pricing-to-market (Marston, 1990; Knetter, 1994; Goldberg, 1995) – that implies the possibility of asymmetric reactions of stock returns to currency appreciations and depreciations. There is also a wide range of FC options, including caps, collars, Asians, baskets, binaries, lookbacks, and swaptions, to mention only a few, that make asymmetric hedging strategies cheap and practical. Finally, there is a small but growing literature on asymmetric currency exposure (e.g. Choi and Prasad, 1995; Di Iorio and Faff, 2000; Krishnamoorthy, 2001; Koutmos and

See, for example, Shapiro (1975), Dumas (1978), Hodder (1982), Flood and Lessard (1986), Booth and Rottenberg (1990), Levi (1994), Marston (2001), Allayannis and Ihrig (2001) and Bodner et al. (2002). 4 In his sample of 109 Turkish firms from 1991 to 1998, close to 50% are significantly exposed to exchange rate movements. 5 See Muller and Vershoor (2006) for a comprehensive review of the literature. 6 In Allayannis and Ofek (2001), hedging explains less than 9% of exposure at most while in Hagelin and Prambourg (2004), the inclusion of hedging variables increases the R2 by less than 2% at most.

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Martin, 2003, and Oh and Lee, 2004) with some evidence that FC exposure asymmetry is frequent and widespread. For example, using data from four major economies, namely, Germany, Japan, the United Kingdom, and the United States over the period 1992 to 1998 Koutmos and Martin (2003) find that in approximately 40% of the country-sector models they study, there is significant exchange rate exposure and over 40% of the significant exposures turn out to be asymmetric. This study contributes to the literature by providing evidence on the asymmetric sensitivity of exchange rate risk at the firm level. More specifically, it investigates whether returns of 176 of the largest French non-financial firms for the year 2004 are asymmetrically affected by exchange rate movements and tests the extent to which FC derivatives use is effective for hedging this asymmetric risk. The French case is well adapted to the research we propose. Research by Capstaff et al. (2007) suggests that FC derivatives use by French firms was widespread both before and after the introduction of the euro and that FC derivatives use actually intensified relative to the reduction in exposure after the introduction of the euro. They find that the reduction in FX derivatives usage was less than proportional to the reduction in FC exposure while the number of sample firms using FC derivatives was virtually unchanged. The effectiveness of derivatives use by these firms seems to have remained unchanged as well. Nguyen et al. (2007) find that the use of FC derivatives was effective in managing exchange rate exposure both before and after the introduction of the euro with no apparent difference between the two periods. France is also one of the most important trading nations in the world. In 2004 it??™s economy measured as gross domestic product was the world??™s 5th largest7 and, according to the 2005 World Trade Organization International Trade Statistics, it was the world??™s fifth
7

See United Nations Statistics Division http:/ /unstats.un.org.

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leading merchandise exporter and fourth leading importer.8 Foreign trade represented 51% of GDP (gross domestic product), and, according to the 2004 CAF-FAB survey, 25.6% of that (13% of GDP) was outside the euro zone.9 It has a large number of firms with substantial foreign operations.10 The economy is highly industrialized and open with developed, generally unrestricted capital markets and trading partners that are predominantly in the same conditions. Thus, the financing and hedging decisions by the firms in our sample are likely to reflect economic and financial criteria rather than constraints imposed by shallow domestic capital markets, bureaucratic controls and the like. The data in our sample is recent and, in 2004, the transitional year for the application of the International Accounting Standards 32 and 39 that require disclosure on hedging practices and derivatives use, most French firms began compliance by making formerly unreported information available.11 This paper contributes to the literature by providing evidence on the asymmetric sensitivity of stock returns of French firms to exchange rate risk and the effect of FC derivative use in alleviating this risk. The results show that exposure is frequently asymmetric. When asymmetry is not considered, only about 22% of the exposure coefficients are significant. However, when asymmetry is considered, approximately 40% of the firms in the sample show significant exchange rate exposure and 61% of the significant exposures are asymmetric. The results also show that the significance of asymmetric exposure depends on the currency. When the total index is broken down into its dollar and non-dollar

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In commercial services, it was the fourth leading exporter and fifth leading importer. ???Le commerce exterieur de la France – Annee 2004 ?», site du ministere l??™Economie, de l??™industrie et de l??™emploi, http://www.minefe.gouv.fr 10 According to the CAF-FAB survey 155,800 French firms had foreign trade transactions. 11 Disclosure requirements of IAS32 include: risk management and hedging policies; hedge accounting policies and practices, and gains and losses from hedges; terms and conditions of, and accounting policies for, all financial instruments; information about exposure to interest rate risk and credit risk; fair values of all financial assets and financial liabilities, except those for which a reliable measure of fair value is not available. IAS39 requires that all financial assets and financial liabilities, including all derivatives and certain embedded derivatives, must be recognised on the balance sheet.

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components,12 18.75% (16.48%) are significantly exposed to the depreciating (appreciating) USD index and 15.91% (20.45%) are significantly exposed to the depreciating (appreciating) non-dollar index. Overall there are 126 significant coefficients associated with 93 different firms. Cross sectional analysis provides evidence that FC derivatives use has a significant negative effect on FC exposure levels. However, this effect is sensitive to the hedging strategy and it varies with respect to different currencies. Strategies that use FC derivatives only are effective against appreciations and depreciations of the residual index. Strategies that use FC derivatives only or FC derivatives along with FC debt are effective in hedging appreciations of the euro with respect to the USD, but no strategy is effective in hedging depreciations of the euro with respect to the USD. The rest of the paper is organized as follows. Section 2 describes the sample. Section 3 presents the methodology and results for estimating the exposure coefficients. Section 4 presents the cross sectional analysis for the determinants of currency exposure. Section 5 concludes.
2. SAMPLE DESCRIPTION

Data on FC exposure, FC risk management and derivatives use was collected manually from annual reports published in 2004 for a sample of the largest 240 French non financial firms. We excluded 25 firms that reported no FC exposure and 39 firms were also excluded due to the lack of accounting and financial information reported by Thomson One Banker. This left 176 firms in the final sample. The stock return data are from DataStream. Table 1 provides summary statistics for the sample. Panel A presents an industry classification of the firms in the sample using the Campbell (1996) classification. The sample spans 11 industries. Services and consumer durables have the highest representation
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The dollar accounts for 81.25% of French non-euro trade flows.

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comprising 22.16% and 20.45% of the sample respectively while petroleum (1.14%), transportation (2.27%), and construction (3.41%) have the lowest. Panel B provides the descriptive statistics of the key characteristics of the firms in the sample. Book value of total long term debt averages about EUR 1117.51 million and ranges from zero to EUR 41175 million. The firms have average total assets of EUR 4986.22 million, ranging from EUR 4.632 million to EUR 89207 million. Finally, the firms have average turnover of EUR 4264.60 million with a minimum of EUR 2.51 million and a maximum of EUR 122700 million. Average net income is about EUR 143.90 million. We use long-term debt/total assets as our measure of leverage. The ratio of foreign sales to total sales is a measure of foreign operations. Table 2 presents the statistics on the use of FC derivatives for the firms in the sample. Panel A shows 58.52% of firms disclose that they use FC derivatives and 41.48% are classified as non-users of FC derivatives. Panel B provides descriptive statistics of the extent of derivatives use represented by the total FC derivatives notional value deflated by total assets (DERIV). The average of DERIV is 0.0632 for all firms in our sample. For the subsample of FC derivatives users, DERIV averages 0.1079 and ranges from 0.00005 to 1.0111.
[INSERT TABLES 1 AND 2 HERE]

3. EMPIRICAL METHODOLOGY

Following Jorion (1990), Allayannis and Ofek (2001) and Nguyen and Faff (2003), we use a two stage empirical framework to examine the effect of foreign derivatives use on the exchange rate exposure. In the first stage, we estimate the stock exposure of each firm in our 2004 sample over three years from January 2003 to December 2005. In the second stage, we examine the relationship between exchange rate exposure already estimated and FC derivatives use. Allayannis and Ofek (2001) argue that this technique is appropriate to

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measure the contemporaneous impact of foreign currency derivatives on a firm??™s exchange rate exposure.13
3.1 Time series analysis: stock price exposure without asymmetry

Dumas (1978), Adler and Dumas (1980), and Hodder (1982) define currency risk exposure as the effect of unanticipated exchange rate fluctuations on firm value. Thus, foreign currency exposure can be measured through a simple model with the change in firm value as the dependent variable and the exchange rate changes as the regressor. Jorion (1990), conscious that other macroeconomic variables can co-vary simultaneously with the currency rate, proposes measuring the firm-specific exchange rate exposure by estimating a two-factor model:

Rit = ? i 0 + ? im Rmt + ? ix R xt + ? it

t = 1KT

(1)

where Rit is the rate of return on the ith??™ firm??™s common stock, Rmt is the rate of market return and Rxt is the rate of change of the exchange rate for period t. Many studies in the literature use trade-weighted exchange rate indices instead of separate currencies (see, for example, Jorion, 1990; Bodnar and Gentry, 1993; He and Ng, 1998; Allayannis and Ofek, 2001; Nguyen et al., 2007). In the spirit of these studies, we use a trade-weighted exchange rate index, the Euro effective index.14 that measures the value of one unit of EUR in foreign currency. To account for France??™s integration in international capital markets and avoid a

There were no major events or structural changes in the French economy and/or its tax structure during the period January 2003 to December 2005 that would have widespread effects on exchange rate exposure through changes in profit margins, demand elasticities, the opportunity cost of capital or tax rates. 14 The trade weighted Euro effective exchange covers 22 currencies: in order of weighting they are Great Britain, USA, Japan, Switzerland, Sweden, China, Hong Kong, Taiwan, Denmark, South Korea, Poland, Singapore, Czech Republic, Russia, Turkey, Hungary, Malaysia, India, Norway, Canada, Thailand and Brazil. This group of countries covers almost 97% of all foreign trade between the Euro area and the rest of the world. The weights adopted are those calculated by the OECD, after a double weighting that takes into account not only direct foreign trade between two counties but also of the presence other competing third party countries. (This definition is given by Datastream??™s staff)

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potential omitted variable problem with the local index, we use the MSCI World Index as the market risk factor.15 Table 3 gives the descriptive statistics of the exposure coefficients estimated for the 176 firms in the sample.16 Panel A shows that 22% of exposure coefficients are significant at the 10% level, which is small but similar to other studies.17 This compares to a rate of significant exposure coefficients of 13% when estimated with the Jorion approach in equation (1). Williamson (2001) points out that tests using a trade weighted basket of currencies may lack power if a firm is mostly exposed to only a few currencies within the basket and Miller and Reuer (1998) argue that a trade weighted index disregards the problem of low and negative correlations among exchange rates. To consider this point we note that the USD represents more than 80% of French non-euro foreign trade transactions. Thus, we decompose the Euro-Index into its dollar component and its non-dollar component to examine whether exposure coefficients are sensitive to the specification of the currency risk factor. To this end we regress the Euro-Index returns R x,t on the bilateral EUR/USD returns, noted Rusd ,t , and save the residuals. The residuals, noted Rres ,t , represent the percentage change in the exchange rate due to currencies other than the USD. In results, not reported here, we find that returns on the EUR/USD bilateral exchange rate are significant with a p-value of 0.0000 and account for
???The MSCI World IndexSM is a free float-adjusted market capitalization index that is designed to measure global developed market equity performance. As of June 2006 the MSCI World Index consisted of the following 23 developed market country indices: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Hong Kong, Ireland, Italy, Japan, Netherlands, New Zealand, Norway, Portugal, Singapore, Spain, Sweden, Switzerland, the United Kingdom and the United States??? (This definition is given by Morgan Stanley Capital International). 16 As a robustness check, we also estimated the exposure coefficients using the Jorion approach in equation 1, where the returns on SBF represent the market factor. The results from this estimation, available on request, are weaker with a substantially lower rate of significant coefficients. 17 The seminal empirical research of Jorion (1990) shows that only 5.2% of his sample exhibit significant exchange rate exposure. Choi and Prasad (1995) document that only 15% of their sample experience significant exchange risk sensitivity. He and Ng (1998) report that about 25% of their sample have significant exchange rate exposure. For French firms, Nguyen et al. (2007) find 32% significant exposure rates in the pre-euro year of 1996 and 11% in the post euro year of 2000.
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80% of returns on the total index (adjusted R 2 = 0.80). We then rewrite equation (1) with two currency risk factors (the bilateral EUR/USD exchange rate and the vector of residuals) instead of one:
Rit = ? 0 + ? im Rmt + ? i ,usd Rusd ,t + ? i ,res Rres,t + ? it

(2)

where ? i,usd measures FC exposure to the USD and ? i,res measures exposure to the non-USD components of the Euro-Index. The results, reported in the last two columns of table 3, suggest that although the dollar counts for 80% of overall exchange rate risk, corporate exposure, measured as absolute values, is relatively low with a mean of ? 0.1012 , a median of ? 0.1347 and a rate of significant exposure coefficients of 15.34%. The absolute values of the exposure coefficients of the residual index are much higher with a mean of ? 2.5981 , a median of ? 2.0045 and a rate of significant exposure coefficients of 28.41%. This suggests that before accounting for asymmetry corporate exposure to FC risk is more pronounced for non-USD currencies.
[INSERT TABLE 3 HERE]
3.2 The asymmetric exposure model

The standard model for investigating whether corporate FC exposure reacts differently to positive and negative moves in the exchange rate involves modifying equation (1) by decomposing R( X t ) into its positive and negative components as follows:
P P N Rit = ? 0 + ? im R mt + ? ix R x ,t + ? ix R xN,t + ? it

(3)

P where R xt represents positive moves in the exchange rate index and zeroes everywhere else

N and R xt represents negative moves in the exchange rate index and zeroes everywhere else. P N ? ix measures exposure to appreciations of the euro and ? ix measures exposure to

depreciations.

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While the standard model makes it possible to directly estimate coefficients associated with currency appreciations and depreciations, it does not offer a direct test of asymmetry. The Koutmos and Martin (2003) (KM) model offers a direct test for asymmetry, but, since it does not directly compute the coefficients for euro depreciations, there is no way to test whether these coefficients are significant. The KM model involves rewriting equation (3) as:
Rit = ? 0 + ? im Rmt + ( ? ix + ? D , Rx Dt ) R xt + ? it

(4)

P P N where ? ix = ? ix , ? D , R = ( ? ix ? ? ix ) and Dt = 1 if R x < 0 and 0 otherwise. The test for asymmetry is equivalent to testing that ? D, R is statistically significant, irrespective of the sign of the coefficient. For a given value of the market portfolio, the response of Rit will be ? ix when R x > 0 and ( ? ix + ? D , Rx )

when R x < 0 . Panel A of table 4 shows the results using equation (3). 17.61% of the firms have significant exposure to appreciations of the euro while 22.16% have significant exposure to depreciations. Overall, when asymmetry is considered, approximately 40% of the coefficients show significant exchange rate exposure with respect to the total index. When we use equation (4) for a direct test of asymmetry, we find that 61% of the significant exposure is asymmetric. To account for sensitivity of exposure coefficients to the USD and non-USD components of the Euro-Index, we break down Rusd ,t and Rres ,t into its positive and negative values and rewrite equation (3): P N P N Rit = ? 0 + ? im R mt + ? iPusd Rusd ,t + ? iNusd Rusd ,t + ? iPres R res ,t + ? iNres R res ,t + ? it , , , , (5) P where ? i,usd ( ? i,Nusd ) measures exposure to appreciations (depreciations) of the euro with respect to the USD and ? iPres ( ? iNres ) measures exposure to appreciations (depreciations) of the , , euro with respect to currencies other than the USD. 12 The results in Panel B of table 4 show that exposure is sensitive to different types of currency exposure. Significant exposure coefficients represent 18.75% on the depreciating USD (appreciating euro), 16.48% on the appreciating USD, 15.91% on the depreciating residual index (appreciating euro) and 20.45% on the appreciating residual index. Overall, there are 126 significant coefficients associated with 93 different firms. Interestingly, when we test for asymmetry using the KM model, we find that 77.7% of the USD coefficients are asymmetric while only 34.4% of the significant coefficients on the residual index are asymmetric. As in table 3, the absolute values of the mean and the median exposure coefficients on the non-USD components of the index, both positive and negative, are higher than the corresponding exposure coefficients for the USD component, which is more evidence that French firms seem relatively more exposed to non-USD currencies. [INSERT TABLE 4 HERE] 4. FC DERIVATIVES USE AND EXPOSURE: CROSS SECTIONAL ANALYSIS Earlier studies (He and Ng, 1998; Nydahl, 1999; Wong, 2000, Allayannis and Ofek, 2001, Nguyen and Faff, 2003 and Hagelin and Prambourg, 2004) investigate the effectiveness of hedging activities by examining the determinants of currency exposure in a cross sectional regression with the absolute value of the exposure coefficient as the dependent variable: ? ? ix = ? 0 + ? ? j Z ji + ? i j =1 n (6) where the Z ji are the explanatory variables. For the explanatory variables, Jorion (1990) Allayannis and Ofek (2001) and Nguyen and Faff (2003), consider the percentage of sales in foreign currency, a proxy for foreign operations, and the use of FC derivatives as the main determinants of foreign exchange exposure along with dummy variables to account for differences across industries. Allayannis 13 and Ofek (2001), Keloharju and Niskanen (2001), Kedia and Mozumdar (2003), Elliot et al. (2003) and Bartram et al. (2006) find strong evidence for the use of FC debt as a hedge for foreign currency exposure. Bodnar et al. (1996) suggest that firm size may also play a role in the relationship between hedging and exposure levels. There is also evidence that FC derivatives use is determined by an overall strategy that includes FC debt (e.g. Clark and Judge, 2008) and, thus, it is possible that FC exposure is sensitive to the hedging strategies of the individual firms. To control for this, we follow Hagelin and Pramborg (2004) and classify firms into four groups, using three dummy variables: CD, FD, and CDFD. CD is equal to one if the firm uses only currency derivatives but not foreign debt and zero otherwise. FD is set to one if the firm uses foreign debt but not FC derivatives and zero otherwise. CDFD is set to one if the firm uses both currency derivatives and foreign debt and zero otherwise. The equation we test is: ? ? i = ? 0i + ?1 FSTS + ? 2 SIZE + ? 3 CD + ? 4 CDFD + ? 5 FD + ? ? j + 5,i IND j + ?i 1 10 (7) ? where ? i represents the absolute value of the estimated betas, FSTS is the ratio of foreign sales to total sales, SIZE is the natural logarithm of firm total assets and IND are industry dummies classed according to Campbell ???1976), which take the value of 1 if the firm belongs to the industry j and 0 otherwise. Columns 1 and 2 of table 5 show that there is no significant relationship between FC hedging and FC exposure measured against the total Euro-Index or the USD component of the Euro-Index. None of the three strategies, CD, CDFD or FD, is significant at any conventional level. In fact, none of the variables is significant and the adjusted R-squares are negative. In column 3, however, when FC exposure is measured against the non-USD component of the Euro-Index, CD, hedging with derivatives only, has a negative sign and is significant with a p-value of 0.0343. The adjusted R-squared is also positive, albeit very small. These results for 14 column 3 suggest that FC derivatives use is effective in reducing FC exposure against both appreciations and depreciations of the non-USD currencies. To test whether FC derivatives use affects appreciations and depreciations differently, we break the non-USD betas into betas associated with appreciations and those associated with depreciations. In results not reported here, the relationship breaks down and none of the strategies is significant at any conventional level. The direction of the currency move is important, however, with respect to the USD. When we break the USD betas into betas associated with appreciations and those associated with depreciations, column 4 shows that two strategies are significant in reducing exposure against appreciations of the euro against the USD. CD is highly significant with a p-value of 0.0137 and CDFD, the use of currency derivatives and foreign debt, has a p-value of 0.0871. FSTS is also significant, but size is not, and the adjusted R-squared is small but positive. For exposure against depreciations of the euro with respect to the USD in column 5, none of the three strategies is significant and the R-squared is negative. This suggests that the use of FC derivatives is effective for hedging against a USD depreciating against the euro but not against a USD appreciating against the euro. [INSERT TABLE 5 HERE] In a series of robustness checks, we tested derivatives use defined as the notional amount of FC derivatives divided by the firm??™s total assets (DERIV) against the total EuroIndex, the USD component and the residual index. Neither derivatives use nor FSTS are significant at any conventional level and the adjusted R 2 are negative. To account for the possibility that whether or not the exposure coefficient is significant might affect the model, we run a probit model with the same explanatory variables where the exposure coefficient 15 takes a value of 1 if it is significant and 0 otherwise. The results are unchanged. Derivatives use is never significant and the model has low explanatory power. We also tested whether the explanatory variables (FSTS, DERIV, FDEBT, SIZE) have a differential impact on positive and ? negative ? ix and found that none of the independent variables are significant at any conventional level. 5. CONCLUSION This paper uses a sample of 176 large, non-financial French firms to investigate the relationship between foreign currency exposure and FC derivatives use. The results show that exposure is sensitive to appreciations and depreciations of the Euro-Index. Significant exposure coefficients go from 22% when asymmetry is not considered to 40% when asymmetry is considered. The KM test shows that 61% of the significant coefficients are asymmetric. The results also show that asymmetric exposure is sensitive to different currency exposures. When the total index is broken down into its dollar and non-dollar components, significant exposure coefficients represent 16.48% on the appreciating euro with respect to the USD, 18.75% on the depreciating euro with respect to the USD, 20.45% on the appreciating euro/residual index and 15.91% on the depreciating residual index. Overall there are 126 significant coefficients associated with 93 different firms. Cross sectional analysis provides evidence that FC derivatives use has a significant negative effect on FC exposure levels, but only when asymmetry is considered. This effect is sensitive to the hedging strategy and it varies with respect to different currencies. Strategies that use FC derivatives only are effective against appreciations and depreciations of the residual index. Strategies that use FC derivatives only or FC derivatives along with FC debt are effective in hedging appreciations of the euro with respect to the USD, but no strategy is effective in hedging depreciations of the euro against the USD. 16 REFERENCES Adler, M. and B.Dumas, ???Exposure to currency risk: definition and measurement??™, Financial Management, 13, 1984, pp. 41??“50. Alexander, S. S., ???Effects of devaluation on a trade balance??™, IMF Staff Papers, 2, 1952, pp. 263-278. Alexander, S. 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Panel A: Industry classification of the sample firms using Campbell (1996) classification Industry SIC codes Number of firms Percentage of total Petroleum 13, 29 2 1.14 Consumer durables 25, 30, 36, 37, 50, 55, 57 36 20.45 Basic industry 10, 12, 14, 24, 26, 28, 33 21 11.93 Food and tobacco 1, 2, 9, 20, 21, 54 9 5.11 Construction 15, 16, 17, 32, 52 6 3.41 Capital goods 34, 35, 38 20 11.36 Transportation 40, 41, 42, 44, 45, 47 4 2.27 Utilities 46, 48, 49 11 6.25 Textiles and trade 22, 23, 31, 51, 53, 56, 59 12 6.82 Services 72, 73, 75, 76, 80, 82, 87, 89 39 22.16 Leisure 27, 58, 70, 78, 79 15 8.52 Total 176 100.00 Values in millions of Euros Panel B: Descriptive statistics of the sample Variable Min 0 Total LT Debt 4.632 Total Assets 2.514 Sales -3 610 Net Income Long-term Debt/Total 0.00000 Assets 0 Foreign Sales/Total Sales Q1 3.63475 83.19125 87.7275 0.69175 0.03502 0.2213 Median 28.4545 325.753 349.8905 8.336 0.14499 0.43305 Mean 1 117.51162 4 986.2168 4 264.60215 143.89849 0.11711 0.446 Q3 196.42175 1 409.91864 1 460.250 43.85025 0.21071 0.674535 Max 41 175 89 207 122 700 9 612 0.836261 1 Table 2. Foreign currency derivatives use This table describes the use of FC derivatives for the sample of 176 firms that are deemed to have FC exposure as of year-end 2004. Panel A provides data on the number of FC hedging firms and non FC hedging firms. Panel B reports statistics for the extent of derivatives use by firm. The extent of derivatives use is calculated as the total derivatives notional value deflated by total assets. Panel A : Number of derivatives users and non users Total Sample Derivatives Users Non Users Panel B: Extent of Derivatives use: Notional Amount/Total Assets Number of Observations Minimum q1 Mean Median q3 Maximum Standard Deviation All Firms 176 0 0 0.0632 0.0137 0.0535 1.0111 0.1379 Derivatives Users 103 4.96127E-05 0.0216 0.1079 0.0471 0.1057 1.0111 0.1666 Number of firms 176 103 73 Percentage of total 100.00 58.52 41.48 21 Table 3. Exchange rate exposure This table reports descriptive statistics of ? ? ix , the FC exposure coefficient, estimated from the following equations for the period January 2003 to December 2005 : Rit = ? 0 + ? im R mt + ? ix R xt + ? it Rit = ? 0 + ? im Rmt + ? i ,usd Rusd ,t + ? i ,res Rres,t + ? it Rit is the firm??™s i common stock return. Rmt is the rate of return of MSCI world index. Rxt represents the rate of change on the Euro effective index in period t Panel A: Descriptive statistics of exchange rate exposure coefficients ? ? ix Median Mean Minimum Maximum Standard deviation No. of significant cases % of significant cases (at 10%) No. of observations -0.6353 -0.6798 -3.8437 1.9816 1.1212 40 22.72 176 ? i ,usd -0.1347 -0.1012 -1.5368 1.6107 0.5923 27 15.34 176 ? i,res -2.0045 -2.5981 -12.142 6.4567 3.0870 50 28.41 176 22 Table 4. Descriptive statistics of asymmetric foreign exchange exposures This table reports descriptive statistics of the foreign exchange exposure estimated from the following equations for the period January 2003 to December 2005: P N Panel A: Rit = ? 0 + ? im Rmt + ? ix R xP,t + ? ix R xN,t + ? it P N P N Panel B: Rit = ? 0 + ? im Rmt + ? iPusd Rusd ,t + ? iNusd Rusd ,t + ? iPres Rres ,t + ? iNres R res ,t + ? it , , , , Panel A Panel B ? Median Mean Minimum Maximum Standard deviation No. of significant cases % of significant cases (at 10%) No. of observations P ix ? N ix ? P i,usd ? N i,usd P ? i,res N ? i,res 0,0501 0,1837 -6,9881 14,5950 2,6027 31 17.61 176 -1,1472 -1,4827 -10,4420 4,6894 2,1382 39 22.16 176 0,1009 0,3583 -3,1972 9,8994 1,4293 29 16.48 176 -0,5286 -0,6862 10,5680 3,4704 1,6267 33 18.75 176 -1,9491 -2,1678 25,8840 14,4700 6,0297 36 20,45 176 -1,3166 -2,2915 24,1500 16,9630 6,3131 28 15.91 176 23 Table 5. Controlling for hedging strategies and asymmetry This table provides parameter n j =1 estimates for the following regression using OLS: exp osure coefficient = ? 0i + ? ? ji Z ji + ? i The sample consists of 176 French non-financial firms. Financial data and data on derivatives use are as of the end of 2004 fiscal year. The p-values, based on White??™s heteroscedasticity-consistent robust standard errors, are between parentheses. ? ? ix , ? i,usd P ? i ,res , ? i,usd , ? i,Nusd and are the absolute value of estimated coefficients by the following regressions presented in tables 3 and 4: Rit = ? 0 + ? im R mt + ? ix R xt + ? it Rit = ? 0 + ? im Rmt + ? i ,usd Rusd ,t + ? i ,res Rres,t + ? it P N P N Rit = ? 0 + ? im Rmt + ? iPusd Rusd ,t + ? iNusd Rusd ,t + ? iPres R res ,t + ? iNres Rres ,t + ? it , , , , FSTS is the ratio of foreign sales to total sales. CD is equal to one if the firm uses only currency derivatives but not foreign debt and zero otherwise. CDFD is set to one if the firm uses both currency derivatives and foreign debt and zero otherwise. FD is set to one if the firm uses only foreign debt and zero otherwise. SIZE is the natural logarithm of the firm??™s total assets. We include 10 industry dummies, Dij (j varies from 1 to 10) to account for differences across the industries using the Campbell (1996) classification. Dij is equal to 1 if the firm i belongs to industry j and 0 otherwise. The p-values, based on the White??™s heteroscedasticity-consistent robust standard errors, are between parentheses beside the estimated coefficients. *** ** , , * denote significance at the 1%, 5%, and 10% levels, respectively. Dependent variable ? ? ix Column 1 ? i,usd Column 2 176 0.6894* (0.0303) -0.0002 (0.1079) -0.1205 (0.3537) -0.1109 (0.2570) -0.0173 (0.8677) -0.0059 (0.7032) YES 0.0513 -0.0376 ? i ,res Column 3 176 8.2300*** (0.0002) -0.0003 (0.6577) -1.6321** (0.0343) -0.4990 (0.4742) -0.9129** (0.0411) -0.2249 (0.8261) YES 0.1082 0.0246 P ? i,usd N ? i,usd Column 4 176 1.4215 (0.1385) -0.0007** (0.0389) -0.6284** (0.0137) -0.4414* (0.0871) 0.0696 (0.8393) -0.0154 (0.7569) YES 0.117 0.0342 Column 5 176 2.9357*** (0.0067) -0.0012*** (0.0011) -0.3261 (0.3569) -0.1755 (0.5512) 0.1532 (0.6900) -0.0787 (0.1431) YES 0.0712 -0.0160 Observations INTERCEPT FSTS CD CDFD FD SIZE Industry dummies R-squared Adjusted Rsquared 176 1.4191* (0.0593) 0.0002 (0.2562) -0.0315 (0.8880) 0.0988 (0.6621) 0.1713 (0.4441) -0.0126 (0.7291) YES 0.0472 -0.0421 24