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 Judge

b

aISCTE Business School, Av. Prof. Aníbal Bettencourt, 1600-189 Lisbon, Portugal

bMiddlesex 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Very preliminary draft: Please do not quote without permission.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.

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


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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


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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


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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


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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.


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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.




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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.



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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


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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.


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%.


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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 hedgers FC 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

dummy0.1253 **

(2.4800)

0.1384 *** 0.1488 ***

(2.8800) (2.7600)

FC hedging

dummy0.1363 ** 0.1456 ** 0.0697

(2.4400) (2.3700) (0.9800)

IR hedging

dummy0.1338 *** 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

dummy0.2132 **

(2.1400)

0.1898 ** 0.2247 **

(2.1200) (1.9800)

FC hedging

dummy0.2234 ** 0.2605 ** 0.2392

(2.2000) (2.1300) (1.4500)

IR hedging

dummy0.1649 * 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

dummyyes 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 0.3796 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

dummy0.1229 **

(2.1900)

0.1143 ** 0.1299 **

(2.0800) (2.2400)

FC hedging

dummy0.0955 * 0.1106 * 0.0072

(1.6500) (1.8400) (0.0900)

IR hedging

dummy0.1250 ** 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


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 A irlines

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 P roviders

48 Personal Products

95 Pharmaceuticals

103 M edical Supplies

132 M edical Equipment

157 B iotechno logy

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,Who lesale

90 Specialty Retailers

IN D 7 Utilities 47 Waste, Disposal Svs.

74 Renewable Energy Eq.

91 M ultiutilities

96 A lt. 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 Explo ration & Prod.

51 Oil Equip. & Services

52 P ipelines

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 ho ld Industry 59 Dur. Househo ld P rod.

60 Furnishings

61 Toys

62 Nondur.Househo ld P rod

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 o f the 12 Industry Dummies, including the info rmation about how many and how much of them are

derivative hedgers

77.3%22

72.7%11

T o ta l Indrusty

F irms

D erivat ive

H edgers (%)

Ind D ummy D escriptio n

A ppendix 1

Industry D ummies D ef init io ns

Industria l Gro uping D ata type


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 HedgNon-Deriv.

HedgDiff. Pval

FC Deriv. Hedger

Non-FC Deriv.Hedg

Diff. PvalIR 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 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


32

1000

Deriv HedgDeriv Non

Hedg Diff PvalFC Deriv.

HedgerNon FC

Deriv.Hedg Diff PvalIR Deriv. Hedger

Non IR Deriv.Hedg Diff Pval

(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 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


33

1000

Deriv Hedg

Deriv Non 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 0.32 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

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


34

FC(IR)

Hedgers


Hedging

dummy0.1260 ***

(2.7700)

0.1386 *** 0.1468 ***

(3.1600) (3.0400)

FC hedging

dummy0.1521 *** 0.1629 *** 0.0703

(2.9200) (2.8500) (0.9200)

IR hedging

dummy0.1277 *** 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



Indrustry


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

dummy0.3025 ***

(3.4800)

0.2424 *** 0.3127 ***

(2.9900) (3.2900)

FC hedging

dummy0.3126 *** 0.3964 *** 0.0102

(3.3800) (3.7100) (0.0600)

IR hedging

dummy0.2262 *** 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


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

dummy0.1228 **

(2.3200)

0.1124 ** 0.1274 **

(2.1400) (2.3000)

FC hedging

dummy0.1321 * 0.1492 ** 0.0357

(2.0200) (2.2000) (0.4000)

IR hedging

dummy0.0966 * 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


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



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

dummy0.1146 **

(2.3000)

0.1290 *** 0.1352 **

(2.7200) (2.5500)

FC hedging

dummy0.1345 ** 0.1410 ** 0.0603

(2.5300) (2.4100) (0.7700)

IR hedging

dummy0.1241 *** 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


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 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.

Foreign Currency (FC) Hedgers Interest Rate (IR) HedgersFC(IR) Derivative Hedgers


38

FC(IR)

Hedgers


Hedging

dummy0.2589 ***

(2.8300)

0.2177 *** 0.2700 ***

(2.7200) (2.6900)

FC hedging

dummy0.2572 *** 0.3152 *** 0.1268

(2.9000) (2.9400) (0.6800)

IR hedging

dummy0.2001 *** 0.2645 *** 0.0717

(2.6200) (2.6500) (0.7000)

Size 0.0089 0.0045 0.0098 0.0012 0.0092 -0.0066 0.0014 0.0050 0.0036

(0.5900) (0.2900) (0.5400) (0.0700) (0.4300) (-0.1800) (0.0900) (0.2600) (0.1100)

LEV -1.5132 *** -1.5250 *** -1.6374 *** -1.5456 *** -1.6902 *** -1.6234 *** -1.4825 *** -1.5632 *** -1.7828 ***

(-8.0100) (-7.8600) (-7.4800) (-7.1400) (-6.9700) (-4.6400) (-6.8000) (-5.7800) (6.0600)

IG 0.3031 *** 0.2905 *** 0.3244 *** 0.2849 ** 0.3216 *** 0.4250 ** 0.2159 ** 0.2369 *** 0.3906 **

(3.5700) (3.3500) (3.7100) (2.5600) (2.8700) (2.1300) (2.5600) (2.7400) (2.2800)

ID dummy -0.0019 0.0266 0.0211 0.0260 0.0198 0.0614 0.0413 0.0500 0.0703

(-0.0300) (0.3700) (0.2800) (0.3000) (0.2200) (0.5400) (0.6100) (0.7600) (0.6200)

GD -0.1931 -0.2316 * -0.2687 * -0.2538 * -0.3161 * -0.1772 -0.1653 -0.1823 -0.1970

(-1.6500) (-1.8900) (-1.8700) (-1.7000) (-1.7500) (-0.7300) (-1.4800) (-1.3600) (-0.8300)

DY -4.7237 ** -4.5410 ** -4.9368 ** -4.7996 ** -5.1530 ** -4.2029 -4.5077 ** -5.5404 ** -4.2228

(-2.5900) (-2.3800) (-2.4200) (-2.2900) (-2.2000) (-1.1900) (-2.0500) (-2.4700) (-1.1700)

ROCE 0.4304 0.3941 0.3835 0.4305 0.4185 0.3413 0.1378 0.1056 0.3727

(1.1100) (1.0400) (1.0000) (1.1000) (1.0600) (0.8700) (0.4200) (0.3100) (0.9200)

C 0.6378 *** 0.7736 *** 0.7530 *** 0.8096 *** 0.7690 *** 1.1286 ** 0.6669 *** 0.6037 ** 1.1026 **

(2.9100) (3.7700) (2.9800) (3.6600) (2.7900) (2.2200) (3.6600) (2.5700) (2.1700)


Indrustry


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 0.5353 0.5309 0.5320 0.5297 0.5331 0.5382 0.5363 0.5374 0.5359

Deriv. Hedging

dummy

FC(IR) Derivative Hedgers Foreign Currency (FC) Hedgers

Appendix 6



Effects of Derivatives use on firm's value - regression results: Appendi 6, Panel B, presents the results for Pooled OLS Standard Adjusted for

Clustering at the Firm Level . 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, Models 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


Interest Rate (IR) Hedgers


39

FC(IR)

Hedgers


Hedging

dummy0.1066 *

(1.9000)

0.0976 * 0.1121 *

(1.7100) (1.8800)

FC hedging

dummy0.0989 * 0.1141 * -0.0065

(1.7400) (1.9200) (-0.0800)

IR hedging

dummy0.1005 0.1147 * 0.1228

(1.6300) (1.8200) (1.5400)

Size -0.0115 -0.0113 -0.0155 -0.0139 -0.0188 -0.0487 ** -0.0095 -0.0134 -0.0530 **

(-0.8700) (-0.85000) (-1.1400) (-0.9300) (-1.2200) (-2.0500) (-0.7000) (-0.9800) (-2.3100)

LEV -0.9696 *** -0.9659 *** -0.9519 *** -1.0414 *** -1.0319 *** -0.6154 *** -0.9223 *** -0.9028 *** -0.6734 ***

(-7.6300) (-7.5300) (-7.2500) (-7.7800) (-7.4900) (-3.8800) (-6.4900) (-6.2200) (-4.2000)

IG 0.1053 *** 0.1057 *** 0.1050 *** 0.1236 0.1304 0.1008 *** 0.1058 *** 0.1052 *** 0.0956 ***

(5.1100) (5.0500) (5.0200) (0.8100) (0.7500) (3.7800) (4.8800) (4.8500) (3.5600)

ID dummy 0.0185 0.0156 0.0236 0.0078 0.0204 0.0116 0.0270 0.0352 0.0177

(0.4400) (0.3700) (0.5400) (0.1700) (0.4100) (0.1900) (0.6000) (0.7400) (0.2900)

GD 0.1905 *** 0.1859 *** 0.2009 *** 0.1984 *** 0.2133 *** 0.2934 *** 0.2005 *** 0.2173 *** 0.2570 ***

(3.0100) (2.8400) (3.0200) (2.6600) (2.8000) (3.3400) (2.8700) (3.0900) (2.7100)

DY 0.5343 0.5546 0.5893 0.5969 0.6525 1.3410 0.4992 0.5398 1.2382

(1.1400) (1.1800) (1.2000) (1.3900) (1.4400) (0.9300) (1.0900) (1.1100) (0.8100)

ROCE -0.0694 -0.0696 -0.0716 0.0021 -0.0005 -0.0544 -0.0726 -0.0746 -0.0562

(-1.0000) (-1.0100) (-1.0500) (0.1300) (-0.0300) (-0.8700) (-1.0100) (-1.0400) (-0.9200)

C 0.6733 *** 0.6805 *** 0.7054 *** 0.7417 *** 0.7742 *** 0.9018 *** 0.6343 *** 0.6519 *** 0.9569 ***

(3.6500) (3.6400) (3.6500) (3.7600) (3.7300) (3.1200) (3.1800) (3.1900) (3.3900)


Indrustry


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 0.4947 0.4932 0.4938 0.5254 0.5248 0.4714 0.5008 0.5022 0.4848



Effects of Derivatives use on firm's value - regression results: Apendix 6, Panel C, presents the results for Pooled OLS Standard Adjusted for

Clustering at the Firm Level . 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


Deriv. Hedging

dummy


Appendix 6


Documents

Florbela Curto Judge Porto Paper 5 June11