Florbela Curto Judge Porto Paper 5 June11

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 Judgeb
a
ISCTE Business School, Av. Prof. Aníbal Bettencourt, 1600-189 Lisbon, Portugal
b
Middlesex University Business School, The Burroughs, Hendon, London NW4 4BT, UK
FGCunha@montepio.pt
dias.curto@iscte.pt
a.judge@mdx.ac.uk
Very preliminary draft: Please do not quote without permission.
1
Corresponding author: Florbela Galvão da Cunha.

The value effects of foreign currency and interest rate derivative use: Italy, Spain and Portugal
I
The Value Effects of Foreign Currency and Interest Rate Derivatives
Use: Evidence from Italy, Spain and Portugal
ABSTRACT
This study presents empirical evidence on the valuation effects of Foreign Currency
(FC) and Interest Rate (IR) hedging with derivatives for Italian, Spanish and Portuguese
firms. Using Tobin’s Q as a proxy for firm value, we find a significant hedging premium for
our full sample. These results seem to be driven by Spanish and Italian firms. When we carry
out separate analyses by country we find evidence of a significant foreign currency and
interest rate hedging premium for firms in Spain and Italy ranging between 11 and 39 percent
but no hedging premium for Portuguese firms.
Keywords: Firm’s value; Corporate hedging; Derivatives; Foreign currency hedging; Interest
rate hedging.
JEL Classification: F30; G32

2
1. INTRODUCTION
In the perfect Modigliani and Miller (M&M) World (1958), risk management as part of a
firm’s corporate financing policy is deemed not to increase firm value, since shareholders can
mitigate the adverse effects of financial price volatility by holding well-diversified portfolios.
Under this M&M framework, corporate hedging policy seems to be irrelevant. The positive
theory of corporate hedging, developed by Smith and Stulz (1985), argues that imperfect
capital markets provide a justification for corporate hedging. Smith and Stulz’s (1985)
seminal work has stimulated many empirical studies looking at why firms hedge. Only
recently have researches asked the more important question does hedging increase firm value.
In this paper, we contribute to this literature by examining the value effects of hedging with
derivatives for a sample of Portuguese, Spanish and Italian non-financial listed firms. We
employ hedging and derivatives dated disclosed in annual reports for the years 2006 to 2008.
Our sample period encompasses the recent financial crisis and ensuing recession and therefore
provides an opportunity to examine the value of hedging during a period when its benefits are
likely to be greatest, that is, during a period of large economic and financial distress. The
issue of whether hedging increase firm value is also important in the context of recent
proposals on the regulation of the use of “Over the Counter” (OTC) derivatives which aims to
prohibit their use.
In October 2008, a month after the collapse of Lehman Brothers, financial market
regulators in the European Union began an investigation into the global derivatives market
looking at ways of reducing systemic risk within the financial sector. The concern for
European regulators is that when a derivatives trade goes “bad”, an outcome that is more
likely when derivatives are used for speculation, they have the potential to spread the negative
consequences of defaults to all corners of the global financial market. Regulators in both the
US and Europe are primarily concerned about the systemic risks arising from positions in the
OTC derivatives market. Establishing central clearing houses or central counterparties (CCPs)
is considered a way of reducing systemic risk related to derivatives transactions. Instead of
being exchanged privately via the OTC market, they could be processed through an
intermediary, a move which is expected to improve transparency and reduce risk. However,
non-financial firms using derivatives to hedge their risks would be required to keep large
amounts of extra financing available for the purposes of putting up margin dependent on daily
mark to market valuations. Capital and undrawn lines of credit will need to be held against

3
potential margin for significant price changes in the price of the asset underlying the
derivative transaction.
Companies will be required to be able to pay margin to their contracted counterparty
for negative positions during the life of a derivative contract although the offsetting, hedged,
underlying cashflows will not materialize until the maturity of the underlying exposure. While
margin payments would be received for derivatives positions showing a gain, they could not
be used in the business prior to maturity as this cash could flow out again just as quickly as
underlying prices moved in the opposite direction.
One of the advantages of OTC derivates is that they usually require no cash flows
prior to maturity. But if the move to CCPs will require non-financial firms to provide
collateral to their counterparty daily during the life of the derivative hedge, the hedge cash
flows become immediate and companies would have to finance them up to maturity. This
could be a significant financial burden for many companies particularly at a time when the
flow of bank credit to the corporate sector is running at historically low levels. The net result
could be an increase in liquidity risk for firms. Another problem with enforcing clearing on
non-financial firms is that it could stop them meeting hedge accounting requirements, as
standardised, exchange-traded contracts would not match the financial exposures on their
balance sheet.
Many voices from the corporate sector are arguing that there is a strong possibility that
compulsory clearing will hamper firms’ ability to hedge because they would have to post
initial and variation margin, utilizing a firm’s scarce working capital. For example, Richard
Raeburn, chairman of the European Association of Corporate Treasurers in London, is
lobbying hard for non-financial firms to be exempt from being required to post margin.
Speaking to Risk Magazine (16 June 2010 - Corporates should be forced onto central
counterparties – BIS, Christopher Whittall, http://www.risk.net/risk-
magazine/news/1686244/corporates-forced-central-counterparties-bis), he says,
"Forcing corporates into central clearing creates an unmanageable liquidity risk challenge.
You can also argue that incremental systemic risk is created because of the hazards
corporates will face if they are required to set aside almost unlimited liquidity to meet
uncertain future margin calls. I would argue that faced with the volatility of currency and
interest rate markets, corporates are left with a very large contingent exposure to post
collateral if the mark to market goes against them…If corporates don't get some kind of

4
exemption from central clearing, they'll basically just see prices go up, as banks will have to
pass prices on. That's the biggest issue at the moment."
Christopher Whittall from Risk Magazine points out that many corporate treasurers have
previously told Risk of their opposition to central clearing. He provides the following quote
from a treasurer of a major airline,
"When fuel prices spiked prior to the financial crisis and then dropped significantly, the
mark-to-market impact was huge. Margin calls would have tied up a good few $100 million at
the very time we needed the money. Clearing would be a disaster: all it will do is stop people
hedging as they can't afford it." (16 June 2010 - Corporates should be forced onto central
counterparties – BIS, Christopher Whittall, http://www.risk.net/risk-
magazine/news/1686244/corporates-forced-central-counterparties-bis)
Corporate end-users are lobbying hard to be exempt from any clearing obligations, arguing
that their use of derivatives doesn’t impose any systemic risk and that any mandatory clearing
requirement would require them to eat into vital working capital to meet margin calls by
CCPs. Derivative end–users are concerned that the requirement to centrally clear all OTC
derivatives trades will force them to put aside large amounts of cash for margin calls and
consequently increase their costs of hedging. This will lower the net benefits of hedging and
hence decrease firm value. The tying up of cash in this way has the potential to adversely
affect firm value in another way, (as firms may be forced to forego valuable investment
opportunities) as that cash could otherwise be deployed in the firm, such as for investment
purposes. For practitioners it seems that there are clear economic and financial implications
to the proposed clearing rules. Firstly, increased costs of hedging leading to less hedging and
therefore firms subjected to greater financial price exposure. It follows that this could result
in greater credit risk for firms’ financial counterparties (such as the banks that lend to
corporates) which could increase systemic risk within the financial sector. This outcome
would be opposite to that envisaged by regulators. Secondly, firms cash resources being
diverted away from productive use, such as funding value increasing investment, for the
purposes of meeting margin and collateral requirements on their derivative transactions. The
implications of this would be a likely reduction in corporate economic activity with obvious
consequences for employment, growth and the real economy.
Given the strong possibility that the proposed clearing and margin obligations could
significantly hinder firms’ ability to hedge their financial price exposures an important
question is whether hedging with derivatives is value enhancing. If it can be demonstrated
that derivatives hedging increases firm value then this may help to dissuade regulators of

5
imposing central clearing on the corporate users of derivatives which might deter such value
generating activity.
In this study we examine the value effects of hedging for the whole sample that
combines firms from Portugal, Spain and Italy and then separately for each country. For the
sample as a whole we find a significant positive hedging premium of around 13 percent.
However, this masks significant variation in the value of hedging across our sample countries.
We find no hedging premium for Portuguese firms, a hedging premium of 12 percent for
Italian firms and around 20 percent for Spanish firms. Our Portuguese sample is relatively
small and the insignificant premium might be a symptom of this. The remainder of the paper
proceeds as follows. Section 2 presents an overview of the empirical literature on the value
effects of hedging. Section 3 discusses the sample construction, defines the variables used and
discusses our empirical results,. Section 4 presents our concluding remarks.
2. Overview of the Empirical Literature (to finish)
The study by Allayannis and Weston (2001) is one of the first papers to look at whether
hedging increase firm’s value. Using data on the use of foreign currency derivatives (FCDs)
by 720 large US non-financial firms they that, on average, non-financial firms that hedge
currency risks with derivatives have 4.9 percent higher value than firms that don’t use FCDs.
Kapitsinas (2008) analyzes the impact of derivatives usage on the value of 81 Greek
non-financial firms listed on the Athens stock exchange for the years 2004-2006. Using
Tobin´s Q to proxy for firm value he finds that Greek firms using derivatives had a hedging
premium of 4.6 percent, similar in magnitude to that found by Allayannis and Weston (2001).
Mackay and Moeller (2007) estimate the value of corporate risk management for 34 US
oil refiners. They find that hedging concave revenues and leaving concave costs exposed,
generates between 2% and 3% increase in a refiner´s firm’s value.
There have been many studies that have looked into the reasons for why non-financial firms
hedge, in UK (Clark and Judge, 2006; Judge, 2006) or in USA markets (Nance et al., 1993;
Graham and Rogers, 2002), but also in Portugal (Mota, 2002; Ferreira and Mota, 2005), Spain
(González et al., 2007), Italy (Bodnar et al., 2000; Bodnar et al., 2008) or even including
several countries all over the world (Bartram et al, 2006; Foo and Yu, 2005).
3. Sample, Data and Methodology

6
One of the key obstacles any study on corporate hedging faces is the availability of
reliable data on firms hedging practices. Because of the lack of disclosure in financial
statements early studies on corporate hedging made use of surveys to CFOs and corporate
treasurers to identify whether and how firms were hedging. However, as successive
International Financial Reporting Standards (IFRS) have been implemented, the quality of
disclosure on hedging practices and the use of financial derivative instruments in firms’
annual reports has improved. Firms in countries that have signed up to these accounting
standards are required to disclose the use of financial derivatives and whether they are used
for hedging or trading. Therefore, recent studies have employed hedging and derivative
disclosures in annual reports to determine whether firms are hedging and which types of
derivatives firms are using for hedging. As financial disclosures in annual reports of listed
firms in Italy, Spain and Portugal come under the regulation of IFRS we use financial
instrument disclosures to determine whether firms are hedging and using derivatives.
Our sample comprises 966 firm year observations of non-financial firms quoted in the
Lisbon, Madrid and Milan stock markets from 2006 to 2008. As a proxy for the firm’s value,
we employ Tobin’s Q. The main goal of this work is to examine whether derivatives hedging
by non-financial firms quoted in Lisbon, Madrid and Milan stock markets, is value enhancing.
For the sample as a whole and each country sample we analyzed 9 different combinations of
hedging/non-hedging firms, defined as follows: (1) Model 1, comparing financial risk hedgers
against non-financial hedgers; (2) Model 2 and 3, comparing derivative financial risk hedgers
against non-derivative hedgers and non-financial hedgers, respectively; (3) Models 4 and 5,
comparing FC derivative hedgers against non-derivative hedgers and non-financial hedgers,
respectively; (4) Model 6, comparing FC derivative only hedgers against non-financial
hedgers; (5) Models 7 and 8, comparing IR derivative hedgers against non-derivative hedgers
and non-financial hedgers, respectively; (6) Model 9, comparing IR derivative only hedgers
against non-financial hedgers (as described in Appendix 2).
3.1 Variable Definitions
Tobin’s Q (Q1), the proxy for the firm value, is the dependent variable and is defined as
the sum of total assets and market value of equity minus the book value of equity, all divided
by total assets (Jin and Jorion, 2006; Belghitar et al., 2008; Pramborg, 2004). For robustness
we also use two additional proxies for Tobin’s Q: (1) Tobin’s Q2, computed as the market
value of equity to the book value of total assets (Mackay and Moeller, 2007) and (2) Tobin’s

7
Q (Q3), as market value of equity to book value of equity (Kapitsinas, 2008). Our results were
qualitatively similar across the three different definitions of Tobin’s Q. In the paper we report
the results for Q1 as this is the more commonly used measure of Tobin’s Q.
To infer that hedging increases firm’s value we have to control the effect of all other
variables that could impact on firms’ value. In common with previous studies, we control for
(1) Size, (2) Profitability, (3) Leverage, (4) Investment grow, (5) Access to Financial Markets,
(6) Industrial Diversification, (7) Geographical Diversification and (8) Industry dummies.
1. Size:
There is no clear evidence about size influence on firm’s value. According to Peltzman
(1977) analysis, size leads to a higher efficiency. Also, there are several previous studies
consistent with the fact that firm’s size tends to increase the derivatives use, because of their
economies of scale in hedging costs. Ross (1996) argued that economies of scale exist in
hedging. His results were confirmed by Tufano (1996), Mian (1996) and Berkman and
Bradbury (1996). Dolde (1993) concluded that large firms would use more derivatives
because of their higher investment in personnel, training and software to set up an in-house
risk management program.
Even though there are some evidences that small firms would better benefit from
derivatives hedging activity than the biggest ones which could mitigate financial risks with
naturally offsetting positions in their vast operations (Crabb, 2003). According to this author,
the unique definitive tools for financial risk management that is available for small business
are the financial derivatives. However, some studies indicate that smaller businesses do not
use derivatives as extensively as large ones. Some reasons are referred to explain this
behavior, as hedging costs and treasurer academic qualification.
In our work, we decided to control the effect of Size in firm’s value using natural
logarithm of total Assets as a proxy for it. Allayannis and Weston (2001) also used the natural
log of Total Assets to control the effect of size and alternatively also used the log of total sales
with similar.
2. Profitability:
It is expected that firm’s profitability has a positive impact on firm’s value. Profitability
was used as a control variable in previous studies. We used Return on Capital Employed
(ROCE), defined as the pre-tax profit plus total interest charges as a portion of total capital
employed plus borrowing repayable within 1 year less total intangibles. A positive sign for
the estimated coefficient is expected.

8
3. Leverage:
To control for the effect of Leverage we used the book value of total debt divided by the
book value of total debt plus the market value of equity. Allayannis and Weston (2001) also
used Leverage as a control variable, but defined it as the long-term debt divided by
shareholders equity. A positive sign for the relation is expected.
4. Investment Growth:
Because hedging firms are more likely to have larger investment opportunities
(Allayannis and Weston, 2001; Belghitar et al., 2008), such control is important.
Additionally, Myers (1977) and Smith and Watts (1992) have also argued there are evidences
that firm’s value also depends on the future investment opportunities. Regarding this
reference, we also decided to include this variable. Similar to Yermack (1996), Servaes
(1996) and Allayannis and Weston (2001), we used the ratio of capital expenditure to sales as
a proxy for investment opportunities. Some previous studies had also used R&D expenditures
as a proxy for investment opportunity. A positive relation to the firm’s value is expected.
5. Access to Financial Markets:
If firms have limited access to financial markets, their Q ratios may be higher because
they tend to undertake only positive net present value (NPV) projects. As a proxy for the
ability to access to financial markets, we chose the dividend yield. Some studies used a
dividend dummy (Allayannis and Weston, 2001). We therefore expect a negative coefficient.
Both, dividend yield or dividend dummy, are referred in previous studies with negative
relation expectation.
6. Industrial Diversification:
Several theoretical arguments suggest that diversification increases value (Williamson,
1970; Lewellen, 1971), while other arguments suggest that diversification is negatively
related to the firm’s value, due to the agency problems between managers and shareholders
(Jensen, 1986). Even though, there are substantial empirical evidences suggesting that
industrial diversification is negatively related to firm’s value (Berger and Ofek, 1995; Lang
and Stulz, 1994; Servaes, 1996; Allayannis and Weston, 2001).

9
To control for the industrial diversification, we used a dummy variable that equals 1 if
the firm operates in more than one segment and 0 otherwise. In our full sample, 69% of the
firms are diversified across industries. Allayannis and Weston (2001) found in their sample a
63% of the firms that diversified industrial segments. A negative relation is expected.
7. Geographic diversification:
Several previous studies suggest that operating in several countries increases firm’s
value (Morck and Yeung, 1991; Bodnar et al., 2000). Considering foreign sales as operations
abroad, we choose the foreign sales to total sales ratio as a proxy for geographic
diversification. This ratio was also used in several previous studies (Allayannis and Weston,
2001; Belghitar et al., 2008). A positive relation is expected.
8. Industry Dummies
To control for the Industry effects, we include 12 different Industry Groups: Vehicles
& Transportation; Food Industry; Healthcare & Pharmaceutical; Equipments (Electrics and
Electronics); Business Support; Distribution & Where housing; Utilities; Energy Sources &
Chemicals; Show Business & Accommodation; Construction Industry; House Hold Industry
and Textile Industry (See Appendix 1).
Table 1 presents the independent variables and their expected relationship with firm
value.
INSERT TABLE 1. ABOUT HERE
3.2 Sample and Descriptive Statistics
The sample includes all 966 firm-year observations of non-financial firms quoted in
Lisbon, Madrid and Milan stock markets during the period 2006 to 2008. We restrict our
sample to non-financial firms because financial firms are usually both users and
intermediaries in derivative transactions. Financial firms often act as market makers and
therefore their motives and behavior are likely to be very different from those of non-financial
firms and hence their inclusion could bias our results.

10
Since the International Financial Reporting Standards (I.F.R.S.) impose firms to report
the information of hedging activities and the derivative usage in their annual reports, it is
easier to get qualified and standard hedging activity information. All firms in the three
analyzed countries, Portugal, Spain and Italy were obliged to reflect IFRS rules in their annual
reports. All data included in our tests was collected from annual reports and Datastream
database.
This study classifies as IR (FC) hedgers firms those that clearly refer this matter in their
2006, 2007 and 2008 annual reports. We found, in general, that non-financial firms use
derivatives to reduce the financial risk exposure, rather than to speculate.
Table 2 contains information about the number of FC (IR) hedgers amongst the sample
of 966 firm-year observations. 74.2% of these firms hedge and 90.0% of hedgers are
derivative users (Panel A). About 61.4% of derivative users are classified as both foreign
currency and interest rate hedgers. While 16.3% of them only hedge foreign currency
exposure, 22.3% hedge exclusively interest rate exposure (Panel B).
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.
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.

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.

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

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
BVEMVETotA
sQTobin
−
+=
−
+=
−+
= 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.

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%.

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.
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

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.
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.
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.

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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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
Employed (ROCE) , Leverage, Investment Grow , Dividend Yield , Dummy Industrial Diversification and Geographic
Diversification are used as control variables in the multivariate approach. Following the previous studies, we chose these
control variables as the main ones that can also influence firm's value and were also used in previous studies.

21
Full Sample
Nr % Nr % Nr %
Portugal 120 84 70.0% 75 89.3% 9 7.5%
Spain 351 270 76.9% 228 84.4% 42 12.0%
Italy 495 363 73.3% 342 94.2% 21 4.2%
Total 966 717 74.2% 645 90.0% 72 7.5%
Derivative
FC(IR) users
Nr % Nr % Nr %
Portugal 75 51 68.0% 6 8.0% 18 24.0%
Spain 228 147 64.5% 39 17.1% 42 18.4%
Italy 342 198 57.9% 60 17.5% 84 24.6%
Total 645 396 61.4% 105 16.3% 144 22.3%
Full Sample FC + IR hedgers
Nr % Nr % Nr %
Portugal 120 57 47.5% 69 57.5% 51 42.5%
Spain 351 186 53.0% 189 53.8% 147 41.9%
Italy 495 258 52.1% 282 57.0% 198 40.0%
Total 966 501 51.9% 540 55.9% 396 41.0%
Full Sample
Nr % Nr %
Portugal 120 6 5.0% 18 15.0%
Spain 351 39 11.1% 42 12.0%
Italy 495 60 12.1% 84 17.0%
Total 966 105 10.9% 144 14.9%
Table 2 presents data on the number of Foreign Currency (FC) and Interest Rate (IR) hedgers
amongst the sample of 966 observations of non-financial firms quoted in Lisbon, Madrid and Milan
stock exchange, in 2006, 2007 and 2008. A firm is defined as a FC(IR) hedger if it provides a
qualitative disclosure of any FC(IR) hedging activity on its Annual Report. Panel A provides data on
the number of FC (IR) hedging and the FC(IR) derivatives hedging. A firm is defined as a derivative
hedger if this information is clearly referred on its Annual Report. Panel B presents information about
FC, IR and FC + IR derivatives hedging firms, amongst the 645 observations of Derivative users,
while Panel C displays the same information but comparing to the full sample. Panels D displays
information about FC and IR only hedgers.
FC only hedgers IR only hedgers
Panel D: Proportion of Firms using FC(IR) derivatives only
Panel C: Proportion of Firms using FC(IR) derivatives in full sample
Table 2
Foreign Currency (FC) and Interest Rate (IR) Hedging
Firms using IR and FC
derivatives
Firms hedging IR and
FC exposures
FC + IR Derivative
users FC only hedgers
Panel A: FC (IR) hedgers
Panel B: Derivative FC (IR) users, as a proportion of derivative users
Firms out of full
sample
FC only hedgers
IR hedgersFC hedgers

22
Variables N Mean Median Std.Dev Min Max
Tobin's Q 963 1.64 1.32 1.77 0.43 28.97
Market Value of Equity (millions) 964 6,191.6 431.5 29,250.5 0.3 463,646.1
Book Value of Equity (millions) 964 12,011.5 204.5 177,302.1 -126.6 3,697,213.0
Total Assets (millions) 964 45,542.2 637.7 693,176.9 0.0 14,452,740.0
Return on Capital Employed - ROCE
(%)
941 63.7% 6.9% 38.4% -501.4% 89.3%
Leverage (%) 963 34.5% 31.5% 23.4% 0% 99.5%
Investment Growth (%) 952 12.6% 5.3% 53.4% 0% 1380%
Dividend Yield (%) 952 1.6% 1.1% 2.3% 0% 39.6%
Industry Diversification (dummy) 963 0.69 1 0.46 0 1
Geographic Diversification- Foreign
sales ratio (%)
932 34.5% 29.6% 30.4% 0% 100.0%
Table 3 summarizes statistical information about variables used in this study. Tobin's Q is computed as the sum
of total assets and market value of equity minus the book value of equity, all divided by total assets. Market
Value of Equity is defined as the share price multiplied by the number of shares in issue (ordinary and
preferences) and Book Value of Equity is defined as equity capital plus reserves, both used to calculate Tobin's
Q variable, as well as total assets. Total Assets refers to book value of total assets. Return on Capital
Employed (ROCE) is calculated as Pre-tax profit plus total interest charges divided by total capital employed
plus borrowing repayable within 1 year less total intangibles. Leverage is measured as book value of total debt
as a proportion of the book value of total debt plus the market value of equity. Investment Grow is calculated
as a ratio of Capex (Capital Expenditure) to total sales. Dividend Yield is the gross dividend divided by share
price. Industry Diversification dummy takes on the value of 1 if the firm operates in more than one business
segment. Geographic Diversification is the foreign sales divided by total sales. We consider foreign exportation
even if it is refers to an European Economic and Monetary Union (EMU) country.
Table 3
Descriptive Statistics
Panel A: Full sample - non-financial firms quoted in Lisbon, Madrid and Milan Stock Markets
Variables N Mean Median
Full Sample 963 1.639 1.32
Portuguese Market 120 1.33 1.23
Spanish Market 350 1.98 1.38
Italian Market 493 1.47 1.30
Tobin's Q1
Table 4
Table 4 summarizes statistical information about Tobin's Q
definitions used in this study, considering three years observations
(2006, 2007 and 2008). Full sample was separated in their three
different susamples: Portuguese, Spanish and Italian markets.
Tobin's Q Descriptive Statistics Information

23
Correlation
t-Statistic
Probability LNQ SIZE LEV IG ID GD DY ROCE
LNQ 1.0000
-----
-----
SIZE -0.0796 1.0000
-2.3842 -----
0.0173 -----
LEV -0.5719 0.1749 1.0000
-20.8081 5.3025 -----
0.0000 0.0000 -----
IG 0.1096 0.0123 0.0532 1.0000
3.2927 0.3671 1.5914 -----
0.0010 0.7137 0.1119 -----
ID 0.0173 0.2271 0.0367 -0.0486 1.0000
0.5168 6.9592 1.0954 -1.4520 -----
0.6054 0.0000 0.2736 0.1469 -----
GD 0.0061 0.1276 0.0022 -0.0673 -0.0273 1.0000
0.1815 3.8392 0.0660 -2.0137 -0.8145 -----
0.8561 0.0001 0.9474 0.0443 0.4156 -----
DY -0.0801 0.2512 0.0603 0.0103 -0.0043 -0.0141 1.0000
-2.3981 7.7481 1.8027 0.3085 -0.1275 -0.4202 -----
0.0167 0.0000 0.0718 0.7578 0.8986 0.6744 -----
ROCE 0.0530 0.0954 -0.1861 -0.2361 0.0262 0.0164 0.0578 1.0000
1.5854 2.8606 -5.6532 -7.2521 0.7835 0.4890 1.7273 -----
0.1132 0.0043 0.0000 0.0000 0.4335 0.6250 0.0845 -----
Table 5
Pearson correlation
Table 5 reports Pearson Corrrelation coefficients of variables used in the tests. LNQ is the natural log of sum of total
assets and market value of equity minus the book value of equity, all divided by total assets. Size is a natural log of
total assets and represents the firm size. ROCE, is a proxy for profitability. LEV is the Leverage. IG is the Investment
Grow. DY is Dividend Yield, the proxy for access to the financial markets. ID is a dummy variable and represents the
Industrial Diversification. GD is the Geographic Diversification, calculated as a foreign ratio. The definition of the
variables are presented in Table 1.

24
FC(IR)
Hedgers
Tobin's Q Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9
Hedging
dummy
0.1253 **
(2.4800)
0.1384 *** 0.1488 ***
(2.8800) (2.7600)
FC hedging
dummy
0.1363 ** 0.1456 ** 0.0697
(2.4400) (2.3700) (0.9800)
IR hedging
dummy
0.1338 *** 0.1470 *** 0.1043 *
(2.7900) (2.7300) (1.8100)
Size -0.0150 -0.0184 -0.0253 * -0.0192 -0.0269 * -0.0420 -0.0195 * -0.0275 ** -0.0418 **
(-1.3500) (-1.6200) (-2.0100) (-1.5600) (-1.9000) (-1.9400) (-1.8200) (-2.3000) (-1.9700)
LEV -1.0593 *** -1.0650 *** -1.0675 *** -1.0667 *** -1.0621 *** -0.9328 *** -1.0547 *** -1.0576 *** -0.9640 ***
(-13.1700) (-13.2700) (-12.6700) (-12.0700) (-11.3900) (-10.6600) (-12.3100) (-11.7200) (-11.0500)
IG 0.0281 0.0282 0.0259 0.0254 -0.0023 0.0287 0.0316 * 0.0293 0.0266
(1.5600) (1.5800) (1.4900) (0.5700) (-0.0500) (1.4300) (1.7400) (1.6300) (1.3600)
ID dummy 0.0730 * 0.0736 * 0.1072 *** 0.0945 ** 0.1440 *** 0.0431 0.0845 ** 0.1216 *** 0.0427
(1.8900) (1.9000) 0.0000 (2.0000) (2.8700) (0.9400) (2.1900) (3.0700) (0.9300)
GD 0.0905 0.0783 0.0685 0.1058 0.0946 0.0762 0.1469 ** 0.1448 ** 0.0867
(1.4300) (1.2400) (1.0300) (1.4400) (1.2100) (0.6600) (2.3900) (2.2300) (0.7700)
DY -0.1347 -0.1364 0.0077 -0.0981 0.0720 -0.3677 -0.1312 0.0057 -0.3701
(-0.3300) (-0.3400) (0.0200) (-0.2300) (0.2000) (-0.4300) (-0.3400) (0.0200) (-0.4400)
ROCE -0.0925 -0.0928 -0.0992 -0.0324 -0.0415 -0.0998 -0.0905 -0.0948 -0.0977
(-1.1900) (-1.6100) (-1.6300) (-0.7200) (-0.8300) (-1.5200) (-1.6000) (-1.6000) (-1.5200)
C 0.7486 *** 0.7933 *** 0.8612 *** 0.7834 *** 0.8572 *** 1.0899 *** 0.7404 *** 0.8096 *** 1.0930 ***
(4.7400) (5.0600) (5.2600) (4.8100) (4.8500) (3.9900) (4.9300) (5.2200) (4.0500)
Country
dummy
yes yes yes yes yes yes yes yes yes
Year dummy yes yes yes yes yes yes yes yes yes
Indrustry
dummy
Nr observ. 893 893 823 763 693 454 794 724 454
Hedgers 668 598 598 468 468 99 499 499 130
Non Hedg 225 295 225 295 225 355 295 225 324
R2 0.5021 0.5025 0.5059 0.4829 0.4859 0.5092 0.5020 0.5069 0.5089
FC(IR) Derivative Hedgers
Table 6
Effects of Derivatives use on firm's value - regression results: Table 6 presents Panel Regression Random Effects results. The dependent
variable is the natural logarithm of Tobin's Q as a proxy for firm's value and 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 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 stands 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, respectively.
Foreign Currency (FC) Hedgers Interest Rate (IR) Hedgers
Deriv. Hedg.
dummy
Panel Regression Random Effects
Full Sample - non-financial firms quoted in Lisbon, Madrid and Milan Stock Markets

25
FC(IR)
Hedgers
Hedging
dummy
0.2132 **
(2.1400)
0.1898 ** 0.2247 **
(2.1200) (1.9800)
FC hedging
dummy
0.2234 ** 0.2605 ** 0.2392
(2.2000) (2.1300) (1.4500)
IR hedging
dummy
0.1649 * 0.2237 ** -0.0401
(1.9200) (2.0200) (-0.3700)
Size -0.0030 -0.0075 -0.0096 -0.0104 -0.0111 -0.0361 -0.0119 -0.0171 -0.0201
(-0.1700) (-0.4200) (-0.4500) (-0.5000) (-0.4400) (-1.1100) (-0.7000) (-0.8100) (-0.6600)
LEV -1.4626 *** -1.4647 *** -1.5068 *** -1.4992 *** -1.5502 *** -1.1768 *** -1.4343 *** -1.4605 *** -1.3094 ***
(-8.9400) (-8.9600) (-8.2600) (-8.5200) (-7.9100) (-5.2800) (-7.7400) (-6.7800) (-6.8800)
IG 0.0979 * 0.0930 0.0672 0.0850 0.0602 0.0363 0.0681 0.0452 0.0044
(1.7100) (1.6100) (1.0900) (1.3100) (0.8700) (0.2100) (1.3000) (0.8400) (0.0300)
ID dummy 0.1093 * 0.1167 * 0.1572 ** 0.1461 * 0.2044 ** 0.1744 ** 0.1258 ** 0.1721 *** 0.1744 **
(1.7400) (1.8400) (2.4000) (1.8500) (2.4600) (2.2000) (1.9600) (2.6800) (2.1400)
GD -0.0586 -0.0782 -0.1203 -0.0705 -0.1271 -0.0324 0.0380 0.0121 -0.0163
(-0.4500) (-0.5900) (-0.8200) (-0.4600) (-0.7300) (-0.1300) (0.3200) (0.0900) (-0.0600)
DY -2.4798 ** -2.4132 ** -1.8290 * -2.8651 ** -2.2221 * 0.1117 -2.8408 ** -2.3637 * 0.1317
(-2.1900) (-2.1400) (-1.7100) (-2.3300) (-1.9000) (0.0800) (-2.1400) (-1.8500) (0.1000)
ROCE -0.0006 -0.0176 -0.0677 0.0126 -0.0283 -0.1388 -0.0319 -0.0927 -0.1219
(0.0000) (-0.0700) (-0.2800) (0.0500) (-0.1100) (-0.5900) (-0.1200) (-0.3400) (-0.5000)
C 0.7373 *** 0.8557 *** 0.8819 *** 0.8669 *** 0.8642 *** 1.3347 *** 0.7276 *** 0.7104 *** 1.2803 **
(2.6700) (3.1200) (3.1900) (2.9800) (2.8500) (2.6300) (2.8700) (2.9900) (2.4800)
Indrustry
dummy
Nr observ. 326 326 286 288 248 150 288 248 150
Hedgers 252 212 212 174 174 38 174 174 38
Non Hedg 74 114 74 114 74 112 114 74 112
R2 0.3796 0.3818 0.3831 0.3734 0.3730 0.4557 0.3893 0.3895 0.4481
Deriv. Hedging
dummy
FC(IR) Derivative Hedgers Foreign Currency (FC) Hedgers
Table 7
Spanish subsample - non-financial firms quoted in Madrid Stock Market
Effects of Derivatives usage on firm's value - regression results: Table 7 presents the results for Panel Regression Random Effects. The dependent
variable is the natural logarithm of Tobin's Q, as a proxy for firm's value and 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 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, respectively.
Interest Rate (IR) Hedgers

26
FC(IR)
Hedgers
Hedging
dummy
0.1229 **
(2.1900)
0.1143 ** 0.1299 **
(2.0800) (2.2400)
FC hedging
dummy
0.0955 * 0.1106 * 0.0072
(1.6500) (1.8400) (0.0900)
IR hedging
dummy
0.1250 ** 0.1414 ** 0.1473 *
(2.1500) (2.3100) (1.7100)
Size -0.0207 -0.0204 -0.0247 -0.0185 -0.0230 -0.0555 * -0.0188 -0.0227 -0.0602 **
(-1.4300) (-1.4000) (-1.6200) (-1.0800) (-1.2400) (-2.1000) (-1.2600) (-1.4600) (-2.3500)
LEV -0.8416 *** -0.8393 *** -0.8326 *** -0.8037 *** -0.7980 *** -0.6552 *** -0.8456 *** -0.8363 *** -0.6860 ***
(-9.4900) (-9.4500) (-9.2700) (-8.3100) (-8.1200) (-5.2600) (-8.800) (-8.5800) (-5.5500)
IG 0.0300 0.0301 0.0302 0.0371 0.0397 0.0326 0.0318 0.0320 0.0306
(1.2700) (1.2900) (1.3200) (0.3400) (0.3500) (1.4600) (1.3600) (1.4000) (1.4500)
ID dummy 0.0122 0.0091 0.0176 0.0043 0.0156 0.0038 0.0181 0.0264 0.0069
(0.2900) (0.2200) (0.4100) (0.0900) (0.3200) (0.0600) (0.4100) (0.5700) (0.1100)
GD 0.1601 ** 0.1559 ** 0.1674 ** 0.1969 ** 0.2083 ** 0.2675 ** 0.1563 * 0.1681 * 0.2368 *
(2.0600) (1.9800) (2.0600) (2.1900) (2.2300) (2.2300) (1.8300) (1.9100) (1.9300)
DY 0.3158 0.3213 0.3732 0.4266 ** 0.4873 ** 0.6504 0.2371 0.2858 0.5447
(1.3500) (1.3900) (1.6100) (2.0200) (2.2300) (0.5700) (1.000) (1.2200) (0.4600)
ROCE -0.0700 -0.0708 -0.0729 0.0068 0.0038 -0.0618 -0.0723 -0.0744 -0.0612
(-1.1800) (-1.2000) (-1.2400) (0.5200) (0.2900) (-1.1000) (-1.1800) (-1.2200) (-1.1400)
C 0.7607 *** 0.7675 *** 0.7954 *** 0.7344 *** 0.7670 *** 1.0189 *** 0.7431 *** 0.7650 *** 1.0633 ***
(3.9300) (3.9400) (3.9300) (3.3400) (3.2500) (3.3200) (3.6900) (3.6800) (3.5500)
Indrustry
dummy
Nr observ. 456 456 435 378 357 249 401 380 249
Hedgers 340 319 319 241 241 55 264 264 78
Non Hedg 116 137 116 137 116 194 137 116 171
R2 0.6788 0.6789 0.6758 0.6794 0.6754 0.6382 0.6834 0.6796 0.6397
Italian subsample - non-financial firms quoted in Milan Stock Market
Effects of Derivatives usage on firm's value - regression results: Table 8 presents the results for Panel Regression Random Effects. The dependent
variable is the natural logarithm of Tobin's Q, as a proxy for firm's value and is calculated as the division of the sum of total assets and market
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 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, respectively.
Deriv. Hedging
dummy
Table 8

27
nr Name
IN D 1 53 Tires
63 Auto Parts
64 Transport Services
65 Automobiles
98 Aerospace
99 M arine Transportation
117 Comm. Vehicles,Trucks
129 Airlines
IN D 2 Food Industry 35 Farming & Fishing
67 Brewers
68 Distillers & Vintners
71 Food Products
72 Restaurants & Bars
79 Tobacco
114 Soft Drinks
IN D 3 45 Healthcare Providers
48 Personal Products
95 Pharmaceuticals
103 M edical Supplies
132 M edical Equipment
157 Biotechnology
IN D 4 34 Computer Hardware
37 Electrical Equipment
43 Industrial M achinery
44 Defense
56 Iron & Steel
57 Electronic Equipment
101 Divers. Industrials
130 Semiconductors
IN D 5 Business Support 41 M edia Agencies
58 Software
82 Paper
84 Publishing
86 Business Support Svs.
150 Computer Services
151 Internet
167 Real Estate Services
IN D 6 70 Containers & Package
87 Broadline Retailers
88 Food Retail,Wholesale
90 Specialty Retailers
IN D 7 Utilities 47 Waste, Disposal Svs.
74 Renewable Energy Eq.
91 M ultiutilities
96 Alt. Electricity
126 Telecom. Equipment
142 Fixed Line Telecom.
143 M obile Telecom.
144 Water
169 Con. Electricity
IN D 8 Energy Sources & Chemicals 31 Gas Distribution
33 Specialty Chemicals
49 Coal
50 Exploration & Prod.
51 Oil Equip. & Services
52 Pipelines
54 Nonferrous M etals
92 Commodity Chemicals
97 Integrated Oil & Gas
122 General M ining
IN D 9 Show Business & Accommodation 55 Recreational Services
80 Hotels
100 Gambling
115 Broadcast & Entertain
IN D 10 Construction Industry 30 Building M at.& Fix.
36 Home Construction
39 Heavy Construction
IN D 11 House hold Industry 59 Dur. Household Prod.
60 Furnishings
61 Toys
62 Nondur.Household Prod
156 Spec.Consumer Service
IN D 12 Textile Industry 66 Apparel Retailers
69 Clothing & Accessory
153 Footwear
Healthcare & Pharmaceutical
Equip (Electrics and Electronics)
Distribution & Where housing
Vehicles & Transportation
79.3%
66.7%
71.4%14
75.6%41
29
24
69.4%36
36.4%22
75.6%46
74.1%27
67.5%40
70.0%10
Appendix 1presents de definitions of the 12 Industry Dummies, including the information about how many and how much of them are
derivative hedgers
77.3%22
72.7%11
T o tal Indrusty
F irms
D erivative
H edgers (%)
Ind D ummy D escriptio n
A ppendix 1
Industry D ummies D efinitio ns
Industrial Gro uping D atatype

28
Models Model Descriptions Comparison
Model 1 All interest rate and/or foreign currency risk hedger firms
are defined as hedgers. Non-hedging sample includes all
firms that don’t hedge interest rate and/or foreign currency.
Comparing Financial risk
hedgers against non-financial
hedgers
Model 2 All firms that hedge interest rate and/or foreign currency
risks with derivatives are defined as hedgers. In this model,
non-hedging sample includes firms that don't hedge or that
use other kind of hedging methods.
Comparing Derivative Financial
risk hedgers against non-
derivative users
Model 3 All firms that hedge interest rate and/or foreign currency
risks with derivatives are defined as hedgers. Non hedging
sample includes only non-financial hedgers
Comparing Derivative Financial
risk hedgers against non-
financial hedgers
Model 4 All firms that hedge FC risk with derivatives are consider as
hedgers. Remain firms were included in non-hedging
sample, except if they are IR users.
Comparing FC Derivative
hedgers against non-derivative
users
Model 5 All firms that hedge FC risk with derivatives are consider as
hedgers. Non hedging sample includes only non-financial
hedgers
Comparing FC Derivative
hedgers
Model 6 This Model includes derivative FC only hedgers, excluding all
interest rate hedgers from the hedging sample. Non hedging
sample includes only non-financial hedgers.
Comparing FC Derivative only
hedgers
Model 7 All firms that hedge IR risk with derivatives are consider as
hedgers. Remain firms were included in non-hedging
sample. Remain firms were included in non-hedging sample,
except if they are FC users.
Comparing IR Derivative
hedgers against non-derivative
users
Model 8 All firms that hedge IR risk with derivatives are consider as
hedgers. Non hedging sample includes only non-financial
hedgers
Comparing IR Derivative
hedgers
Model 9 This Model includes derivative IR only hedgers, excluding all
interest rate hedgers from the hedging sample. Non hedging
sample includes only non-financial hedgers.
Comparing IR Derivative only
hedgers
Appendix 2 displays the eight models description used in multivariate approach. Each one has a different
combination of firms included in hedging and in non-hedging samples.
Appendix 2
Model Definitions

29
Tobin's Q 120 1.33 1.23 0.52 0.74 4.70
Market Value of Equity (millions) 120 1,609.9 200.5 3,087.4 1.3 14,662.7
Book Value of Equity (millions) 120 577.5 133.1 1,093.3 -35.7 6,365.2
Total Assets (millions) 120 2,990.4 559.2 6,532.8 20.2 35,169.2
(%)
119 4.2% 6.4% 11.8% -70.7% 35.0%
Leverage (%) 120 51.0% 47.2% 24.9% 6.5% 99.5%
Investment Growth (%) 119 10.6% 4.5% 19.5% 0% 128.7%
Dividend Yield (%) 119 1.6% 0.6% 2.8% 0% 21.8%
sales ratio (%)
114 30.9% 19.8% 31.8% 0% 97.2%
Panel A: Portuguese subsample - non-financial firms quoted in Lisbon Stock Market
Appendix 2 - Panels A to C - summarizes statistical information about variables used in this study in
Portuguese, Spanish ando Italian subsamples separately. Tobin's Qis computed as the sum of total assets and
market value of equity minus the book value of equity, all divided by total assets. Market Value of Equity is
defined as the share price multiplied by the number of shares in issue (ordinary and preferences) and Book
Value of Equity is defined as equity capital plus reserves, both used to calculate Tobin's Q variable, as well as
total assets. Total Assets refers to book value of total assets. Return on Capital Employed (ROCE) is calculated
as Pre-tax profit plus total interest charges divided by total capital employed plus borrowing repayable within 1
year less total intangibles. Leverage is measured as book value of total debt as a proportion of the book value
of total debt plus the market value of equity. Investment Grow is calculated as a ratio of Capex (Capital
Expenditure) to total sales. Dividend Yield is the gross dividend divided by share price. Industry Diversification
dummy takes on the value of 1 if the firm operates in more than one business segment. Geographic
Diversification is the foreign sales divided by total sales. We consider foreign exportation even if it is refers to
an European Economic and Monetary Union (EMU) country.
Appendix 3
Descriptive Statistics

30
Tobin's Q 350 1.98 1.38 2.77 0.43 28.97
Market Value of Equity (millions) 351 13,144.5 1,008.2 46,611.8 0.3 463,646.1
Book Value of Equity (millions) 351 31,158.9 369.1 293,070.5 0.0 3,697,213.0
Total Assets (millions) 348 118,718.9 1,261.8 1,145,997.0 0.0 14,452,740.0
(%)
343 10.3% 8.6% 20.6% -120.1% 184.4%
Leverage (%) 351 32.8% 29.6% 24.1% 0% 97.9%
Dividend Yield (%) 347 1.5% 1.1% 1.8% 0% 12.4%
sales ratio (%)
339 31.5% 29.2% 26.1% 0% 100.0%
Appendix 3
Panel B: Spanish subsample - non-financial firms quoted in Madrid Stock Market
Tobin's Q 493 1.47 1.30 0.73 0.51 6.85
Market Value of Equity (millions) 493 2,356.5 294.6 8,510.1 100,374.1 100,374.1
Book Value of Equity (millions) 493 1,162.2 143.4 4,214.8 -126.6 44,436.0
Total Assets (millions) 493 3,800.2 402.1 14,006.8 11.5 127,326.0
(%)
479 4.1% 5.2% 50.5% -501.4% 893.2%
Leverage (%) 492 31.8% 29.3% 20.8% 0.2% 90.4%
Dividend Yield (%) 486 1.6% 1.1% 2.6% 0% 39.6%
sales ratio (%)
479 37.5% 36.8% 32.6% 0% 100.0%
Panel C: Italian subsample - non-financial firms quoted in Milan Stock Market
Appendix 3

31
1000
Deriv Hedg
Non-Deriv.
Hedg
Diff. Pval
FC Deriv.
Hedger
Non-FC
Deriv.Hedg
Diff. Pval
IR Deriv.
Hedger
Non-IR
Deriv.Hedg
Diff. Pval
(Ln)Tobin's Q
Mean 0.35 0.28 0.07 0.045 0.36 0.28 0.08 0.033 0.34 0.28 0.06 0.124
Median 0.30 0.20 0.10 0.014 0.28 0.20 0.08 0.015 0.29 0.20 0.09 0.020
Stdev 0.46 0.49 0.48 0.49 0.43 0.49
N 643 248 500 248 538 248
Size
Mean 14.16 12.64 1.52 0.000 14.44 12.64 1.80 0.000 14.30 12.64 1.66 0.000
Median 13.88 12.24 1.63 0.000 14.24 12.24 1.99 0.000 14.07 12.24 1.83 0.000
Stdev 1.99 1.79 2.00 1.79 2.02 1.79
N 643 247 500 247 538 247
ROCE
Mean 0.06 0.07 -0.01 0.684 0.08 0.07 0.01 0.719 0.05 0.07 -0.02 0.458
Median 0.08 0.04 0.03 0.000 0.08 0.04 0.04 0.000 0.07 0.04 0.03 0.000
Stdev 0.28 0.61 0.15 0.61 0.28 0.61
N 630 240 492 240 529 240
LEV
Mean 0.36 0.32 0.05 0.012 0.36 0.32 0.04 0.040 0.39 0.32 0.07 0.000
Median 0.35 0.24 0.11 0.000 0.35 0.24 0.11 0.000 0.36 0.24 0.12 0.000
Stdev 0.22 0.27 0.21 0.27 0.21 0.27
N 643 248 500 248 538 248
IG
Mean 0.14 0.10 0.03 0.431 0.10 0.10 0.00 0.912 0.15 0.10 0.05 0.282
Median 0.06 0.04 0.01 0.012 0.06 0.04 0.01 0.016 0.06 0.04 0.02 0.001
Stdev 0.63 0.19 0.18 0.19 0.68 0.19
N 640 240 499 240 535 240
DY
Mean 0.02 0.01 0.01 0.000 0.02 0.01 0.01 0.000 0.02 0.01 0.01 0.000
Median 0.01 0.00 0.01 0.000 0.02 0.00 0.01 0.000 0.01 0.00 0.01 0.000
Stdev 0.03 0.02 0.03 0.02 0.03 0.02
N 636 246 496 246 533 246
ID
Mean 0.70 0.46 0.24 0.155 0.72 0.65 0.07 0.034 0.72 0.65 0.07 0.044
Median 1.00 1.00 0.00 0.147 1.00 1.00 0.00 0.030 1.00 1.00 0.00 0.039
Stdev 0.65 0.48 0.45 0.48 0.45 0.48
N 644 248 501 248 539 248
GD
Mean 0.41 0.24 0.17 0.000 0.45 0.24 0.22 0.000 0.41 0.24 0.17 0.000
Median 0.43 0.09 0.34 0.000 0.50 0.09 0.41 0.000 0.42 0.09 0.33 0.000
Stdev 0.30 0.29 0.28 0.29 0.29 0.29
N 620 240 480 240 515 240
Appendix 4
Univariate Approach
Panel A reports univariate test results withLN Tobin's Q andcontrol variables used in multivariate approach.In particular it shows the mean,median andstandard
deviation for derivative hedgers and non-derivative hedgers, including firms quoted in Lisbon, Madrid and Milan stock market. Moreover, it also displays the
differenceinthe 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 was
used 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
presentedinTable1andAppendix3,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

32
1000
Deriv Hedg
Deriv Non
Hedg Diff Pval
FC Deriv.
Hedger
Non FC
Deriv.Hedg Diff Pval
IR Deriv.
Hedger
Non IR
(Ln)Tobin's Q
Mean 0.43 0.34 0.09 0.238 0.48 0.34 0.14 0.108 0.38 0.34 0.04 0.629
Median 0.32 0.32 0.00 0.271 0.34 0.32 0.02 0.132 0.30 0.32 -0.02 0.559
Stdev 0.59 0.65 0.64 0.65 0.54 0.65
N 228 80 186 80 189 80
Size
Mean 15.00 13.37 1.63 0.000 15.18 13.37 1.82 0.000 15.04 13.37 1.67 0.000
Median 14.78 12.40 2.38 0.000 15.11 12.40 2.70 0.000 15.03 12.40 2.63 0.000
Stdev 2.09 2.37 2.15 2.37 2.15 2.37
N 228 80 186 80 189 80
ROCE
Mean 0.10 0.13 -0.03 0.340 0.10 0.21 -0.10 0.382 0.08 0.13 -0.05 0.032
Median 0.09 0.08 0.02 0.085 0.10 0.08 0.02 0.053 0.09 0.08 0.01 0.518
Stdev 0.19 0.26 0.13 0.26 0.12 0.26
N 224 78 184 78 185 78
LEV
Mean 0.35 0.31 0.03 0.280 0.33 0.31 0.02 0.549 0.39 0.31 0.07 0.031
Median 0.33 0.28 0.05 0.148 0.30 0.28 0.03 0.337 0.36 0.28 0.08 0.007
Stdev 0.23 0.26 0.23 0.26 0.22 0.26
N 228 81 186 81 189 81
IG
Mean 0.13 0.16 -0.03 0.395 0.13 0.16 -0.03 0.431 0.14 0.16 -0.02 0.636
Median 0.08 0.09 -0.01 0.374 0.08 0.09 -0.01 0.496 0.08 0.09 -0.01 0.677
Stdev 0.22 0.26 0.23 0.26 0.24 0.26
N 226 79 185 79 187 79
DY
Mean 0.02 0.01 0.01 0.017 0.02 0.01 0.01 0.009 0.02 0.01 0.00 0.032
Median 0.01 0.01 0.01 0.001 0.01 0.01 0.01 0.000 0.01 0.01 0.01 0.003
Stdev 0.02 0.02 0.02 0.02 0.02 0.02
N 225 81 184 81 187 81
ID
Mean 0.89 0.84 0.05 0.271 0.91 0.84 0.07 0.107 0.89 0.84 0.05 0.245
Median 1.00 1.00 0.00 0.232 1.00 1.00 0.00 0.073 1.00 1.00 0.00 0.210
Stdev 0.31 0.37 0.28 0.37 0.31 0.37
N 228 81 186 81 189 81
GD
Mean 0.37 0.22 0.15 0.000 0.39 0.22 0.17 0.000 0.37 0.22 0.15 0.000
Median 0.42 0.13 0.29 0.000 0.43 0.13 0.29 0.000 0.42 0.13 0.29 0.000
Stdev 0.25 0.25 0.24 0.25 0.24 0.25
N 220 77 178 77 181 77
Panel B reports univariate test results with LN Tobin's Qand 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 the
comparison of medians and to give the corresponding p-values. N is the number of observations (firms). Tests were conducted separatelyfor 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 1and Appendix3,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

33
1000
Deriv
Hedg
Deriv Non
Hedg Diff Pval
FC Deriv.
Hedger
NonFC
IR Deriv.
Hedger
Non IR
(Ln)Tobin's Q
Mean 0.32 0.28 0.04 0.358 0.31 0.28 0.03 0.508 0.32 0.28 0.04 0.285
Median 0.28 0.25 0.04 0.346 0.25 0.25 0.00 0.525 0.28 0.25 0.04 0.245
Stdev 0.37 0.39 0.37 0.39 0.37 0.39
N 340 132 257 132 280 132
Size
Mean 13.66 12.24 1.42 0.000 13.89 12.24 1.64 0.000 13.88 12.24 1.64 0.000
Median 13.29 12.21 1.08 0.000 14 12 1.32 0.000 13.58 12.21 1.37 0.000
Stdev 1.75 1.29 1.77 1.29 1.80 1.29
N 340 131 257 131 280 131
ROCE
Mean 0.03 0.06 -0.03 0.595 0.07 0.06 0.01 0.863 0.03 0.06 -0.03 0.559
Median 0.07 0.03 0.04 0.000 0.07 0.03 0.04 0.000 0.06 0.03 0.04 0.000
Stdev 0.35 0.81 0.12 0.81 0.37 0.81
N 332 126 251 126 276 126
LEV
Mean 0.35 0.24 0.11 0.000 0.35 0.24 0.11 0.000 0.36 0.24 0.12 0.000
Median 0.34 0.18 0.16 0.000 0.35 0.18 0.17 0.000 0.35 0.18 0.17 0.000
Stdev 0.20 0.22 0.19 0.22 0.20 0.22
N 340 131 257 131 280 131
IG
Mean 0.14 0.08 0.06 0.458 0.07 0.08 -0.02 0.194 0.16 0.08 0.08 0.351
Median 0.04 0.03 0.02 0.011 0.04 0.03 0.01 0.720 0.05 0.03 0.02 0.001
Stdev 0.83 0.15 0.09 0.15 0.92 0.15
N 339 126 257 126 279 126
DY
Mean 0.02 0.01 0.01 0.006 0.02 0.01 0.01 0.005 0.02 0.01 0.01 0.004
Median 0.01 0.00 0.01 0.000 0.01 0.00 0.01 0.000 0.01 0.00 0.01 0.000
Stdev 0.03 0.02 0.03 0.02 0.03 0.02
N 337 129 256 129 278 129
ID
Mean 0.56 0.54 0.02 0.723 0.57 0.54 0.03 0.603 0.59 0.54 0.05 0.318
Median 1.00 1.00 0.00 0.723 1.00 1.00 0.00 0.602 1.00 1.00 0.00 0.317
Stdev 0.50 0.50 0.50 0.50 0.49 0.50
N 341 131 258 131 281 131
GD
Mean 0.45 0.21 0.24 0.000 0.51 0.21 0.30 0.000 0.46 0.21 0.24 0.000
Median 0.52 0.04 0.49 0.000 0.58 0.04 0.54 0.000 0.54 0.04 0.51 0.000
Stdev 0.31 0.29 0.29 0.29 0.32 0.29
N 331 127 248 127 271 127
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
standarddeviationforderivativehedgers andnon-derivativehedgers,including firms quotedin Milanstock market. Moreover,it also displays thedifferencein
themeans and medians as well as p-values of meantests,usingLevene's Test forequality of variance and t-test for equality of means. Wilcoxonwas usedto
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:DerivativeHedgers (Model 3);FC DerivativeHedgers (Model 5) andIR DerivativeHedgers (Model 8).Thedefinitionof variables andmodels
arepresented inTable1and Appendix3,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

34
FC(IR)
Hedgers
Hedging
dummy
0.1260 ***
(2.7700)
0.1386 *** 0.1468 ***
(3.1600) (3.0400)
FC hedging
dummy
0.1521 *** 0.1629 *** 0.0703
(2.9200) (2.8500) (0.9200)
IR hedging
dummy
0.1277 *** 0.1363 *** 0.0896
(2.9100) (2.8400) (1.3700)
Size 0.0006 -0.0029 -0.0056 -0.0046 -0.0084 -0.0167 -0.0043 -0.0080 -0.0162
(0.0500) (-0.2400) (-0.4300) (-0.3400) (-0.5700) (-0.7500) (-0.3600) (-0.6100) (-0.7400)
LEV -1.1207 *** -1.1355 *** -1.1465 *** -1.2093 *** -1.2376 *** -1.0080 *** -1.0539 *** -1.0489 *** -1.0665 ***
(-11.6100) (-11.7800) (-11.0200) (-11.2300) (-10.5400) (-6.7600) (-10.6500) (-9.7200) (-7.2000)
IG 0.1868 *** 0.1864 *** 0.1894 *** 0.2314 0.2625 0.2260 *** 0.1749 *** 0.1768 *** 0.2175 ***
(3.8800) (3.8900) (3.8500) (1.4700) (1.5800) (3.7700) (3.8200) (3.7700) (3.6300)
ID dummy 0.0206 0.0228 0.0321 0.0046 0.0174 0.0179 0.0414 0.0525 0.0166
(0.4400) (0.4900) (0.6500) (0.0900) (0.3000) (0.2600) (0.8800) (1.0600) (0.2400)
GD 0.0457 0.0281 0.0309 0.0171 0.0152 0.0319 0.0892 0.1021 0.0391
(0.6500) (0.4000) (0.4100) (0.2100) (0.1700) (0.2900) (1.2400) (1.3200) (0.3600)
DY -0.7030 -0.6867 -1.1082 -0.7569 -1.2455 -1.3515 -0.7499 -1.2585 -1.3223
(-0.6600) (-0.6500) (-0.9900) (-0.6400) (-0.9800) (-0.6600) (-0.7200) (-1.1300) (-0.6500)
ROCE 0.0158 0.0134 0.0151 0.0128 0.0130 0.0146 -0.0134 -0.0118 0.0126
(0.4100) (0.3500) (0.3800) (0.3000) (0.3000) (0.3200) (-0.3600) (-0.3100) (0.2800)
C 0.5044 *** 0.5385 *** 0.5793 *** 0.5865 *** 0.6456 *** 0.7781 ** 0.4265 ** 0.4596 ** 0.7997 **
(2.7000) (2.8900) (2.9100) (2.8200) (2.8700) (2.5100) (2.2900) (2.3000) (2.5900)
Country
dummy
Indrustry
dummy
Nr observ. 893 893 823 763 693 454 794 724 454
Hedgers 668 598 598 468 468 99 499 499 130
Non Hedg 225 295 225 295 225 355 295 225 324
R2 0.4205 0.4250 0.4183 0.4393 0.4349 0.3703 0.4209 0.4137 0.3748
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
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,
respectively.
Panel Regression Between Effects
Appendix 5
Deriv. Hedging
dummy

35
FC(IR)
Hedgers
Hedging
dummy
0.3025 ***
(3.4800)
0.2424 *** 0.3127 ***
(2.9900) (3.2900)
FC hedging
dummy
0.3126 *** 0.3964 *** 0.0102
(3.3800) (3.7100) (0.0600)
IR hedging
dummy
0.2262 *** 0.3072 *** 0.1339
(2.7500) (3.1700) (0.9100)
Size 0.0153 0.0105 0.0209 0.0082 0.0275 0.0034 0.0077 0.0171 0.0065
(0.7600) (0.5000) (0.8700) (0.3500) (1.0100) (0.0800) (0.3600) (0.6600) (0.1600)
LEV -1.4519 *** -1.4886 *** -1.6289 *** -1.5041 *** -1.7145 *** -1.6540 *** -1.4574 *** -1.5855 *** -1.7722 ***
(-8.3000) (-8.3900) (-7.9800) (-7.5700) (-7.4600) (-4.5900) (-7.5900) (-6.6000) (-5.2700)
IG 0.5860 *** 0.5773 *** 0.6595 *** 0.7026 *** 0.8322 *** 0.6118 0.4392 ** 0.4938 ** 0.6291
(2.8800) (2.7900) (3.0700) (2.8000) (3.2100) (1.5800) (2.0700) (2.2100) (1.6900)
ID dummy -0.3878 * -0.2940 -0.4161 * -0.5190 * -0.7315 ** -0.0465 -0.3017 -0.4096 -0.0091
(-1.7400) (-1.3000) (-1.7400) (-1.9100) (-2.5400) (-0.1000) (-1.2600) (-1.6000) (-0.0200)
GD -0.1797 -0.2242 -0.2481 -0.2498 -0.3102 -0.2181 -0.2019 -0.2156 -0.2685
(-1.2200) (-1.4600) (-1.4700) (-1.4000) (-1.5400) (-0.8000) (-1.2800) (-1.2000) (-0.9800)
DY -7.6588 *** -7.1420 *** -8.3136 *** -8.3592 *** -10.1251 *** -6.9608 -6.5106 * -8.4247 *** -7.6399
(-3.1200) (-2.8900) (-2.9700) (-2.8700) (-2.9600) (-1.3700) (-2.6600) (-2.9900) (-1.5100)
ROCE 0.9684 *** 0.8987 0.9643 *** 0.9523 *** 1.0086 *** 1.1946 *** 0.4836 0.5547 1.2137 ***
(4.3200) (3.9800) (4.0300) (3.9100) (3.9100) (3.6000) (1.5600) (1.5800) (3.7800)
C 0.2656 0.3203 0.1911 0.3534 0.0932 0.4709 0.3558 0.2048 0.4576
(0.7600) (0.9000) (0.4800) (0.8700) (0.2000) (0.7600) (1.0200) (0.5100) (0.7600)
Indrustry
dummy yes yes yes yes yes yes yes yes yes
Nr observ. 326 326 286 288 248 150 288 248 150
Hedgers 252 212 212 174 174 38 174 174 38
Non Hedg 74 114 74 114 74 112 114 74 112
R2 0.6573 0.6465 0.6724 0.6592 0.6998 0.7174 0.6340 0.6577 0.7248
Appendix 5
Between Effects
Panel B: Spanish subsample - non-financial firms quoted in Madrid Stock Market
Effects of Derivatives usage on firm's value - regression results: Appendix 5, Panel B, 7 presents the results for Panel Regression Bettween Effects.
The dependent variable is the natural logarithm of Tobin's Q, as a proxy for firm's value and 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 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 Appendix 1 and Appendix 3, respectively.
Interest Rate (IR) HedgersForeign Currency (FC) Hedgers
Deriv. Hedging
dummy

36
FC(IR)
Hedgers
Hedging
dummy
0.1228 **
(2.3200)
0.1124 ** 0.1274 **
(2.1400) (2.3000)
FC hedging
dummy
0.1321 * 0.1492 ** 0.0357
(2.0200) (2.2000) (0.4000)
IR hedging
dummy
0.0966 * 0.1120 * 0.0966
(1.7800) (1.9700) (1.2600)
Size -0.0149 -0.0147 -0.0189 -0.0185 -0.0237 -0.0539 * -0.0121 -0.0161 -0.0562 ***
(-1.0200) (-1.0000) (-1.2500) (-1.1000) (-1.3600) (-1.9500) (-0.8200) (-1.0500) (-2.0500)
LEV -0.9613 *** -0.9549 *** -0.9396 *** -1.0532 *** -1.0405 *** -0.5648 *** -0.9036 *** -0.8820 *** -0.6108 ***
(-7.7900) (-7.7200) (-7.4700) (-7.7900) (-7.4800) (-3.0600) (-6.9300) (-6.6400) (-3.2800)
IG 0.2210 *** 0.2212 *** 0.2205 *** 0.1982 0.2173 0.2164 *** 0.2293 *** 0.2294 *** 0.2070
(5.3700) (5.3600) (5.2900) (0.8000) (0.8000) (4.3000) (5.6100) (5.5700) (4.1400)
ID dummy 0.0302 0.0273 0.0361 0.0069 0.0190 0.0360 0.0524 0.0632 0.0337
(0.7100) (0.6400) (0.8100) (0.1400) (0.3700) (0.5500) (1.1800) (1.3500) (0.5300)
GD 0.2422 *** 0.2374 *** 0.2560 *** 0.2242 ** 0.2438 *** 0.3412 *** 0.2812 *** 0.3032 *** 0.3062 **
(3.1800) (3.0800) (3.2000) (2.5400) (2.6300) (2.9200) (3.4200) (3.5500) (2.5900)
DY 0.9104 0.9669 0.9724 0.6881 0.6953 2.0173 1.2004 1.2413 1.8442
(0.8000) (0.8500) (0.8400) (0.5500) (0.5400) (0.9600) (1.0300) (1.0400) (0.8800)
ROCE 0.0116 0.0100 0.0092 0.0027 0.0022 0.0136 0.0139 0.0155 0.0099
(0.3400) (0.2900) (0.2700) (0.0800) (0.0600) (0.3400) (0.3900) (0.4300) (0.2500)
C 0.5769 *** 0.5799 *** 0.5956 *** 0.6840 *** 0.7054 *** 0.8242 ** 0.4043 * 0.4161 * 0.8781 **
(2.6800) (2.6900) (2.7100) (2.8700) (2.8700) (2.2700) (1.7900) (1.8100) (2.4400)
Indrustry
dummy yes yes yes yes yes yes yes yes yes
Nr observ. 456 456 435 378 357 249 401 380 249
Hedgers 340 319 319 241 241 55 264 264 78
Non Hedg 116 137 116 137 116 194 137 116 171
R2 0.4509 0.4479 0.4533 0.4488 0.4529 0.4475 0.4680 0.4553 0.4587
Panel C: Italian subsample - non-financial firms quoted in Milan Stock Market
Effects of Derivatives usage on firm's value - regression results: Appendix 5 - Panel C presents the results for Panel Regression Between Effects.
The dependent variable is the natural logarithm of Tobin's Q, as a proxy for firm's value and 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 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, respectively.
Deriv. Hedging
dummy
Appendix 5
Between Effects

37
FC(IR)
Hedgers
Hedging
dummy
0.1146 **
(2.3000)
0.1290 *** 0.1352 **
(2.7200) (2.5500)
FC hedging
dummy
0.1345 ** 0.1410 ** 0.0603
(2.5300) (2.4100) (0.7700)
IR hedging
dummy
0.1241 *** 0.1325 ** 0.0976 *
(2.6400) (2.5100) (1.7600)
Size 0.0002 -0.0033 -0.0070 -0.0051 -0.0096 -0.0181 -0.0050 -0.0098 -0.0180
(0.0200) (-0.3200) (-0.6300) (-0.4600) (-0.7700) (-0.8200) (-0.4900) (-0.9000) (-0.8200)
LEV -1.1103 *** -1.1216 *** -1.1322 *** -1.1860 *** -1.2110 *** -0.9924 *** -1.0557 *** -1.0515 *** -1.0443 ***
(-10.6900) (-10.7700) (-9.8900) (-10.5000) (-9.7000) (-7.0400) (-9.2600) (-8.3300) (-7.1000)
IG 0.0964 *** 0.0961 *** 0.0961 *** 0.1444 ** 0.1511 ** 0.1083 *** 0.0894 *** 0.0893 *** 0.1036 ***
(4.5300) (4.4900) (4.4500) (2.0500) (2.1100) (3.7300) (4.4900) (4.4200) (3.6500)
ID dummy 0.0290 0.0302 0.0451 0.0272 0.0482 0.0105 0.0469 0.0645 * 0.0120 *
(0.7900) (0.8300) (1.1700) (0.6700) (1.1000) (0.2000) (1.2700) (1.6500) (0.2400)
GD 0.0239 0.0072 0.0055 0.0167 0.0120 0.0027 0.0714 0.0799 0.0136
(0.3600) (0.1100) (0.0700) (0.2100) (0.1400) (0.0200) (1.1700) (1.2200) (0.1300)
DY -0.4082 -0.4104 -0.5415 -0.4142 -0.5383 -1.1145 -0.4833 -0.6619 -1.0488
(-0.6400) (-0.6400) (-0.8100) (-0.6000) (-0.7400) (-0.7400) (-0.7300) (-0.9700) (-0.7000)
ROCE -0.0241 -0.0254 -0.0293 0.0408 0.0350 -0.0313 -0.0642 -0.0673 -0.0295
(-0.3700) (-0.4000) (-0.4600) (0.5000) (0.4600) (-0.4800) (-1.2600) (-1.2800) (-0.4600)
C 0.6233 *** 0.6699 *** 0.7099 *** 0.7000 *** 0.7547 *** 0.8634 *** 0.6102 *** 0.6467 *** 0.8718 ***
(4.4300) (4.7600) (4.5500) (4.7900) (4.5600) (2.9800) (4.3600) (4.1800) (3.0900)
Country
dummy
Year
dummy
Indrustry
dummy
Nr observ. 893 893 823 763 693 454 794 724 454
Hedgers 668 598 598 468 468 99 499 499 130
Non Hedg 225 295 225 295 225 355 295 225 324
R2 0.4306 0.4340 0.4259 0.4512 0.4445 0.3779 0.4355 0.4271 0.3832
Deriv. Hedging
dummy
Appendix 6
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 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
appear under variables coefficients. The definition of the variables and Models are presented in Table 1 and Appendix 3, respectively.
Foreign Currency (FC) Hedgers Interest Rate (IR) HedgersFC(IR) Derivative Hedgers

Florbela Curto Judge Porto Paper 5 June11

Florbela Curto Judge Porto Paper 5 June11

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