The Value Effects of Foreign Currency and Interest Rate Derivatives Use: Evidence from Italy, Spain and Portugal JUNE 5 TH , 2011 Florbela Galvão da Cunha a1 , José Dias Curto a and Amrit Judge b a ISCTE Business School, Av. Prof. Aníbal Bettencourt, 1600-189 Lisbon, Portugal b Middlesex University Business School, The Burroughs, Hendon, London NW4 4BT, UK [email protected][email protected][email protected]Very preliminary draft: Please do not quote without permission. 1 Corresponding author: Florbela Galvão da Cunha.
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The Value Effects of Foreign Currency and Interest Rate
Derivatives Use: Evidence from Italy, Spain and Portugal
JUNE 5TH
, 2011
Florbela Galvão da Cunhaa1
, José Dias Curtoa and Amrit Judge
b
aISCTE Business School, Av. Prof. Aníbal Bettencourt, 1600-189 Lisbon, Portugal
bMiddlesex University Business School, The Burroughs, Hendon, London NW4 4BT, UK
Regarding the full sample data, we found that IR hedging is slightly more important
than FC hedging; 55.9% of firms are IR derivative hedgers, whilst only 51.9% hedge their
foreign currency risks (Panel C). This difference in favor of IR hedging is verified in the three
analyzed markets. Even though, in Spain the difference is less significant. In the UK, FC
hedging is much more important than IR hedging. Judge (2006) reports that 70.4% of UK
firms are FC derivative hedgers, whilst only 44.4% hedge their IR risks with derivatives.
INSERT TABLE 2. ABOUT HERE
Table 3 presents descriptive statistics of the variables use in this study for the combined
sample. The descriptive statistics by country (Portugal, Spain and Italy) can be found in
Appendix 3. Tables 3 and 4 present descriptive statistics for Tobin’s Q for our sample. Like
previous studies the median Tobin’s Q is smaller than its mean, indicating that the distribution
of Tobin’s Q is skewed to the left.
INSERT TABLE 3. ABOUT HERE
INSERT TABLE 4. ABOUT HERE
The value effects of foreign currency and interest rate derivative use: Italy, Spain and Portugal
11
3.4 Empirical Results
In common with previous empirical studies, we use the natural log of Tobin’s Q as the
dependent variable in our regression analysis. With natural log we can interpret the changes in
Tobin’s Q value as an approximate percentage change in the firm’s value. Hedging is
measured using a dummy variable with value 1 for the firms that hedge and 0 for non-
hedgers. We define hedgers as those firms that indicate in their annual reports that they hedge
foreign currency or interest rate exposure using either derivatives or other hedging techniques.
In this study we estimate the following nine models:
Model 1: All FC and/or IR hedging firms are defined as hedgers. Non-hedging sample
includes all non hedgers;
Model 2: all FC and/or IR derivative hedgers are included in hedging sample. Non-
hedging sample includes non hedgers and non derivative users;
Model 3: all FC and/or IR derivative hedgers are included in hedging sample. Non-
hedging sample includes only non hedgers;
Models 4 to 6: both Models 3 and 5 include all FC derivative hedgers in the hedging
sample, nevertheless Model 3 defines non-hedging sample as non-derivatives users and
Model 4 defines it as non-financial hedgers. Model 5 compares FC Derivative only hedgers
against non-financial hedgers.
Models 7 to 9: both Models 6 and 7 include all IR derivative hedgers in the hedging
sample, nevertheless Model 6 defines non-hedging sample as non-derivatives users and
Model 7 defines it as non-financial hedgers. Model 8 compares IR Derivative only hedgers
against non-financial hedgers (see definition in Appendix 2).
Table 5 presents the Pearson correlation coefficients between variables used in our
empirical analysis. We define Tobin’s Q as the sum of total assets and market value of equity
minus the book value of equity, all divided by total assets. Consistent with a priori
expectations, Table 5 shows that Profitability (ROCE), Geographical Diversification (GD)
and Investment Growth (IG) are positively correlated with the log of Tobin’s Q, whereas the
Access to Financial Markets (DY) is negatively correlated with the log of Tobin’s Q.
Contrary to the expectations, Industrial Diversification (ID) is positively correlated with
Tobin’s Q and Leverage (LEV) is negatively correlated with firm’s value. Firm size (Size)
has a negative correlation, but statistically significant at a 10% level only.
INSERT TABLE 5. ABOUT HERE
The value effects of foreign currency and interest rate derivative use: Italy, Spain and Portugal
12
B. Firm’s Value and Foreign Currency (FC) and Interest Rate (IR) hedging: a Tobin’s Q
Analysis
B.1. Univariate tests
We firstly compare the characteristics of hedgers and non-hedgers by testing for
equality of means and medians. Tests are performed for our full sample and separately for the
Spanish and Italian subsamples. Moreover, we also tested separately derivative hedgers
(Model 3), FC derivative hedgers (Model 5) and IR derivative hedgers (Models 8), as shown
in Appendix 4 (Panels A to C). The three chosen Models compare derivative hedgers against
non-financial hedgers, whether using derivatives or not, as described in Appendix 2 (Models
Definition).
Panel A presents the full sample results of the t-test for the equality of means and the
Wilcoxon test for the equality of medians between: (i) derivative hedgers and non-financial
hedgers; (ii) FC derivative users and non-financial hedgers; (iii) IR derivative users and non-
financial hedgers. Panels B and C present the same tests for Spanish and Italian subsamples,
respectively.
In the full sample (Panel A), the test reveals that the differences in the mean’s value of
Tobin’s Q are positive and statistically significant at 5% level, with Models 3 and 5,
supporting the hypothesis that derivative hedgers and FC derivative hedgers are higher
rewarded than non-hedgers. The differences in the mean’s value of Tobin’s Q are positive in
all the comparisons, as well as with Spanish (Panel B) and Italian (Panel C) subsamples.
The means difference in control variables Size (Size), Dividend Yield (DY) and
Geographic Diversification (GD) are always positive and statistically significant at 1%, in the
full sample and Italian subsample.
When we isolated subsamples Spanish and Italian one, Panels B and C, we didn’t find
any statistical significance for the differences in the mean’s value of Tobin’s Q.
In the Spanish subsample, the test outputs positive and statistically significant at 1%
level results only with control variables Size (Size) and Geographic Diversification (GD).
Our univariate results only support the hypothesis that on average derivatives hedging
usage increases the firm’s value, comparing with non-derivative hedgers, when using all
observation (full sample).
B.2. Multivariate analysis – Panel Data
The value effects of foreign currency and interest rate derivative use: Italy, Spain and Portugal
13
The univariate analysis in the previous section does not control for the effect of other
variables that could impact on firm’s value. Therefore we need to conduct our analysis within
a multivariate setting, controlling for the effect of the following variables: (1) Size, by using
the natural log of total assets (Size) as a proxy; (2) Profitability, using Return On Capital
Employed (ROCE) as a proxy; (3) Leverage (LEV), using book value of total debt as a
proportion of the book value of total debt plus the market value of equity as a proxy; (4)
Investment grow (IG), using ratio of capital expenditure to total sales as a proxy; (5) Access to
financial markets, using the Dividend Yield (YD) as a proxy; (6) Industrial Diversification
(ID) dummy, taking value one if the firm operates in more than one business segment as a
proxy and 0 otherwise; (7) Geographical Diversification (GD), using the ratio of foreign sales
to total sales as a proxy and we also included Industry dummies to control for the Industry
effects. Over the sample period we observed very little variation in the decision to hedge
amongst firms therefore we restricted our panel data analysis to random effect specification.
The analysis was based on the linear regression model of Allayannis and Weston (2001)
formulated as:
ititititit
ititititit
GDIDDYIG
LEVROCESizemyHedgingdumsQnNatLogTobi
εββββ
ββββα
+++++
++++=
8765
4321' (1)
Adding Industry dummies, we got the following equation
ititititititit
ititititit
INDINDGDIDDYIG
LEVROCESizemyHedgingdumsQnNatLogTobi
εββββββ
ββββα
++++++++
++++=
11...1
'
2098765
4321 (2)
Tobin’s Q: Defined as the sum of total assets and market value of equity minus the book
value of equity, all divided by total assets, represented as:
TotA
BVEMVE
TotA
BVEMVE
TotA
TotA
TotA
BVEMVETotAsQTobin
−+=
−+=
−+= 1' (3)
TotA: Book Value of total Assets
MVE: Market Value of Equity
BVE: Book Value of Equity
Results:
Our results, presented in Tables 6 to 8, display Regression Random Effects analysis.
Table 6 reports full sample results, listed non-financial firms from Spain, Italy and Portugal.
The value effects of foreign currency and interest rate derivative use: Italy, Spain and Portugal
14
Under each column, the 9 Models results are displayed according to the definitions in
Appendix 2.
As observed in previous studies, a statistically significant premium comes up when
firms use derivatives on their hedging activities. Regarding the hedging dummy coefficients,
almost all estimated coefficients are statistically significant except in Model 6 (FC derivative
only hedgers) and Model 9 (IR derivative only hedgers) for the Spanish subsample.
We got different results when full sample is separated in three subsamples: (i)
Portuguese Market; (ii) Spanish Market and (iii) Italian Market. Table 7 displays results for
Spanish firms and Table 8 reports the Italians’ firms ones. Portuguese results didn’t output
any statistical significance.
Spanish results evidence that FC hedging activity is higher rewarded than IR one, whilst
in the Italian market IR hedging seems to be the most important for the market. Comparing to
the Spanish market, Italy is more regional and focused on Economic European Community
commercial relationship, whereas Spain developed a strong Latin American countries
relationship. Several Firms quoted in Madrid stock market have their Head Office located in
that region, using a different currency from euro.
Regarding control variables, we observed that Leverage (LEV) is always negative and
statistically significant at a 1% level, within full sample or Spanish and Italian subsamples.
We can also find positive statistically significant coefficients in Geographic Diversification
(GD) and Industrial Diversification (ID). GD seems to be more important for Italian market,
whereas in Spain ID has more statistically significant coefficients.
Table 6 displays full sample test results. Hedging dummy coefficients are all positive
and statistically significant at 1% and 5% level as expected, except in Models 6 and 9. The
last one is statistically significant, at 10% level. We also found evidences that, on average,
hedging with derivatives is a higher rewarded activity (Models 2 and 3), comparing to
hedging with any kind of security (Model 1), plus 1.31% to 2.35%. Hedgers against non-
hedgers display a 12.53% premium, whilst FC(IR) derivative hedgers against non hedgers
output premiums of 13.84% and 14.88%.
The results from IR and FC derivative hedgers separately are very similar. Except with
FC(IR) derivative only users (Models 6 and 9). Model 9, IR only hedgers against non-hedgers
displays a coefficient statistically significant at 10% level, whilst the results with FC
derivative only hedgers didn’t display any statistical significance. Models 4 to 6, FC
derivative hedges, output premiums from 13.63% to 14.56%, and in Models 7 to 9 (IR
derivative hedgers) we have premiums from 10.43% to 14.70%.
The value effects of foreign currency and interest rate derivative use: Italy, Spain and Portugal
15
Several control variables’ coefficient output the expected signal, but only some of them
are statistically significant. The natural log of total assets (Size), a proxy for firm size,
displays a negative sign as in Lang and Stulz (1994), but rarely output statistical significance.
Contraire to expectations, on average, firms with higher leverage (LEV) have lower value and
the corresponding estimated coefficients are statistically significant, in all models, at 1%
level, as it was found in Greek stock market analyzed by Kapitsinas (2008).
The Investment Grows (IG) is statistically significant only in Model 7, at a 10% level,
and the average effect is positive as expected, in line with most previous research, as well as
the Geographic Diversification (GD). However there are some theories suggesting that
Geographic Diversification is an outgrowth of Agency problems, suggesting a negative
relation with the firm’s value.
Also Industrial Diversification (ID) outputs several statistically significant coefficients,
but positive against our expectations. Although, Profitability (ROCE) coefficients didn’t
display any statistical significance and the relation with firm’s value is negative, against a
priori expected.
Dividend Yield (DY) level is almost always negatively related with firm’s value as
expected, supporting the theory that ability of the firm to access to the financial markets are
negatively correlated with firms’ value, as they tend to invest in several projects even without
properly expected profits. Though, the model didn’t display any statistical significance.
INSERT TABLE 6. ABOUT HERE
To better recognize any differences between each country, we separated full sample in
three subsamples: Portuguese, Spanish and Italian markets. As already explained, Portuguese
subsample results did not output any statistical significance relationship between hedging
activity and firm’s value. So, we didn’t include its results in our paper.
Comparing coefficient premiums’ level, values are much higher in Spanish market than
in Italian one. In Spanish subsample, we got statistically significant coefficients from 18% to
26%, at 5% level, against 11% to 14% on Italian one.
Regarding control variables, we also found some differences. Whilst in Spanish Market,
the proxy for capacity to access to financial markets, Dividend Yield –DY, evidences a
negative statistically significant relationship with firm’s value, in Italian Market is positive
and rarely statistically significant. Geographic Diversification (GD) seems to be more
rewarded by Italian Investors, whilst Spanish one better reward Industrial Diversification
The value effects of foreign currency and interest rate derivative use: Italy, Spain and Portugal
16
(ID). Leverage (LEV) is equally high statistically significant and negatively correlated with
firm’s value.
Table 7 displays Spanish subsample results performed by Random Effects Regression.
As already referred, there is evidence that derivative financial hedging is highly rewarded by
Spanish market. Also FC derivative hedging activity displays higher statistical significant
premiums, at a 5% level, than IR hedging activity: 22% and 26% in Model 4 and 5,
comparing to 16% and 22% in Models 7 and 8.
INSERT TABLE 7. ABOUT HERE
Table 8 displays Italian subsample results performed by Panel Random Effects
Regression. As already referred, results also evidence that financial hedging activity is
rewarded by Italian market. Moreover, Italian market seems to better reward IR derivative
hedging activity. Models 7 and 8 display statistically significant premiums of 12% and 14%,
at 5% level, whereas FC hedging activity premium is only 9% and 11% (Models 4 and 5), at a
only 10% level significance.
INSERT TABLE 8. ABOUT HERE
In order to robust our full sample and subsamples results we also performed Panel
Between Effects Regression and Pooled OLS regression with robust standard errors
(Appendix 5 and 6, Panels A to C). Considering hedging dummies coefficient statistical
significance, results are consistent with Random Effects Regression ones, except that control
variable Investment Growth (IG) coefficients are mostly statistically significant and positively
correlated with firms’ value with full samples and both subsamples, Spanish and Italian one.
4. CONCLUSIONS (TO FINISH)
This study examines the value effects of FC and IR derivative hedging activity for large
non-financial firms quoted in Lisbon, Madrid and Milan stock markets during the period 2006
to 2008. During a period of extreme economic and financial distress our empirical results
indicated a hedging premium of 14 percent for the combined sample.
The value effects of foreign currency and interest rate derivative use: Italy, Spain and Portugal
17
When we carry out separate analysis for firms in each country we find that the hedging
premium is higher for Spanish firms, around 20 percent, and approximately 11 percent for
Italian firms. For the Portuguese firms in our sample there is no evidence that hedging activity
is rewarded by investors. We also found evidence that FC hedging activity is higher rewarded
in Spain, whilst Italian market better rewards IR hedging activity. It might be because the
Spanish economy is far more open than the Italian economy. Spanish firms have developed
strong trading ties with economic agents in Latin America.
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The value effects of foreign currency and interest rate derivative use: Italy, Spain and Portugal
20
Variables Variable Description Source
Tobin's Q Q Defined as the sum of total assets and market value of equity minus
the book value of equity, all divided by total assets.
Datastream
Market Value of
Equity
MVE Share price multiplied by the number of shares in issue (ordinary and
preferences).
Datastream
Book Value of
Equity
BVE Equity capital and Reserves. Datastream
Total Assets TotA Book value of total assets. Datastream
Return On Capital
Employed
ROCE Pre-tax profit plus total interest charges divided by total capital
employed plus borrowing repayable within 1 year less total intangibles
(Obtained directly from Datastream database - WC08376).
Datastream
Leverage LEV Book value of total debt as a proportion of the book value of total
debt plus the market value of equity.
Datastream
Investment Grow IG Calculated as a ratio of Capex (Capital Expenditure) to total sales Datastream
Dividend Yield DY Gross dividend divided by share prices. Datastream
Industry
diversification
ID Dummy : Industry diversification dummy takes on the value of the 1 if
the firm operates in more than one business segment and 0, else.
Annual Report
Geographic
Diversification
GD Foreign sales divide by total sales (Foreign sales ratio). Annual Report
& DataStream
All Variable Definitions (Except Industry Dummies)
TABLE 1
TABLE 1 presents de definitions of variables employed on the analysis of hedging value for non-financial firms quoted in
Lisbon, Madrid and Milan Stock Markets. It provides the variable's definition and their source.
Tobin's Q s the dependent variable, proxy for the firm value. The following variable: Total Assets, Return On Capital
Panel A reports univariate test results withLN Tobin's Q andcontrol variables used in multivariate approach.In particular it shows the mean,median andstandarddeviation for derivative hedgers and non-derivative hedgers, including firms quoted in Lisbon, Madrid and Milan stock market. Moreover, it also displays the
difference in the means and medians as well as p-values of mean tests,using Levene's Test for equality of variance and t-test for equality of means. Wilcoxon wasused to the comparison of medians and to give the corresponding p-values. N is the number of observations (firms). Tests were conductedseparately for three
different Models:Derivative Hedgers (Model 3);FC Derivative Hedgers (Model 5) and IR Derivative Hedgers (Model 8).The definition of variables and models are
presented in Table 1 and Appendix 3, respectively.
Model 3 - Derivative Hedgers Model 5 - FC Derivative Hedgers Model 8 - IR Derivative Hedgers
Panel A: Full Sample, includes Lisbon, Madrid and Milan Stock Markets
The value effects of foreign currency and interest rate derivative use: Italy, Spain and Portugal
Panel B reports univariate test results with LN Tobin's Q and control variables used in multivariate approach. In particular it shows the mean, median and standard deviation for derivative hedgers and non-derivative hedgers, including firms quoted in Madrid stock market. Moreover, it also displays the difference in the
means and medians as well as p-values of mean tests, using Levene's Test for equalityof varianceand t-test for equalityof means.Wilcoxon was used to thecomparison of medians and to give the corresponding p-values. N is the number of observations (firms). Tests were conducted separatelyfor three differentModels: Derivative Hedgers (Model 3); FC Derivative Hedgers (Model 5) and IR Derivative Hedgers (Model 8). The definition of variables and models arepresented in Table 1 and Appendix 3, respectively.
Model 3- Derivative Hedgers Model 5 - FC Derivative Hedgers Model 8 - IR Derivative Hedgers
Panel B: Spanish Sample, includes non-financial firms quoted in Madrid Stock Market
The value effects of foreign currency and interest rate derivative use: Italy, Spain and Portugal
Panel C reports univariate test results with LN Tobin's Q and control variables used in multivariate approach. In particular it shows the mean, median and
standard deviation for derivative hedgers and non-derivative hedgers, including firms quoted in Milan stock market. Moreover, it also displays the difference in themeans and medians as well as p-values of mean tests,usingLevene's Test for equality of variance and t-test for equality of means. Wilcoxonwas used to
the comparison of medians and to give the corresponding p-values. N is the number of observations (firms). Tests were conducted separately for three
different Models: Derivative Hedgers (Model 3); FC Derivative Hedgers (Model 5) and IR Derivative Hedgers (Model 8). The definition of variables and models
are presented in Table 1 and Appendix 3, respectively.
Model 3 - Derivative Hedgers Model 5 - FC Derivative Hedgers Model 8 - IR Derivative Hedgers
Panel C: Italian Sample, includes non-financial firms quoted in Milan Stock Market
The value effects of foreign currency and interest rate derivative use: Italy, Spain and Portugal
34
FC(IR)
Hedgers
Tobin's Q Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9
Panel A: Full Sample - non-financial firms quoted in Lisbon, Madrid and Milan Stock Markets
Effects of Derivatives usage on firms' value - regression results: Appendix 5, Panel A, presents the results for Regression Between Effects. The
dependent variable is the natural logarithm of Tobin's Q, as a proxy for firm's value, is calculated as the division of the sum of total assets and market
value of equity minus the book value of equity, all divided by total assets. Under each column we analyzed a different definition of hedging sample,
Models 1 to 9. The variable Hedging is always a dummy variable, equal to 1 when firm hedge according to the question of each Model (hedger, Model
1; derivative hedger, Model 2 amd 3; FC derivative hedger, Models 4 to 6; IR derivative hedger, Models 7 to 9). Size is the natural logarithm of total
assets, a proxy for firm value. LEV stand for Leverage. IG stands for investment grows. ID dummy stands for diversification in industrial segments. GD
stands for geographic diversification. DY stands for dividend yield, a proxy for the access to financial markets. ROCE stands for the return on capital
employed, a proxy for profitability. And C stands for the constant. ***, ** and * denote significance at the 1%, 5% and 10%, respectively. t-statistics
are based on White standard errors and appears between (). The definition of the variables and Models are presented in Table 1 and Appendix 3,
Pooled OLS Standards Errors Adjusted for Clustering at the Firm Level Analyze
Panel A: Full Sample - non-financial firms quoted in Lisbon, Madrid and Milan Stock Markets
Effects of Derivatives use on firm's value - regression results: Appendix 6, Panel A, presents the results for Pooled OLS Standard Adjusted
for Clustering at the Firm Level - Firm is a variable that assume values from 1 to 3, depending on the market: 1 for Portuguese Market; 2 for
Spanish Market and 3 for Italian Market. The dependent variable is the natural logarithm of Tobin's Q, as a proxy for firm's value, is
calculated as the division of the sum of total assets and market value of equity minus the book value of equity, all divided by total assets.
Under each column we analyzed a different definition of hedging sample, Models 1 to 9. The variable Hedging is always a dummy variable,
equal to 1 when firm hedge according to the question of each Model (hedger, Model 1; derivative hedger, Model 2 and 3; FC derivativehedger, Models 4 to 6; IR derivative hedger, Models 7 to 9). Size is the natural logarithm of total assets, a proxy for firm value. LEV stand
for Leverage. IG stands for investment grows. ID dummy stands for diversification in industrial segments. GD stands for geographic
diversification. DY stands for dividend yield, a proxy for the access to financial markets. ROCE stands for the return on capital employed, a
proxy for profitability. And C stands for the constant. ***, ** and * denote significance at the 1%, 5% and 10%, respectively. t-statistics
appear under variables coefficients. The definition of the variables and Models are presented in Table 1 and Appendix 3, respectively.