The Relationship between Exchange Rate Exposure and Hedging for
Listed Companies in the European Union
Wouter Jan van de Pol
Supervisor: Dr. B. Qin
Second Supervisor: Dr. H. Gonenc
March 2011
University of Groningen
Faculty of Economics and Business
The Relationship between Exchange Rate Exposure and Hedging for
Listed Companies in the European Union
Wouter Jan van de Pol1
ABSTRACT
This study found that 14% of the entire sample of 453 non-Financial companies listed in the Euro zone experienced exchange rate exposure to the US Dollar, 34% to Japanese Yen, 17% to Canadian Dollar and 16% to South Korean Won when the stock returns are lagged with one month for the period January 1999 until December 2009. These levels of exchange rate exposure are remarkably high compared to existing empirical studies. Next to that, the level of exchange rate exposure is positively related to the economic crisis. The extent to which a company is exposed could be explained by the firm size and growth opportunities of a company, both proxies of hedging have a significant negative relationship with the level of exposure of a listed company in the EU.
JEL Classification: F23, F31, G01.
Keywords: European Union, Exposure, Hedging, Economic Crisis.
TABLE OF CONTENTS
1. INTRODUCTION ... 4
2. LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT ... 8
2.1. Firm Size ... 11
2.2. Leverage ... 12
2.3. Growth Opportunities ... 13
2.4. Liquidity ... 14
3. RESEARCH METHODOLOGY ... 15
3.1. Sample Selection Process and Data Sources ... 16
3.2. First Level Regression ... 18
3.3. The Effect of Lagged Exchange Rate Changes on Stock Returns ... 19
3.4. Cross Sectional Analysis of Exchange Rate Exposure ... 20
4. EMPIRICAL RESULTS ... 21
4.1. First-level Regression Results ... 21
4.1.1. The Impact of the Economic Crisis on the Level of Exposure ... 22
4.1.2. The Lagged Effect of Exchange Rate Exposure ... 24
4.1.2. Exchange Rate Exposure of European Companies ... 30
4.2. The Determinants of Exchange Rate Exposure ... 30
4.2.1. Firm size ... 31
4.2.2. Leverage ... 32
4.2.3. Growth Opportunities ... 32
4.2.4. Liquidity ... 33
4.2.5. Relationship between Exchange Rate Exposure and Hedging ... 33
5. CONCLUSION AND FUTURE RESEARCH ... 34
5.1. Conclusion ... 34
5.2. Limitations and Future Research ... 35
1. INTRODUCTION
The last two decades, exchange rate exposure became one of the most important subjects in the world of international finance. The popularity of this subject started to rise since the breakdown of the Bretton Woods fixed parity system (Muller and Verschoor, 2006a) and its popularity grew even further with the discussion and introduction of one currency in the European Union (EU) in 1999.
In this study, exchange rate exposure is defined as the coefficient that is actually the elasticity of the impact of an unexpected exchange rate change on corporate stock returns (Bartram and Bodnar, 2007). Nowadays there is even an expression - the exchange rate exposure puzzle - for the contradictions with regard to exchange rate exposure (Bartram and Bodnar, 2007). This expression illustrates the contradictions between the theory and the empirical evidence of over 60 studies, and due to these contradictions the exposure puzzle is still seen as an unresolved issue in financial literature (Baldwin, 2006). In theory it is widely accepted that whether a company is involved in exports or imports, foreign investments, or if there exists another involvement with the international environment, the company has to deal with exchange rate exposure (Bodnar and Gentry, 1993). On the other hand, existing literature, regardless of the study (Bodnar and Wong, 2003), shows that the level of exchange rate exposure is lower than the expectations based on the theory. Therefore, Bartram and Bodnar (2007) have coined the term exposure puzzle to define the contradiction between theory and literature.
closed economy in comparison with the economy of the EU, meaning that the percentages of import and export against GDP are higher in the EU compared to the percentages of import and export against GDP in the USA (He and Ng, 1998; Muller and Verschoor, 2006b; Eurostat, 2010). Therefore, a study to a more open economy is more suitable for a study of exchange rate exposure and based on the theory it will lead to a higher level of companies that experience exchange rate exposure. Another explanation of the exposure puzzle could be that the stock prices of a company are not instantaneously related to changes in exchange rates and that there exists a lagged relationship (Bartov and Bodnar, 1994). This study will try to solve the exposure puzzle by determining the relationship between the level of exchange rate exposure and hedging, and by measuring if there exists a lagged relationship between the stock returns of a company and the unexpected change of an exchange rate.
one of the main reasons for the members of the EU for the introduction of the Euro in 1999 was to eliminate the exchange rate exposure for bilateral trade in the Euro-zone (Frankel and Rose, 2002). The motivation for implementing the Euro in the Euro zone will make it interesting to measure the level of exposure since the introduction of the Euro on the capital markets in 1999. Finally, the existing empirical research of exchange rate exposure in the EU is outdated and covers only several years since its introduction in 1999 (Baldwin, 2006; Muller and Verschoor, 2006b; Hutson and O’Driscoll, 2010). These reasons will make it interesting to conduct a research about the relationship between exchange rate exposure and hedging in the EU.
This study consists of two major steps. In the first step, the exchange rate exposure of companies listed in the EU is measured. According to the European Commission Trade (2010) the main trading partners of the EU without a pegged currency are the USA, Japan, Canada, and South Korea. Consequently, the exchange rate is measured and defined as the domestic currency price of the foreign currency (Euro/U.S. Dollar, Euro/Japanese Yen, Euro/Canadian Dollar, and Euro/South Korean Won). The second step of this study consists of determining the relationship between exchange rate exposure and hedging. Since information about hedging is difficult to obtain, this study will use six variables that are seen as proxies for hedging in literature. These variables are firm size, leverage, market to book ratio, capital expenditure ratio, quick ratio, and dividend yield (Smith and Stulz, 1985; Froot, Scharfstein and Stein, 1993; Nance, Smith and Smithson, 1993). Therefore the following research question is stated:
In which way can hedging explain exchange rate exposure of listed companies in the European Union for the period 1999-2009?
crisis. During a crisis there is a lot of macroeconomic uncertainty on the capital markets and due to the increase in macroeconomic uncertainty the higher level of companies that are exposed during the economic crisis could be explained (Muller and Verschoor, 2006a; Chue and Cook, 2008). The extent to which a company is exposed could be explained by the variables firm size and growth opportunities, both proxies of hedging have a significant negative relationship with the level of exposure of a listed company in the EU.
The main innovation of this paper is that it will determine the relationship between exchange rate exposure and hedging for EU companies since the introduction of the Euro on the capital markets in 1999. Furthermore, this study will measure the impact of the economic crisis on the level of exposure. Moreover, this study will measure the level of exposure when the stock returns are lagged with one, two and three months. A unique feature of this study is that it will determine the relationship between the level of exchange rate exposure and hedging by six variables that are seen in the literature as significant with the degree of hedging of a company. To the best knowledge of the author, measurements, between exchange rate exposure and hedging in the EU to this extent, have so far never been assessed in a published paper of exchange rate exposure of listed companies in the EU since the introduction of the Euro.
2. LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT
The expression exchange rate exposure puzzle illustrates the contradictions between the theory and evidence of over 60 empirical studies (Baldwin, 2006). Based on theory and financial models the firm value of companies that are operating in an international environment should be affected by changes in the exchange rate. On the other hand, empirical evidence shows that the level of exchange rate exposure, regardless of the study (Bodnar and Wong, 2003), is less significant than the expectations based on the theory. Moreover, the level of exchange rate exposure is also lower than expected when companies are pre-identified on variables that show the international involvement of a company. According to Bartram and Bodnar (2007) a percentage between 10 and 25 of the companies that have been studied show a significant level of exchange rate exposure. Therefore, Bartram and Bodnar (2007) call this contradiction between theory and literature the exchange rate exposure puzzle.
When a company is involved in exports or imports, foreign investments or if there exist another involvement with the international environment it is widely accepted that a company will experience exchange rate exposure (Bodnar and Gentry, 1993). In addition, unexpected changes in the exchange rate will affect the cash flows of a company and due to these influences on the cash flow the stock prices and firm value of listed companies will be influenced. Besides, exchange rates are seen as a significant determinant of macroeconomic uncertainty for a company that has foreign-currency based activities, assets or competition (Muller and Verschoor, 2006b; Bartram and Bodnar, 2007).
Choi and Prasad (1995) who found that approximately 15% of their sample of US companies experienced significant exposure at a 10% level. Choi and Prasad (1995) concluded that the level of exchange rate exposure depends on the level of foreign operations of a company. The term exchange rate exposure puzzle was first coined by Bartam and Bodnar (2007) due to the differences between empirical studies and theory. Bartam and Bodnar (2007) concluded after an extensive study that companies that are acting in a rational way will not experience exchange rate exposure. Focusing on the European market, Muller and Verschoor (2006b) found that 14% of their sample of 817 European multinationals was significantly exposed. In general, Muller and Verschoor (2006b) concluded that the short-term exposure of European companies is hedged well. On the other hand, long term exposure seems to be very difficult to detect. In conclusion, European companies will experience more long term exposure than short term exposure.
In conclusion, it is widely accepted that when a company has a connection with the international environment that a company has to deal with exchange rate exposure. In addition, above presented existing empirical studies showed that the level of exposure varies. Nevertheless, these studies also showed that there are always some companies exposed. Therefore, the following hypothesis is stated:
H1: The stock returns of EU companies are affected by unexpected changes of the exchange rate.
The articles of Shapiro (1975), Marston (2001), and Muller and Verschoor (2006a) are in line with the article of Dumas (1978) who concluded that the total exposure of a company is uncertain due to difficulties in estimating the macroeconomic environment. During a crisis and especially during the recent economic crisis investors experience a lot of macroeconomic uncertainty. Next to that, unexpected changes in the exchange rate will affect the cash flows of a company and these changes will influences the stock prices and firm value of a company (Muller and Verschoor, 2006a). Due to the increase in macroeconomic uncertainty during a crisis combined with the influence of exchange rates on stock prices the percentage of companies that are exposed during the economic crisis should be higher compared to a non-crisis period. Therefore, the following hypothesis is stated:
H2: The economic crisis is positively related to the level of exchange rate exposure.
reduced with financial and operational hedging. According to Stulz (2003) these two forms of hedging a company could apply to reduce or offset the exposure of their company. The first method is called financial hedging and could be seen as the use of financial products to take financial positions to offset the possible exposure in the near future. Eiterman, Stonehill and Mofett (2001) stated that the second method - operational hedging - is the most suitable option to reduce the exchange rate exposure of a company. Operational hedging could be seen as structuring the operations of a company in a way that different operations in different parts of the world will automatically offset the exposure of the company. Stated differently, operational hedging could be seen as trying to match the cash flows in different currencies as best as possible. Both ways of hedging will minimize the level of exchange rate exposure a company is facing and that means that the relationship between the level of exchange rate exposure and hedging is a negative relationship (Allayannis and Weston, 2001). To determine this relationship this paper will test the following hypothesis:
H3: Hedging is negatively related to the level of exchange rate exposure of a company.
To test hypothesis 3 this study will use variables that are seen in the literature as significant with the degree of hedging of a company. This paper will now both explain why these variables are seen as proxies for hedging and describe the relationship between these variables and hedging.
2.1. Firm Size
(1998) who stated that larger companies could obtain economics of scale in their hedging activities. In addition, He ad Ng (1998) found that 25% of their sample of 171 Japanese multinationals faced significant exposure and concluded that the level of exchange rate exposure could be explained by the size of a company. Secondly, larger companies are more likely to operate in a greater number of countries and hence automatically offsetting, operational hedging, their exposure (Agarwal and Ramaswami, 1992). However, the literature is ambiguous about the relationship between firm size and hedging. Muller and Verschoor (2006b) stated in their article that smaller companies have a higher probability of financial distress and as a result smaller companies have a larger incentive to hedge. Nevertheless, the negative relationship between firm size and hedging is more common in existing literature. When a company applies a hedging strategy, financial or operational hedging, to decrease their exchange rate exposure it will be an expensive and difficult process to undertake for a company. Consequently, larger companies will hedge their exchange rate exposure more frequently than smaller companies due to economics of scale that a larger company can obtain more easily in comparison with smaller companies.
Therefore, the following hypothesis will be tested to determine the relationship between exchange rate exposure and firm size:
H3a: The size of a company is negatively related to the exchange rate exposure of a company.
2.2. Leverage
flows and hence the probability to go bankrupt will decline (Géczy, Minton and Schrand, 1997; Chow and Chen, 1998). This negative relationship between the level of exposure and leverage ratio is supported by empirical evidence of He and Ng (1998) and Choi and Kim (2003) who both found that companies with a higher leverage ratio will have a lower level of exchange rate exposure.
Therefore, the following hypothesis will be tested to determine the relationship between exchange rate exposure and the leverage ratio of a company:
H3b: The leverage of a company is negatively related to the exchange rate exposure of a company.
2.3. Growth Opportunities
A growth opportunity could be defined as an investment that has the potential to be profitable in the (near) future of a company (Bekaert, Harvey, Lundblad, and Siegel, 2007). When a company has good investment opportunities it is important that the company is able to execute these opportunities. An investment could not be executed when a company has to deal with a cash flow problem, this is in line with the study of Lin and Chang (2009) who stated that companies engage in hedging to ensure their future cash flows. Therefore, companies with more growth opportunities have a strong advantage to decrease the volatility of their cash flows. Consequently, a company with more growth opportunities will have a lower level of exchange rate exposure (Choi and Kim, 2003). Stated differently, hedging will reduce the volatility of cash flows and will ensure the possibility to exercise investment opportunities (Froot, Scharfstein and Schrand, 1993). In the literature about the determinants of exchange rate exposure it is common to use market to book ratio and capital expenditures ratio as proxies for growth opportunities (Allanyannis and Weston, 2001; Hutson and O’Driscoll, 2010).
H3c: The market to book ratio of a company is negatively related to the exchange rate exposure of a company.
H3d: The capital expenditure ratio of a company is negatively related to the exchange rate exposure of a company.
2.4. Liquidity
In literature, the proxies quick ratio and dividend yield, are frequently used for measuring the liquidity of a company (Froot, Scharfstein and Schrand, 1993; Nance, Smith and Smithson, 1993). According to Froot, Scharfstein and Schrand (1993) the quick ratio captures the internal wealth of a company. Furthermore, whether a company pays dividend depends on the liquidity of a company. In addition, a dividend payment is not likely if a company has to deal with capital restrictions. To conclude, a strong liquidity position will result in a high quick ratio and dividend payments by the company, this means that there is a positive relationship between the proxies for liquidity and exchange rate exposure of a company.
Therefore, the following hypotheses will be tested to determine the relationship between exchange rate exposure and the liquidity of a company:
H3e: The quick ratio of a company is positively related to the exchange rate exposure of a company.
H3f: The dividend yield of a company is positively related to the exchange rate exposure of a company.
3. RESEARCH METHODOLOGY
3.1. Sample Selection Process and Data Sources
In the first step the level of exposure for listed companies in the Euro zone will be measured. In addition, the five largest economies of the EU in the Euro zone, Germany, France, Italy, Spain and the Netherlands, are taken in this study (Eurostat, 2010). The existing literature is ambiguous whether the selected companies should have a certain import or export level or that all the listed companies should be selected for an exchange rate exposure study. According to Dominguez and Tesar (2006) companies without a connection to the international environment are still facing (indirect) exchange rate exposure due to the fact that their competition has a connection with the international environment. These companies are not aware of their exposure, and consequently do not apply a hedging strategy with a result that a company without a connection to the international environment will probably experience exchange rate exposure. Therefore, this paper will include all the listed companies in the above-mentioned countries. However, the financial companies are excluded due to the differences in business objectives and complex financial risks compared to non-financial companies. The final sample consists of 153 companies from Germany, 143 companies from France, 61 companies from the Netherlands, 53 companies from Italy, and 43 companies from Spain.
Therefore, February 2007 could be seen as the beginning of the economic crisis (Mizen, 2008). The first sub-sample period is without the economic crisis, from January 1999 until January 2007, and the second sub-sample period is the period of the economic crisis from February 2007 until December 2009.
Furthermore, the data will be taken on a monthly basis, because the level of exchange rate exposure of a company is higher when the stock returns are measured over a longer time period (Muller and Verschoor, 2006a). Bodnar and Wong (2003) showed that data on a monthly basis is the common standard for an empirical research that is comparable with a research executed in this paper.
In step 2 the relationship between the level of exposure and hedging will be determined. All the variables are retrieved from the DataStream database for the period 1999-2009. The method of measurement of the variables that are in the existing literature seen as proxies of hedging will now be presented. First of all, firm size is measured as the sum of total current assets, long term receivables, investment in unconsolidated subsidiaries, other investments, net property plant and equipment and other assets. In addition, firm size is measured as a logarithm in step 2 to control for size effects. Secondly, leverage is measured as the long-term debt and short-term debt divided by the total capital of a company. The capital expenditure ratio is calculated as the total sales divided by the total capital expenditure of a company. Fourthly, market to book ratio is measured as the market value of the common equity divided by the balance sheet value of the common equity in the company. The quick ratio is calculated as the cash and equivalents and (net) receivables divided by the current liabilities. Lastly, dividend yield is measured as the dividend per share as a percentage of the share price.
3.2. First Level Regression
The first step, measuring the exchange rate exposure, will be executed with Jorion’s (1990) model. The technique of Jorion has become the new standard in empirical research of exchange rate exposure instead of the model of Adler and Dumas (1984), because Jorion’s model is controlling for market effects. Consequently
exchange rate exposure are
difference between the total exposure of a company and the exposure of the market portfolio.
The model of Jorion involves a time
exchange rate against the return on a companies’ stock while controlling for market effects (Hutson and O’Driscoll, 2010). The following formula will be applied in the first step:
(Equation 1)
Where Ri,t is the monthly rate of return on stock of return on the market portfolio in period rate (measured as €/$, €/¥,
period t. Consequently, the slop
exchange rate exposure of a company.
regression line, could be seen as the amount of change in the dependent variable, stock returns, that is correlated with
(Huizingh, 2010).
The results of the European Commission Trade (2010) showed that the EU has six main trading partners, USA, China, Japan, Canada, South Korea, and Hong Kong, and that the economy of the EU is interdependent for a high degree with the economies of these countries. The currencies of these countries could not be taken without testing for a multicollinearity problem.
. First Level Regression
The first step, measuring the exchange rate exposure, will be executed with Jorion’s technique of Jorion has become the new standard in empirical research of exchange rate exposure instead of the model of Adler and Dumas (1984), because Jorion’s model is controlling for market effects. Consequently
exchange rate exposure are residual levels of exposure and this level of exposure is the difference between the total exposure of a company and the exposure of the market
The model of Jorion involves a time-series regression of changes in the trade
e against the return on a companies’ stock while controlling for market effects (Hutson and O’Driscoll, 2010). The following formula will be applied in the first
is the monthly rate of return on stock i in period t (1999-2009) of return on the market portfolio in period t, Xt the unexpected change of the
¥, €/C$, and €/₩), and
ε
i,t is the error term for company i in period t. Consequently, the slope coefficient of the equation, yi, shows the level of exchange rate exposure of a company. The absolute value of yi, the slope of the regression line, could be seen as the amount of change in the dependent variable, stock returns, that is correlated with one unit change of the independent variable, exchange rateThe results of the European Commission Trade (2010) showed that the EU has six main trading partners, USA, China, Japan, Canada, South Korea, and Hong Kong, and that the he EU is interdependent for a high degree with the economies of these countries. The currencies of these countries could not be taken without testing for a multicollinearity problem. The Pearson correlation coefficient will show the level of The first step, measuring the exchange rate exposure, will be executed with Jorion’s technique of Jorion has become the new standard in empirical research of exchange rate exposure instead of the model of Adler and Dumas (1984), because Jorion’s model is controlling for market effects. Consequently, the levels of residual levels of exposure and this level of exposure is the difference between the total exposure of a company and the exposure of the market
series regression of changes in the trade-weighted e against the return on a companies’ stock while controlling for market effects (Hutson and O’Driscoll, 2010). The following formula will be applied in the first
2009), Rm,t the rate the unexpected change of the exchange is the error term for company i in i, shows the level of , the slope of the regression line, could be seen as the amount of change in the dependent variable, stock change of the independent variable, exchange rate
dependence between two currencies. Table 1 shows the correlation between all the currencies and the correlation value should not exceed 0.800 (Cooper and Schindler, 2006). The level of correlation between the US Dollar with the Chinese Yuan and Hong Kong Dollar exceeds the value of 0.800 and these currencies will, due to a multicollinearity problem, be excluded from this study.
As a result, the exchange rate is defined as the domestic currency price of the foreign currency (Euro/U.S. Dollar, Euro/Japanese Yen, Euro/Canadian Dollar and Euro/South Korean Won), meaning that an increase of the exchange rate is a depreciation of the local currency and an appreciation of the foreign currency.
3.3. The Effect of Lagged Exchange Rate Changes on Stock Returns
The exchange rate exposure puzzle could be explained in different ways. One explanation is that companies could be aware of their exposure and consequently try to decrease their exposure by means of hedging. As a result, that the level of exposure is lower than the expected level of exposure based on the theory. A second explanation of the exposure puzzle is that the stock returns of a company and the exchange rate have a lagged relationship and not a contemporaneous relationship. According to Ahmihud (1994) and Bartov and Bodnar (1994) reactions on financial information are not instantaneous, because the reaction of investors on financial information will occur sometime. Moreover, investors cannot always obtain the hedging strategy of a company. This ignorance could result in systematic errors by investors that could lead to mispricing of shares of a company over limited periods of time (Bartov and Bodnar, 1994). Investors will correct
US Dollar Chinese Yuan Japanese Yen Canadian Dollar South Korean Won Hong Kong Dollar
US Dollar 1,00 - - - -
-Chinese Yuan 0,98* 1,00 - - -
-Japanese Yen 0,63 0,63 1,00 - -
-Canadian Dollar 0,62 0,60 0,35 1,00 -
-South Korean Won 0,43 0,42 0,33 0,53 1,00
-Hong Kong Dollar 1,00* 0,98* 0,63 0,62 0,43 1,00
Note: *val ue exceedi ng l evel of 0.800
their errors when new (financial) information about the results of a company
A listed company is obligatory to publish their results every three months and this newly released information could
possible existence of a lagged relationship will be
This study will examine whether the level of exchange rate exposure is a contemporaneous or a lagged relationship for listed companies in the EU. This will be measured with the following formula:
(Equation 2)
Where Ri,t is the monthly rate of return on stock of return on the market portfolio in period rate (€/$, €/¥, €/C$, and €/
on exchange rate changes,
coefficient of the equation, shows the level of exchange rate exposure of a company. This equation will determine the level of exchange rate exposure when the stoc
lagged with one, two, and three
3.4. Cross Sectional Analysis of Exchange Rate Exposure
The second step of this paper consists of determining the relationship between exchange rate exposure of listed companies in the EU, measured in
means of the variables that are seen as proxies for hedging
measured by testing the hypotheses stated in the literature overview by running a cross sectional analysis. The following formula will be
their errors when new (financial) information about the results of a company
A listed company is obligatory to publish their results every three months and this newly could lead to corrections by investors if necessary. Therefore, the
lagged relationship will be measured for three months.
This study will examine whether the level of exchange rate exposure is a contemporaneous or a lagged relationship for listed companies in the EU. This will be measured with the following formula:
is the monthly rate of return on stock i in period t (1999-2009)
of return on the market portfolio in period t, Xt the unexpected change of the exchange €/₩),
measures the effect of lagged changes, and
ε
i,t is the random error. Consequently, ycoefficient of the equation, shows the level of exchange rate exposure of a company. This equation will determine the level of exchange rate exposure when the stoc
lagged with one, two, and three months.
. Cross Sectional Analysis of Exchange Rate Exposure
The second step of this paper consists of determining the relationship between exchange rate exposure of listed companies in the EU, measured in the first step, and
means of the variables that are seen as proxies for hedging. This relationship will be measured by testing the hypotheses stated in the literature overview by running a cross sectional analysis. The following formula will be used:
their errors when new (financial) information about the results of a company is released. A listed company is obligatory to publish their results every three months and this newly lead to corrections by investors if necessary. Therefore, the
r three months.
This study will examine whether the level of exchange rate exposure is a contemporaneous or a lagged relationship for listed companies in the EU. This will be
2009), Rm,t the rate the unexpected change of the exchange measures the effect of lagged stock returns he random error. Consequently, yi, the slope coefficient of the equation, shows the level of exchange rate exposure of a company. This equation will determine the level of exchange rate exposure when the stock returns are
(Equation 3)
Where yi is the estimated exposure from step 1, where D is a dummy variable that will take the value of 1 if the currency of the dummy
find the level of exposure for yi, and where F(i) is a variable that is a proxy for hedging. In the second step yi, dependent variable, will be related to
The independent variables, proxies of hedging, are
ratio, capital expenditure ratio, quick ratio and dividend yield
4. EMPIRICAL RESULTS
The results of the two different steps of this study will now be presented and discussed. The first step, measuring the level of exchange rate exposure
measurements: the level of exposure for the entire period, for different sub periods, and when the stock returns
the relationship between the level of
4.1. First-level Regression Results
Table 2 reports summary statistics for firm
the entire sample consisting of 453 European companies over, consecutively, the entire period, January 1999 until
Table 2 reports the mean, median, variance, minimum and maximum values of yi, percentage of companies that are significantly exposed at the 5% and at the 10% level. Where yi is the estimated exposure from step 1, where D is a dummy variable that will take the value of 1 if the currency of the dummy matches the currency that was used to nd the level of exposure for yi, and where F(i) is a variable that is a proxy for hedging. In the second step yi, dependent variable, will be related to the independent variable The independent variables, proxies of hedging, are firm size, leverage,
ratio, capital expenditure ratio, quick ratio and dividend yield.
4. EMPIRICAL RESULTS
The results of the two different steps of this study will now be presented and discussed. The first step, measuring the level of exchange rate exposure, consists of the following measurements: the level of exposure for the entire period, for different sub
stock returns are lagged with one, two and three
the relationship between the level of exposure and hedging will be determined.
level Regression Results
Table 2 reports summary statistics for firm-level exposure coefficients, yi, estimated for the entire sample consisting of 453 European companies over, consecutively, the entire until December 2009, and two sub-sample periods. In addition, able 2 reports the mean, median, variance, minimum and maximum values of yi, of companies that are significantly exposed at the 5% and at the 10% level. Where yi is the estimated exposure from step 1, where D is a dummy variable that will he currency that was used to nd the level of exposure for yi, and where F(i) is a variable that is a proxy for hedging. the independent variable F(i). firm size, leverage, market to book
The results of the two different steps of this study will now be presented and discussed. , consists of the following measurements: the level of exposure for the entire period, for different sub-sample three months. Secondly, hedging will be determined.
Finally, Table 2 presents the number of companies with a positive and a negative level of exchange rate exposure that are significant at the 10% level.
The results presented in Table 2 shows that about 13% of the entire sample of 453 companies for the entire sample period is exposed to the US dollar, about 15% is exposed to the Japanese Yen, about 11% is exposed to the Canadian Dollar and about 16% is exposed to the South Korean Won at a significance level of 10%. The results presented in Table 2 show a lot of similarity compared to the level of companies that are exposed of existing studies. Choi and Prasad (1995) found significant exposure for approximately 15%, at a significance level of 10%, of their sample of US companies. Focusing on the European market, Muller and Verschoor (2006b) found that 14% of the 817 European multinationals was significantly exposed at a significance level of 5%. In general, Bartram and Bodnar (2007) concluded that 10% till 25% of the companies that have been studied show a significant level of exchange rate exposure. When a company is exposed to a currency it does not mean that a company is doing business in a country with that currency. On the other hand, it does mean that an exposed company is influenced in a way by products that have a connection with that currency (Choi and Kim, 2003).
4.1.1. The Impact of the Economic Crisis on the Level of Exposure
The increase in macroeconomic uncertainty on the capital markets could lead to the higher number of companies that are exposed during the economic crisis. At this moment, there are no empirical studies that have measured the level of exchange rate exposure during the recent economic crisis, and, therefore, this study is not able to compare the level of exposure during the economic crisis showed in Table 2. However, Chue and Cook (2008) measured the level of exchange rate exposure immediately after the various This table reports summary statistics for firm-level coefficients, yi, estimated for the entire sample consisting of 453 European companies over, consecutively, the entire period 01/01/1999 - 12/31/2009 and two sub-sample periods from the following model:
(Equation 1)
where Ri,t is the monthly rate of return on stock i in period t , Rm,t the rate of return of the market portfolio in period t, ∆Xt the unexpected change of the exchange rate measured as the Euro price of foreign currencies and εi,t is the error term for company i in period t.
Cross-sectional distribution of summary statistics
Sample Period N*(%) N**(%) Min Max Mean Median Variance N*(+) N*(-)
Panel A: US Dollar exchange rate exposure
01/01/1999 - 12/31/2009 13,02 7,51 -1,805 2,732 0,21639 0,586 1,12796 24 35
01/01/1999 - 01/31/2007 10,38 3,75 -1,9720 4,4190 0,5309 0,9130 2,0004 31 16
02/01/2007 - 12/31/2009 15,01 8,61 -2,8410 3,6480 -0,1820 -1,1475 3,5622 27 41
Panel B: Japenese Yen exchange rate exposure
01/01/1999 - 12/31/2009 15,23 8,61 -1,8350 1,7150 0,1764 0,5110 0,5940 24 45
01/01/1999 - 01/31/2007 8,39 3,97 -3,7730 1,8270 -0,4800 -0,7285 1,2356 13 25
02/01/2007 - 12/31/2009 17,66 11,48 -3,2010 2,2920 0,2946 0,9475 1,8849 52 28
Panel C: Canadese Dollar exchange rate exposure
01/01/1999 - 12/31/2009 11,04 7,51 -2,3830 1,8060 -0,2228 -0,6095 0,9163 30 20
01/01/1999 - 01/31/2007 7,28 3,31 -3,6920 2,7010 0,4083 0,7890 1,6490 23 10
02/01/2007 - 12/31/2009 13,91 6,40 -2,6800 2,2530 -0,3415 -0,8650 1,7667 22 38
Panel D: South Korean Won exchange rate exposure
01/01/1999 - 12/31/2009 16,11 9,71 -1,9590 1,0320 -0,3797 -0,5160 0,5215 52 21
01/01/1999 - 01/31/2007 13,69 5,96 -3,8960 1,6260 -0,6255 -0,7615 1,3399 28 44
02/01/2007 - 12/31/2009 18,98 13,96 -2,5280 2,1950 0,3514 0,7965 1,5098 59 29
TABLE 2
Cross-Sectional Distribution of Exchange Rate Exposure Coefficients of European Companies
emerging market crises of the 1990s. In the sub-sample period directly after the different crises, the number of companies that were significantly exposed was substantially higher compared to the entire period and the other sub-sample period. Comparing the results of Chue and Cook (2008) and the results presented in Table 2 lead to an assumption that the relationship between the level of exchange rate exposure and a crisis is positively related. Based on the results showed in Table 2, H2, the economic crisis is positively related to the level of exchange rate exposure, will be accepted.
4.1.2. The Lagged Effect of Exchange Rate Exposure
This table reports summary statistics for firm-level coefficients, yi, estimated for the entire sample consisting of 453 European companies over the entire period 01/01/1999 - 12/31/2009 ,where the stock returns and exchange rate exposure have a
contemporaneaous relationship (Lagging 0), and where the time period is lagged with one period, two periods or with three periods from the following model:
(Equation 2)
Where Ri,t is the monthly rate of return on stock i in period t , Rm,t the rate of return of the market portfolio in period t, ∆Xt the
unexpected change of the exchange rate measured as the Euro price of foreign currencies, measures the effect of
lagged exchange rate changes on stock returns, and εi,t is the random error.
Lagging 0 Lagging 1 Lagging 2 Lagging 3
Currency N* N*(%) N*(+) N*(-) N* N*(%) N*(+) N*(-) N* N*(%) N*(+) N*(-) N* N*(%) N*(+) N*(-)
US Dollar 59 13,02 24 35 66 14,57 14 52 37 8,17 23 14 33 7,28 13 20
Japanese Yen 69 15,23 24 45 155 34,22 150 5 94 20,75 76 18 58 12,80 36 22
Canadian Dollar 50 11,04 30 20 79 17,44 74 5 92 20,31 5 87 40 8,83 22 18
South Korean Won 73 16,11 52 21 74 16,34 21 53 49 10,82 31 18 54 11,92 15 39
Note: N* Number of companies that are significant at the 10% level, N*(%) percentage of companies of the entire sample that are significant at the
10% level. N*(+) number of companies with a positive level of exchange rate exposure at the 10% level, N*(-) number of companies with a negative level of exchange rate exposure at the 10% level.
Cross-sectional distribution TABLE 3
In conclusion, comparing the level of exchange rate between a contemporaneous and a lagged relationship shows that the number of companies that are significantly exposed will increase to a remarkably high level when stock returns are lagged with one period. As a result, this study now will focus on the statistics of the level of exposures when stock returns are lagged with one month. Next to that, due to a higher percentage of companies that are exposed when stock returns are lagged with one period these levels of exposure will be applied in the second step by determining the relationship between exchange rate exposure and hedging.
In Table 4 summary statistics for firm-level exposure coefficients, yi, estimated for the entire sample consisting of 453 European companies over the entire period, January 1999 until December 2009, when the time period is lagged with one period are reported. In addition, Table 4 reports the mean, median, variance, minimum and maximum values of yi, number and percentage of companies that are significantly exposed at the 5% and at the 10% level and it reports the number of companies with a positive and a negative coefficient of exchange rate exposure at a 10% level.
Table 4 shows that about 14% of the entire sample is exposed to the US dollar (approximately 3% yield a positive exposure coefficient and approximately 11% yield a negative exposure coefficient), about 34% is exposed to the Japanese Yen (approximately 33% yield a positive exposure coefficient and approximately 1% yield a negative exposure coefficient), about 17% is exposed to the Canadian Dollar (approximately 16% yield a positive exposure coefficient and approximately 1% yield a negative exposure coefficient), and about 16% is exposed to the South Korean Won (approximately 4% yield a positive exposure coefficient and approximately 12% yield a negative exposure coefficient). In addition, these companies are exposed at a 10% significance level.
This table reports summary statistics for firm-level coefficients, yi, estimated for the entire sample consisting of 453 European companies over the entire period 01/01/1999 - 12/31/2009 ,where the stock returns are lagged with one month, from the following model:
(Equation 4)
Where Ri,t is the monthly rate of return on stock i in period t , Rm,t the rate of return of the marker portfolio in period t, ∆Xt the unexpected change of the exchange rate measured as the Euro price of foreign currencies, measures the effect of lagged exchange rate changes on stock returns, and εi,t is the random error.
Curreny N* N*(%) N** N**(%) Min Max Mean Median Variance N*(+) N*(-)
US Dollar 66 14,57 30 6,62 -2,7340 1,6020 -0,6894 -0,9430 0,7722 11 55
Japanes Yen 155 34,22 99 21,85 -0,8110 2,3520 0,8419 0,8080 0,2110 150 5
Canadian Dollar 79 17,44 43 9,49 -0,8530 2,2790 0,8752 0,8530 0,2948 74 5
South Korean Won 74 16,34 41 9,05 -4,0500 1,7760 -0,4111 -0,6265 0,6506 20 54
Note: N* Number of compani es that are si gni fi cant at the 10% l evel , N* percentage of companies that are signi ficant at the 10% level , N** number of compani es that are si gnifi cant at the 5% l evel , N**(%) Percentage of companies that are signi ficant at the 5% level. N* (+) number of compani es wi th a posi tive l evel of exchange rate exposure at the 10% l evel, N* (-) number of compani es wi th a negati ve level of exchange rate exposure at the 10% level .
Cross-sectional distribution of summary statistics TABLE 4
other currencies has a positive (negative) impact on the stock returns of EU companies and that EU companies could be classified as net-exporters (net-importers) to these countries. (He and Ng, 1998; Muller and Verschoor, 2006b; Hutson and O’Driscoll, 2010). According to Table 4, EU companies could be classified as net-importers from the USA and South Korea and as net-exporters to Japan and Canada. When these results are compared with the results of a study of the European Commission Trade (2010) it shows that EU companies are net-importers from the USA, Canada and South Korea and that EU companies are exporters to Japan. The fact that EU companies are mainly net-importers from Canada does not correspondent with the results stated in Table 4. However, it should be noted that the values of imports and exports of EU companies for Japan, Canada and South Korea are more equally balanced compared to the import and export values of the USA. Next to that, the study of the European Commission Trade (2010) is based on the values of imports and exports for the whole European Union and not only on the imports and exports of the countries that were taken in this study. These arguments could help explain the difference found by the European Commission Trade (2010) compared to this study.
The results presented in Table 2 are the levels of exposure for EU companies against different currencies. In order to improve the understanding of the levels of exposure the levels of exposure for the five different countries will also be investigated. Table 5 presents the percentage of companies that are significantly exposed at the 5% and at the 10% level, and it reports the number of companies with a positive and a negative level of exchange rate exposure that are significant at the 10% level. The minimum level of exposure of any country against any used currency is 5.66% and the maximum level is 45.90%.
This table reports the cross-sectional distribution of exchange rate exposure of European companies by country for the entire sample consisting of 453 European companies over the entire period 01/01/1999 -12/31/2009, where the stock returns are lagged with one month, from the following model:
(Equation 4)
where Ri,t is the monthly rate of return on stock i in period t , Rm,t the rate of return of the market portfolio in period t, ∆Xt the unexpected change of the exchange rate measured as the Euro price of foreign currencies and εi,t is the error term for company i in period t.
Country N* N** N*(%) N*(-) N*(+)
Panel A: US Dollar exchange rate exposure
Germany 16 5 10,46 16 0 France 31 19 21,68 30 1 Italy 8 2 15,09 1 7 Spain 6 3 13,95 5 1 The Netherlands 5 1 8,20 3 2 All 66 30 55 11
Panel B: Japanese Yen exchange rate exposure
Germany 40 20 26,14 1 39 France 59 41 41,26 0 59 Italy 10 5 18,87 4 6 Spain 18 9 41,86 0 18 The Netherlands 28 24 45,90 0 28 All 155 99 5 150
Panel C: Canadian Dollar exchange rate exposure
Germany 25 13 16,34 3 22 France 35 21 24,48 0 35 Italy 3 1 5,66 1 2 Spain 8 3 18,60 1 7 The Netherlands 8 5 13,11 0 8 All 79 43 5 74
Panel D: South Korean Won exchange rate exposure
Germany 15 8 9,80 6 9 France 20 10 13,99 17 3 Italy 12 5 22,64 9 3 Spain 9 5 20,93 7 2 The Netherlands 18 13 29,51 15 3 All 74 41 54 20
Note: N* Number of compani es that are signi fi cant at the 10% l evel , N** number of companies that are si gnificant at the 5% l evel. N*(%) percentage of companies that are si gnificant at the 10% l evel. N* (+) number of companies wi th a posi ti ve l evel
TABLE 5
Cross-Sectional Distribution of Exchange Rate Exposure of Europen Companies by Country
45.90% of the Dutch companies are exposed against the Japanese Yen. According to Table 4 EU companies could be classified as net-exporters to Japan and in Table 5 is showed unambiguous that Germany, France, Spain and the Netherlands are net-exporters to Japan. As stated in the introduction, whether a company is involved in exports or imports, foreign investments or when there exists another involvement with the international environment, the company has to deal with exchange rate exposure (Bodnar and Gentry, 1993). According to Choi and Kim (2003) this connection with the international environment could also mean that a company has to deal with competition from a foreign market. Focusing on the relationship between the above mentioned EU countries and Japan could this mean that EU companies are not completely aware of all the competitors that are influencing the Japanese market.
4.1.2. Exchange Rate Exposure of European Companies
To conclude, the level of exchange rate exposure is measured for the entire period, for different sub-sample periods, and when the stock returns are lagged with one, two and three months. In all these measurements the level of exchange rate exposure varies between 5.66% (Stock returns of Italian companies are lagged with one period against the US Dollar) and 45.90% (Stock returns of Dutch companies are lagged with one period against the Japanese Yen). Therefore, H1, the stock returns of EU companies are affected by unexpected changes of the exchange rate, will be accepted.
4.2. The Determinants of Exchange Rate Exposure
approximately 0.2 and the explanatory power of the Japanese Yen and Canadian Dollar is very weak with values of 0.01 and 0.036 respectively.
4.2.1. Firm size
Table 6 shows a significant, negative relationship between firm size and exchange rate exposure to the US Dollar and Japanese Yen. According to the literature, firm size is one of the most important variables as a proxy for hedging (Nance, Smith, and Smithson, 1993). The results stated in Table 6 are in line with the theory that larger companies will hedge their exchange rate exposure more frequently due to the economics of scale that a
This table reports estimates of the relationship between Yi and the variables that are used as proxies for hedging (firm size, leverage, market to book ratio, capital expenditure ratio, quick ratio and dividend yield). The above mentioned relationship is determined with the following model:
(Equation 3)
Where D is a dummy variable that will take the value of 1 if the currency of the dummy is correlating with the currency of the exchange rate that was applied to found the level of exposure for yi in step 1.
Variable US Dollar Japanese Yen Canadian Dollar South Korean Won
Firm Size -1,336* -3,653* -1,289 1,048
Leverage 0,002* 5,269 0* 3,281
Market to Book ratio -0,001* 0,003 -0,012 -0,001
Capital Expenditure ratio -0,01* 0 -3,892 -0,005*
Quick Ratio -0,364* 0,012 0,013 -0,059
Dividend Yield 0,001* -2,322 4,733* -1,481
Adjusted R² 0,204 0,01 0,036 0,231
Note: * signi ficant at the 10% l evel .
Table 6
The Determinants of Exchange Rate Exposure
larger company can obtain. Therefore, H3a, the size of a company is negatively related to the exchange rate exposure of a company, will be accepted.
4.2.2. Leverage
The sign of the coefficients between leverage and exchange rate exposure to the currencies are not consistent. The coefficient between leverage and exposure to the US Dollar is slightly positive and the coefficient between leverage and exposure to the Canadian Dollar is exactly zero. In addition, the coefficients of the relationships are very weak. According to the literature, companies with a higher leverage ratio have to deal with higher costs of financial distress due to higher costs regarding issuing debt. Therefore, companies with a higher leverage ratio have a higher incentive to implement a hedging strategy (Géczy, Minton and Schrand, 1997; Chow and Chen, 1998; He and Ng, 1998). The existing literature is not in line with the results presented in Table 6 and as a consequence that H3b, the leverage of a company is negatively related to the exchange rate exposure of a company, will be rejected.
4.2.3. Growth Opportunities
4.2.4. Liquidity
The liquidity of a company is measured with the variables quick ratio and dividend yield. Table 6 shows that the sign of the coefficients for both variables of liquidity are not consistent. There is a significant, negative relationship between the quick ratio and exchange rate exposure to the US Dollar and there is a significant, positive relationship between dividend yield and exposure to the US Dollar and the Canadian Dollar. According to the literature, it is more likely that a company with a low liquidity level would apply a hedging strategy to reduce the volatility of their cash flows in order to be able to execute investment opportunities in the future (Nance, Smith, and Smithson, 1993; Lin and Chang, 2009). The significant, negative coefficient of the variable quick ratio is not in line with existing literature. In contrast, the significant, positive coefficients of the variable dividend yield those are both in line with the literature. Hence, H3e, the quick ratio of a company is positively related to the exchange rate exposure of a company, will be rejected and H3f, the dividend yield of a company is positively related to the exchange rate exposure of a company, will be accepted.
4.2.5. Relationship between Exchange Rate Exposure and Hedging
5. CONCLUSION AND FUTURE RESEARCH
5.1. Conclusion
This study examines whether there exists a relationship between exchange rate exposure of listed companies in the EU and hedging. A study to the relationship between exposure and hedging in EU since the introduction of the Euro on the capital markets in 1999 is interesting for several reasons. First of all, existing studies are focused mainly on listed companies in the USA. Secondly, the economy of the EU could be characterized as an open economy while the economy of the USA could be seen as a closed economy (Muller and Verschoor, 2006b; Eurostat, 2010). Thirdly, the percentage of companies that applies a hedging strategy is higher in Europe than in the USA (Bodnar, de Jong and Macrae, 2003). The results of this study shows that the level of exchange rate exposure of the sample that consists of 453 non-financial companies for the period January 1999 until December 2009 varies between the 11% (to the Canadian Dollar) and 17% (to the South Korean Won) depending on the exchange rate. The entire period is divided into two sub-sample periods to measure the possible impact of the economic crisis on the level of exposure. This study found that during the economic crisis the level of exchange rate exposure is higher compared to a non-crisis period. During a crisis there is a lot of macroeconomic uncertainty on the capital markets and due to the increase in macroeconomic uncertainty the higher level of companies that are exposed during the economic crisis could be explained (Muller and Verschoor, 2006a; Chue and Cook, 2008).
negative (positive) impact on the stock returns of an EU company, and EU companies could be classified as a net-importer from the USA and South Korea. On the other hand, the signs of the coefficients for Japan and Canada are mainly positive, which means that a depreciation (appreciation) of the Euro against the Japanese Yen/Canadian Dollar has a positive (negative) impact on the stock returns of a EU company and EU companies could be classified as a net-exporter to Japan and Canada.
The second step of this study consists of determining the relationship between the level of exchange rate exposure and the variables that are proxies for hedging. This study found that larger EU companies and companies with more growth opportunities will have a lower level of exchange rate exposure. The sign of the variables of liquidity are not consistent and this study is as a consequence not able to determine the relationship between liquidity and hedging. This paper also found that a company with a higher leverage ratio will have a higher level of exchange rate exposure, however, this relationship is not correlating with the existing literature.
In conclusion, this study found evidence that there exists a relationship between exchange rate exposure and hedging. Another important finding is that there exist a strong lagged relationship between stock returns and the level of exchange rate exposure. Furthermore, this study found that the level of companies that are exposed during the economic crisis is higher compared to a non-crisis period. The main common explanation of the exchange rate exposure puzzle in the existing literature is the engagement of companies in hedging. Due to the fact that this study found evidence of the existence of the use of hedging, of the influence of a crisis on the level of companies that are exposed, and of a strong lagged relationship, the contradictions between the theory and the empirical evidence of over 60 studies, the exposure puzzle, could now be explained in a better way.
5.2. Limitations and Future Research
Euro-zone. The main limitation of this research is that this study is not able to draw any conclusion regarding the influence of the implementation of the Euro on the level of exchange rate exposure. Only several studies (Baldwin, 2006; Muller and Verschoor, 2006b; Hutson and O’Driscoll, 2010) have studied the effect of the introduction of the Euro on the exchange rate exposure of listed companies in the EU. Baldwin (2006) stated that the implementation of one currency will result in a net reduction in exchange rate exposure for listed companies in the EU. The results of Hutson and O’Driscoll (2010) show that the number of companies that are significantly exposed is higher for companies located in the non-Euro zone compared to companies located in the Euro zone. Another study, in line with the theory of Baldwin (2006), is the empirical study of Bartram and Karolyi (2006) who found that the level of exposure was declining in absolute value after the implementation of the Euro in 1999. The key limitation of these studies is that these studies are outdated and only cover several years since the introduction of the Euro. A second limitation is that some of these studies are not related to variables that are proxies for hedging. Therefore, it will be interesting to compare the level of exchange rate exposure of listed companies in the Euro zone with companies located in non-Euro countries over a longer period. Next to that, it will be interesting to compare the level of exposure before and after the implementation of the Euro in 1999. However, this study does not have any data regarding the exposure before 1999 and does not have any data of companies located in non-Euro countries. Therefore, this study will not be able to make a comparison and draw a conclusion of the consequences of the implementation of the Euro on the level of exchange rate exposure. As a result, a comparison with the level of exchange rate exposure of this study would be an interesting area for future research.
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