Financial distress and corporate risk management

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Journal of Financial Economics 87 (2008) 706–739
www.elsevier.com/locate/jfec

Financial distress and corporate risk management:
Theory and evidence$
Amiyatosh PurnanandamÃ
Ross School of Business, University of Michigan, Ann Arbor, MI 48109, USA
Received 28 February 2006; received in revised form 2 April 2007; accepted 10 April 2007
Available online 14 December 2007

Abstract
This paper extends the current theoretical models of corporate risk-management in the presence of ﬁnancial distress
costs and tests the model’s predictions using a comprehensive data set. I show that the shareholders optimally engage in expost (i.e., after the debt issuance) risk-management activities even without a pre-commitment to do so. The model predicts
a positive (negative) relation between leverage and hedging for moderately (highly) leveraged ﬁrms. Consistent with the
theory, empirically I ﬁnd a non-monotonic relation between leverage and hedging. Further, the effect of leverage on
hedging is higher for ﬁrms in highly concentrated industries.
r 2008 Elsevier B.V. All rights reserved.
JEL classification: G30; G32
Keywords: Hedging; Risk-shifting; Asset substitution; Derivatives

1. Introduction
This paper develops and tests a theory of corporate risk management in the presence of ﬁnancial distress
costs. The existing literature shows that hedging can lead to ﬁrm value maximization by limiting deadweight
losses of bankruptcy (see Smith and Stulz, 1985).1 These models justify only ex-ante risk-management
$
This paper is based on a chapter of my Ph.D. dissertation at Cornell University. I would like to especially thank an anonymous referee
for several useful suggestions during the reviewing process. I am grateful to George Allayannis, Warren Bailey, Sugato Bhattacharya,
Sreedhar Bharath, Sudheer Chava, Thomas Chemmanur, Wayne Ferson, Ken French, John Graham, Robert Goldstein, Yaniv Grinstein,
Jerry Haas, Pankaj Jain, Kose John, Haitao Li, Roni Michaely, M.P.Narayanan, Maureen O’Hara, Paolo Pasquariello, Mitch Petersen,

Uday Rajan, William Schwert (the editor), David Weinbaum, Rohan Williamson, and seminar participants at Boston College, Cornell,
Darden, Emory, London Business School, University of Michigan, Notre Dame, University of Rochester, The Lehman Brothers Finance
Fellowship Competition 2003, and the Western Finance Association’s 2005 meetings for valuable comments and suggestions. I am
particularly grateful to Bob Jarrow and Bhaskaran Swaminathan for their advice. All remaining errors are mine.
ÃTel.: +1 734 764 6886; fax: +1 734 936 8715.
E-mail address:
1
Other motivations for corporate hedging include convexity of taxes, managerial risk-aversion (Stulz, 1984; Smith and Stulz, 1985)
underinvestment costs (Froot, Scharfstein, and Stein, 1993), and information asymmetry (DeMarzo and Dufﬁe, 1991, 1995). See also
Breeden and Viswanathan (1996) and Stulz (1996).

0304-405X/$ - see front matter r 2008 Elsevier B.V. All rights reserved.
doi:10.1016/j.jﬁneco.2007.04.003

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behavior on the part of the ﬁrm; ex-post, shareholders of a levered ﬁrm may not ﬁnd it optimal to engage in
hedging activities due to their risk-shifting incentives (Jensen and Meckling, 1976).2 I extend the current
literature by explaining the ex-post risk-management motivation of the ﬁrm.3 I provide a simple model that
generates new cross-sectional predictions by relating ﬁrm characteristics such as leverage, ﬁnancial distress
costs, and project maturity to risk-management incentives. I test the key predictions of the model with hedging
data of COMPUSTAT-CRSP ﬁrms meeting some reasonable sample selection criteria for ﬁscal years
1996–1997. The empirical study presents the ﬁrst large-sample evidence on the determinants of the extent of
ﬁrms’ hedging activities and provides new ﬁndings.
The key assumption underlying my theory is the distinction between financial distress and insolvency.
I assume that apart from the solvent and the insolvent states, a ﬁrm faces an intermediate state called financial

distress. Financial Distress is deﬁned as a low cash-ﬂow state in which the ﬁrm incurs losses without being
insolvent. The notion that ﬁnancial distress is a different state from insolvency has some precedence in the
literature. Titman (1984) uses a similar assumption to study the effect of capital structure on a ﬁrm’s
liquidation decisions.
There are three important sources of ﬁnancial distress costs. First, a ﬁnancially distressed ﬁrm may lose
customers, valuable suppliers, and key employees.4 Opler and Titman (1994) provide empirical evidence that
ﬁnancially distressed ﬁrms lose signiﬁcant market share to their healthy counterparts in industry downturns.
Using data from the supermarket industry, (Chevalier 1995a, b) ﬁnds evidence that debt weakens the
competitive position of a ﬁrm. Second, a ﬁnancially distressed ﬁrm is more likely to violate its debt covenants5
or miss coupon/principal payments without being insolvent.6 These violations impose deadweight losses in the
form of ﬁnancial penalties, accelerated debt repayment, operational inﬂexibility, and managerial time and
resources spent on negotiations with the lenders.7 Finally, a ﬁnancially distressed ﬁrm may have to forgo
positive NPV projects due to costly external ﬁnancing, as in Froot, Scharfstein, and Stein (1993). In this paper
I focus on the ﬁrst of these costs, i.e., the product market-related costs of ﬁnancial distress.
I develop a dynamic model of a ﬁrm that issues equity capital and zero-coupon bonds to invest in a risky
asset. The ﬁrm makes an initial investment with the consent of its bondholders. At a later date, shareholders
can modify the ﬁrm’s investment risk by replacing the existing asset with a new one. The ﬁrm’s asset value
evolves according to a stochastic process. The ﬁrm is in ﬁnancial distress if the asset value falls below some
lower threshold during its life. In this state, the ﬁrm loses market share to its competitors and therefore is
unable to realize its full upside potential, even when the industry condition improves at a later date. Insolvency
occurs on the maturity date if terminal ﬁrm value is below the face value of debt, in which case debtholders
gain control of the ﬁrm. Shareholders’ ﬁnal payoffs depend on the terminal asset value as well as on the path
taken by the ﬁrm’s asset over its life.8
2

Throughout the paper, I use the terms ex ante and ex post with respect to the time of borrowing.
Other papers analyzing shareholders’ ex-post risk-management decisions include Leland (1998) and Morellec and Smith (2003). Leland
(1998) provides a justiﬁcation for the ﬁrm’s ex-post hedging behavior in the presence of tax-beneﬁts of debt. In Morellec and Smith (2003),
the manager-shareholder conﬂict reduces shareholders’ ex-post asset-substitution incentives. My model, in contrast, is based on the cost of
ﬁnancial distress and provides new empirical predictions.

4
For example, in the mid-1990s Apple Computers had ﬁnancial difﬁculties leading to speculation about its long-term survival (see
Business Week, January 29 and February 5, 1996). Software developers were reluctant to develop new application software for Mac-users,
which led in part to a decline of 27% in the unit sales of Mac computers from 1996 to 1997 (see Apple’s 1998 10-K ﬁlings with the SEC).
Similarly, when Chrysler faced ﬁnancial difﬁculties in the early 1980s, Lee Iacocca (former CEO of the company) observed that ‘‘its share
of new car sales dropped nearly two percentage points because potential buyers feared the company would go bankrupt’’ (quoted from
Titman, 1984).
5
Lenders often impose debt covenants such as maintenance of minimum networth or maximum debt-to-equity ratio by the borrowing
ﬁrms. See Smith and Warner (1979), Kalay (1982), and Dichev and Skinner (2001).
6
Moody’s Investor Service Report (1998) shows that during 1982–1997 about 50% of the long-term publicly traded bond defaults
(including missed or delayed payment of coupon and principal) didn’t result in bankruptcy ﬁlings.
7
For example, when Delta airlines violated a debt-to-equity ratio covenant in 2002, it was required by its lenders to maintain a minimum
of $1 billion in cash and cash equivalents at the end of every month from October 2002 until June 2003. See Delta’s 2002 10-K ﬁlings with
the SEC.
8
This approach is similar (but not the same) to valuation of equity as a path-dependent (down-and-out call) option. The equity value in
my model differs from the corresponding barrier option by the amount of losses incurred in ﬁnancial distress. Brockman and Turtle (2003)
provide some empirical evidence in support of equity’s valuation as a path-dependent option.
3

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The optimal level of ex-post investment risk, from the shareholders’ perspective, is determined by the tradeoff between the costs of ﬁnancial distress and value associated with the limited liability of the ﬁrm’s equity.9

Unlike in the risk-shifting models such as Jensen and Meckling (1976), equity value is not always an increasing
function of ﬁrm risk in my model. While a high risk project increases the value of equity’s limited liability, it
also imposes a cost on shareholders by increasing the expected cost of ﬁnancial distress. Due to these losses,
the shareholders ﬁnd it optimal to implement a risk-management strategy ex-post even in the absence of an
explicit pre-commitment to do so.
The optimal investment risk in my model depends on ﬁrm leverage, the ﬁnancial distress boundary, the time
horizon of the project, and the costs of ﬁnancial distress. As in the extant models (Smith and Stulz, 1985),
I show that a ﬁrm with high leverage has a higher incentive to engage in hedging activities. However, the riskmanagement incentives disappear for ﬁrms with extremely high leverage. The incentive to hedge arises from
the product market-related ﬁnancial distress costs and these costs are more likely to be present when a ﬁrm is
vulnerable to losing market share to its competitors. Empirical studies by Opler and Titman (1994) and
Chevalier (1995a, b) show that debt weakens the competitive position of a ﬁrm in its industry. Further, the
adverse consequences of leverage are more pronounced in concentrated industries. Motivated by these studies
my model argues that industry concentration provides a good proxy for ﬁnancial distress costs. Highly
leveraged ﬁrms in concentrated industries are more likely to experience a deterioration in their competitive
position in the event of ﬁnancial distress i.e., are expected to incur higher ﬁnancial distress costs. Thus, the
model predicts a stronger hedging incentive for highly levered ﬁrms in concentrated industries.
The model shows that hedging incentives increase with project maturity because the likelihood of
experiencing ﬁnancial distress as well as the expected loss of default increases with the life of the asset. Riskmanagement motivation in my model arises from costs incurred by the ﬁrm in states in which the ﬁrm hits the
ﬁnancial distress barrier but remains solvent on the maturity date. If there are no ﬁnancial distress costs, riskmanagement incentives disappear. On the other hand, if these costs are very high, the distinction between
ﬁnancial distress and insolvency diminishes along with any ex-post risk-management motivations.
Intermediate levels of losses create risk-management incentives within the ﬁrm. Therefore, my model predicts
a U-shaped relation between ﬁnancial distress costs and hedging.
The predictions of my model have important implications for the empirical research. To test the existing
theories, empirical studies regress some measure of ﬁnancial distress (typically leverage) on ﬁrms’ riskmanagement activities. If ﬁrms with extreme distress are less likely to hedge, these models may be misspeciﬁed.
The bias can be particularly severe in small-sample studies. It is not surprising that existing empirical studies
ﬁnd mixed evidence in support of the distress cost-based theories of hedging.10
I contribute to the empirical risk-management literature by analyzing foreign currency and commodity riskmanagement activities of a comprehensive sample of nonﬁnancial ﬁrms. Since data on ﬁrms’ hedging activities
(by means of derivatives) are not readily available, empirical studies in this area are based on small samples or
investigate only the yes–no decision to hedge.11 This has created two major challenges. First, our current
understanding is mostly based on analyses that treat ﬁrms with different hedging intensities as similar, which

limits our ability to investigate ﬁrms’ hedging motivations. Second, we have been able to gain only limited
insight into the effect of industry-speciﬁc factors on hedging decisions.
I test the predictions of my model with data on the extent of hedging of more than 2,000 ﬁrms for the ﬁscal
year 1996–1997. Due to the large sample size drawn from different industries, I provide new empirical evidence
relating industry structure to hedging decisions. Consistent with the theory, I ﬁnd strong evidence that ﬁrms
with higher leverage hedge more, although the hedging incentives disappear for ﬁrms with very high leverage.
Also in line with my theory, I ﬁnd that ﬁnancially distressed ﬁrms in highly concentrated industries hedge
9

In the context of swap markets, Mozumdar (2001) demonstrates the trade-off between risk-shifting and hedging incentives in the
presence of information asymmetry about the ﬁrm type. His model relates hedging incentives to ﬁrm type.
10
For example, while Haushalter (2000) and Graham and Rogers (2002) ﬁnd a positive relation between the two variables, Nance, Smith
and Smithson (1993), Mian (1996), and Tufano (1996) fail to ﬁnd such evidence.
11
For example, Geczy, Minton, and Schrand (1997) use 372 ﬁrms with 154 hedgers; Graham and Rogers (2002) use about 400 ﬁrms with
158 hedgers. Studies by Mian (1996) and Bartram, Brown, and Fehle (2003) use large samples to investigate the yes–no decision of
hedging. Tufano (1996) and Haushalter (2000) provide detailed evidence from gold and oil & gas industries, respectively. Brown (2001)
provides evidence from a detailed case study. Purnanandam (2007) investigates the risk-management decisions of commercial banks.

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more. My empirical results are robust to alternative proxies of ﬁnancial distress (such as leverage, industryadjusted leverage and Altman Z-score), alternative ways of measuring the hedging activities (yes–no decision
to hedge and total notional amount of hedging) and various controls for nonderivative-based hedging
strategies. Further for a subsample of 200 manufacturing ﬁrms, I obtain data on the ﬁrms’ hedging activities
for ﬁscal years 1997–1998 and 1998–1999 and show that the basic results remain similar for a regression model

involving changes in hedging activities. While ﬁrms with a moderate increase in leverage increase their hedging
activities, ﬁrms with an extreme increase in leverage decrease their hedging positions. As long as ﬁrms do not
frequently change their operational hedging strategies (such as opening plants in foreign countries to hedge
their foreign currency risk), the analysis based on change regressions provides a robust control for
nonderivative-based hedging strategies of the ﬁrm. The change regressions also allow me to partially
disentangle the effects of ex-ante and ex-post hedging incentives.
The rest of the paper is organized as follows. In Section 2, I provide the model description. Section 3
analyzes the optimal risk-management policy of the ﬁrm. The empirical tests are provided in Section 4, and
Section 5 concludes the paper. Without any loss in continuity, readers mostly interested in the empirical part
of the paper can skip to Section 3.1, which provides a self-contained summary of the key features of the
theoretical model.
2. Model
I consider a stylized model of a continuous trading economy with time horizon ½t0 ; T. There are three
important dates in the model discussed below. Though a discrete time model can also be used to capture the
key feature of my model, the continuous time version allows for an easier analytical solution at the expense of
additional mathematical overhead. In addition, the continuous time model provides additional prediction
relating the time to maturity of the ﬁrm’s project to its hedging incentives.
At t ¼ t0 , the ﬁrm makes its capital structure decision and invests in risky asset Ai (i stands for the initial
investment), which I refer to as an ‘‘EBIT-generating machine’’ (Goldstein, Ju and Leland, 2001). These
decisions may or may not be made with the consent of the ﬁrm’s debtholders. The risky asset ðAi Þ is acquired
at the market-determined price and ﬁnanced through a mix of zero-coupon debt and equity capital. Let L be
the face value of the zero-coupon debt, payable at time T, and Et be the time t-value of the ﬁrm’s equity. There
is a tax beneﬁt of debt, which provides the incentive to issue debt in my model. For simplicity the tax beneﬁt is
assumed to be a fraction t of the face value of debt L. Optimal capital structure is determined by a trade-off
between the tax beneﬁt of debt and bankruptcy costs. For simplicity, I do not endogenize the capital structure
decisions. However, the key predictions of the model remain similar for a more general model (unreported)
that solves for capital structure decisions as well. The cash generated by the machine and its asset value Ait are
driven by a Brownian motion with the usual properties.
At some later time t ¼ t1 ðt1 2 ðt0 ; TÞÞ, the shareholders (or managers acting on their behalf) make a riskmanagement decision. At this time, which can be an instant or days or months after the capital structure
decisions, they have an opportunity to change the asset’s risk without the bondholder’s approval. To capture

the risk-shifting incentives, I assume that the bondholders are unable to recontract with the shareholders at
t ¼ t1 . Further, I assume that the two parties cannot contract on the risk-management choice at time t0
through the use of bond covenants. This latter assumption is what gives rise to the risk-shifting incentive in my
model. This assumption is in the spirit of a large literature on incomplete contracting in economics and ﬁnance
(see for example, Bolton and Dewatripont, 2005). The premise here is that it is too costly to specify every state
of the world and write down debt covenants that will limit shareholders behavior with respect to ﬁrm risk in
each of those states. Even if such covenants could be written to tie down the manager’s risk-management
behavior, it would be too costly to implement them especially in very high leverage states when shareholders
have a large incentive to default on covenants.12
This assumption is in the spirit of Jensen and Meckling’s argument that ‘‘To completely protect the
bondholders from the incentive effects, these provisions would have to be incredibly detailed and cover most
12

As long as there are nontrivial costs in writing, monitoring and enforcing these contracts, some residual risk-management decisions are
always optimally left with the shareholders/managers, which is sufﬁcient to generate the main results of my model.

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operating aspects of the enterprise including limitations on the riskiness of the projects undertaken. The costs
involved in writing such provisions, the costs of enforcing them and the reduced proﬁtability of the ﬁrm
(induced because the covenants occasionally limit management’s ability to take optimal actions on certain
issues) would likely be nontrivial. In fact, since management is a continuous decision making process it will be
almost impossible to completely specify such conditions without having the bondholders actually perform the
management function.’’
After the risk-management decisions have been made, the ﬁrm acquires a new EBIT-generating machine.
This EBIT-generating machine generates cashﬂows dt forever that evolves according to a geometric Brownian

motion. The value of this EBIT-generating machine, i.e., the value of a similar unlevered ﬁrm, is denoted by
At .13 One can think of dt as the state vector representing the state of the ﬁrm’s industry. I assume that the
change in the investment risk of the asset (from Ai to AÞ has no cashﬂow impact on the ﬁrm at t ¼ t1 . This
provides an initial boundary condition in the model, namely At1 ¼ Ait . Further, for analytical simplicity I
1
assume that the total payout (to debtholders and shareholders) by the ﬁrm is zero during ½t0 ; TÞ, with the ﬁnal
payoffs realized at t ¼ T. The shareholders receive the terminal equity value of the ﬁrm.14 The bondholders
receive the face value of debt (L) if the ﬁrm remains solvent on the maturity date t ¼ T 15; otherwise they
receive the residual value of the ﬁrm. The model can be represented by the following timeline:
m
t ¼ t0
Capital structure
Initial investment

m
t ¼ t1
Risk-management
decisions

m
t¼T
Payoffs

This modeling framework allows me to address the issue of ex-ante vs. ex-post risk-management behavior of
the ﬁrm in the presence of the shareholders’ risk-shifting incentives. I now discuss the main assumption of the
paper, namely, the distinction between ﬁnancial distress and insolvency.
2.1. Financial distress and insolvency
If during (t0 ; TÞ the ﬁrm’s asset value At falls below a boundary KðLÞ;16 the ﬁrm is in the state of ﬁnancial
distress. Insolvency, on the other hand, occurs on the terminal date T if the terminal ﬁrm value ðV T ) is less
than the debt obligations. Therefore, in the state of ﬁnancial distress, control of the ﬁrm does not shift to the

bondholders immediately, but the ﬁrm does incur costs that increase with leverage. Opler and Titman (1994)
show that ﬁnancially distressed (highly leveraged) ﬁrms lose signiﬁcant market share to their healthy
competitors during industry downturns. The drop in sales faced by Apple Computers and Chrysler during
periods of ﬁnancial difﬁculty provide anecdotal evidence in support of such deadweight losses. In a sample of
31 high-leveraged transactions (HLTs), Andrade and Kaplan (1998) isolate the effect of economic distress
from ﬁnancial distress and estimate the cost of ﬁnancial distress as 10–20% of ﬁrm value. Asquith, Gertner
and Scharfstein (1994) show that on average ﬁnancially distressed ﬁrms sell 12% of their assets as part of their
restructuring plans.
Chevalier (1995a, b) uses detailed information from the local supermarket industry to provide evidence in
support of predatory behavior in this market. She shows that following supermarket leveraged buyouts
13
The value of the levered ﬁrm of my model differs from At by the amount of the tax beneﬁt of debt as well as the costs associated with
ﬁnancial distress and bankruptcy. Throughout this paper I denote the value of the levered ﬁrm by V t and the value of its assets (EBITgenerating machine) by At .
14
For analytical simplicity I assume that the model’s terminal date corresponds to the maturity date of the ﬁrm’s debt, at t ¼ T. This
assumption should not be confused with the assumption that the ﬁrm’s life is ﬁnite. It simply states that at time T initial shareholders sell
the ﬁrm to some other investors at the fair market value of the ﬁrm as an ongoing concern.
15
Other maturity structures are possible. To illustrate the main results of the paper in its simplest form, I prefer to work with zero
coupon debts.
16
I refer to K as the distress barrier in the rest of this paper. K is assumed to be an increasing function of leverage. This deﬁnition of
ﬁnancial distress is equivalent to assuming that when industry conditions deteriorate, ﬁrms with high leverage become ﬁnancially
distressed.

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Asset Value

Healthy
Financially
Distressed
At1
f-1(L)
L
K

Insolvent

t1

τ

T
Time (t)

Fig. 1. This ﬁgure plots three paths for the evolution of the ﬁrm’s asset value. I assume zero tax shield of debt for this presentation. These
paths correspond to three states of the ﬁrm in my model. In the top-most path, the asset value never hits the ﬁnancial distress barrier (K).
This corresponds to the ‘Healthy’ state. The middle path represents the state in which the distress barrier is hit (at time tÞ, but the ﬁrm
remains solvent at time T. This is the state of ‘Financial Distress.’ In this state the terminal ﬁrm value, net of deadweight losses (i.e.,
f ðAT Þ), remains above the face value of debt (i.e., L). Thus, this is the state where f ðAT Þ4L or alternatively AT 4f À1 ðLÞ, as depicted in the
ﬁgure. Finally, the bottom-most path corresponds to the state of ‘Insolvency.’

(LBOs), prices fall in local markets in which rival ﬁrms have low leverage and are concentrated. Further, these
price drops are associated with LBO ﬁrms exiting the local market. These ﬁndings suggest that rivals attempt
to prey on LBO chains. Phillips (1995) studies the interactions between product market and ﬁnancial structure

for four industries and ﬁnds evidence consistent with debt weakening the competitive positions of ﬁrms (see
also Kovenock and Phillips, 1997; Arping, 2000). Using deregulation of the trucking industry as an exogenous
shock, Zingales (1998) studies the interplay between ﬁnancial structure and product market competition and
provides evidence that leverage reduces the probability of a ﬁrm’s survival after an increase in competition.
The overall message from these papers is that ﬁnancial distress may impose a real cost on ﬁrms by weakening
their competitive position in the product market.
Motivated by the empirical ﬁndings of above papers and anecdotal evidence, I assume that a ﬁrm in
ﬁnancial distress loses a fraction of its market share to its healthy competitors.17 In my model, this is achieved
by assuming that the ﬁnancially distressed ﬁrm’s EBIT-generating machine produces less cashﬂow resulting in
a lower value for the distressed ﬁrm. If the ﬁrm does not experience ﬁnancial distress during t 2 ½t1 ; T, the
terminal ﬁrm value is V T . However, if the distress boundary is hit, the terminal value falls to f ðV T Þ, where
f ðV T ÞoV T (see Fig. 1). The function f represents the losses caused by ﬁnancial distress.
2.2. Valuation of equity
The shareholders receive liquidating dividends at T. Due to equity’s limited liability, the ﬁnal payoff to the
shareholders ðxT Þ is zero if the terminal ﬁrm value is below L. Let us deﬁne inf t1 ptpT At mT for
the minimum value of the asset during ½t1 ; T. In the event of no distress (i.e., mT 4K) and solvency on the
terminal date (i.e., V T 4L), the shareholders get a liquidating dividend of ðV T À LÞ. If ﬁnancial distress is
experienced (i.e., mT pK), but on the terminal date the ﬁrm remains solvent (i.e., f ðV T Þ4L), the shareholders
17
In a more general industry equilibrium setting, ﬁrms can make strategic decisions about their leverage, investment risk, and hedging
(see e.g., Adam, Dasgupta, and Titman, 2004; Nain, 2006). My model abstracts from such considerations and focuses on the ﬁrm’s
decision, taking industry structure as given.

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receive liquidating dividends of f ðV T Þ À L. In the event of insolvency, shareholders receive nothing and ﬁrm

value drops by the fraction g 2 ½0; 1. The shareholders’ payoff under different states is summarized as
State at t ¼ T

Corresponding ﬁrm values

Payoff to shareholders

Healthy
Financial distress
Insolvency
Insolvency

V T 4L; mT 4K
f ðV T Þ4L; mT pK
V T pL; mT 4K
f ðV T ÞpL; mT pK

VT À L
f ðV T Þ À L
0
0

Proposition 1. Under mild technical conditions, the equity valuation at t ¼ t1 is given by:
xt1 ¼ eÀrT E Q ½ðV T À LÞ À ðV T À f ðV T ÞÞ1ff ðV T Þ4L;mT pKg þ ðL À V T Þf1fV T pLg þ 1ff À1 ðLÞ4V

T 4L;mT pKg

g.
(1)

Proof. See Appendix A.1. &
The equity value, as shown in Proposition 1, has three components. The ﬁrst term ðE Q ½V T À LÞ represents
the equity value without the distress costs and the limited liability feature. The second term ðE Q ½ðV T À
f ðV T ÞÞ1ff ðV T Þ4L;mT pKg Þ represents the cost of ﬁnancial distress. Because the shareholders of a ﬁnancially
distressed but solvent ﬁrm bear this cost, the equity value decreases by this amount. The risk avoidance
incentive results from this cost. The third term ðE Q ½ðL À V T Þf1fV T pLg þ 1ff À1 ðLÞ4V 4L;m pKg gÞ represents the
T

T

savings enjoyed by the shareholders of a levered ﬁrm due to the limited liability feature of equity. This term
captures shareholders’ risk-shifting incentives. By increasing the asset’s risk, the shareholders can make
themselves better off by increasing the call option value (the third term). At the same time, however, the
expected loss in the event of ﬁnancial distress also increases with an increase in asset risk. The optimal level of
investment risk is determined by the trade-off between the two.
2.2.1. Financial distress costs
Proposition 1 provides a general valuation formula in my model. To proceed further I need to be
explicit about the form of ﬁnancial distress cost that is borne by the shareholders of a ﬁnancially
distressed ﬁrm. In addition, I make some simplifying assumptions for analytical tractability. I assume that in
the event of distress (i.e., mT pK), the ﬁrm’s cashﬂows drop to ldt ; l 2 ð0; 1 and never reach beyond some
arbitrary upper bound Uo1 at time T, i.e., dT pU. Therefore, the losses take the form of lost upside
potential. This representation of ﬁnancial distress cost is motivated by existing empirical ﬁndings and
anecdotal evidence, and captures the intuition that distressed ﬁrms lose cashﬂows due to lost sales to
competitors. If industry conditions improve in the future, the distressed ﬁrms continue to feel the negative
effect of distress due to lost customers. This representation of distress is also consistent with the view that
when ﬁnancially distressed ﬁrms restructure themselves by selling assets (Asquith, Gertner and Scharfstein,
1994), their EBIT-generating machine produces lower contemporaneous cashﬂows and in addition it limits
their ability to capitalize on very good industry conditions in the future. To concentrate on the effect of
ﬁnancial distress costs (as opposed to tax-motivated incentives of hedging as in Leland, 1998), in the rest of the
paper I set t ¼ 0.18 Under this assumption and the assumption l ¼ 1, the distressed ﬁrm’s asset value can be

represented as19:
f ðAT Þ ¼ AT if fdT pUg; and M 0 if fdT 4Ug for some constant M 0 .

(2)

18
In unreported analysis, I solve the model with tax beneﬁts and obtain the ﬁrm’s optimal capital structure. However, to keep the focus
of this paper on risk-management decisions, I do not present these results in the paper. With tax beneﬁts, the ﬁrm’s payoffs increase by tL
without qualitatively changing the results of the analysis.
19
If lo1, then ﬁnancial distress costs are even higher and the results become stronger. This assumption is made only for analytical
simplicity.

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713

Equity Value In
Healthy State

Equity Value
in my model

L

Equity Value in
Financial Distress
0

L

L+M

Asset Value at T
Fig. 2. This ﬁgure plots the equity value as a function of the terminal asset value of the ﬁrm. For illustrative purposes I set the tax rate to
zero and g ¼ 1 for this diagram. The equity value in my model is depicted by the solid line. The upper dotted line represents the equity
value for the Healthy state. The lower dotted line depicts the equity value in the state of Financial Distress. The equity value in my model is
a weighted average (weight is decided by the relative likelihood of the two states) of the equity value in these two states.

Let us denote the asset value ðAT Þ corresponding to dT ¼ U by L þ M. The shareholders’ liquidating
dividends are given as
States

Payoff to shareholders

Firm value

AT 4L; mT 4K
AT 4L; AT pL þ M; mT pK
AT 4L þ M; mT pK
AT pL

AT À L
AT À L
M

0

AT
AT
LþM
gAT

The ﬁnancial distress costs can be expressed as ðAT À MÞ:1fAT 4LþM;mT pKg . A higher value of M
corresponds to lower deadweight losses in the model. In line with Proposition 1, the equity value can be
expressed as follows:
xt1 ¼ eÀrT E Q ½ðAT À LÞ1fAT 4L;mT 4Kg þ ðAT À LÞ1fAT 4L;AT pLþM;mT pKg
þ M1fAT 4LþM;mT pKg .

ð3Þ

Fig. 2 plots the equity value as a function of the terminal asset value of the ﬁrm. As the diagram shows, the
equity value is not a strictly convex function of the underlying ﬁrm value as in the classical approach where
equity is valued as a call option on ﬁrm value. The deadweight loss of distress introduces a concavity in the
equity value, which results in risk-management incentives for the ﬁrm.
3. Optimal choice of investment risk
Without loss of generality, I set the risk-free interest rate to zero in the rest of the analysis. At t ¼ t1 , the
shareholders make a decision about the optimal investment risk of the ﬁrm. There are two possibilities for
changing the investment risk: (a) the ﬁrm can directly choose an optimal level of s at t ¼ t1 , or (b) the asset’s
risk, s, may be ﬁxed and the ﬁrm can alter its risk proﬁle by buying derivative contracts such as futures and
options. I analyze the problem of ﬁnding optimal s assuming that investment risks can be costlessly modiﬁed.
Proposition 2. The shareholders have a well-founded incentive to engage in risk-management activities ex-post.
At t ¼ t1 , the shareholders optimally choose a level of risk sÃ in the interior of all possible risks.

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Proof. As shown in Appendix A.2 and A.3, the optimum level of investment risk is obtained by the following
ﬁrst-order condition:
At1 fðh1 Þ ¼ Kfðc1 Þ

(4)
pﬃﬃﬃﬃﬃ0
pﬃﬃﬃﬃﬃ0 0
2
0
2
=LÞ
where
ﬃﬃﬃﬃﬃ0 þ ðs =2ÞT Þ=s T , h2 ¼ h1 À s T , T ¼ T À t1 , c1 ¼ lnðK =At1 ðL þ MÞÞ þ ðs =2ÞT =
pﬃﬃﬃﬃﬃ0 h1 ¼ ðlnðAt1p
s T , c2 ¼ c1 À s T ; and f stands for the probability density function of the standard normal distribution.
Further simpliﬁcation leads to the following closed-form solution:
!

K2
K 2L
ln
ln
LðL þ MÞ
A2t ðL þ MÞ
1

2 Ã

1
ðs Þ ¼ 0
:
&
(5)
LþM
T
ln
L
2

0

As a result of the trade-off between the risk-shifting and risk-avoidance incentives, an interior solution for
the optimal risk is obtained in the model. This result differs from that of the earlier models. In risk-shifting
models such as Jensen and Meckling (1976), the shareholders take as much risk as possible, whereas in riskmanagement models such as Smith and Stulz (1985), the optimal level of risk is obtained at s ¼ 0. By
obtaining an interior solution for the optimal investment risk of the ﬁrm, my model provides insights into the
risk-management policies of the ﬁrm, as discussed below.20
Proposition 3. The firm chooses a lower level of investment risk if (a) it faces a higher distress barrier (K), and (b) it
has a longer project maturity ðT 0 ¼ T À t1 Þ. The relation between the deadweight losses and the optimal investment
pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
2ð lnðAt =KÞ lnðL=KÞÞ
1
risk is U-shaped. Let M c ¼ L exp
À L. When M4M c , the optimal investment risk decreases
with an increase in the deadweight losses, otherwise it increases with an increase in the deadweight losses.
Proof. The proof follows from direct differentiation of the optimal solution for s given in expression 5
(see Appendix A.5). &

The investment risk decreases (i.e., the risk-management incentive increases) with the distress boundary (K). As
expected, a higher boundary increases the likelihood of ﬁnancial distress. Therefore, the shareholders optimally
choose a lower investment risk to avoid the ﬁnancial distress costs. The results show that the ﬁrm with a longer
operational horizon ðT 0 ¼ T À t1 Þ ﬁnds it optimal to engage in increased risk-management activities. With longer
time-horizon, the probability of hitting the lower barrier increases. Further, consequent to entering the state of
distress expected losses increase with time to maturity because there is a higher probability of improvements in
industry conditions and the distressed ﬁrm will not be able to capitalize on these opportunities. There is
considerable empirical evidence that large ﬁrms hedge more than small ﬁrms. The pursuit of economies of scale has
been suggested as one possible explanation for this empirical regularity. My model suggests another explanation:
the time horizon of operations. If ﬁrms with longer time horizons grow larger over time, the researcher would ﬁnd
a positive association between risk-management activities and ﬁrm size at any given point in time.
Finally, I ﬁnd a U-shaped relation between the risk management incentives and the cost of ﬁnancial distress.
Recall that the deadweight losses in my model are parameterized by M (losses are given by
ðAT À MÞ:1fAT 4LþM;mT pKg ). In the event of ﬁnancial distress, the ﬁrm loses its upside potential beyond
L þ M. Thus, the higher the M, the lower the lost upside potential and therefore the lower the deadweight losses.
If the deadweight losses are absent (i.e., M ¼ 1), the shareholders lose nothing in the state of ﬁnancial distress
and hence there is no risk-management incentive. On the other hand, if deadweight losses are very high (i.e.,
M ¼ 0) the distinction between default and insolvency disappears along with the risk-management incentives.21
It’s the intermediate cases that generate risk-management incentives in the model. Fig. 3 illustrates this relation.
20
With nonzero tax rates (in unreported analysis), the optimal s is even lower. The additional incentives for risk reduction, in the
presence of the tax-beneﬁt of debt, comes from the potential loss in the tax shield of debt for a bankrupt ﬁrm. This additional effect
generates ex-post hedging as in Leland (1998). See also Fehle and Tsyplakov (2005).
21
In this case, equity value becomes similar to a down-and-out barrier option. Since the value of this option is increasing in the volatility
of the underlying assets, the shareholders do not have any risk-management incentives at t1 .

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2.94
2.92
2.9
Investment Risk

2.88
2.86
2.84
2.82
2.8

2.

25

00
3.

75

50

3.

4.

25

00

5.

6.

75
6.

50

25

7.

8.

00

0

75

9.

9.

.5

5

10

0

.2

.0

11

5

12

0

.7

.5

12

5

13

.2
14

15

.0

0

2.78

Deadweight Loss Parameter (M)
Fig. 3. This ﬁgure plots the optimal investment risk as a function of deadweight losses. The model has been calibrated with the following
parameter values: At1 ¼ 2; L ¼ 1; T 0 ¼ 1 and K ¼ 0:5: On the x-axis, I plot the value of M. M measures the upside potential lost by
the ﬁrm in the event of ﬁnancial distress. I plot M from higher-to-lower value so that the deadweight losses increase as one moves along the
x-axis.

Investment Risk vs. Leverage
14
12

Investment Risk

10
8
6
4
2

95
0.

91

87

0.

83

0.

0.

79
0.

75
0.

71
0.

67
0.

63
0.

59

55

0.

51

0.

47

0.

0.

43
0.

39
0.

35
0.

31
0.

27
0.

23
0.

19
0.

15
0.

0.

11

0
Leverage
Fig. 4. This ﬁgure plots the optimal investment risk of the ﬁrm against the debt-asset ratio. For this graph I assume the following structure
on the distress boundary and deadweight losses: K ¼ 1 À expÀ0:1Ãlev and M ¼ 7 À exp2Ãlev . Amount of debt raised at time zero (L) if ﬁxed
at 1. lev equals L scaled by At1 . T is set to one.

Leverage and risk management: To study the relation between leverage and risk management, I differentiate
the optimal s with respect to ﬁrm leverage at time 1 ðlev ¼ L=AÞ. The details are provided in Appendix A.5.
After some simpliﬁcation it can be shown that the optimal sigma decreases (i.e., risk-management incentives
increase) with an increase in leverage for a wide range of speciﬁcations of the distress boundary and

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deadweight loss parameter. This relation reverses when leverage is very high due to the risk-shifting incentives.
At very high leverage, the value associated with the call option of equity dominates the cost borne by

shareholders and thus they lose risk-management incentive. Using a parametric speciﬁcation of K and M,
I solve for optimal risk as a function of leverage and report the results in Fig. 4. The relation is summarized
below:
Proposition 4. Risk-management incentives increase with leverage; this relation reverses for extremely high levels
of leverage.
Combining this result with the results between deadweight losses and risk-management, the effect of
leverage on hedging intensity is predicted to be higher for ﬁrms operating in industries with a higher incidence
of predatory behavior. The key friction underlying my model consists of the costs incurred by a ﬁrm after it
enters the state of ﬁnancial distress. These costs come in the form of lost customers and deterioration in the
competitive position of the ﬁrm within its industry. Based on the empirical studies of Opler and Titman (1994)
and Chevalier (1995a, b) such costs are more likely to be incurred by a ﬁrm in concentrated industries. Thus, in
the context of my model industry concentration provides a good proxy for the ﬁnancial distress costs.
Accordingly, high leverage ﬁrms in concentrated industries are predicted to have greater hedging incentives.
3.1. Summary of theoretical model
In this section I present a self-contained summary of the theoretical part of the paper that serves as the basis
for the empirical tests to follow. In my stylized model, a ﬁrm starts with some mix of debt and equity at time
zero and buys a productive asset. At this time the capital structure of the ﬁrm is determined by trading off the
tax beneﬁt of debt against the expected ﬁnancial distress and bankruptcy costs. I do not solve for the optimal
leverage policy in my theoretical model to keep the focus of my analysis on risk-management decisions.
However, making capital structure decisions endogenous does not change the key results of the paper. In
unreported analyses, I solve for optimal leverage and as expected show that the debt ratio increases with the
tax beneﬁts and decreases with bankruptcy and ﬁnancial distress costs.22
Given a level of debt determined at time t0 , the ﬁrm experiences some random shocks to its value till t1 ,
which perturbs its leverage ratio. At this point the shareholders make the key decision in the model, i.e., a riskmanagement decision so as to maximize equity value. This modeling structure allows me to focus on the expost hedging incentives. Subsequent to the risk-management decision at t1 , the asset value evolves according to
a stochastic process from time t1 to T in the model. If the ﬁrm’s asset value breaches a lower threshold before
the terminal date T, then the ﬁrm enters ﬁnancial distress. Financial distress imposes costs on the ﬁrm such as
lost customers to the competitors, which in turn prohibits it from capitalizing on its full upside potential.
Motivated by the earlier empirical ﬁnding, I assume that highly levered ﬁrms lose more when they enter the
state of ﬁnancial distress.
After the distress boundary is hit, the ﬁrm can either stay solvent on the terminal date or go bankrupt,

depending on whether its value, net of distress costs, is above or below the debt value. The state in which the
ﬁrm enters ﬁnancial distress but remains solvent at time T imposes a real cost on shareholders. In this state
they incur the ﬁnancial distress costs without being able to use their limited liability option. An increase in ﬁrm
risk increases the probability of ﬁnancial distress and the associated deadweight losses that are borne by the
shareholders, not the debtholders. On the other hand, by increasing ﬁrm risk they beneﬁt on account of the
usual limited liability feature. The optimal risk-management policy trades off these two incentives. For
moderate levels of leverage, the risk-management incentive dominates. But when leverage becomes too high at
22

In a rational expectation framework ﬁrm value at time t0 should be maximized keeping in mind the expected level of risk that will be
optimally undertaken by the shareholders at time t1 . This expected sigma along with the tax beneﬁt of debt and bankruptcy costs will
determine the optimal amount of debt raised by the ﬁrm at time t0 . Indeed the actual leverage at time t1 will be different from the rationally
expected value of leverage, depending on the shocks experienced by the ﬁrm in the intervening period. Depending on the realizations of
these shocks in the interim period, the ﬁrm’s leverage at time t1 will be different and shareholders may deviate from the rationally
anticipated risk policy that is based on the expected level of leverage and not on realized leverage. The main result that shareholders will
have risk-management incentives as long as their leverage doesn’t go up too much follows. When the asset value realization is too low (i.e.,
leverage is too high as compared to expectations) the risk-shifting incentive follows.

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time t1 , the value associated with the call option feature of equity dominates the expected ﬁnancial distress
costs and shareholders ﬁnd it optimal not to engage in risk-management activities. Thus, the model predicts a
nonmonotonic relation between leverage and hedging. Further, the positive relation between leverage and
hedging is expected to be stronger for ﬁrms operating in industries with a higher incidence of predatory
behavior such as concentrated industries. I test these predictions of the model in the rest of the paper.
4. Empirical evidence

There are three important challenges in empirically testing the above theory. First, data on a ﬁrm’s hedging
decision are very limited. Second, leverage and hedging are likely to be determined jointly by ﬁrms, leading to
an endogeneity problem. Theories based on ex-ante incentives suggest that ﬁrms can increase their debt
capacity by engaging in hedging activities, which in turn lead to reverse causation from hedging to leverage.
Third, to capture both the ex-ante and ex-post incentives in a clean empirical setting, I need data on the timing
of debt issues and hedging decisions, which unfortunately are not available. Below I begin with discussing the
sample and data collection procedure followed by econometric strategies used to account for the endogeneity
problem. Empirical results follow these discussions. I then discuss the issues related to ex-ante vs. ex-post
incentives in a later section.
4.1. Sample selection and data
I test the key predictions of my model using the foreign currency and commodity derivatives holdings of a
large cross-section of ﬁrms during the ﬁscal years 1996 and 1997. I start with all ﬁrms in the intersection of the
CRSP and COMPUSTAT with 10-Ks available on the SEC website. I remove ﬁnancials and utilities since the
risk-management incentives of these ﬁrms are not necessarily comparable to other industrial ﬁrms. From this
sample, I exclude ﬁrms that fall in the last quartile of the size distribution based on total sales. Earlier
empirical studies and survey evidence suggest that such small ﬁrms are very unlikely to use derivative products
for hedging purposes (Dolde, 1993), arguably due to the lack of economies of scale.
For the remaining ﬁrms, I collect data on derivative usage from the 10-K ﬁlings. In the ﬁrst step I obtain all
available 10-K ﬁlings of ﬁrms in the intersection of COMPUSTAT and CRSP from the SEC for the calendar
year 1997.23 I obtain data by searching the entire 10-K ﬁlings for the following text strings: ‘‘risk
management,’’ ‘‘hedg,’’ ‘‘derivative’’, and ‘‘swap.’’ If a reference is made to any of these key words, I read the
surrounding text to obtain data on foreign currency and commodity derivatives. I obtain data on the notional
amount of foreign currency derivatives used for hedging purposes across various derivative instruments such
as swaps, forwards, futures, and options.24 For commodity hedging I only obtain data on whether a ﬁrm uses
derivatives for hedging or not, since the reporting requirement for commodity derivatives doesn’t allow for an
easy quantiﬁcation in terms of dollar value. If there are no references to the key words, the ﬁrm is classiﬁed as
a nonhedger. I require that data on net sales, leverage and market capitalization be available for a ﬁrm to be
included in the sample.
In addition, to capture the dynamic behavior of a ﬁrm’s hedging and leverage decisions, I focus on a smaller
subset of 200 manufacturing ﬁrms (one-digit SIC code 2) and collect data using the same procedure for two

additional years, i.e., 1998 and 1999. This smaller subsample allows me to relate the changes in a ﬁrm’s
hedging activities to changes in ﬁnancial conditions, which in turn allows me to draw sharper inferences as
outlined in the subsequent sections.
I limit my analysis to only those ﬁrms that have well-deﬁned exposures to foreign currency and commodity
risks. I conduct my analysis for foreign currency derivatives on the subsample of ﬁrms with an exposure to
foreign currency risk and similarly commodity derivatives on the subsample of ﬁrms with an exposure to
23
For some ﬁrms (most of the ﬁrms with a ﬁscal year ending in October, November, or December) this corresponds to ﬁscal year 1996,
while for others this corresponds to the ﬁscal year 1997.
24
The break-up of the notional amount across various instrument types was not easy to obtain for some sample ﬁrms. For these ﬁrms,
I collect data on the aggregate notional amount of derivatives only. Since most of the analysis is conducted with the aggregate amount
of derivatives, this doesn’t create any bias in the study.

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commodity price risk. This sample selection criteria ensures that I can treat the lack of derivative usage as a
ﬁrm’s choice variable to not hedge rather than an absence of exposure to the risk. I identify a ﬁrm’s exposure
to these risks in the following manner.
4.1.1. Exposure to foreign currency risk
I closely follow Geczy, Minton, and Schrand (1997) to identify ﬁrms with pre-deﬁned exposure to foreign
currency risks. A ﬁrm is classiﬁed as being exposed to foreign currency risk if any of the following criteria is
met: (a) it reports foreign currency sales in the COMPUSTAT geographical segment ﬁle in the ﬁscal year of
derivative usage or within þ=À one year; (b) it reports foreign income taxes, deferred foreign currency taxes,
or pre-tax foreign income in its annual statements; (c) it reports foreign currency adjustments in its annual
report; or (d) it discloses an exposure hedged with foreign currency derivatives in its footnotes identiﬁed by

hand-collected data.
Based on these screens, I identify 1,781 ﬁrms as exposed to foreign currency risk.25 In the subsequent
regression analysis I lose additional ﬁrms due to missing data on the explanatory variables used to estimate the
multivariate models.
4.1.2. Exposure to commodity price risk
Compared to foreign currency exposure, identifying ﬁrms with an exposure to ﬂuctuations in commodity
prices is harder to measure. This arises because current accounting standards do not require ﬁrms to
disclose much information with respect to their exposure to commodity price risk. In the absence of any
balance sheet information, I identify a ﬁrm’s exposure to commodity price risk by estimating the sensitivity of
its earnings to movements in various indices of commodity prices. As an alternative speciﬁcation, one can use
a simpler approach and take the set of all commodity-producing industries as the sample of ﬁrms that are
exposed to commodity price risk. However, with such an approach it would be hard to detect ﬁrms that are
exposed to commodity price risk on the input side (such as airline industry). Thus, for the sake of
comprehensiveness, I adopt the more involved methodology of detecting ﬁrms with exposure to commodity
price risk.
In particular, I regress the quarterly earnings before interest and taxes obtained from COMPUSTAT’s
quarterly ﬁles on the quarterly changes in several commodity price indices and classify a ﬁrm as having an
exposure to commodity price risk if the resulting coefﬁcient is signiﬁcant at the 10% level or better. I take data
from the last 60 quarters (or the maximum available) to estimate this model. Most of the effect of commodity
price movements is reﬂected in a ﬁrm’s sales or its cost of production, such as raw material or energy costs.
Therefore, I take EBIT as the relevant measure of earnings for the purpose of sensitivity analysis.26
There are two important issues with this estimation methodology. First, the use of derivatives can make a
ﬁrm’s earnings less sensitive to movements in commodity prices, rendering my methodology ineffective for
hedger ﬁrms. However, I already have hand-collected data on whether these ﬁrms use commodity derivatives
to hedge a well-speciﬁed risk. I, therefore, add the commodity hedgers to the set of ﬁrms that I detect as having
an exposure to commodity risk based on the above methodology.
Second, ﬁrms may be exposed to various types of commodity risks, ranging from oil price shocks to metals
to farm produce. Based on the ﬁrm’s disclosure in the footnotes of their annual statements as well as the
contract volume of various futures contracts on the futures exchanges, it is clear that the main sources of
commodity risk facing U.S. nonﬁnancial ﬁrms are the following: (a) crude oil and related products; (b) metals

such as copper and iron; (c) farm products such as corn; and (d) various industrial chemicals. Noting this,
I obtain data on the quarterly price changes for a basket of these commodities from the Bureau of Labor
25

Geczy, Minton, and Schrand (1997) also consider ﬁrms with a high concentration of foreign importers in the industry as exposed to
foreign currency risk. I don’t consider this screening criterion since they show that very few ﬁrms are identiﬁed as having a foreign currency
exposure based solely on this criterion. In their sample of about 370 ﬁrms, only three ﬁrms are identiﬁed as having exposure based solely
on this criterion.
26
I also repeat my analysis with other measures such as cashﬂows, EBIT/TA, NI/TA, the seasonally adjusted earnings, and obtain
similar set of ﬁrms. Note that scaling EBIT by total assets doesn’t make any qualitative difference because the regression is estimated on a
ﬁrm-by-ﬁrm basis with fairly stable total asset values (as compared to EBIT). Therefore, I only present results with the EBIT-based
sensitivity analysis to conserve space.

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Studies. Further, I obtain data on quarterly changes in the aggregate Producer Price Index (PPI), which
reﬂects price changes based on a basket of commodities including oil, farm products, industrial chemicals,
metals, and other commonly used products by the industrial producers. Thus I have ﬁve price indices (crude
oil, metals, farm products, chemicals, and all commodities) and I estimate a ﬁrm’s commodity price sensitivity
with respect to each of these indices separately. Since my analysis doesn’t distinguish ﬁrms based on the
speciﬁc source of risk they face, I consider a ﬁrm as exposed to commodity price risk if I obtain a signiﬁcant
coefﬁcient in any of the ﬁve regressions. This methodology identiﬁes 1,238 ﬁrms as having an exposure to
commodity price risk in my sample. When I merge this sample with the sample of ﬁrms with ex-ante exposure
to foreign exchange movements, I ﬁnd that I have a total of 2,256 ﬁrms with an exposure to at least one of
these sources of risk.

4.1.3. Derivatives as a proxy for hedging
I use two deﬁnitions of hedging based on derivative usage. The ﬁrst deﬁnition is based on the ﬁrm’s binary
decision of whether to use derivatives for hedging purposes. This speciﬁcation uses both types of derivative
contracts—foreign currency and commodity. In the second speciﬁcation, I use the total notional amount of
foreign currency derivatives. The notional amount-based deﬁnition of hedging captures the ﬁrm’s total
ownership of risk-management instruments and is thus able to distinguish between ﬁrms with different
hedging intensities.
There are two important concerns associated with the use of derivatives as a proxy for hedging activities.
First, though I obtain data on derivatives classiﬁed as risk-management tools, there may still be a concern
about their intended use—are ﬁrms indeed using these instruments for hedging purposes or not? Earlier
empirical studies ﬁnd strong evidence in support of risk-reducing (i.e., hedging) effects of derivatives on
various measures of a ﬁrm’s risk. Guay (1999) ﬁnds that the new users of derivatives experience a decline in
their earnings and stock price volatility after the initiation of derivatives contracts. Similarly Allayannis and
Ofek (2001) show that using derivatives reduces currency exposure, and Hentschel and Kothari (2001) do not
ﬁnd any evidence that derivatives are used for speculative purposes. Thus, there is enough evidence in the
literature to suggest that the majority of ﬁrms use derivative instruments for hedging purposes and not for
speculative reasons.
The second concern with the use of derivatives data relates to the importance of derivatives on the
overall cashﬂows of the ﬁrms. Allayannis and Weston (2001) and Graham and Rogers (2002) ﬁnd a
signiﬁcant impact of derivative instruments on ﬁrm value and the ﬁrm’s debt capacity, respectively.
These ﬁndings suggest that derivative instruments have a signiﬁcant impact on ﬁrm performance and
thus are good instruments for the ﬁrm’s risk-management activities. Guay and Kothari (2003) show
that the median ﬁrm’s derivatives cashﬂow sensitivity (deﬁned as the level of cashﬂows that derivative
instruments can generate in extremely adverse scenarios of interest rate, foreign currency or commodity
prices) is modest at only about 10% (mean of 45%) of the average year’s operating cash-ﬂows of the
ﬁrms.27 At an extreme, if the median ﬁrm’s operating cash-ﬂows drops to 25% of its normal level, the
impact of derivative instruments can be as high as 40% of a bad year’s operating cash-ﬂows. However,
at the same time the study by Guay and Kothari underscores the importance of nonderivative
based risk-management strategies for ﬁrm-value. The study by Petersen and Thiagarajan (2000) illustrates
the importance of nonderivative based hedging strategies for a ﬁrm’s overall risk-management decisions.

In my empirical study I provide various robustness tests to account for nonderivative-based methods of
hedging.
4.1.4. Descriptive statistics of hedging variables
Table 1 provides the descriptive statistics of hedging activities. In Panel A, I provide the frequency
distribution of hedgers of different risks. Out of a total of 1,781 ﬁrms with an exposure to foreign currency
risk, 497 (about 28% of the ﬁrms) use derivatives to hedge their exposure to movements in foreign exchange
rates. For commodity price risk, there are 211 hedgers (about 20% of the ﬁrms) out of a total sample size of
1,238 ﬁrms. If I consider exposure to either type of risk, I ﬁnd a total of 645 hedgers from a sample of 2,256
27

The sensitivity varies from 9% to 39% depending on the scaling variable used (see Table 4 of Guay and Kothari, 2003).

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Table 1
Descriptive statistics—derivatives usage
This table provides the descriptive statistics of derivatives usage by sample ﬁrms. Panel A provides the number of ﬁrms that use foreign
currency (FX) or commodity (CM) derivatives for hedging purposes for the ﬁscal year ending between September 1996 to August 1997.
The ‘Any’ column represents the number of ﬁrms that use either FX or CM (or both) derivatives for hedging purposes. Panel B provides
the details on the notional amount of FX derivatives. Panel C provides the instrument-wise break-up of FX derivatives across swaps,
forwards/futures, and options. This panel is based on a smaller subsample of 435 foreign currency hedgers for which the instrument-wise
break-up is available. Statistics in Panel C are based on only those observations that have nonzero values for the respective hedging
instrument.
Panel A
Firms with Exposure
Number of Derivative Users

Number of non-users

Foreign Currency
1781
497
1284

Commodity
1238
211
1027

Any
2256
645
1611

Mean
359.15
8.62
10.74

Median
40.28
4.43
4.07

Std. Dev.
1010.99
22.36

50.48

Frequency
63
360
82

Mean
344.82
259.72
316.83

% of Sales
8.07
6.73
21.61

Panel B
Notional Amount
As a % of Assets
As a % of Sales
Panel C
Swap
Forward/Futures
Options

ﬁrms. Panel B provides the summary statistics for the aggregate notional amount of foreign currency
derivatives used for risk-management purposes. The mean (median) notional amount of foreign currency
derivatives is $359.15 million ($40 million). The average level of derivatives holdings in my sample is smaller
than that of earlier studies such as Graham and Rogers (2002). This is not surprising, since these studies focus

mostly on large ﬁrms, whereas my sample contains many medium and small ﬁrms as well. The notional value
of derivatives scaled by the book value of the ﬁrm’s total assets (sales) amounts to 8.62% (10.74%) for the
average ﬁrm in the sample. These numbers are comparable to earlier studies.
Table 1 (Panel C) also provides the break-up of foreign currency derivatives across instrument types.
Forward and futures contracts are the most widely used instruments for managing foreign currency risk.
Among the foreign currency hedgers, about 80% of ﬁrms use forward and futures contracts. In unreported
analyses, I ﬁnd that there are comparable levels of transactions for both buying and selling in the foreign
currency forward markets.
My main tests are based on the relation between leverage and hedging. In the next section, I brieﬂy describe
the control variable used in the analysis before turning to the issue of endogenous modeling of riskmanagement and leverage decisions.
4.1.5. Control variables
Earlier theoretical and empirical work in this literature proposes several variables that can explain a ﬁrm’s
hedging incentives. My control variables are motivated by these studies. First, I control for ﬁrm size (size) as
measured by log of total sales to capture the well-known size effects in derivative usage (see Dolde, 1993). I use
the ratio of research and development (R&D) expenses to total sales as a proxy for ﬁrm’s growth
opportunities. Froot, Scharfstein, and Stein (1993) predict a positive relation between growth opportunities
and hedging incentives since hedging can minimize the underinvestment problem in low cash-ﬂow states of the
world. I also use a ﬁrm’s market-to-book ratio as an additional control variable for growth opportunities and
obtain similar results. However, I do not include it in my base model since market-to-book has been taken as a
measure of ﬁrm-value in several studies in corporate ﬁnance and ﬁrm value may itself depend on derivative
usage. Second, I model leverage in an endogenous setting, which requires regressing leverage on all

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explanatory variable in the ﬁrst stage regression. Given the illusionary nature of relation between leverage and
market-to-book ratio, this speciﬁcation poses additional challenges.28 The underinvestment problem of a ﬁrm

can be reduced by keeping more liquid assets. I include the quick ratio of the ﬁrm (quick) as a measure of the
ﬁrm’s liquid assets. The quick ratio is constructed as a ratio of cash and short-term investments to the current
liabilities of the ﬁrm.29
Motivated by earlier studies (see Geczy, Minton, and Schrand, 1997; Graham and Rogers, 2002), I include
institutional shareholdings as an explanatory variable in the model to control for risk-management incentives
due to information asymmetry between ﬁrm’s insiders and outsiders. DeMarzo and Dufﬁe (1991) and Breeden
and Viswanathan (1996) argue that ﬁrms with higher information asymmetry between managers and
shareholders should hedge more. Assuming that higher institutional share-holdings leads to lower information
asymmetry between the managers and shareholders of the ﬁrm, the coefﬁcient on this variable should be
negative as predicted by these theories. The inst variable measures the fraction of common shares of the ﬁrm
held by institutional investors. The data are obtained from the 13-F ﬁlings. In an alternative unreported
speciﬁcation, I also use the number of analysts following the ﬁrm as a proxy of (inverse) information
asymmetry and obtain similar results.
Next, I control for tax convexity-based hedging incentives. If a ﬁrm faces a progressive tax structure, then its
post-tax value becomes a concave function of its pre-tax value. The ﬁrm can lower its expected tax liability by
engaging in hedging activities (Smith and Stulz, 1985). I use the methodology suggested by Graham and Smith
(1999) to measure the tax-convexity incentive of hedging. A brief description of their methodology is provided
in Appendix A.6. The tax-convexity variable measures the expected tax beneﬁts (in dollars) from a 5%
reduction in the ﬁrm’s income volatility. I scale this measure by the total sales of the ﬁrm. Since this variable is
estimated by using other accounting variables of the ﬁrm, in my base-case analysis I do not control for the taxconvexity measure to ensure that my key results are not driven by the inclusion of this imputed variable.
Subsequently, I control for this effect and show that the results with respect to the key variables of interest
remain robust to the inclusion of this control variable in the model.
In the foreign currency hedging model, I include foreign currency sales as a percentage of a ﬁrm’s total sales
as an additional control variable (fsale). Jorion (1991) shows that foreign currency sales is a good proxy of the
ﬁrm’s exchange rate risk exposure. Thus, this variable controls for two effects. First, it controls for the extent
of exposure faced by the sample ﬁrms, and second, it proxies for economies of scale that can be exploited in
hedging foreign currency risks. High exposure ﬁrms should have a lower cost of hedging if there are signiﬁcant
economies of scale in these activities.
Firms can achieve signiﬁcant reductions in their foreign currency risk exposure by operating in multiple
geographical locations around the world (see Allayannis, Ihrig, and Weston, 2001). A ﬁrm with more

diversiﬁed geographical operations has a natural foreign currency hedge if currencies in different markets are
not highly correlated. I control for these effects by including the number of geographical segments reported by
sample ﬁrms as a control variable. In an unreported analysis, I also control for the entropy of a ﬁrm’s foreign
sales in diverse geographical regions and obtain similar results.30

28
In one of the unreported analyses, I also use the analyst growth forecast obtained from I/B/E/S as a proxy for the growth option of the
ﬁrm. Since my results remain qualitatively similar, I don’t report the results of this model.
29
See also Acharya, Almeida, and Campello (2004), who argue that cash can serve as a hedge against future cash shortfalls for
ﬁnancially constrained ﬁrms.
30
If a ﬁrm operates in n foreign segments (as deﬁned in the COMPUSTAT segments ﬁles) and the percentage share of sales of foreign
segment i is Pi , then the entropy is computed as follows:

Entropy ¼

n
X

Pi lnð1=Pi Þ.

(6)

i¼1

The entropy measure is a proxy of the ﬁrm’s geographical diversiﬁcation—ﬁrms with higher entropy values have more diversiﬁed
operations across various foreign markets. As an additional robustness check, I also experiment with the ﬁrm’s geographical Herﬁndahl
index to control for this effect. All results remain similar to these alternative control variables.

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4.2. Endogenous modeling of leverage and hedging
My theory predicts a positive relation between leverage and hedging for ﬁrms with moderate levels of
leverage, and a negative relation at extremely high levels of leverage. In addition, the relation between leverage
and hedging is expected to be stronger for ﬁrms operating in industries with greater likelihood of predatory
behavior such as concentrated industries. Thus, my key tests are based on the relation between hedging
and leverage.
For expositional simplicity and analytical tractability, at the time the hedging decision is made in the
theoretical model (i.e., at time t1 in the model) the debt level is pre-determined. However, we know from prior
theoretical work that a ﬁrm’s debt capacity and hence leverage can itself increase due to hedging. For example,
consider a variant of my model where ﬁrms hedge ﬁrst and obtain debt at a later date. In the context of my
model, hedging lowers the volatility of ﬁrm value, which in turn lowers the probability of bankruptcy and thus
allows ﬁrms to borrow more for a given level of the tax beneﬁt of debt. This leads to endogeneity between
leverage and hedging. It, therefore, becomes important for my empirical study to explicitly account for this
endogeneity bias. To do so, I need a structural model for the capital structure choice and hedging decisions of
the ﬁrm. In the absence of a consensus on an ideal model for debt choices, it is advantageous to have a
theoretical model linking capital structure and hedging choices. I keep the empirical estimation tightly linked
to the theoretical model. In particular, I estimate the following structural model:
leverage ¼ b0 þ b1 Ã hedging þ Sg Ã X i þ ei ,

(7)

hedging ¼ a0 þ a1 Ã leverage þ a2 Ã leverage2 þ Sy Ã Y i þ i .

(8)

This model is estimated in a two-stage instrumental variable (IV) regression framework. The ﬁrst-stage
equation is an OLS model for the leverage decision, whereas the second equation models a ﬁrm’s hedging
(derivative) decisions. In the second stage, the risk-management equation is estimated using the predicted value
of the leverage ratio as the explanatory variable in the Logit or Tobit estimation. I try alternative econometric
speciﬁcations to this model in later sections.31 The leverage (leverage) of a ﬁrm is deﬁned as the ratio of total
debt (long-term debt plus debt included in the current liabilities) to the book value of total assets. To
investigate the effect of extreme leverage on hedging, I include leverage2 as an additional explanatory variable
in the second equation. I expect a positive sign on leverage and a negative sign on leverage2 in the regression
involving various measures of hedging as the dependent variable. X and Y represent control variables affecting
ﬁrms’ leverage and hedging decisions, respectively.
As argued earlier, industry concentration provides a good measure of ﬁnancial distress costs in my model.
In such industries, highly levered ﬁrms are more vulnerable to losing their competitive position in the industry
in the event of ﬁnancial distress. Opler and Titman (1994) provide empirical evidence in support of this
assumption. Based on this argument, my model predicts a positive relation between hedging and industry
concentration for highly levered ﬁrms. To capture this effect empirically, I include industry concentration
measure and its interaction with leverage in the hedging model. This measure is constructed by summing the
market shares (based on sales in 1996) of the top four players in the ﬁrm’s three-digit SIC code. Then I create a
dummy variable (concd) that equals one if the concentration ratio is above the median, and zero otherwise.
4.2.1. Identification strategy
To estimate this model I need to ﬁnd proper instrument(s) for the ﬁrst-stage leverage regression. A large
literature studies corporations’ capital structure determinants (see Frank and Goyal, 2003 for a survey) and
researchers have proposed several determinants of a ﬁrm’s leverage such as size, tangible assets, the book-tomarket ratio, earnings volatility, proﬁtability, and marginal tax rates (see Bradley, Jarrell, and Kim, 1984;
Titman and Wessels, 1988; Lang, Ofek, and Stulz, 1996; Graham, Lemmon, and Schallheim, 1998 among
others). For my identiﬁcation strategy to work, one has to argue that one or more of these variables affect a
ﬁrm’s hedging decision only through their impact on leverage and not independently by themselves. Finding a
31

In particular, I estimate an alternative econometric model suggested by Wooldridge (2002) for IV estimations involving the presence of
a function of the endogenous variable (i.e., leverage2 ) in the second stage.

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truly exogenous instrument for leverage is an extremely challenging task. Given this, I propose an
identiﬁcation strategy that is motivated by the theoretical model itself.
In the theoretical section, the ﬁrm’s capital structure follows a trade-off model. As in standard trade-off
models, the advantage of debt ﬁnancing is its tax beneﬁt, whereas its cost is ﬁnancial distress and deadweight
losses of bankruptcy. The ex-ante debt ratio is determined by the relative costs and beneﬁts of this trade-off.
This provides a dispersion in the debt ratio at time zero in the model. Subsequently, in the intervening time
period the debt ratio is further perturbed by random shocks to the ﬁrm’s proﬁtability. Thus, at the time the
hedging decision is made (at time t1 in the model), the cross-sectional leverage ratio is an outcome of the firm’s
marginal tax benefit of debt, the bankruptcy and distress costs of debt, as well as its recent profit history. The
theoretical model focuses on how the leverage ratio affects hedging decisions at this point in time, which I call
the ex-post hedging decision. In that sense, leverage becomes pre-determined in the model at the time the
hedging decision is made. At this time, shareholders engage in hedging activities as long as the ﬁrm’s leverage
is not too high, beyond which point the risk-shifting incentives begin to dominate.
I ﬁrst note that in my model the ﬁrst-order effect of hedging on leverage (i.e., the concern about reverse
causation) is through its affect on the cost of leverage and not through its effect on its tax beneﬁt. Thus, at
least in the context of my stylized model the marginal tax beneﬁt of debt provides one key source of dispersion
in the ex-ante debt ratio that remains largely unaffected by the extent of hedging. It is the deadweight cost of
ﬁnancial distress and bankruptcy that decreases due to hedging, allowing ﬁrms to borrow more. Thus,
marginal beneﬁt of debt seems like a reasonable instrument for identifying the leverage equation in my
empirical model. Motivated by this logic, I consider two instruments—the before-ﬁnancing simulated
marginal tax rate (MTR) of Graham, Lemmon, and Schallheim (1998) and a ﬁrm’s nondebt tax shield
(NDTS).
Marginal tax rates provide a reasonably direct proxy for the tax beneﬁt of debt. Thus, it is directly in the

spirit of my theoretical motivation. To avoid problems associated with negative spurious correlation between
the leverage and marginal tax rates, I use the before-ﬁnancing simulated tax rates. Further, I take the historical
averages of MTRs under the assumption that the current level of debt is an outcome of historical incremental
capital structure decisions. I take the past 10 year’s average MTR as a proxy for a ﬁrm’s current leverage ratio.
As discussed later, this variable has a signiﬁcant explanatory power in leverage regression. I also repeat my
analysis with last ﬁve year’s average and the current level of MTR (without averaging) as instruments and
obtain similar results.32
My second instrument is the nondebt tax shield enjoyed by a ﬁrm. Following earlier literature, I use
depreciation and amortization (da) scaled by the total assets of the ﬁrm as a measure of the ﬁrm’s nondebt tax
shield. This instrument measures the disincentive of using debt rather than directly measuring the incentive to
use debt based on tax considerations as captured by MTR. Thus, it is capable of detecting the ﬁrm’s leverage
ratios in response to the tax incentives as proposed in my model. At least controlling for ﬁrm’s size, PPE, and
other key characteristics, it can be argued that nondebt tax shield is a reasonable instrument for leverage in my
leverage-hedging model.
Both these instruments (MTR and DA/TA) possess good statistical properties for an instrument. They both
are signiﬁcant determinants of ﬁrm’s debt ratios in the ﬁrst stage regression reported in the next section. I also
check for their strength and ﬁnd that they do not suffer from any weak instrumentation bias in the sense of
Bound, Jaeger, and Baker (1995) and Staiger and Stock (1997). I repeat all my analyses after considering only
MTR and NDTS (one at a time) as my instrument and all results remain qualitatively similar. In order to save
space and due to the statistical advantage of having more instruments, I consider them both in my leverage
model for the results that I present in the paper. In addition, I use a ﬁrm’s net income to sales ratio (ni) in the
leverage regression as an additional instrument to capture the effect of recent proﬁtability on a ﬁrm’s capital
structure at the time of hedging in the spirit of my theoretical model. As I show later, this variable performs
well in the ﬁrst stage regression as well.
I include additional control variables in the spirit of Titman and Wessels (1988) and Graham, Lemmon and
Schallheim (1998) to control for well-known drivers of cross-sectional dispersion in leverage ratios. First,
32

Historical average MTR has better statistical properties in explaining the leverage-ratio than the current MTR. Therefore, I prefer the
average MTR on statistical grounds as well.

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I include Property, Plant and Equipment (ppe) scaled by total assets to control for the collateral available for
borrowing. I include a ﬁrm’s modiﬁed Z-score (see Graham, Lemmon, and Schallheim, 1998) to control for
the effect of ﬁrms that may currently be in ﬁnancial distress. The modiﬁed Z-score (modz) excludes the effect
of leverage from the original Altman Z-score to avoid a mechanical relation between leverage and this
variable. I also include two-digit SIC codes to control for industry-speciﬁc drivers of capital structure in my
leverage model. In addition, ﬁrm size and R&D-to-Sales ratio enter both the hedging and leverage
speciﬁcations. Overall, my model is in line with the theoretical arguments and is also close to earlier empirical
studies in corporate risk-management such as Geczy, Minton, and Schrand (1997) and Graham and Rogers
(2002). The base model is presented below:
X
lev ¼ b0 þ b1 hedging þ b2 size þ b3 rnd þ b4 MTR þ b5 ppe þ b6 modz þ b7 ni þ b8 da þ
Ind þ ei .
hedging ¼ a0 þ a1 lev þ a2 lev2 þ Sy Ã Y i þ i .
I add several other variables to this speciﬁcation in additional tests. As an alternative test, I use three-year
panel data of 200 manufacturing ﬁrms and regress changes in hedging activities on changes in leverage ratios.
Change regressions are less likely to suffer from endogeneity biases and face a tougher hurdle in detecting an
association between the variables of interest. My results are qualitatively similar for both the cross-sectional
IV regression model and the change regression model. Further, it should be noted that the nonlinear
speciﬁcation that I use in my modeling approach gives additional conﬁdence that my results are not driven by
reverse causality. This obtains because the endogeneity in my model comes from the fact that hedging can lead
to higher debt levels; this is true for all levels of leverage and especially so for higher levels. Thus, the
endogeneity argument will predict a positive relation between hedging and both leverage and leverage2 ,
something that is opposite to what my theory predicts.

4.3. Univariate tests
Table 2 presents the median values of key ﬁrm-level variables across hedgers and nonhedgers. To prevent
outliers from affecting my analysis, all variables used in this paper are winsorized at 1% from both tails. In
Panel A, I present the median characteristics of hedgers and nonhedgers of foreign currency risk in the sample
of ﬁrms with exposure to this risk. Panel B provides the same statistics for hedgers and non-hedgers for ﬁrms
with exposure to commodity price risk. Panel C is based on pooled observations across both types of risk.
I ﬁnd that the hedgers have signiﬁcantly different characteristics from the nonhedgers. The hedgers are
signiﬁcantly larger ﬁrms—the median hedger ﬁrm is about four to ﬁve times bigger than the nonhedger ﬁrm in
terms of market capitalization or total sales. The median leverage for hedgers is signiﬁcantly higher than the
median leverage of nonhedgers, with stronger results for commodity hedging sample. Hedgers keep less liquid
assets as compared with the nonhedgers as shown by the quick ratios of the two groups. As expected, the
foreign currency hedgers have higher foreign currency sales as compared with the nonhedgers. Not surprising,
there is no difference in the extent of foreign sales across hedgers and nonhedgers in the sample of ﬁrms with
commodity exposure. I also ﬁnd that hedgers have signiﬁcantly larger institutional shareholdings than nonhedgers. While foreign currency hedgers have higher growth opportunities (as proxied by R&D to sales and
market-to-book ratio) than their nonhedger counterparts, this pattern reverse for the commodity hedgers.
I explore these effects more carefully in the multivariate models presented below.
4.4. Regression analysis
In this section I present the regression results relating a ﬁrm’s hedging incentives to leverage and other
control variables.
4.4.1. First stage estimation
As a starting point, I present the regression results from the ﬁrst-stage estimation of leverage as reported in
the ﬁrst panel of Table 3. I ﬁnd a positive and signiﬁcant coefﬁcient on MTR indicating that ﬁrms with higher
tax beneﬁts obtain higher debt. As expected the coefﬁcient on depreciation and amortization, da=ta the proxy

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Table 2
Summary statistics
This table presents the descriptive statistics for the key explanatory variables used in the analysis. Panel A presents the median
characteristics of users and nonusers of foreign currency (FX) derivatives based on 1,781 observations that are identiﬁed as ﬁrms with
exposure to foreign currency risk. Panel B is based on commodity (CM) derivatives (1,238 observations with exposure to commodity price
risk), and Panel C is based on the usage of any of these two derivatives (2,256 observations). In every panel, I provide the median
characteristics of hedgers and nonhedgers as well as the entire sample. The last row in each sample gives the p-Value for the test that
median characteristics for hedger and nonhedger groups are equal based on a Wilcoxon-Mann-Whitney test. Sales represent the total sales
of the ﬁrm as reported under item 12 of COMPUSTAT tapes. mv stands for market value obtained by multiplying COMPUSTAT item 25
by item 199. lev measures the ratio of total liabilities (sum of COMPUSTAT items 9 and 34) to total assets (item 6). Quick ratio is
constructed as the ratio of cash and short-term investments (item 1) to current liabilities (item 5). fsale represents the ratio of foreign sales
to total sales of the ﬁrm. The foreign sales data are obtained from the COMPUSTAT geographical segments ﬁle. inst measures the
percentage institutional ownership in the ﬁrm. rnd stands for percentage research and development expenses (item 46) scaled by the sales of
the ﬁrm (item 12). mtb stands for the market-to-book ratio of the ﬁrm’s assets (COMPUSTAT (item 6 minus 60 plus ð25 Ã 199Þ) scaled by
item 6).
sales

mv

lev

quick

fsale

inst

rnd

mtb

Panel A: FX derivatives
Nonhedgers
228.5150
Hedgers
1147.0000
All
334.4900
p-Value
0.01

256.6120
1313.4053
392.5571
0.01

0.1744
0.2082
0.1877
0.05

0.2632
0.2159
0.2460
0.04

0.0685
0.3480
0.1416
0.01

44.1235
58.0480
47.7017
0.01

0.0000
2.1435
0.7773
0.01

1.6588
1.7048
1.6723
0.02

Panel B: Commodity derivatives
Nonhedgers
212.0220
Hedgers
768.4550
All
244.8135
p-Value
0.01

214.9539
798.7656
263.7081
0.01

0.2194
0.2829
0.2331
0.01

0.2378
0.1412
0.2091
0.01

0.0000
0.0000
0.0000
0.33

38.3827
53.0766
41.1219
0.01

0.0000
0.0000
0.0000
0.01

1.5692
1.5024
1.5501
0.09

Panel C: Any derivatives
Nonhedgers
201.7550
Hedgers
917.1540
All
285.0805
p-Value
0.01

207.9030
977.1712
305.7399
0.01

0.1983
0.2267
0.2071
0.04

0.2505
0.1995
0.2271
0.02

0.0000
0.2828
0.0241
0.01

39.3921
56.2881
44.4876
0.01

0.0000
1.0701
0.0000
0.01

1.6002
1.6659
1.6136
0.06

for nondebt tax shields, is negative and signiﬁcant. Further, consistent with my model ﬁrms with higher
proﬁtability have lower leverage as indicated by a negative and signiﬁcant coefﬁcient on net income to sales
ðni=salesÞ. These results are consistent with the motivations behind the use of these variables in the leverage
regression model. Other results are in line with the earlier empirical literature. Once I obtain the predicted
values of leverage from the ﬁrst-stage model, I use it in the second-stage model to explain a ﬁrm’s foreign
currency and commodity hedging decision. To save space, I do not present the results from the ﬁrst stage
estimation in the rest of the paper.

4.4.2. Foreign currency hedging
I start with the ﬁrm’s foreign currency hedging decision and subsequently analyze the commodity hedging
decisions.
Yes/No decision: I present the results of a second-stage Logit regression in Table 3. The dependent variable
equals one if a ﬁrm uses foreign currency derivatives and zero otherwise. The model is estimated with only
those ﬁrms that have a pre-deﬁned exposure to foreign currency risks. In the ﬁrst model, leverage is positive

and signiﬁcant at 1% whereas leverage2 is negative and signiﬁcant at the 1% level. For easier interpretation, I
present the marginal effect (on the probability of hedging) of the explanatory variable evaluated at the mean
rather than the raw estimated coefﬁcient from the logit model. In the next model, I include the interaction of
leverage and a dummy indicating whether the ﬁrm belongs to a highly concentrated industry or not. I ﬁnd a
positive coefﬁcient on the interaction of leverage and industry concentration (concd). As expected, the

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Table 3
Foreign currency hedging—yes/no decision
This table presents logistic regression results for foreign currency hedging by means of derivatives. In the ﬁrst stage I estimate an OLS
regression model for leverage. The estimation results from this regression are presented in the ﬁrst two columns. In addition to the
coefﬁcients reported in this table, this regression also includes industry dummies based on two-digit SIC code (coefﬁcients suppressed). In
the second stage, a logistic model is estimated with ﬁrm’s foreign currency derivative usage as the dependent variable (one for hedgers and
zero for nonhedgers). levÃ denotes the predicted value of leverage from the ﬁrst-stage regression. The marginal effect of explanatory
variables (evaluated at the mean) on the probability of hedging along with associated t-Values are presented in the table. Columns 3-8
present results from the second-stage estimation of hedging model. size represents the log of total sales of the ﬁrm. quick is the ratio of cash
and short-term investments to current liabilities. rnd stands for research and development expenses scaled by the sales of the ﬁrm. concd is
a dummy variable based on the four-ﬁrm concentration ratio of the ﬁrm’s industry (based on three-digit SIC code). concd equals one if the
ﬁrm belongs to an industry with a concentration ratio above the median, zero otherwise. fsale represents foreign sales as a percentage of
total sales. inst measures the percentage institutional ownership in the ﬁrm. mtr stands for the historical average of ﬁrm’s marginal tax
rates. ppe/ta stands for plant, property, and equipment scaled by total assets. Modified Z is the Altman Z-score without the leverage effect.
ni/sales stands for the ratio of net income to total sales. taxconvexity measures the dollar tax beneﬁt from a 5% volatility reduction in the
ﬁrm’s income scaled by the sales of the ﬁrm. mtb stands for the market-to-book ratio of the ﬁrm. segno stands for the number of
geographical segments in which the ﬁrm operates. The number of observations and R2 (for OLS regression) are provided at the end of the
table.

Leverage

size
levÃ
levÃ2
lev Ã concd
quick
rnd
concd
fsale
inst
mtr
ppe=ta
modifiedz
ni=sales
da=ta
taxconvexity
mtb
segno
R2
N

FX Derivatives

Estimate

t-Value

Estimate

t-Value

Estimate

t-Value

Estimate

t-Value

0.0116

(3.61)

0.1348
1.1181
À2.3759

(12.70)
(3.04)
(À3.34)

(9.80)
(2.52)
(À2.95)

(À5.73)
(À6.52)
(1.01)
(À0.94)

(À2.91)
(2.79)
(2.56)
(À12.90)
(À2.95)
(À2.64)

0.0203
0.0103
À0.0479
0.3457
0.0007

(1.10)
(5.20)
(À1.75)
(9.34)
(1.20)

(12.65)
(1.94)
(À3.23)
(2.42)
(0.82)
(5.19)
(À2.87)
(9.39)
(1.16)

0.1170

0.9519
À2.1029

À0.0295
À0.0060
0.0112
À0.0123
À0.0006
0.2634
0.1105
À0.0783
À0.1729
À0.6776

0.1346
0.7657
À2.3041
0.5295
0.0155
0.0104
À0.1706
0.3497
0.0007

0.0149
0.0101
À0.0499
0.1918
0.0006

(0.78)
(4.95)
(À1.83)
(3.95)
(1.00)

À0.7850
À0.0074
0.0670

(À1.54)
(À0.62)
(4.60)

0.402
1,421

1,421

1,421

1,418

introduction of this interaction term lowers the signiﬁcance of leverage variable, but it still remains signiﬁcant
at almost the 5% level. These ﬁndings are consistent with the key predictions of the model.
Other results indicate that ﬁrms with higher foreign currency sales are more likely to use hedging products,
indicating that highly exposed ﬁrms have higher incentives to hedge. There is a strong relation between growth
opportunities as measured by R&D expenses and hedging. This ﬁnding is consistent with the theoretical
predictions of Froot, Scharfstein, and Stein (1993) and earlier empirical ﬁndings of Geczy, Minton, and
Schrand (1997). I ﬁnd a positive relation between institutional shareholdings and hedging. Assuming an

inverse relation between institutional shareholdings and the extent of information asymmetry between the
insiders and outsiders of the ﬁrm, this result is inconsistent with information asymmetry-based models of
hedging. However, more analysis is needed to draw stronger inferences for this theory since the measurement
of information asymmetry remains a difﬁcult task for empirical researchers. In the ﬁnal model I include three
more control variables: tax convexity, market-to-book ðmtbÞ, and the number of geographical segments
(segno) in which the ﬁrm operates. All key results remain similar. I don’t ﬁnd evidence in support of tax-based

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Table 4
Foreign currency hedging—extent of hedging
This table presents the Tobit regression results for foreign currency hedging by means of derivatives. In the ﬁrst stage (unreported), I
estimate an OLS regression model for leverage. In the second stage, a Tobit model is estimated with ﬁrm’s foreign currency derivative
usage as the dependent variable. This variable takes the value of the notional amount of foreign currency derivatives scaled by total sales
of the ﬁrm (zero for nonhedgers). I provide the second-stage estimation results for three different model speciﬁcations in the table below.
levÃ denotes the predicted value of leverage from the ﬁrst-stage regression. The marginal effect of explanatory variables (evaluated at the
mean) on the expected value of uncensored observations along with associated t-Values are presented in the table. size represents the log of
total sales of the ﬁrm. quick is the ratio of cash and short-term investments to current liabilities. rnd stands for research and development
expenses scaled by the sales of the ﬁrm. concd is a dummy variable based on the four-ﬁrm concentration ratio of the ﬁrm’s industry (based
on three-digit SIC code). concd equals one if the ﬁrm belongs to an industry with concentration ratio above the median, zero otherwise.
fsale represents foreign sales as a percentage of total sales. inst measures the percentage institutional ownership in the ﬁrm. taxconvexity
measures the dollar tax beneﬁt from a 5% volatility reduction in the ﬁrm’s income scaled by the sales of the ﬁrm. mtb stands for the
market-to-book ratio of the ﬁrm. segno stands for the number of geographical segments in which the ﬁrm operates. The number of
observations is provided at the end of the table.

size

levÃ
levÃ2
levÃ Ã concd
quick
rnd
concd
fsale
inst
taxconvexity
mtb
segno
N

Estimate

t-Value

Estimate

t-Value

Estimate

t-Value

0.0117
0.1435
À0.2741

(10.51)

(3.21)
(À3.19)

(8.47)
(3.19)
(À3.17)

(1.46)
(4.21)
(À1.05)
(8.27)
(1.37)

(10.45)
(2.27)
(À3.14)
(2.21)
(1.33)
(4.17)
(À2.37)
(8.33)
(1.36)

0.0104
0.1459
À0.2721

0.0032
0.0010
À0.0033

0.0370
0.0001

0.0116
0.1074
À0.2699
0.0572
0.0029
0.0010
À0.0162
0.0372
0.0001

0.0036
0.0009
À0.0041
0.0176
0.0001
0.0226
0.0005
0.0086

(1.66)
(3.55)
(À1.31)
(3.06)
(1.49)
(0.46)
(0.38)
(4.99)

1,421

1,421

1,418

motivations for hedging. Finally, ﬁrms operating in more diverse foreign markets hedge more as evident by a
positive and highly signiﬁcant coefﬁcient on this variable. This result points toward the possibility that
derivative instruments act as complements to a ﬁrm’s natural hedging strategies.
Extent of hedging: In Table 4, I present results from the Tobit estimation with the notional amount of
foreign currency derivatives scaled by total sales of the ﬁrm as the dependent variable. Since the estimated
coefﬁcients in a Tobit model don’t represent the marginal effect of explanatory variables on the observed
dependent variable, for easier economic interpretation I report the slope coefﬁcients at the mean level.
I present results from three different model speciﬁcations and ﬁnd that ﬁrms with high leverage hedge more
and the relation between hedging and leverage reverses at very high levels of leverage. Highly levered ﬁrms in
concentrated industries have higher hedging incentives as well. These results are in line with both the model’s
predictions and the results obtained from the logit model described earlier, as well as with the earlier
theoretical models based on bankruptcy costs (Smith and Stulz, 1985). Economically, these results suggest that
if leverage increases from 10% to 20%, the ﬁrm increases its foreign currency derivative holdings by
approximately 6.4%, which is about 60% of the average level of foreign currency derivatives held by the
sample ﬁrms (see Table 1; these are only rough estimates with linear extrapolation around the mean.)
Earlier studies provide mixed evidence in support of hedging theories based on ﬁnancial distress costs. Mian
(1996) studies the binary (i.e., yes–no) hedging decision for a large sample of ﬁrms and ﬁnds no support for the
distress cost theories.33 My results suggest that linear models seeking to test theories of risk-management,
33

There are three possible explanations for the different results between my study and Mian (1996). First, his sample comes from 1992—
a period before the strict FASB regulation on derivatives disclosure. Second, his model doesn’t test for nonlinearity and therefore it doesn’t
have any quadratic terms. Finally my modeling technique is different as I use a two-stage estimation technique that considers leverage and

hedging as endogenous variables.

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especially if conducted on small sample sizes, may fail to detect a positive relation between ﬁnancial distress
and derivative activities for moderately leveraged ﬁrms.
To understand the nonmonotonic relation between hedging and leverage, I conduct a semi-parametric test.
I break the sample of ﬁrms into two groups based on whether their predicted leverage is below or above the
70th percentile of the empirical distribution of leverage in my sample. For this estimation I run a Tobit
regression with the same set of variables as in Model 1 of Table 4 after dropping leverage2 . The spline
regression results show that for the ﬁrst group, i.e., for the group with moderate leverage the marginal effect of
leverage on hedging is positive with a slope coefﬁcient of 0.0452, which is signiﬁcant at the 1% level. However,
the marginal effect of leverage on hedging becomes negative for ﬁrms in the other group, i.e., for ﬁrms with
leverage in top 30% of the sample. For this group, the marginal effect of leverage on hedging is estimated to be
À0:1504 with a signiﬁcance level of 2%. The semi-parametric test conﬁrms the non-monotonic relation
obtained in parametric regressions.
4.4.3. Commodity hedging
Table 5 provides logistic regression results for the commodity hedging decision. This regression is estimated
on a sample of ﬁrms with exposure to commodity price risk only. As in the foreign currency derivative
regression, I ﬁnd a positive and signiﬁcant coefﬁcient on leverage, and a negative and signiﬁcant coefﬁcient on
leverage2 . Both these relations are signiﬁcant at the 1% level. When I include the interaction of leverage with
high concentration industry, I ﬁnd the coefﬁcient on the interaction term to be positive and signiﬁcant at the
6% level. These results show that the predictions of my theory are supported by both foreign currency and
commodity hedging data. As in the case of foreign currency hedging, larger ﬁrms are more likely to use
commodity derivatives as well. However, in this regression the coefﬁcient on the quick ratio becomes positive
and signiﬁcant, while it is positive but insigniﬁcant in the foreign currency hedging models. Commodity

hedgers keep more liquid assets as well, which can be taken as evidence that hedgers complement their hedging
policies with liquid assets.
The most noticeable difference between the two models is the coefﬁcient on the R&D variable. This variable
has a positive and highly signiﬁcant coefﬁcient in the logit and Tobit model of foreign currency hedging

Table 5
Commodity hedging—yes/no decision
This table presents logistic regression results for commodity hedging by means of derivatives. In the ﬁrst stage (unreported) I estimate an
OLS regression model for leverage. In the second stage, a logistic model is estimated with ﬁrm’s commodity derivative usage as the
dependent variable (one for hedgers and zero for non-hedgers). levÃ denotes the predicted value of leverage from the ﬁrst stage regression.
The marginal effect of explanatory variables (evaluated at the mean) on the probability of hedging along with associated t-Values are
presented in the table. size represents the log of total sales of the ﬁrm. quick is the ratio of cash and short-term investments to current
liabilities. rnd stands for research and development expenses scaled by the sales of the ﬁrm. concd is a dummy variable based on the fourﬁrm concentration ratio of the ﬁrm’s industry (based on three-digit SIC code). concd equals one if the ﬁrm belongs to an industry with
concentration ratio above the median, zero otherwise. inst measures the percentage institutional ownership in the ﬁrm. taxconvexity
measures the dollar tax beneﬁt from a 5% volatility reduction in the ﬁrm’s income scaled by the sales of the ﬁrm. mtb stands for the
market-to-book ratio of the ﬁrm. The number of observations is provided at the end of the table.

size
levÃ
levÃ2
levÃ concd
quick
rnd
concd
inst
taxconvexity
mtb
N

Estimate

t-Value

Estimate

t-Value

Estimate

t-Value

0.0360
0.9815
À1.7470

(5.20)
(3.23)
(À3.46)

(4.79)
(3.13)
(À3.40)

(2.13)
(À6.76)
(1.23)
(2.16)

(5.25)
(2.47)

(À3.50)
(1.94)
(2.20)
(À7.19)
(À1.27)
(2.20)

0.0354
0.9680
À1.7337

0.0229
À0.0216
0.0206
0.0008

0.0355
0.7556
À1.7267
0.3107
0.0229
À0.0220
À0.0737
0.0008

0.0224
À0.0215
0.0212
0.0008
À0.0764

À0.0013

(2.04)
(À6.56)
(1.26)
(2.02)
(À0.33)
(À0.15)

948

948

947

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Table 6
Alternative models
This table presents the results for derivatives usage for various alternative model speciﬁcations. The ﬁrst two models use alternative
deﬁnitions of ﬁnancial distress, whereas the last two models use leverage as the measure of ﬁnancial distress but use bootstrapped standard
errors in estimating the t-Values. FD stands for the measure of ﬁnancial distress for the given model and FD2 is its squared term. The ﬁrst
model uses the industry-adjusted leverage ratio based on two-digit SIC codes as a measure of FD. For this model FD2 equals leveragesquared if the ﬁrm’s leverage is above industry average, zero otherwise. In the second model, I use Altman’s Z-score as a measure of the
ﬁrm’s ﬁnancial distress and estimate a logistic model using the ﬁrm’s usage of foreign currency or commodity derivatives as the dependent
variable (one for users, zero for nonusers). For consistency with other models, I set FD equal to the inverse of the Z-score such that a
higher value of FD corresponds to ﬁrms closer to ﬁnancial distress. The third model replicates the base-case logistic regression with

bootstrapped standard errors. The dependent variable is one for the users of commodity or foreign currency exposure, zero otherwise. In
the fourth model I estimate a Tobit model for the extent of foreign currency derivatives using bootstrapped standard errors. For these last
two models FD stands for predicted leverage from the ﬁrst-stage regression, and FD2 is simply the squared predicted leverage. All
regression results provide the slope estimates evaluated at the mean of the explanatory variable along with corresponding t-statistics. size
represents the log of total sales of the ﬁrm. quick is the ratio of cash and short-term investments to current liabilities. rnd stands for
research and development expenses scaled by the sales of the ﬁrm. concd is a dummy variable based on the four-ﬁrm concentration ratio of
the ﬁrm’s industry (based on three-digit SIC code). concd equals one if the ﬁrm belongs to an industry with a concentration ratio above the
median, zero otherwise. fsale represents foreign sales as a percentage of total sales. inst measures the percentage institutional ownership in
the ﬁrm. Number of observations used for the estimation is provided in the last row.
Estimate

t-Value

N

0.1089
0.2536
À0.7713
0.0111
0.0039
À0.0255
0.2833
0.0010

(13.20)
(3.07)
(À2.70)
(0.89)
(2.35)
(À1.16)

(9.07)
(2.05)

2,089

Estimate

Z-score

Ind Leverage
size
FD
FD2
quick
rnd
concd
fsale
inst

t-Value

Estimate

0.0433
0.0812
À0.0265
0.0039
0.0019
À0.0095
0.1168

0.0004
2,049

t-Value

Estimate

LOGIT
(1.98)
(3.83)
(À11.11)
(0.79)
(1.70)
(À0.95)
(1.97)
(1.48)

0.1167
1.3070
À2.4002
0.0415
0.0057
À0.0250
0.3056
0.0012
1,769

t-Value

TOBIT

(12.05)
(4.01)
(À4.18)
(2.63)
(2.81)
(À1.09)
(8.47)
(2.07)

0.0111
0.1162
À0.2276
0.0029
0.0009
À0.0034
0.0345
0.0001

(5.88)
(2.57)
(À2.71)
(1.41)
(3.58)
(À1.47)
(4.56)
(1.49)

1,421

decisions. However, here it is negative and insigniﬁcant. While high growth ﬁrms manage their foreign

currency hedging more aggressively, they are less likely to manage their commodity risk exposure. Although
exploring the differences in hedging incentives across different types of risks is beyond the scope of this paper,
this ﬁnding is suggestive of ﬁrm’s facing conﬂicting incentives in managing various forms of risk. These
conﬂicting incentives may be driven by factors such as differences in the correlation between the risk being
hedged and the ﬁrm’s investment opportunity set. For example, the argument behind a positive relation
between growth opportunities and hedging relies on the assumption that high growth ﬁrms may need funds to
undertake projects in bad cashﬂow states. If ﬁrms do not have good investment opportunities in states with
poor realizations of cashﬂows, then this incentive disappears. At the extreme, if the investment opportunity set
is highly positively correlated with the realizations of cashﬂows, then such ﬁrms may have a disincentive to
hedge. A better understanding of these issues is left for future research that explicitly incorporates these
correlations in the analysis.
4.5. Alternative model specifications
Industry-adjusted leverage ratios: The results presented so far in the paper are based on a two-stage
speciﬁcation that requires an assumption about the structural model determining a ﬁrm’s leverage choice. For
robustness, I test a model that does not require such a speciﬁcation. Speciﬁcally, I conduct the analysis with
ﬁrms’ industry-adjusted leverage ratios as industry adjustment provides a simpler and perhaps more robust
way to classify ﬁrms into moderately and highly leveraged. The industry-adjusted leverage ratio of a ﬁrm is
deﬁned as the difference between the ﬁrm’s leverage and the industry median based on the two-digit SIC code.

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I reestimate all my results with these ratios. For this model, leverage variable can be either positive or negative
depending on whether the ﬁrm is above or below industry-median. Thus, I cannot use leverage2 as an
explanatory variable to test for the nonlinearity predicted by the model. Instead, I use a variable that equals
leverage2 þ for ﬁrms with higher than industry median leverage and zero otherwise. To conserve space, I pool
foreign currency and commodity hedging decisions to estimate these robustness results. I estimate a logit

model on a sample of ﬁrms that are exposed to either of these two types of risks and present the results in
Model 1 of Table 6. 34 My key results remain the same with this deﬁnition of leverage.
Altman Z-score: I use the Altman Z-score as an alternative proxy of ﬁnancial distress. Lower Z-score values
correspond to ﬁnancially weaker ﬁrms. I, therefore, transform them by taking their inverse to be consistent in
the presentation of results. Results are presented in Model 2 of Table 6. I ﬁnd a nonmonotonic relation based
on this measure as well.
Bootstrapped standard errors: Since I use a two-stage estimation methodology in the logit and Tobit
regressions, there is a potential for overstated t-statistics due to the sampling error of ﬁrst-stage estimation (see
Maddala, 1983). To account for this possibility I reestimate my models with bootstrapped standard errors. In
every replication I create a pseudo-random sample by drawing observations from the base sample with
replacement. Thus, in every replication some of the observations appear more than once and some do not
appear at all. With 100 such replications, I generate an empirical distribution of estimated coefﬁcients in
the logit and Tobit models. The standard deviations of these estimates are then used to obtain bootstrapped
p-Values for my base estimation. This methodology does not rely on any structural form for the estimation of
the variance-covariance matrix and has the advantage of benchmarking base estimates against their empirical
distributions. In Models 3 and 4 of Table 6, I present the Logit and Tobit model estimates with bootstrapped
errors. As shown, all my key results are robust to bootstrapped standard error estimation.
Alternative IV regression specification: Wooldridge (2002) suggests an alternative instrumental variable
regression model for models involving functions of an endogenous variable (such as leverage2 in the secondstage estimation). Potentially, this technique provides econometrically better estimates than the model
that uses predicted values of leverage and its function in the second stage. In this method, rather than
using the squared value of predicted leverage in the second-stage regression, both leverage and leverage2
are treated as endogenous variables and instrumented with their own instruments. To achieve identiﬁcation,
I add the squared terms of all exogenous variables entering the leverage model as instruments for leverage2 .
These instruments are the squared values of: mtr, da=ta, ppe=ta, ni=sales and modified_z. In addition, the
squared predicted value of leverage from the ﬁrst stage estimation is used as an additional instrument for
leverage2 .
With both leverage and leverage2 as endogenous variables and these instruments in hand, I estimate an
instrumental variable model in a two-stage regression framework. I estimate the binary decision to hedge
foreign currency or commodity derivatives with an IV Probit model and the extent of foreign currency hedging
with an IV Tobit model. The results are presented in Table 7. Note that the parameter estimates in this model

are not directly comparable to the earlier tables since, due to computational simplicity, I report the coefﬁcients
from the regressions directly rather than the slope coefﬁcients presented earlier. I ﬁnd that my key results
remain robust to this alternative IV estimation technique. All other results remain similar to the earlier basecase speciﬁcation.
4.6. Dynamic analysis
Change regression: I focus on a three-year panel of 200 manufacturing ﬁrms to exploit the variations in the
individual ﬁrm’s leverage and hedging intensities in a dynamic setting. This empirical strategy closely
resembles my theoretical model and presents several econometric advantages. The change regression controls
for unobserved ﬁrm-speciﬁc factors. In particular, unless a ﬁrm’s nonderivative-based hedging strategies have
changed substantially over this time period, change regressions control for operational and natural hedging in
a more precise manner. Second, the endogeneity argument is less severe for change regressions since by
construction it removes ﬁrm-speciﬁc unobservable effects that could be correlated with both hedging and
34

Individual regressions estimated separately on foreign currency and commodity samples are qualitatively similar.

Financial distress and corporate risk management

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