Electronic copy available at: http://ssrn.com/abstract=799524

Is Operational Hedging a Substitute for or a Complement to Financial Hedging?

Young Sang Kim Dept of Economics and Finance Northern Kentucky University

kimy1@nku.edu

Ike Mathur Dept of Finance

Southern Illinois University imathur@siu.edu

Jouahn Nam Dept of Finance and Economics

Pace University jnam@pace.edu

This version: July 21, 2005 We thank Wallace N. Davidson III, Jim Musumeci, Mark Peterson, Subhash Sharma, an anonymous reviewer and seminar participants at Southern Illinois University at Carbondale, Midwest Finance Association 2004 Annual Conference, and the Financial Management Association 2004 annual meeting for helpful comments.

Correspondence Address: Ike Mathur

Department of Finance Southern Illinois University Carbondale, IL 62901-4626 Phone: 618-453-1421 Fax: 618-453-5626 Email: imathur@cba.siu.edu

Electronic copy available at: http://ssrn.com/abstract=799524

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Is operational hedging a substitute for or a complement to financial hedging?

Young Sang Kim a, Ike Mathur b,*, Jouahn Nam c

aDepartment of Economics and Finance, Northern Kentucky University, Highland Heights, KY 41099 bDepartment of Finance, Southern Illinois University, Carbondale, IL 62901

cDepartment of Finance and Economics, Pace University, New York, NY 10038

Abstract

This paper investigates operational hedging by firms and how operational hedging is related to financial hedging by using a sample of 424 firm observations, which consist of 212 operationally-hedged firms (firms with foreign sales) and a size and industry matched sample of 212 non-operationally-hedged firms (firms with export sales). We find that non-operationally-hedged firms use more financial hedging, relative to their levels of foreign currency exposure, as measured by the amount of export sales. On the other hand, though operationally-hedged firms have more currency exposure, their usage of financial derivatives becomes much smaller than that of exporting firms. These results can explain why some global firms use very limited amount of financial derivatives for hedging purpose despite much higher levels of currency risk exposure. We also show that hedging increases firm value

JEL classification: F23, F21, F31, G32, Keywords: Operational hedging; Financial hedging: Foreign exchange exposure; Financial derivatives

*Corresponding author, tel.: +1 618 453 1421 fax: +1 618 453 5626. Email address: imathur@cba.siu.edu (I. Mathur)

1. Introduction

Stulz (2004) reports that the use of foreign exchange derivatives by industrial firms has a

notional value of $5 trillion, and the similar figure for interest rate derivatives has a notional

value of $15 trillion. Bartram et al. (2004) report that 65% of U.S. firms use derivatives and

37.4% of U.S. firms use foreign exchange derivatives. The large volume of derivatives use by

U.S. firms emphasizes the importance of hedging for firms, and has stimulated significant

research, resulting in the emergence of a rich body of literature that explores the various channels

through which hedging can contribute to increased firm value (e.g., Froot et al., 1993; DeMarzo

and Duffie, 1995; Smith and Stulz, 1985; Geczy et al., 1997; Allayannis and Weston, 2001;

Haushalter, 2000; Graham and Rogers, 2002; Carter et al., 2004b). Earlier studies have advanced

our understanding of how financial hedging can enhance firm value. However, much less

attention has been given to firms operational hedging activities. Thus, this paper addresses the

somewhat less explored but increasingly important issues of the relation between operational and

financial hedging and the valuation effects of operational hedging activities.

Operational hedging can be viewed as either a substitute or a complement to a firms

financial hedging strategy when it intends to reduce the volatility of future cash flows and, thus,

to possibly increase firm value. The relationship between operational and financial hedging has

received less attention and remains largely unexplained. Lim and Wang (2001) show that

financial hedging and corporate diversification are more often complementary than substitutive.

Allayannis et al. (2001) find that operational hedging is not an effective substitute for financial

risk management. Operational hedging strategies increase firm value only when used in

combination with financial hedging strategies. In a similar vein, Pantzalis et al. (2001) find that

operational hedging leads to lower foreign exchange risk exposures for MNCs.

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Though these studies shed light on the important role of operational hedging for MNCs,

none of them examines how such firms being exposed to foreign currency risk but not being

operationally hedged, manage their foreign currency risk exposures. More importantly, there are

no empirical studies that explore how firms view operational hedging, as opposed to financial

hedging. For most exporting firms, the need for financial hedging should be greater since they

are not operationally hedged or less hedged. Thus, this paper investigates how financial hedging

can be more effectively used to reduce cash flow variability (transaction exposure), while

operational hedging can be used to mitigate more long-term and permanent risk exposures

(economic exposure).

It is also important to examine whether operational hedging can contribute to increasing

firm value. When an operational hedging strategy serves to stabilize a firms future cash flows,

the value of the firm should increase through a rationale similar to that for financial hedging.

Despite the importance of this potential value effect of a firms operational hedging, no studies

have provided evidence on this relationship. Thus, we investigate whether, and more importantly

how, operational hedging can contribute to increasing firm value.

We investigate this relation between financial and operational hedging for a unique

sample of 424 U.S. firms over the period of 1996 to 2000. A firm should report its sales to

foreign countries as export sales if it manufactures the products in the U.S. However, when it has

foreign manufacturing operations (foreign assets), sales from these operations to foreign

countries should be recorded as foreign sales. These accounting treatments allow separating

firms into operationally hedged and non-operationally hedged firms. Firms reporting foreign

sales are operationally hedged, while firms reporting only export sales are non-operationally

hedged. Operational hedging is defined as the degree of geographic diversification.

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This paper contributes to the literature in several ways by providing evidence on the

important role of operational hedging. First, this paper investigates whether operational hedging

is a substitute or a complement to financial hedging. Using a sample of 424 firm observations,

we find that non-operationally hedged firms use more financial hedging relative to their levels of

foreign exchange risk exposure as measured by the amount of export sales. On the other hand,

though firms reporting foreign sales have more currency risk exposure in terms of the level of

foreign activity, their usage of financial derivatives becomes much smaller than those of

exporting firms. These results can explain why some globally diversified firms use very limited

amounts of financial derivatives for hedging purposes despite much higher levels of currency

exposure. We also find a significant positive relationship between operational and financial

hedging in several multivariate regression models, thus supporting the complementary nature of

these hedging strategies.

Second, this paper contributes to the literature by examining the effects of operational

hedging and financial hedging on foreign exchange risk exposure. We find that both financial

hedging and operational hedging are associated with reducing a firms currency exposure. The

results show that a financial hedging strategy is more effective in reducing foreign exchange risk

exposure.

Finally, this paper contributes to the literature by examining the valuation effects of

operational hedging strategies. We find that operational hedging and financial hedging are

associated with increases in firm value. These results support the evidence of Allayannis and

Weston (2001), and Carter et al. (2004b) that financial hedging increase firm value,1 and extends

it to operational hedging strategies.

1 Allayannis and Weston (2001) and Nain (2004) also show that foreign currency hedging increases firm value by 4.8% and 5.0%, respectively.

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The remainder of the paper is organized as follows. Section 2 discusses previous studies

and empirical hypotheses. Section 3 describes the sampling procedure. Section 4 describes the

methodology used in this paper. Section 5 presents the results of tests and Section 6 concludes

the paper.

2. Literature review and hypothesis development

2.1. Operational hedging

Researchers argue that operational hedging through geographic diversification is

beneficial for MNCs for reducing the volatility of cash flows. MNCs, which have operations

located in different countries, may benefit from offsetting unexpected changes in foreign

currency exchange rates due to operational hedges. However, Allayannis et al. (2001) find that

operational hedging is not an effective substitute for financial risk management, and support the

conditional complement hypothesis. Pantzalis et al. (2001) find that the ability to construct

operational hedges leads to lower currency exposures for the pooled sample as well as for firms

with positive exposure (net importers) and negative exposure (net exporters). Carter et al. (2001)

find that the combined use of operational and financial hedges is associated with decreased

exchange rate exposure. These results support the complementary hypothesis. Based on this

literature, we hypothesize that operational hedging is complementary to financial hedging since

operational and financial hedging strategies are used for managing different types of risk

exposures, i.e., operational hedging for long-term exposure (economic exposure) and financial

hedging for short term exposure (transaction exposure).

2.2. The rationale for financial hedging

Financial risk management theory, which stems from market imperfections and violations

of the perfect world assumptions, argues that risk management can add value if it reduces

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expected tax liabilities (Smith and Stulz, 1985), bankruptcy costs (Myers, 1977; Smith and Stulz,

1985), and the underinvestment problem from costly external financing (Froot et al., 1993).

Smith and Stulz (1985) argue that hedging can reduce expected tax liability since

volatility is costly for firms with convex tax functions. The convexity of the tax code creates tax

advantages that follow from a smoother profit stream through financial hedging (derivatives) and

operational hedging (diversification). In this paper, we use the tax measure, TAX, the book value

of net operating tax loss carry forwards divided by total assets, as a proxy for the tax theory. We

expect to find a positive relationship between TAX and hedging activities.

The cost of financial distress theory argues that hedging, by reducing the variability of

cash flows, reduces the probability of incurring bankruptcy costs (Smith and Stulz, 1985). The

increase in firm value comes from the reduction in the deadweight costs of bankruptcy.

Diversification may also reduce the likelihood of bankruptcy. The proxy for the costs of financial

distress is DEBT, the ratio of total debt to total assets. We expect a positive relationship between

DEBT and hedging activities.

The reduction in the underinvestment problem theory holds that firms can reduce the

need for costly external financing through hedging (Froot et al., 1993). Gczy et al. (1997) find

that firms use of currency derivatives is positively related to research and development

expenditures for a sample of Fortune 500 firms. To test the reduction in the underinvestment

problem theory, this study includes RND, the ratio of research and development expenditures to

total assets. Allayannis and Ofek (2001), Gay and Nam (1998), and Graham and Rogers (2002)

all find that hedging increases with the level of R&D expenditures. A positive relationship

between RND and derivatives usage is expected.

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The economies of scale involved in establishing a hedging program is a common

explanation for the relationship between size and hedging. Previous empirical studies find a

strong positive relationship between the size of the firm and the likelihood of hedging activity

(Geczy et al., 1997; Mian, 1996; Haushalter, 2000; Allayannis and Ofek, 2001; Graham and

Rogers, 2002). We include SIZE, the logarithm of total assets, to control for the size effect. We

expect a positive relationship between SIZE and hedging activities.

We also include the quick ratio QUICK, current assets less inventories divided by current

liabilities, to measure the availability of internal funds (Geczy et al., 1997). We expect a negative

relationship between QUICK and hedging activities. In order to control for industry effects we

use dummy variables based on two-digit SIC codes for different industries (INDUSTRY). We

use SEGN, number of industrial segments, as another control variable. Some firms may seek to

derive potential benefits of financial hedging through the use of foreign currency denominated

debt. Thus, we use the indicator dummy variable FDEBT equal to 1 if a firm reports use of

foreign debt and equal to 0, otherwise.

Many of the past empirical studies on risk management utilize the ratio of the notional

amount of total derivatives use to total assets as a proxy for the firms risk exposure and/or to

analyze the use of derivatives as a dichotomous variable. In this paper, we employ both the

dichotomous variable and the continuous variable, the ratio of notional amount of derivatives to

total foreign currency risk exposure, in the regression models for robustness tests.

3. Sample

3.1. Operational hedging data procedure

In June 1997, the Financial Accounting Standards Board (FASB) issued Statement of

Financial Accounting Standards (SFAS) No. 131, Disclosure about Segments of an Enterprise

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and Related Information, which became effective for fiscal years starting after December 31,

1997. SFAS 131 rules require that enterprises report information about operating segments,

products and services, the geographic areas in which they operate, and their major customers.

Under SFAS 131, segment reporting is more consistent with the organizational structure of the

firm and provides more detailed information about geographic segments.

The initial sample of the paper is obtained from the COMPUSTAT Geographic Segment

(CGS) files for 1998. Since firms effectively adopted the new segment rule SFAS 131 in 1998,

we use fiscal year end 1998 for the sample. We initially identified 6,086 companies that have

foreign exchange rate exposure due to foreign sales or export sales. We restrict the sample with

1998 total sales greater than $300 million. We exclude from the sample firms in the financial

industries (SIC codes 6000-6999) or the utility industries (SIC codes 4900-4999). After the size

and industry screening procedure, we obtain 1,643 firm observations from the C.G.S files.

Among these firms, we define firms that report only export sales in the geographic segment file

as non-operationally hedged firms. Export sales represent the amount or percentage of each

segments revenue generated by domestically produced goods or services, sold outside the

domestic country.

To examine the effects of operational hedging on firm value and the relation between

operational and financial hedging, we collect a sample of operationally hedged firms that have

foreign assets or operations in foreign countries and report foreign sales. We match non-

operationally hedged firms with operationally hedged firms of similar size (total sales within

+10%) and in the same industry (SIC code) in the CGS segment files.2 We also require that the

2 For the industry and size matching process, we match 34.0 percent (72 firms) of the sample using four-digit SIC codes, 34.4 percent (73 firms) of the sample using three-digit SIC codes, and the remaining 31.6 percent (67 firms) of the 212 firm sample using two-digit SIC codes. We lose 49 firms in the matching process since we could not find the appropriate size matches.

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sample firms stock returns be available from CRSP. After this size and industry matching

procedure, and financial data requirements, we obtain a final sample of 424 firm observations

that consists of 212 non-operationally hedged and 212 operationally hedged firms. We collect the

number of subsidiaries located in foreign countries, and the number of foreign countries in which

a firm operates from the Directory of Corporate Affiliations: U.S. Public Companies, which was

prepared by National Register Publishings Database Publishing Group in 1999.3 We count only

level 1 non-U.S. subsidiaries, which report directly to the parent company.

3.2. Financial hedging data procedure

Next, for our sample firms, we collect financial derivatives information using the search

terms notional, hedge, forwards, swaps, options, market risk, and derivatives from annual

proxy statements (EDGAR database) in the fiscal year end of 1998. We identify financial

derivatives user as a firm that reports any type of currency derivatives instruments or reports the

notional amount of currency derivatives. We obtain 236 firms that report the use of financial

derivatives and 188 firms that do not report any type of financial derivatives. We record the total

notional amount of currency derivatives use and the types of currency derivatives including

forwards, futures, options, and swaps as well as interest rate derivatives information.

The increase in the complexity of derivatives has also increased the need for corporate

disclosures of positions. SFAS No. 119, Disclosure about Derivative Financial Instruments and

Fair Value of Financial Instruments, added to and amended two other statements (SFAS No. 105

and SFAS No. 107) and requires firms to disclose their objectives and strategies. Firms must

report the contract notional amount of instruments and hedging purpose. The FASB also issued

SFAS No. 133, Accounting for Derivative Instruments and Hedging Activities, in June 1998,

3 We thanks Allayannis and Weston for providing part of the sample for these operational measures.

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portions of which are augmented by SFAS No.137 and SFAS No.138. Starting with all fiscal

years after June 15, 2000, SFAS No.133 becomes mandatory for all companies. SFAS No.133

requires companies to show changes in all of their derivatives values as assets or liabilities in the

financial statements and measure those instruments at fair value with offsets allocated to current

earnings or other comprehensive income (OCI), even if the derivatives remain in an open

position. Information on other firm characteristics is collected from the COMPUSTAT annual

industrial database and segment database.

4. Methodology

4.1. Operational hedging measures

As proposed by Allen and Pantzalis (1996) and used by Allayannis et al. (2001), we use

four proxies for operational hedging: (i) the log of the number of countries in which a firm

operates (LNCTY); (ii) the number of regions where the firm has subsidiaries (LNSUBS),

NAFTA, Europe, Western Europe, Eastern Europe, East Asia, Other Asia, Central America,

South America, and Africa; (iii) the geographic dispersion of its subsidiaries across different

countries (Dispersion index I); (iv) the geographic dispersion of its subsidiaries across regions

(Dispersion index II). Dispersion indices I and II are calculated as

Dispersion index I = 1 Hirshman-Herfindahl concentration index

= 1 { i [# of countries]2 / [ i (# of countries)]2 } (1)

Dispersion index II = 1 { j [# of regions]2 / [ j (# of regions)]2 } (2)

where # of countries equals the number of foreign subsidiaries in country i, and # of regions

equals the number of foreign subsidiaries in geographic region j. These dispersion indices are

close to zero if a firm has subsidiaries in one country or region and equal to one if a firm has

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subsidiaries in many countries or regions. Thus, if a firm is more operationally diversified, then

this dispersion index measure also increases.

The effects of operational hedging and financial hedging on foreign exchange risk

exposure are measured by using the two-factor model (Jorion, 1990) Rit = i + i Rmt + i FX t +

it (3), where Rit = monthly rate of return on the ith firms stock in month t, Rmt = monthly rate

of return on the equally weighted market portfolio, and FX t = monthly rate of return on the

broad trade weighted foreign exchange rate. We use monthly return data from the CRSP

database over the period of 1996 to 2000 to estimate foreign exchange risk exposure (FXEXP,

). The data for the nominal broad trade weighted index are obtained from the Federal Reserve

Board database.4

4.2. Multivariate regression model

4.2.1. Substitute vs. complement

We examine whether operational hedging and financial hedging are substitutes or

complements. The basic model is as follows:

FINHEDGE = + 1 TOTFOR + 2 OPERH + Control variables + (4)

FINHEDGE is the financial hedge ratio, measured as the total notional amount of currency

derivatives divided by total foreign activities. TOTFOR is total foreign activity, measured by

total foreign sales and export sales to total sales, and OPERH refers to operational hedging

variables Dispersion Index I, Dispersion Index II, LNCNTY, and LNSUBS. The control

variables include FDEBT, TAX, SIZE, RND, QUICK, SEGN and INDUSTRY.

4 We also test by using various estimation periods of 36 month, 48 month, and 60 month, a different market measure (equally weighted index, and value weighted index), and various currency indices (Major index, OITP index). We report the result based on the five year estimation periods with the equally weighted market index (Bodnar and Wong, 2003). The results of alternative methods are similar to the findings in this paper.

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We use different econometric techniques to test this relation. First, we use a logistic

regression model since the dependent variable, CUSER, is dichotomous. CUSER equals one if a

firm uses any type of currency derivatives instruments and otherwise equals zero. Second, we

alternatively use the continuous dependent variable, the financial hedge ratio, FINHEDGE. If the

invoice currency is the foreign currency, then export sales produce the same exposure to

exchange rates as do foreign sales and FINHEDGE might be a good proxy for foreign activity.

FINHEDGE is similar to Haushalter (2000)s measure of the percentage of production hedged in

the gas and oil industry. With this continuous dependent variable, we employ the left censored

Tobit regression model to test the relation.

4.2.2. Foreign exchange risk exposure regression

Next, we examine the impact of operational hedging and financial hedging on foreign

exchange risk exposure by following the methodology of He and Ng (1998) with indicator

variable D, which equals one if a firm has positive foreign exchange risk exposure (i.e., > 0)

and otherwise equals zero (i.e., < 0). This method is also used by Carter et al. (2004a).

ABSFX (| |) = 0 D + 1 D*FINHEDGE + 2 D*OPERH + 3 D*FDEBT + 4

D*TOTFOR + 5 D*SIZE + 6 D* SEGN + 0d (1-D) + 1d (1-D)*FINHEDGE + 2d (1-

D)*OPERH + 3d D*FDEBT + 4d D*TOTFOR + 5d D*SIZE + 6d D* SEGN + (5)

where ABSFX = absolute value of the foreign exchange risk exposure as measured, by the two

factor market model (Jorion, 1990).5 We control heteroscedasticity in the estimation of the

exposure coefficients by using the weighted least squares (WLS) method and using the inverse of

5 The reason that we use the absolute value of foreign exchange risk exposure measure is that the details of import and export activities at the firm level are not available although we obtain export sales information. For this reason, it is not feasible to identify the sign of the currency exposure to the exchange rate change. Since the focus of this paper, however, is how risk management strategies affect foreign exchange risk exposure and firm value, the magnitude of the exposure is more important than the sign of the exposure.

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the squared standard error of the foreign exposure coefficient from equation (3) as the weight

(see Pantzalis et al., 2001; and Carter et al., 2004a, for details).

The level of foreign exchange exposure, i, and financial risk management decisions are endogenous. A firms hedging policies affect the magnitude of i since hedged firms will have lower levels of exposure. On the other hand, i influences a firms hedging policies. A firm that views itself as more exposed will have greater incentive to hedge (or hedge more). Thus,

the level of i and corporate hedging decisions are determined simultaneously and are modeled accordingly. To control for the endogenous nature of the hedging decisions, we use a

system of equations using three-stage least squares methodology (3SLS).6 For the hedging

decision, we use the model in equation (4) and add the endogenous variable absolute value of

foreign exchange exposure in the model. We hypothesize that both financial hedging and

operational hedging are associated with reducing foreign exchange risk exposure.

4.2.3. Tobins q regression

We examine the impact of both hedging strategies on firm value by using the dependent

variable Tobins q (Chung and Pruitt, 1994). The regression model is as follows:

Log (Tobins q) = + 1 FINHEDGE + 2 OPERH + 3 CUSER*OPERH

+ Control variables + (6)

We include the interaction variable, CUSER*OPERH, which is the financial derivatives

user variable multiplied by operational hedging variables. This variable is included to capture

the effects of operational and financial hedging for firms that use both of these hedging strategies.

6 The hedging regression results are not reported. The basic results are similar to our findings in Table 4. The results are available upon request.

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We also include the control variables, SIZE, DEBT, RND, CAPX, ROA, Credit rating and

industry dummy variables in Tobins Q regression.

5. Results

5.1. Univariate tests

Table 1 presents summary statistics for the sample of 424 firm observations. Panel A

shows the number of observations, mean, standard deviation, first quartile, median, and third

quartile for firm characteristics and operational hedging measures. The average [median] of total

assets is $5.5 billion [$1.4 billion]. Average [median] export sales are $541 million [$112 million]

while average foreign sales are $1.7 billion [$0.5 billion]. The amount of foreign sales is larger

than export sales. On average, 23% of total sales is from foreign sales or export sales. The

average number of foreign subsidiaries is 11 while the average number of foreign countries is 7.

The two other operational hedging measures, Dispersion Indices I and II, show how MNCs are

dispersed in different geographic areas. The average for Dispersion Index I (II) is 0.45 (0.32).

Table 1, Panel B, presents financial derivatives usage information. The notional amounts

of foreign currency derivatives as well as the types of instruments are reported. The number of

observations, mean, standard deviation, first and third quartile, and median values are reported.

Approximately 55% of the 424 total sample firms report the use of financial derivatives. Among

financial derivatives users, 78% of firms use foreign currency derivatives (183/236). The results

are close to the Bodnar et al. (1996) survey results. Foreign currency forward contracts are the

most popular instruments (69% of 236 derivatives users).

Table 2 shows univariate tests of firm characteristics. Panel A provides the difference in

mean and median statistics of firm characteristics by operational hedging strategy. Since we

obtain the control sample by matching by size and industry, the average difference in total assets

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and total sales in the two sub-samples is not statistically significant. The average and median

value of total debt to total assets is lower for operationally hedged firms and statistically

significant. However, R&D/total assets, foreign exchange exposure, and the absolute value of

foreign exchange exposure are not significantly different in the two samples while the total

foreign activity ratio is significantly higher for operationally hedged firms. The interesting

finding in this table is the average financial hedge ratio, which for non-operationally hedged

firms is 0.38 while the average for operationally hedged firms is 0.14. The difference in means

for the two samples is statistically significant at the 1 percent level. Since operationally hedged

firms are naturally hedged and have flexibility in their production and marketing strategies

related to exchange rate changes, the need for financial hedging is lower than that for non-

operationally hedged firms.

Table 2, Panel B, provides mean and median difference tests for firm characteristic

variables and operational hedging proxies by financial derivatives usage. In the sample,

approximately 56% of the total sample (236 firms) is derivatives users and 44% of the sample

(188 firms) is derivatives non-users. The size variable is larger for financial derivatives users.

Contrary to the financial distress explanation, total debt to total assets is lower for derivatives

users. Consistent with the growth opportunity hypothesis, however, R&D expenditures to total

assets is higher for derivatives users. Export sales are higher for financial derivatives non-users

while foreign sales are lower.

The main interest in this paper is the relation between operational hedging variables and

financial hedging activities. The average and median values of all four proxies for operational

hedging variables are significantly higher in the derivatives users group. Consistent with the

complementary hypothesis of the relation between operational and financial hedging, the results

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suggest that more operationally hedged firms are likely to use financial derivatives. However, we

draw this inference cautiously since confounding effects such as size are not controlled for in this

relationship.

Table 3 presents the Pearson correlation coefficients for financial and operational

hedging variables. All of the four operational hedging variables (Dispersion index I, Dispersion

index II, number of subsidiaries and number of countries) are very highly inter-correlated (e.g.,

0.90, 0.91, and 0.93 for Dispersion index I and other variables, respectively). Financial and

operational hedging variables are significantly positively correlated. Both the derivatives use and

the financial hedge ratio are positively correlated with all four proxies for operational hedging

variables. The results support the complementary nature of the risk management strategies of

financial and operational hedging. This finding supports the argument that operational hedging is

used for managing long term exposure (economic exposure) and financial hedging is used for

managing short term exposure (transaction exposure). The absolute value of the foreign

exchange risk exposure measure and financial hedging are negatively correlated, consistent with

earlier financial risk management literature (Allayannis et al., 2001).

5.2. Multivariate regression tests

5.2.1. Complement vs. substitute test

Table 4 reports the multiple regression results for the relation between operational and

financial hedging in multiple regression models. The dichotomous dependent variable equals one

if a firm reports the use of any type of currency derivatives use including forwards, swaps, and

options and equals zero otherwise. Logit regression models show that total foreign activity,

which is the sum of foreign export sales and foreign sales, is positively related to financial

derivatives usage. This positive relation between foreign activity and financial derivatives is

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consistent with Geczy et al. (1997) and Allayannis and Weston (2001). They also find a strong

positive relationship between the foreign sales ratio and foreign currency derivatives usage. After

controlling for this foreign activity and other determinants of financial hedging, we find a strong

positive relation between operational hedging (Dispersion index I) and financial hedging. The

results are very similar when other proxies for operational hedging measures are used. We find a

negative but statistically insignificant relation between foreign debt and the financial hedging

variable. To control for the effects of the determinants of financial hedging, we include the

control variables tax loss carry forward, size, debt ratio, growth opportunity, quick ratio, the

number of industrial segments, and industry dummy variables using two digit SIC code in all

other regression models. The results still hold when the control variables are included. The signs

and magnitude of the control variable coefficients are generally consistent with the earlier risk

management literature.

We reestimate the multivariate regression model using the left censored Tobit model and

the continuous financial hedge ratio variable. The basic results on the complementary nature of

operational and financial hedging do not change. All of the four proxies for the operational

hedging variables are statistically significant and positive. The sign and magnitude of other

control variables are similar to the results for the Logit regression models except that the foreign

activity variable is no longer significant. In summary, we identify a strong positive relation

between operational hedging and financial hedging. This result supports the complementary

nature of both hedging strategies.

5.2.2. Foreign exchange risk exposure results

Table 5 provides the results for the effects of operating and financial hedging on foreign

exchange risk exposure. The dependent variable in this regression is the absolute value of foreign

17

exchange risk exposure measure. The effects of financial hedging on currency exposure are

investigated by including the financial hedge ratio. Total foreign activity is measured as the

amount of export sales or foreign sales at fiscal year end 1998. We use three-stage least square

regression model to control for endogeneity between hedging strategies and the level of foreign

exchange risk exposure. In addition, we use the indicator variable, D, to separate the effects of

financial and operational hedging strategies for positive and negative exposure following He and

Ng (1998). We calculate foreign exposure using the two factor market model with equally

weighted market index and monthly foreign exchange rate index for a five year period as

suggested by Bodnar and Wong (2003).

As shown in the regression, we find significant negative effects of financial hedging on

currency exposure for net importer and net exporter. For net importers, financial hedging is

effective in controlling a firms exposure to unexpected currency exchange rate changes. We also

include operational hedging variables in the regression models. The coefficients for the four

operational hedging variables are also negative but insignificant for net importers (i.e., positive

foreign currency risk exposure). For net exporters, we also find that financial hedging reduces

foreign currency risk exposure effectively. The coefficients for the operational hedging variables

are positive but statistically insignificant for the different regression models. The results show

that financial hedging plays an important role in hedging foreign currency risk exposure. The

coefficients for the number of segments for net exporters are positive and statistically significant.

Other control variables are insignificant. Overall, we find that both financial hedging and

operational hedging are associated with foreign exchange risk exposure change.

5.2.3. Firm value effect (Tobins Q)

18

The dependent variable in this regression is Tobins Q (Chung and Pruitt, 1994). In Table

6, the regression model in the first column shows the relation between financial hedging and firm

value. Financial hedging is positively associated with firm value. Also, this result holds when we

include the operational hedging variable and other control variables. The results are consistent

with earlier findings in risk management literature. The regression model in the second column

shows the effect of operational hedging measured by dispersion index I. Operational hedging is

strongly positively associated with an increase in firm value. The results do not change when we

include the various control variables. The sign of the control variables are consistent with

previous literature, in general. Firms with high growth opportunity (R&D expenditures / total

assets) and profitability (return on assets) increase firm value. We also control industry effect for

all regression models. Interestingly, when we use the interaction dummy variable for both

financial and operational hedging, the coefficients of financial and operational hedging variables

are significant. However, the coefficients of the interaction variables are negative and

insignificant in all regression models. This result may be attributed to the complementary nature

of both financial and operational hedging. Overall, the valuation effects of operational hedging

and financial hedging are positive. We also reestimate the models using industry-adjusted

Tobins Q following Lang and Stulz (1994). The results are consistent with the evidence that

financial hedging adds 5.4% to firm value on average7 and operational hedging increase firm

value as a range of 4.8% to 17.9% in Table 6.

6. Conclusions

7 Our results are similar to previous results. For example, Allayannis and Weston (2001) conclude that foreign currency hedging adds 4.8% to firm value on average and Nains (2004) results indicate a 5% increase in firm value. Similarly, Bartram et al. (2004) show 4-9% for a global sample including U.S. firms, and Carter et al. (2004b) give a range of 12-16% for fuel hedging by airlines. The industry-adjusted Tobins q results are available from the authors.

19

This paper investigates the interrelationship between operational and financial hedging

activities, the effects of these hedging strategies on foreign exchange risk exposure, and the

effects of operational and financial hedging on firm value. The sample consists of 212 export

sales firms (non-operationally hedged firms) and 212 size and industry matched foreign sales

firms (operationally hedged firms). The results indicate that operational and financial hedging

strategies are complementary. MNCs employ both operational and financial hedging strategies to

manage their overall risks. These results regarding the complementary nature of the relation are

robust when we use different proxies for operational hedging, and are also robust when we use

different econometric techniques, Logit and Tobit regressions. Operational hedging can be

effective in managing long-term exposure (economic exposure) while financial hedging can be

effective for hedging short-term exposure (transaction exposure).

Consistent with the complementary hypothesis, the results show that both hedging

strategies are effective in reducing foreign exchange risk exposure. Moreover, consistent with

Carter et al. (2004b), both operational and financial hedging strategies are associated with

enhancing firm value. These results emphasize the importance of operational and financial

hedging for managing firm risk. Also, the results explain why some globally diversified firms

use limited amounts of financial derivatives for hedging purposes, despite much higher levels of

foreign exchange risk exposure.

Acknowledgements

We thank Wallace N. Davidson III, Jim Musumeci, Mark Peterson, Subhash Sharma, an anonymous reviewer and seminar participants at Southern Illinois University at Carbondale, Midwest Finance Association 2004 Annual Conference, and the Financial Management Association 2004 annual meeting for helpful comments.

References

20

Allen, L., Pantzalis, C., 1996. Valuation of the operating flexibility of multinational corporations. Journal of International Business Studies 27, 633-653. Allayannis, G., Ofek, E., 2001. Exchange-rate exposure, hedging and the use of foreign currency derivatives. Journal of International Money and Finance 20, 273-296. Allayannis, G., Ihrig, J., Weston, J.P., 2001. Exchange-rate hedging: financial vs. operational strategies. American Economic Review Papers & Proceedings 91 (2), 391-395. Allayannis, G., Lel, U., Miller, D.P., 2004. Corporate governance and the hedging premium around the world. Working paper, Darden School of Business, University of Virginia and Kelley School of Business, Indiana University. Allayannis, G., Weston, J., 2001. The use of foreign currency derivatives and firm market value. Review of Financial Studies 14, 243-276. Bartram, S.M., Brown, G.W., Fehle, F.R., 2004. International evidence on financial derivatives usage. SSRN Working paper. Bodnar, G.M., Hayt, G.S., Marston, R.C., 1996. 1995 Wharton survey of derivatives usage by U.S. non-financial firms. Financial Management 25, 113-133. Bodnar, G.M., Wong, M.H., 2003. Estimating exchange rate exposures: issues in model structure. Financial Management 32, 35-67. Carter, D.A., Pantzalis, C., Simkins, B.J., 2001. Firmwide risk management of foreign exchange exposure by U.S. multinational corporations. SSRN Working paper. Carter, D.A., Pantzalis, C., Simkins, B.J., 2004a. Asymmetric exposure to foreign-exchange risk: Financial and real option hedges implemented by U.S. multinational corporation. SSRN Working paper. Carter, D.A., Rogers, D.A., Simkins, B.J. 2004b. Does fuel hedging make economic sense? The case of the U.S. airline industry. SSRN Working paper. Chung, K.H., Pruitt, S.W., 1994. A simple approximation of Tobins q. Financial management

23, 70-74. DeMarzo, P.M., Duffie, D., 1995. Corporate incentives for hedging and hedge accounting. Review of Financial Studies 8, 743-771. Financial Accounting Standards Board (FASB), 1994. Disclosure about derivative financial instruments and fair value of financial instruments. Statement No. 119. Stamford, CT: FASB. Financial Accounting Standards Board (FASB), 1998. Accounting for derivative instruments and hedging activities. Statement No. 133. Stamford, CT: FASB. Froot, K.A., Scharfstein, D.S., Stein, J.C., 1993. Risk management: coordinating corporate investment and financing policies. Journal of Finance 48, 1629-1958. Gay, G.D., Nam, J., 1998. The underinvestment problem and corporate derivatives use. Financial Management 27, 53-69. Geczy, C., Minton, B.A., Schrand, C., 1997. Why firms use currency derivatives. Journal of Finance 52, 1323-1354. Graham, J.R., Rogers, D.A., 2002. Tax incentives to hedge. Journal of Finance 54, 815-839. Haushalter, D.G., 2000. Financing policy, basis risk, and corporate hedging: evidence from oil and gas producers. Journal of Finance 55, 107-152. He, J., Ng, L.K., 1998. The foreign exposure of Japanese multinational corporations. Journal of Finance 53, 733-753. Jorion, P., 1990. The exchange rate exposure of U.S. multinationals. Journal of Business 63, 331- 345.

21

Lang, L.H., Stulz, R.M., 1994. Tobins q, corporate diversification, and firm performance. Journal of Political Economy 102, 1248-1280. Lim, S., Wang, H.C., 2001. Stakeholder firm-specific investments, financial hedging, and corporate diversification. Working paper, Ohio State University. Mian, S.L. 1996. Evidence on corporate hedging policy. Journal of Financial and Quantitative Analysis 31, 419-439. Myers, S.C., 1977. Determinants of corporate borrowing. Journal of Financial Economics 5, 147- 175. Nain, A., 2004. The strategic motives for corporate risk management. Working paper, University of Michigan. Pantzalis, C., Simkins, B.J., Laux, P., 2001. Operational hedges and the foreign exchange exposure of U.S. multinational corporations. Journal of International Business Studies 32, 793-812. Smith, C.W., Stulz, R.M., 1985. The determinants of firms hedging policies. Journal of Financial and Quantitative Analysis 20, 391-405. Stulz, R.M., 2004. Should we fear derivatives? SSRN Working Paper. Wong, F.M.H., 2000. The association between SFAS No.119 Derivatives disclosures and the foreign exchange risk exposure of manufacturing firms. Journal of Accounting Research 38, 387-417.

22

Table 1 Descriptive statistics This table presents the descriptive statistics for operational and financial hedging measures for the sample of 424 observations for fiscal year end 1998. Firm characteristics are obtained from COMPUSTAT and operational hedging measures (the number of subsidiaries and number of foreign countries) are hand collected from the Directory of Corporate Affiliations in 1999. Panel A provides information about several operational hedging measures. Total assets are measured as book value of total assets for fiscal year end 1998. Total Sales are measured as the amount of total sales. Export sales and foreign sales are collected from the COMPUSTAT geographic segment files. The foreign activity ratio is measured as export sales and foreign sales divided by total sales. Foreign debt dummy variable equals 1 if the company reports the use of foreign debt, otherwise equals zero. The number of subsidiaries (foreign countries) is measured as the number of non-U.S. located subsidiary (foreign countries) from the Directory of Corporate Affiliations. Dispersion index I (Dispersion index II) is measured as one minus Hirshman-Herfindahl index for the number of countries (regions) where the company is located. The Hirshman-Herfindahl index is constructed as the sum of the squared "market shares" for each country (region) where the market share for each country (region) is defined as the proportion of total subsidiaries in each country (region). Panel B shows the financial derivatives usage information including currency and interest rate forwards, swaps, and options. The financial hedge ratio is measured as the total notional amount of currency derivatives divided by total foreign activities. Number of observations, Mean, standard deviation, first quartile, median, and third quartiles are reported. Panel A. Operational hedging measures Variables Number of

observations Mean Standard

deviation First quartile Median Third quartile

Total assets ($ million) 424 5549.5 24741.2 648.5 1430.6 2999.4

Total sales ($ million) 424 4414.1 12877.2 703.2 1389.8 3328.9

Export sales ($ million) 212 541.1 2076.4 48.8 112.1 248.2

Foreign sales ($ million) 212 1725.2 4874.0 204.1 548.8 1112.3

Foreign activity ratio 424 0.2290 0.1931 0.0779 0.1656 0.3365

Foreign debt 424 0.0919 0.2893 0.0000 0.0000 0.0000

Number of subsidiaries 378 11.31 26.84 1.00 3.00 12 (Max: 402)

Number of foreign countries 378 6.86 10.03 1.00 2.00 9 (Max: 94)

Dispersion index I (country based) 378 0.4477 0.4104 0.00 0.50 0.8760

Dispersion index II (region based) 378 0.3196 0.3243 0.00 0.279 0.6400

23

Panel B. Financial hedging information (Financial derivatives users only. All amounts in $ million.)

Variables Number of observations Mean Standard deviation First quartile Median Third quartile

Currency derivatives notional amount 183 741.12 2880.49 19.80 57.90 337.00

Forwards contracts 162 635.55 2756.24 20.00 60.35 306.00

Swaps 18 842.69 2154.80 11.00 138.80 405.00

Options 45 542.61 1075.25 16.70 118.50 368.90

Interest rate derivatives 146 1090.27 8120.84 50.00 139.55 400.00

Forwards/futures 1 134.00 N.A 134.00 134.00 134.00

Swaps 139 1103.00 8322.17 44.00 127.00 400.00

Options 11 478.69 711.14 50.00 200.00 525.00

Total derivatives notional amount 236 1254.50 8757.94 44.00 145.00 530.00

Financial hedge ratio (users only) 236 0.4692 1.1814 0.0026 0.0828 0.3306

Currency notional amount (total sample) 424 319.927 1924.83 0.000 0.000 44.60

Financial hedge ratio (total sample) 424 0.2604 0.9091 0.000 0.000 0.1207

24

Table 2 Univariate tests by operational hedging and financial hedging This table presents the univariate tests for operational and financial hedging measures for the sample of 424 observations in fiscal year end 1998. Firm characteristics are obtained from COMPUSTAT and operational hedging measures (the number of subsidiaries and number of foreign countries) are hand collected from the Directory of Corporate Affiliations in 1999. Panel A provides firm characteristics by operational hedging measures. Firms are operationally hedged if they report foreign sales or foreign assets. Non-operationally hedged firms are firms that report export sales but do not report foreign sales or foreign assets. Total assets are measured as book value of total assets for fiscal year end 1998. Total sales are measured as the amount of total sales. Panel B shows firm characteristics by financial derivatives usage. The number of subsidiaries and foreign countries are measured as the number of non-U.S. located subsidiaries/foreign countries from the Directory of Corporate Affiliations. Dispersion index I (Dispersion index II) is measured as one minus the Hirshman-Herfindahl index of the number of countries (regions) where the company is located. The Hirshman-Herfindahl index is constructed as the sum of the squared "market shares" for each country where the market share for each country (region) is defined as the proportion of total subsidiaries in each country (region). Means, standard deviations, medians, t statistics and Z statistics are reported. Panel A. Firm characteristics by operational hedging

Non-operationally hedged Number of firms = 212

Operationally hedged Number of firms = 212

Mean Standard deviation

Median Mean Standard deviation

Median

t-statistics Z-statistics

Total assets ($ million) 5206.47 25245.48 1062.63 5892.69 24281.38 1663.69 -0.29 -3.77 ***

Total sales ($ million) 3952.33 9872.78 1083.48 4876.04 15314.36 1683.64 -0.74 -3.73 ***

Total debt/total assets 0.263 0.182 0.253 0.224 0.162 0.210 2.26 ** 2.03 **

R&D/total assets 0.047 0.049 0.025 0.053 0.056 0.030 -1.02 0.52

Foreign exchange exposure () -0.165 1.447 -0.217 -0.306 1.489 -0.327 0.98 0.77

Foreign exchange exposure 1.030 1.027 0.756 1.095 1.052 0.759 -0.63 -0.70

Foreign activity ratio 0.128 0.123 0.092 0.330 0.197 0.307 -12.67*** -12.02 ***

Currency derivatives 213.54 988.46 0.000 426.31 2535.30 426.31 -1.14 -2.47 ***

Financial hedge ratio 0.380 1.158 0.000 0.141 0.535 0.002 2.73 *** -0.95

Tobins Q 1.540 0.960 1.246 1.646 1.065 1.286 -1.07 -0.62

Industry adjusted Q -0.075 0.602 -0.108 -0.096 0.651 -0.123 0.32 0.12

25

Panel B. Firm characteristics by financial derivatives usage Derivatives non-users

Number of firms = 188 Derivatives users

Number of firms = 236

Mean Standard deviation

Median Mean Standard deviation

Median

t-statistics Z-statistics

Total assets ($ million) 1502.5 2670.0 736.9 8773.5 32750.8 2088.2 -3.40 *** -9.84 ***

Total sales ($ million) 1408.7 1809.4 760.3 6808.3 16819.4 1991.4 -4.90 *** -9.45 ***

Total debt/total assets 0.274 0.191 0.266 0.219 0.154 0.205 3.24 *** 3.00 ***

R&D/total assets 0.023 0.037 0.009 0.050 0.057 0.025 -5.75 *** -5.97 ***

Tax ratio 0.015 0.062 0.000 0.022 0.077 0.000 -1.10 -2.57 ***

Quick ratio 1.396 0.855 1.167 1.304 0.689 1.094 1.21 0.78

Industrial segments 2.430 1.142 2.00 3.381 1.339 3.000 -7.74 *** -7.50 ***

Export sales 0.075 0.121 0.029 0.054 0.095 0.000 1.94 * 2.48 ***

Foreign sales 0.097 0.153 0.000 0.219 0.242 0.150 -6.32 *** -4.98 ***

Foreign debt 0.064 0.245 0.000 0.114 0.319 0.000 -1.85 * -1.79 **

Foreign exchange exposure ()

-0.126 1.484 -0.246 -0.323 1.453 -0.293 1.35 0.85

Foreign exchange exposure

1.074 1.028 0.750 1.053 1.050 0.757 0.19 0.12

Number of subsidiaries 4.25 9.28 1.00 16.27 33.29 6.00 -5.10 *** -7.44 ***

Number of countries 3.12 4.53 1.00 9.49 11.85 5.00 -7.37 *** -7.31 ***

Dispersion index I 0.274 0.368 0.000 0.569 0.394 0.750 -7.45 *** -7.17 ***

Dispersion index II 0.183 0.278 0.000 0.415 0.320 0.500 -7.48 *** -6.83 ***

Financial hedge ratio 0.000 0.000 0.000 0.467 1.179 0.081 -6.10 *** -16.36 ***

Tobins Q 1.511 0.955 1.257 1.655 1.055 1.246 -1.46 -1.49 *

Industry adjusted Q -0.079 0.627 -0.084 -0.091 0.627 -0.146 0.19 0.38

***, **, * significant at the 1%, 5%, and 10% levels, respectively.

26

Table 3 Correlation matrix This table presents the Pearson correlation coefficients for financial and operational hedging variables. Currency derivative is a dummy variable equal to 1 if a firm reports any notional amount of currency derivatives and otherwise equal to zero. Financial hedge ratio is measured as the total notional amount of currency derivatives divided by total foreign activities (foreign sales or export sales). Foreign activity is a dummy variable equal to 1 if a firm reports foreign sales or foreign assets and otherwise equal to zero. Foreign sales ratio is measured as foreign sales divided by total sales. Total foreign activity is measured as the sum of foreign sales and export sales divided by total sales. foreign exchange exposure is calculated using the two factor model (Jorion, 1990). Dispersion index I (Dispersion index II) is measured as one minus the Hirshman-Herfindahl index of the number of countries (regions) where the company is located. Number of subsidiaries (Countries) is the log of the number of non-U.S. located level 1 subsidiaries (foreign countries where a firm operates). Tobins Q is the ratio of market to book value. Currency

derivative Financial hedge ratio

Foreign activity dummy

Foreign sales ratio

Total foreign activity

Foreign exchange exposure

Foreign exchange exposure

Dispersion Index I

Dispersion Index II

Number of subsidiaries

Number of countries

Tobins Q

Currency derivative

1.0000 0.2559 a 0.1423 a 0.2811 a 0.2605 a -0.1115 b -0.0148 0.3551 a 0.3522 a 0.3789 a 0.3764 a 0.0704

Financial hedge ratio

1.0000 -0.1319 a -0.1080 b -0.1493 a 0.0910 b -0.1120 b 0.1498 a 0.1820 a 0.1329 a 0.1415 a 0.0923 b

Foreign activity dummy

1.0000 0.7649 a 0.5250 a -0.0607 0.0057 0.0923 c 0.0890 c 0.0813 0.0786 0.0538

Foreign sales

1.0000 0.8657 a -0.1346 a 0.0989 b 0.1902 a 0.1923 a 0.2056 a 0.2219 a 0.1562 a

Total foreign activity

1.0000 -0.1566 a 0.1168 b 0.1672 a 0.1683 a 0.1980 a 0.2169 a 0.1897 a

Foreign exchange exposure

1.0000 -0.0787 -0.1347 a -0.1263 b -0.1067 b -0.1193 b -0.1067 b

Foreign exchange exposure

1.0000 -0.0677 c -0.0683 -0.0813 c -0.0622 -0.0209

Dispersion index I

1.0000 0.9054 a 0.9148 a 0.9371 a 0.1229 a

Dispersion index II

1.0000 0.8617 a 0.8904 a 0.1401 a

Number of subsidiaries

1.0000 0.9817 a 0.1219 a

Number of countries

1.0000 0.1358 a

Tobins Q 1.0000 a, b, c significant at the 1%, 5%, and 10% levels, respectively.

27

Table 4 Complement vs. substitute tests This table shows the multiple regression results for the sample of 424 firms for fiscal year end 1998. In the Logit regressions, the dependent variable is a dummy variable equal to one if a firm reports any type of derivatives and otherwise equal to zero. In the Tobit regression, the dependent variable is measured as total currency derivatives notional amount divided by foreign sales or export sales. Total foreign activity is measured as the sum of export sales and foreign sales divided by total sales. Dispersion index I (Dispersion index II) is measured as one minus the Hirshman-Herfindahl index of the number of countries (regions) where the company is located. Foreign debt is a dummy variable equal to 1 if the company reports the use of foreign debt, otherwise zero. The tax ratio is the ratio of tax loss carry forwards to total assets. Industry is a dummy variable using two digit SIC code. T-statistics are reported in parentheses. Logit regression: Dependent = Derivatives use Tobit regression: Dependent = Financial hedge ratio Dispersion index I Dispersion index II Number of

subsidiaries Number of countries

Dispersion index I Dispersion index II Number of subsidiaries

Number of countries

Intercept -7.288*** (-4.31)

-7.288*** (-4.31)

-7.942*** (-6.90)

-7.330*** (-4.34)

-7.022*** (-4.27)

-7.063*** (-4.17)

-3.640*** (-5.42)

-1.711* (-1.85)

-3.792*** (-5.62)

-1.729* (-1.86)

-1.647 * (-1.75)

-1.624* (-1.73)

Operational hedging

1.279*** (2.94)

1.275*** (2.86)

1.043*** (3.13.)

1.016*** (2.99)

0.359*** (3.09)

0.388*** (2.96)

0.927*** (3.26)

0.861*** (3.05)

0.556** (2.50)

0.518** (2.35)

0.117* (1.73)

0.154** (2.00)

Total foreign activity

1.326* (1.70)

1.419* (1.74)

1.370* (1.76)

1.454* (1.79)

1.337* (1.64)

1.334* (1.64)

-1.043** (-2.30)

-1.089** (-2.42)

-1.021** (-2.24)

-1.065** (-2.35)

-1.098** (-2.42)

-1.116** (-2.46)

Foreign debt -0.183 (-0.40)

-0.217 (-0.47)

-0.207 (-0.45)

-0.235 (-0.50)

-0.223 (-0.48)

-0.230 (-0.49)

-0.009 (-0.03)

-0.018 (-0.07)

-0.014 (-0.05)

-0.021 (-0.08)

-0.027 (-0.10)

-0.025 (-0.09)

Tax ratio 1.353 (0.65)

1.422 (0.67)

1.401 (0.67)

1.456 (0.69)

1.374 (0.64)

1.322 (0.62)

2.547** (2.02)

2.641** (2.13)

2.513** (1.97)

2.605** (2.08)

2.562** (2.04)

2.551** (2.04)

Total assets 0.836*** (5.70)

0.848*** (5.68)

0.857*** (5.91)

0.869*** (5.88)

.836*** (5.60)

0.849*** (5.70)

0.313*** (3.83)

0.314*** (3.88)

0.346*** (4.25)

0.345*** (4.28)

0.352*** (4.21)

0.345*** (4.17)

Total debt/ total assets

-0.219 (-0.28)

-0.302 (-0.38)

-0.155 (-0.20)

-0.251 (-0.31)

-0.277 (-0.35)

-0.251 (-0.31)

-0.276 (-0.51)

-0.327 (-0.60)

-0.395 (-0.72)

-0.434 (-0.79)

-0.532 (-0.96)

-0.466 (-0.84)

R&D/total assets

11.888*** (3.69)

11.369*** (3.27)

11.771*** (3.68)

11.369*** (3.29)

11.406*** (3.31)

11.334*** (3.29)

1.978 (1.12)

0.653 (0.36)

2.073 (1.17)

0.768 (0.42)

0.826 (0.45)

0.776 (0.42)

Quick ratio -0.181 (-1.00)

-0.160 (-0.88)

-0.163 (-0.90)

-0.144 (-0.79)

-0.144 (-0.79)

-0.141 (-0.78)

0.215* (1.80)

0.244** (2.07)

0.209* (1.75)

0.241** (2.04)

0.236** (1.99)

0.239** (2.02)

Number of segments

0.406*** (3.50)

0.399*** (3.41)

0.385*** (3.30)

0.380*** (3.23)

0.388*** (3.30)

0.385*** (3.27)

0.158** (2.41)

0.171** (2.57)

0.151** (2.28)

0.162** (2.42)

0.167** (2.50)

0.166** (2.48)

Industry NO YES NO YES YES YES NO YES NO YES YES YES

LR chi2 167.63 170.42 168.81 171.17 172.09 171.23 65.68 80.08 65.73 76.33 73.81 74.81

Pseudo R2 0.298 0.305 0.299 0.306 0.308 0.306 0.069 0.085 0.070 0.081 0.078 0.079 ***, **, * significant at the 1%, 5%, and 10% levels, respectively.

28

Table 5 Foreign exchange risk exposure tests

This table shows the multiple regression results for the sample of 424 firms for fiscal year end 1998. The dependent variable is the absolute value of foreign exchange risk exposure. We use three stage least squares to control endogeneity between hedging and foreign exchange risk exposure. Also, we use the weighted least square method using one over squared standard error of the coefficient of foreign exposure in equation (3). Foreign exchange risk exposure is calculated using the two factor model (Jorion, 1990). To separate foreign exposure, we employ the indicator variable D, equal to 1 if the foreign exposure is positive, otherwise equal to zero, following He and Ng (1998). The financial hedging ratio is measured as total currency derivatives notional amount divided by total foreign activity. Operational hedging is measured by four different proxies: Dispersion index I, Dispersion index II, log of number of subsidiaries and number of foreign countries. Dispersion index I (Dispersion index II) is measured as one minus the Hirshman-Herfindahl index of the number of countries (regions) where the company is located. Total foreign activity ratio is the sum of export sales and foreign sales divided by total sales. Foreign debt is a dummy variable equal to 1 if the company reports the use of foreign debt, otherwise zero. Industry is a dummy variable using the two digit SIC code. T-statistics are reported in parentheses. Adjusted R-squares and F-statistics are reported.

Dispersion index I

Dispersion index I

Dispersion index II

Log(Number of subsidiaries)

Log(Number of countries)

D 1.184*** (2.98)

2.016*** (3.26)

1.980*** (3.21)

2.001*** (3.18)

2.039*** (3.26)

D * financial hedging ratio

-0.269*** (-5.90)

-0.293*** (-6.42)

-0.277*** (-6.00)

-0.279*** (-6.06)

-0.292*** (-6.40)

D * operational hedging

-0.024 (-0.11)

-0.014 (-0.06)

-0.077 (-0.45)

-0.017 (-0.30)

-0.006 (-0.09)

D * foreign debt 0.178 (0.71)

0.201 (0.81)

0.194 (0.78)

0.201 (0.80)

0.207 (0.84)

D * total foreign activity

0.198 (0.47)

0.145 (0.34)

0.201 (0.47)

0.179 (0.42)

0.148 (0.35)

D * log(total assets) -0.055 (-0.94)

-0.055 (-0.93)

-0.049 (-0.85)

-0.050 (-0.82)

-0.054 (-0.88)

D * number of segments

-0.002 (-0.03)

-0.008 (-0.14)

-0.007 (-0.14)

-0.009 (-0.16)

-0.008 (-0.15)

(1-D) 1.043*** (3.41)

1.869*** (3.32)

1.881*** (3.34)

1.876*** (3.28)

1.927*** (3.38)

(1-D)* financial hedging ratio

-0.215*** (-3.91)

-0.237*** (-4.37)

-0.225*** (-4.13)

-0.221*** (-4.08)

-0.237*** (-4.41)

(1-D) * operational hedging

0.159 (0.98)

0.162 (1.00)

0.198 (1.51)

0.031 (0.82)

0.054 (1.23)

(1-D) * foreign debt -0.066 (-0.42)

-0.063 (-0.41)

-0.078 (-0.50)

-0.071 (-0.45)

-0.073 (-0.47)

(1-D)* total foreign activity

-0.187 (-0.69)

-0.208 (-0.76)

-0.233 (-0.86)

-0.224 (-0.82)

-0.258 (-0.95)

(1-D) * log(total assets)

-0.051 (-1.16)

-0.055 (-1.26)

-0.060 (-1.39)

-0.054 (-1.18)

-0.058 (-1.31)

(1-D) * number of segments

0.097** (2.25)

0.099** (2.30)

0.095** (2.20)

0.099** (2.29)

0.096** (2.23)

Industry NO YES YES YES YES

Adjusted R square 0.553 0.551 0.553 0.551 0.552

F-statistics 35.98 24.18 24.35 24.16 24.20

***, **, * significant at the 1%, 5%, and 10% levels, respectively.

29

Table 6 Firm value effect tests (Tobins Q) This table shows the multivariate regression results for the sample of 424 firms for fiscal year end 1998. The dependent variable is Tobins Q, measured as the ratio of the market value of assets to book value of assets following Chung and Pruitt (1994). The financial hedge ratio is total currency derivatives notional amount divided by total foreign activity. Operational hedging is measured by four different proxies: Dispersion index I, Dispersion index II, log of number of subsidiaries and log of number of countries. Dispersion Index I (Dispersion Index II) is measured as one minus the Hirshman-Herfindahl index of the number of countries (regions) where the company is located. The interaction variable is financial derivatives user variable multiplied by operational hedging variables. Industry is a dummy variable using the two digit SIC code. White (1980) corrected standard errors are reported in parentheses. Adjusted R-squares and F-statistics are reported. Dispersion

index I Dispersion index

II Log(number of

subsidiaries) Log(number of

countries)

Intercept 0.316 (0.440)

0.333 (0.433)

0.283 (0.435)

0.343 (0.434)

0.289 (0.437)

0.390 (0.439)

0.314 (0.440)

0.390 (0.439)

0.319 (0.440)

Financial hedging ratio

0.054* (0.027)

0.051* (0.028)

0.052* (0.028)

0.056** (0.028)

0.054* (0.028)

Operational hedging

0.179** (0.094)

0.235* (0.142)

0.135* (0.072)

0.178* (0.103)

0.040 (0.021)

0.078** (0.035)

0.048* (0.025)

0.084** (0.042)

Foreign derivatives* operational hedging

-0.116 (0.151)

-0.087 (0.112)

-0.052 (0.036)

-0.053 (0.043)

Log(total assets)

0.015 (0.030)

0.006 (0.031)

0.009 (0.031)

0.009 (0.031)

0.011 (0.030)

0.005 (0.031)

0.009 (0.031)

0.006 (0.031)

0.009 (0.031)

Total debt/ total assets

0.332 (0.250)

0.366 (0.255)

0.364 (0.255)

0.365 (0.255)

0.362 (0.255)

0.358 (0.253)

0.357 (0.253)

0.370 (0.255)

0.365 (0.254)

R&D/ total assets

6.113*** (0.926)

6.027*** (0.929)

6.101*** (0.929)

6.045*** (0.926)

6.107*** (0.924)

6.036 (0.931)

6.131*** (0.924)

6.025*** (0.930)

6.109*** (0.923)

Capital exp / total assets

0.145 (0.662)

0.114 (0.652)

0.168 (0.655)

0.098 (0.657)

0.140 (0.661)

0.118 (0.658)

0.174 (0.664)

0.103 (0.657)

0.155 (0.663)

Return on assets

3.625*** (0.574)

3.675*** (0.578)

3.632*** (0.571)

3.675*** (0.579)

3.630*** (0.572)

3.670 (0.580)

3.615*** (0.571)

3.676*** (0.580)

3.622*** (0.572)

Number of segments

0.015 (0.020)

0.013 (0.020)

0.014 (0.020)

0.011 (0.020)

0.013 (0.020)

0.011 (0.020)

0.013 (0.020)

0.011 (0.020)

0.012 (0.020)

Credit rating YES YES YES YES YES YES YES YES YES

Industry YES YES YES YES YES YES YES YES YES

Adjusted R-squares 0.377 0.377 0.379 0.378 0.378 0.376 0.380 0.377 0.380

F-statistics 13.49 13.49 12.44 13.48 12.44 13.45 12.53 13.49 12.51

***, **, * significant at the 1%, 5%, and 10% levels, respectively.