Electronic copy available at: http://ssrn.com/abstract=1760201

CEO Entrenchment and Corporate Risk

Management1

Praveen Kumar

C. T. Bauer College of Business

University of Houston

pkumar@uh.edu

Ramon Rabinovitch

C. T. Bauer College of Business

University of Houston

ramon@uh.edu

This Version: January 30, 2011

1We thank Yakov Amihud, Jonathan Berk, Sudheer Chava, Peter DeMarzo, Darrell Du¢ e, JohnGraham, Milton Harris, Kose John, Vince Kaminsky, Takao Kobayashi, Ronald Masulis, ErwanMorellec, Annette Poulsen, Paul Povel, Sheridan Titman, Alex Triantis, Arthur Warga, participantsin seminars at the Indian Institute of Management (Bangalore), University of Colorado (Boulder),University of Houston, Texas A&M University, University of Tokyo, and the annual meetings of theFinancial Management Association for helpful comments or discussions on the issues addressed inthis paper. An earlier version of this paper was titled �Corporate Characteristics and the Demandfor Hedging.�

Electronic copy available at: http://ssrn.com/abstract=1760201

Abstract

Will corporations hedge even if risk management does not raise �rm value? We address

this question by examining theoretically and empirically the e¤ects of CEO entrenchment

and overinvestment on corporate hedging. Our theoretical analysis indicates that the avoid-

ance of �nancial distress costs and managerial risk aversion are not necessary for hedging

and risk management by corporations. We also generate novel predictions on the in�u-

ence of entrenchment related factors, CEO equity ownership, and available cash �ows on

hedging. Using a unique hand-collected dataset with detailed quarterly data on hedging by

non-integrated exploration and production �rms in the oil and gas industry, we test these

predictions and �nd support for them. Corporate hedging intensity is positively (or nega-

tively) related to internal and external governance factors that enhance (or weaken) CEO

entrenchment, even after controlling for leverage, size, risk, and the marginal tax rate. Our

study provides a new perspective and evidence on the determinants of corporate hedging.

JEL Numbers: G34, G30

Keywords: Risk management; Managerial entrenchment; Free cash �ows; Corporate gov-

ernance; Oil and gas industry

Electronic copy available at: http://ssrn.com/abstract=1760201

1 Introduction

Recent corporate debacles associated with the use (or misuse) of derivatives have generated

renewed interest in corporate risk management policies, in particular the demand for hedging

through derivatives. It is well known that hedging is a value-neutral activity in a Modigliani-

Miller (1958) world. But, at least theoretically, hedging may enhance �rm value in the

presence of market frictions and distortionary taxation.1 However, the empirical evidence

on the relationship between hedging and �rm value is ambiguous.2 More generally, the

motivations for corporate hedging are still not de�nitively understood and the basic question:

why do corporations hedge?, merits further scrutiny. In particular, will corporations hedge

even if risk management does not raise �rm value?

In this paper, we emphasize the role of managerial agency in corporate hedging: we an-

alyze both theoretically and empirically the implications of top-management entrenchment

and overinvestment on corporate hedging policies. We argue that the level of CEO entrench-

ment, which is positively related to the free cash �ow agency problem for �empire building�

or overinvestment (Jensen, 1986, 1993; Stulz, 1990), is a signi�cant determinant of the de-

mand for hedging for �rms that face external �nancing costs. There are arguments in the

literature that shareholder-manager agency con�icts in�uence corporate risk management

1See Fenn, Post and Sharpe (1997) and Stulz (2003) for excellent surveys of this literature.2Allayannis and Weston (2001) �nd a positive association between �rm value and the use of foreign

currency derivatives; Carter, Rogers and Simkins (2006) suggest that hedging jet fuel prices increases �rmvalue in the airline industry; and Campello et al. (2010) �nd that �rms that use foreign exchange andinterest rate derivatives for hedging purposes pay lower interest rates and are less likely to su¤er expenditurerestrictions in loan agreements. But Tufano (1996) and Jin and Jorion (2006) �nd little empirical evidencethat risk management facilitates �rm value maximization in samples of �rms from the gold mining industryand the oil and gas industry, respectively. Similarly, Guay and Kothari (2003) �nd that the gains fromhedging for non-�nancial �rms are small relative to cash �ow or equity value movements.

1

activities (Amihud and Lev, 1981; Tufano, 1998, Morellec and Smith, 2007).3 However,

to our knowledge, this is the �rst study to theoretically analyze and empirically verify the

e¤ects of CEO entrenchment levels on corporate risk management.

Our theoretical analysis clari�es a number of issues regarding the relationship between

entrenchment and hedging that are ambiguous ex ante. For example, increasing hedging

activity reduces future cash �ow volatility and raises the expected utility of entrenched man-

agers who are risk averse with respect to future cash �ow realizations. However, higher

hedging costs also reduce free cash �ows that are especially valued by entrenched managers.

The former e¤ect suggests that hedging is positively related to the level of entrenchment,

while the latter e¤ect suggests the opposite. Furthermore, with positively serially correlated

cash �ows (Watts, 1975; Gri¢ n, 1977), the relationship between available cash �ows and

hedging demand is complex because cash �ow realizations in�uence not only internal liquid-

ity but also impact expectations of future cash �ows. Yet, the dynamic inter-dependence

between cash �ows and hedging has not been explored in the literature. Finally, incentive

compensation that ties managerial wealth to the �rm�s performance reduces the agency risks

from entrenchment (Lewellen, Loderer and Martin, 1987; Hu and Kumar, 2004). But there

is no available analysis of the e¤ects of managerial equity ownership on corporate risk man-

agement in the context of free cash �ow agency problems due to managerial entrenchment.4

3Amihud and Lev (1981) �nd empirically that �manager-controlled��rms engage in conglomerate diver-si�cation more than managers in �owner-controlled� �rms. Tufano (1998) argues that cash �ow hedgingcan protect managers from capital market scrutiny, potentially exacerbating shareholder-manager con�icts.Taking a shareholder value maximization perspective, Morellec and Smith (2007) argue that hedging cancontrol overinvestment incentives.

4In the existing literature on hedging, a role for managerial equity ownership arises only because risk-averse managers with greater equity ownership are predicted to prefer more risk management (Stulz, 1984;Smith and Stulz, 1985; Tufano, 1996).

2

Our model analyzes the e¤ects of managerial private bene�ts of control, equity ownership,

and cash �ow availability on corporate hedging in a dynamic model with serially correlated

cash �ows. To highlight the role of entrenchment, we abstract from managerial risk aversion

and the avoidance of bankruptcy costs as motives for hedging as these factors have already

been considered in the literature.5 Rather, we build on Froot, Scharfstein and Stein�s (1993)

observation that a role for risk management will arise whenever external �nancing costs

exceed those of internally generated funds. But while Froot et al. conduct their analysis

from the viewpoint of shareholder value-maximization, in our model managerial agency costs

endogenously raise the costs of external �nancing relative to internal funds.

We �nd that even risk neutral entrenched managers of unlevered �rms, with a personal

utility from managing larger assets, will optimally establish costly hedging positions because

such managers face the risk of high �nancing costs for their desired levels of investment. Thus,

the avoidance of �nancial distress costs and managerial risk aversion are not necessary for

hedging and risk management by corporations. Comparative statics of the optimal hedging

policy produces refutable predictions on the covariation of corporate hedging demand with

manager- and �rm-speci�c characteristics. The higher are the manager�s private bene�ts of

control the greater is her expected marginal value from cash �ows and, thus, the higher is the

risk-aversion towards future investment �nancing risk. Consequently, our model predicts a

positive relationship between the manager�s level of entrenchment and the optimal choice of

corporate hedging intensity even when the hedging cost function is convex. We also predict

5The literature on managerial entrenchment also emphasizes the role of debt in attenuating the man-agerial moral hazard for overinvestment (Stulz, 1990; Hart and Moore, 1995; Zwiebel, 1996). However, wedeliberately eschew debt from our model to eliminate bankruptcy risk as a motivation for hedging.

3

a positive relationship between the manager�s equity ownership and hedging intensity even

though the manager is risk neutral. In addition, we derive a dynamic relationship between

free cash �ows and hedging intensity: with serially correlated cash �ows, our analysis predicts

a negative relationship between hedging intensity and the availability of free cash �ows.

The empirical implications of our model can be tested in any industry where cash �ow

volatility is su¢ ciently high to make risk management important. Here, we test these pre-

dictions using a unique hand-collected dataset with detailed quarterly data on the hedging

positions of �rms from the oil and gas industry during 1996-2008. Our sample �rms are

independent exploration and production �rms that are undiversi�ed in terms of physical

assets. Our dataset is of special interest because most empirical studies of corporate risk

management use only information on �rms�decision to hedge or use derivatives. But even in

comparison with the few studies that use more detailed hedging information from commod-

ity producing industries, we have a relatively long time-series. Moreover, we use quarterly,

rather than annual data, which is important because hedging activities in the oil and gas

industry have short horizons.6

Our empirical test design employs dynamic panel data analysis with �xed �rm-level

e¤ects, and we exploit the substantial variation in oil and natural gas price volatility during

the sample period to examine the e¤ects of high commodity price volatility regimes on risk

management, an issue that has not been explored in the literature. We develop a �rm-wide

measure of the level of risk management, or hedging intensity, based on the delta of the

6For example, Haushalter (2000) examines data on the oil and gas industry from 1992-1994; Tufano (1996)considers data from the gold mining industry from 1992-1994; and Mian (1996) examines a broader sampleof �rms for 1992. Similarly, Guay and Kothari (2003) and Gezcy, Minton and Schrand (2007) also use datawith relatively short sample periods.

4

�rms�derivatives portfolio (Tufano 1996), and use proxies for CEO entrenchment from the

empirical literature on this topic (Berger, Ofek and Yermack, 1997; Hu and Kumar, 2004;

Chava, Kumar and Warga, 2010).

In light of the unique aspects of our data, we �rst analyze the determinants of hedging

with factors emphasized in the existing literature. Our main �ndings here are:

1. Hedging intensity is positively related to �nancial leverage (or the debt capitalization

ratio). We note that while the literature has theoretically motivated a positive associa-

tion between hedging and leverage from the viewpoint of lowering �nancial contracting

costs (Smith and Stulz, 1985; Bessembinder, 1991; Froot et al., 1993), barring a few

exceptions (e.g., Haushalter, 2000), most empirical studies have not found a signi�cant

connection between hedging intensity and leverage.

2. Hedging intensity is positively associated with the �rm�s unlevered beta, which is

a proxy for intrinsic cash �ow risk. This �nding is of substantial interest because,

while cash �ow risk is theoretically a central determinant of hedging demand, previous

empirical studies have been unable to document a signi�cant relationship between

theoretically derived direct measures of this risk (i.e., the unlevered beta) and hedging.

3. Hedging intensity is positively related to the estimated marginal corporate income

tax rate, which is consistent with the argument that the progressivity in the corporate

income tax structure allows �rms to reduce expected tax costs through hedging (Smith

and Stulz, 1985; Graham and Smith, 1999).

4. Hedging intensity is positively associated with the value of managerial equity owner-

5

ship, which is also noteworthy because the existing empirical studies on hedging have

found mixed results.7

We then include in the analysis variables that are related to the predictions of our model.

Our main empirical result is that, even when controlling for the above-mentioned factors,

hedging is signi�cantly and positively (or negatively) related to internal and external gov-

ernance factors that enhance (or weaken) CEO entrenchment. For example, the literature

suggests that CEO entrenchment is positively associated with CEO tenure (Shleifer and

Vishny, 1989; Hu and Kumar, 2004), while it is negatively in�uenced by board indepen-

dence (Fama and Jensen, 1983) and the equity ownership of outside blockholders (Shleifer

and Vishny, 1986). We indeed �nd that hedging intensity is positively associated with CEO

tenure and, strikingly, �rms signi�cantly reduce hedging immediately following a change of

the CEO, other things held �xed. On the other hand, both board independence and the

percentage of equity ownership held by outside blockholders are negatively related to hedg-

ing. Finally, we �nd a negative relationship between hedging intensity and the level of recent

cash �ows, which is also consistent with the predictions of our model.

Ours is the �rst empirical analysis to document a highly signi�cant impact of a change

of the CEO, board independence and outside blockholder ownership on �rms�hedging poli-

cies. We are also the �rst to document a signi�cant negative relationship between free cash

�ow availability and hedging. These results support the view that CEO power or level

of entrenchment play a signi�cant role in the determination of corporate risk management

7Tufano (1996) �nds a positive relationship between hedging and managerial equity ownership whileHaushalter (2004) �nds a negative relationship. Similarly, evidence presented by Geczy, Minton and Schrand(1997), Gay and Nam (1998), and Knopf, Nam and Thornton (2002) also presents a mixed picture.

6

policies, which is the main prediction of our theoretical framework.

We organize the paper as follows. Section 2 describes our model and in Section 3 we

present the main theoretical results. Section 4 describes the data, while Section 5 speci�es

the empirical test design. Section 6 describes the results and Section 7 concludes.

2 A Model of Managerial Entrenchment and Hedging

We consider an agency model of the �rm where there is a separation of ownership and

control. The �rm is publicly owned and completely equity-�nanced and is controlled by a

risk-neutral manager. Thus, we remove managerial risk aversion and avoidance of bankruptcy

as motivations for hedging. As we noted above, the e¤ects of these factors on �rms�hedging

demand have already been studied elsewhere in the literature.

The �rm has an investment opportunity, but there is a con�ict on investment preferences

between the equity owners and the manager because the manager derives private bene�ts

from controlling larger investments. That is, there is moral hazard toward taking negative

NPV projects or �empire building�(Jensen, 1986, 1993; Stulz, 1990; Hart, 1995).

The �rm also faces external �nancing constraints. Froot et al. (1993) show rather

generally that if external �nancing costs exceed the (shadow) costs of internal �nancing,

then hedging can be value-maximizing. In our case, the managerial moral hazard for over-

investment leads naturally to a wedge between internal and external �nancing costs because

�nancial markets incorporate the tendency toward ine¢ cient investment (Stulz, 1990; Tirole,

2006). The higher costs of external �nancing, relative to the opportunity costs of internal

funds, generate a value to the manager of having internal liquidity or free cash �ows to

7

�nance her personally desired level of investment.

To minimize extraneous notation, we assume that the �rm is entirely internally funded.

However, our main results are robust to allowing the �rm access to external �nancing at a

cost greater than that of internal funds.

2.1 Timing Conventions

There are three dates in the model, t = 0; 1; 2; and all decisions are made at dates t = 0; 1:

A risk-free asset pays a gross per-period return of R > 1: At t = 0, the �rm has no cash

at hand and its only cash in�ow is through the operational cash �ows c0 � 0; which is a

realization of a serially correlated process; i.e., the net cash �ows at t = 0 are C0 = c0.

Also, at t = 0, the �rm�s CEO, who owns the fraction �0 of the �rm�s equity, decides

on the amount of hedging � which we label as the hedging intensity and denote by �0.8

This decision, together with the resulting net cash �ow at t = 1, C1; determines the amount

of capital that is available for investment I1, which the manager decides at t = 1. This

investment then stochastically generates the �rm�s cash �ows at t = 2, at which time the

�rm and its assets are liquidated.

2.2 Cash Flows and Hedging

We take the �rm�s operational cash �ow process at dates t = 0; 1; namely, fc0; c1g to be

given, based on previously made investment decisions that are outside the purview of our

8The manager�s equity-ownership may be the outcome of previous incentive compensation awards to helpaddress the agency problem, but we take it as a given to facilitate foucs on the derivation of the optimalhedging strategy.

8

framework. Consistent with the empirical facts (see, e.g., Gri¢ n 1977), we take these op-

erational cash �ows to be positively serially correlated. It is convenient parameterically to

model this autocorrelation through a �rst order autoregressive process subject to shocks that

have the Normal distribution.9 That is,

c1 = �c0 + "1 (1)

Here, the cash �ow shock "1 is independent of c0 and is Normal with mean zero and variance

�2"; while � > 0 is the persistence parameter.10

The manager may hedge the risk of low realizations of c1 by investing in securities whose

payo¤s at t = 1 are negatively correlated with the cash �ow shock "1. Speci�cally, the extent

of protection against cash �ow volatility is determined by the choice of the hedging intensity

�0; where 0 � �0 � 1: While our results apply as long as �0 and the volatility of c1 are

negatively related, for analytic tractability, we assume that, conditional on �0;

c1 = �c0 + "1p1� �0 (2)

That is, the payo¤s from the hedge securities are negatively correlated with the operational

cash �ow shocks and the hedging protection increases with the hedging intensity. Indeed, for

�0 = 1; the �rm�s operational cash �ows at t = 1 are perfectly hedged because then c1 = �c0.

9Watts (1975) analyzes serial correlation in forecasting errors for quarterly earnings and �nds that inno-vations in earnings in adjacent quarters are positively correlated. Gri¢ n (1977) reaches a similar conclusionand states that �...the behavior of quarterly earnings may be characterized by a �rst-order autoregressiveprocess...�10If � = 1, then cash �ows follow a random walk. If � > 1; then there is cash �ows growth, while � < 1

corresponds to the regressive and stationary case. Note that in our two-period process, issues of stationarityare not relevant.

9

However, hedging is costly. As we mentioned above, there are signi�cant �xed costs

involved in setting up hedging operations internally. If there are proportional transactional

costs in derivatives (expressed in terms of the percentage of cash �ow exposure), then the

total hedging costs are increasing and convex with respect to the hedging intensity. Moreover,

hedging all risk from future cash �ows will generally not even be economically feasible; there

may not even exist �nancial contracts to insure against certain components of cash �ow risk

� for example, loss of skilled labor (or human capital loss risk) and development of product

substitutes (or competitive risk) can not be insured. We therefore posit an increasing and

convex function g(�0), such that g(0) = 0 , lim�0#0 g0(�0) = 0; and lim�0"1 g

0(�0) =1:11

The net cash �ows at t = 1 are denoted by C1 and are comprised of the operational cash

�ows and the dollar returns from �nancial investments, i.e.,

C1 = c1 + (c0 � g(�0))R (3)

In light of our previous assumptions and (2)-(3), the distribution of C1; conditional on

(C0; �0); is Normal with mean �1 = c0(� + R) � g(�0)R and variance �21 = �2"(1 � �0):

Formally, we write: �C1 C0; �0

�� �(C1;�1; �21) (4)

where �(�;�1; �21) is the cumulative distribution function for the Normal distribution with

the moments (�1; �21):

11Strictly speaking, our results do not require that the hedging cost function be convex everywhere. We onlyrequire that this cost function be eventually convex with a steeply ascending marginal cost as � approaches1. Thus, with additional notational complication, we can accommodate the presence of scale economies intransactions costs upto some intermediate range of cash �ow exposure.

10

2.2.1 Investment Opportunities and Managerial Preferences

At t = 1; the net cash �ow C1 is realized (cf. (2)-(3)). Negative realizations of C1 indicate

the �rm�s inability to meet its obligations to its input suppliers. In such a situation, limited

liability applies and the �rm shuts down and the value of the �rm�s equity falls to zero.

But if C1 > 0; then the manager invests I1; 0 < I1 � C1; in a project which uses

a point input-point output technology; this investment yields dollar returns at t = 2 of

X2(I1; �2) = �2pI1. Here �2 is a non-negative pro�t shock that is realized after the investment

is made, with the cumulative distribution function F (�2), and E(�2) = ��2 > R:12 The net

cash �ows at t = 2 are thus:

C2 = �2pI1 +max(0; (C1 � I1)R) (5)

The �rm is then liquidated upon the realization of C2.

As a stockholder, the manager derives utility from the liquidating net cash �ows C2:

The manager also derives personal utility from the investment under her control, I1, where

> 0: The literature interprets the level of entrenchment to be positively related to the

likelihood of making sub-optimal decisions. Thus, can be interpreted as the manager�s

entrenchment level in our model. The manager discounts future payo¤s through a subjective

discount factor �; 0 < � < 1:We assume that the manager�s marginal utility from controlling

a larger investment is less than her opportunity cost from over-investment, i.e., < ��0R:13

12The assumption that �2 � 0 is for notational convenience only. If �2 < 0 such that the liquidating netcash �ows are negative, then the �rm is simply liquidated with zero equity value.13This condition, which is appealing from introspection, ensures that the manager�s optimal investment

level does not become unbounded.

11

We now turn to characterize the manager�s optimal investment and hedging policies

3 Optimal Managerial Investment and Hedging

We characterize the optimal investment and hedging intensity choices of the manager through

backward recursion. First, we characterize the optimal investment at t = 1 as a function of

the available net cash �ows C1. This also determines the manager�s value function (i.e., the

indirect expected utility function for t = 1 and t = 2) for C1 (viewed as a state variable).

But C1 depends on the hedging intensity choice at t = 0, i.e., �0; we use this relationship to

characterize the optimal hedging choice.

3.1 Optimal Investment

If the �rm is still operational at t = 1 (i.e., C1 > 0), then the manager chooses the investment

I1 to maximize her expected utility, which is composed of her share from the liquidation

equity payo¤s C2 (cf. (5)) and the personal bene�t derived from controlling the investment.

That is,

U1(I1; C1) = ��0

h�2pI1 +max(0; (C1 � I1)R)

i+ I1 (6)

Notice that if the �rm�s investment �nancing constraint is binding, then I1 = C1: Otherwise,

maximizing (6) with respect to I1, yields the manager�s optimal investment choice:

I�1 (C1) = min

C1;

(��2)2

4(R� ��0)2

!(7)

12

That is, with su¢ ciently high C1; the manager�s optimal investment is increasing with the

expected capital productivity (��) and her entrenchment level ( ). But the optimal invest-

ment is decreasing with the manager�s equity holdings �0; which align her interests with the

shareholder, her subjective discount factor �, and the interest rate R:

As a benchmark, the �rm�s value-maximizing (or e¢ cient) investment level, subject to

the �nancing constraint, is given by:

Ie1(C1) = min

�C1;

(��2)2

4R2

�� I�1 (C1) (8)

That is, Ie1 would be the optimal investment (as a function of C1) if there was no managerial

agency problem. Given that < ��0R; the inequality in (8) is strict when there are no

internal �nancing constraints or C1 >(��2)

2

4R2. That is, there is over-investment relative to

the e¢ cient level whenever the manager can �nance her desired level of investment. For

notational ease, we denote Ie1 � ��22=4R

2 and I�1 � ��22=[4(R�

��0)2]:

For any net cash �ow C1; let �(C1) � I�1 (C1)� Ie1(C1) denote the investment distortion

because of managerial entrenchment. It is straightforward to calculate that �(C1) equals 0

if C1 � Ie1 ; equals C1 � Ie1 if Ie1 < C1 � I�1 ; and, equals I

�1 � Ie1 if C1 > I�1 : Hence, at t = 0,

conditional on (C0; �0), the expected investment distortion at t = 1 is:

Eh�(C1) C0; �0

i= E

hC1 � Ie1 Ie1 < C1 � I�1

i+ (I�1 � Ie1)(1� �(I�1 ;�1; �21)) (9)

Eh�(C1) C0; �0

i> 0 because �(C1) � 0 and is strictly positive whenever C1 > Ie1 : That

is, there is expected over-investment ex ante. Moreover, from (9) we can deduce:

13

Proposition 1 Conditional on (C0; �0); the expected over-investment by the manager, i.e.,

Eh�(C1) C0; �0

i, is positively related to the manager�s private bene�ts of control , but it

is negatively related to the manager�s equity-based incentive compensation �0 and the oppor-

tunity cost of capital R:

For shareholders, the agency costs incurred due to the separation between ownership and

control depend on the extent of the investment ine¢ ciency (relative to the value-maximizing

investment level). Proposition 1 veri�es the intuition that these agency costs are positively

associated with the manager�s private bene�ts of control but are attenuated if the manager

has greater equity ownership. Indeed, it is apparent from (7)-(8) that in the polar case of

= 0 there is no moral hazard problem and e¢ cient investment is incentive compatible.

Moreover, for a given the expected over-investment is decreasing in the manager�s equity

ownership 0 < �0 < 1.14

An examination of the manager�s optimal investment policy in (7) also clari�es how

external �nancing costs and the manager�s personal desire for empire-building in�uence the

�rm�s hedging policy ex ante. Other things being the same, a more entrenched manager is

more likely to be �nancing-constrained with respect to the desired level of investment. This

suggests a connection between entrenchment and the optimal demand for hedging that we

pursue next. In particular, we analyze the manager�s optimal hedging policy in terms of her

private bene�ts of control and equity-based compensation.

14If �0 = 1, then the manager owns the �rm and there is e¤ectively no agency problem due to theseparation of ownership and control.

14

3.2 Optimal Hedging

We can represent the manager�s indirect expected utility function at t = 1 in terms of C1 as

follows: V1(C1 ) = 0 when C1 � 0 and for C1 > 0;

V1(C1 ) =

8>><>>:��0(��2

pC1) + C1 0 < C1 < I�1

��0[��2

qI�1 + (C1 � I�1 )R] + I�1 I�1 � C1 <1

(10)

We interpret V1(C1 ) as the manager�s value function for the cash �ows at t = 1: Notice

that this value function incorporates the risk to the manager of losing private bene�ts of

control when C1 � 0: Moreover, even though the manager is risk-neutral in terms of her

intrinsic preferences towards risk, she is strictly risk-averse with respect to C1 over a part of

its domain because of the external �nancing constraints. In fact, it follows from (10) that:

Proposition 2 V1(C1 ); the manager�s value function for cash �ows C1 ; is strictly increasing

and concave for C1 > 0: Moreover, it is strictly concave for C1 < I�1 :

Recursing backwards to t = 0; the manager�s expected utility as a function of the hedging

intensity �0 given the cash �ows C0 is:

U0(�0 C0) /1Z0

V1(C1 )!(C1;�1; �21)dC1 (11)

Here, !(�;�1; �21) is the density function associated with �(�1; �21) (see (4)).15 The man-

ager thus chooses the optimal hedging intensity ��0; which is a solution to the constrained

15In (11), we use the fact that V1(C1 ) = 0 when C1 � 0:

15

maximization problem:

max0��0�1

U0(�0 C0); s.t., g(�0) � C0 (12)

Using (11) and under our assumptions on the hedging cost function, it follows that if C0

is su¢ ciently large, then the necessary and su¢ cient condition for the optimum hedging

intensity ��0 is:

Rg0(��0)

1Z0

�V1(C1 )

@!(C1;�1; �21)

@�1

�dC1 = (��2")

1Z0

�V1(C1 )

@!(C1;�1; �21)

@�21

�dC1 (13)

The left hand side of (13) represents the marginal cost of hedging because it reduces the

expected cash �ow through the opportunity costs of the increased hedging expenditures,

while the right hand side represents the gains due to reduced cash �ow uncertainty. Moreover,

because (from Proposition 2) V1(C1 ) is strictly concave on part of its domain and concave

on the remainder, the latter term is positive (using second order stochastic dominance).16

That is, raising the hedging intensity �0 (at the margin) has two con�icting e¤ects on the

manager�s welfare or expected utility: increasing �0 lowers the volatility of C1 ; which in turn

increases the manager�s expected utility � we call this the hedging e¤ect. But raising �0

also increases the cost of hedging and therefore lowers the expected C1 � we call this the

liquidity e¤ect. At the optimum ��0; the two e¤ects just balance each other.

We now use the optimality condition (13) to derive the e¤ects of some important para-

meters on the optimal hedging intensity ��0.

16Note that, given the properties of V1(C1 );

1Z�1

hV1(C1 )

@!(C1;�1;�21)

@�21

idC1 < 0 from second order stochastic

dominance

16

3.3 Properties of the Optimal Hedging Policy

3.3.1 Managerial Entrenchment and Optimal Hedging

As noted above, the optimal hedging choice by the manager trades o¤ the bene�cial hedging

e¤ect against the costly liquidity e¤ect. But from (10) we can deduce that more entrenched

managers have a higher expected marginal value for cash �ows. Let us de�ne the manager�s

expected marginal value for C1 by

EhMU0(�0 C0)

i=

1Z0

V 01(C1 )!(C1;�1; �

21)dC1 (14)

Proposition 3 EhMU0(�0 C0)

iis strictly increasing in the level of entrenchment .

It follows from Proposition 3 that the net expected bene�ts of hedging intensity, at the

margin, are higher for more entrenched managers. Therefore,

Theorem 1 The optimal hedging intensity, ��0; is positively related to the level of managerial

entrenchment.

3.3.2 Managerial Equity Ownership and Optimal Hedging

Varying the manager�s equity ownership, �0; has two potentially con�icting e¤ects on the

optimal hedging intensity ��0: As the manager�s share of the liquidating pro�ts increases, her

expected marginal value for future cash �ows (cf. (14)) also increases and this raises the

marginal bene�t of hedging. We call this the equity ownership e¤ect. Other things being

�xed, the equity ownership e¤ect indicates a positive relationship between �0 and ��0. But

raising �0 also reduces the managerial moral hazard for over-investment (see Proposition 1),

17

thereby lowering the marginal bene�t of hedging. We call this the incentive e¤ect. Other

things being the same, the incentive e¤ect indicates a negative relationship between �0 and

��0:

However, the envelope theorem suggests that, for small perturbations, the equity owner-

ship e¤ect is a �rst-order e¤ect while the incentive e¤ect is a second order e¤ect. This is

because the incentive e¤ect is composed of the response of the manager�s optimal investment

policy to the perturbation in equity ownership. Hence, we conclude:

Proposition 4 The optimal hedging intensity ��0 is positively related to the manager�s equity

ownership, �0.

3.3.3 Free Cash Flows and Optimal Hedging

Consistent with practice, in our model the demand for hedging is driven by the desire to

reduce the volatility of future cash �ows. However, by modeling the dynamic and serially

correlated nature of cash �ows explicitly, our framework highlights another issue, namely,

the e¤ects of the current or recent cash �ows on the optimal hedging policy.

Prima facie, this relationship is ambiguous because raising C0 should have two con�icting

e¤ects. There is a persistence e¤ect of cash �ows that suggests that a higher C0 raises

the expected cash �ow at t = 1; C1; because in the absence of hedging the unconditional

expectation of C1is E[C1] = C0(�+R): Thus, other things being the same, a higher current

cash �ow should induce a lower demand for costly hedging and conversely a lower current

cash �ow should induce a higher demand for hedging. But, on the other hand, there may

typically also be a wealth e¤ect that goes in the opposite direction: with higher cash �ows

18

in hand, the (�nancially constrained) �rm can incur higher hedging costs.

However, if the hedging cost function is su¢ ciently convex as the hedging intensity ap-

proaches 1 and if the initial liquidity C0 is not too low, then the �rm will not be �nancially

constrained in implementing the optimal hedging policy. In this situation, only the persis-

tence e¤ect will be operational. Thus,

Proposition 5 There exists some �C0 > 0 such that the optimal hedging intensity ��0 is

negatively related to the available net cash �ows C0 for all C0 > �C0.

Proposition 5 has signi�cant implications for the empirical test design for examining the

relationship between the demand for hedging and cash �ows. Axiomatically, there should

be a positive correlation between the current hedging intensity and future cash �ows, other

things held �xed. However, Proposition 5 implies a negative correlation between hedging

intensity and recent cash �ow realizations. Thus, the timing of the hedging positions and

the cash �ow realizations is important in empirical investigations, and our empirical analysis

below addresses this issue carefully.

3.3.4 Cash Flow Risk and Optimal Hedging

Finally, in the literature the link between cash �ow risk and hedging is typically derived

indirectly, for example, by linking cash �ow risk to the default probability or transactions

costs in external �nancing. However, in our model there is a direct e¤ect:

Proposition 6 The optimal hedging intensity ��0 is positively related to the intrinsic cash

�ow risk �2":

19

We now empirically test the predictions derived above. In the following sections, we

describe the data, specify our empirical test design, and present the results.

4 Data

We use a hand-collected dataset from the oil and gas exploration and production industry

for our empirical analysis.

4.1 The Oil and Gas Industry

Commodity producing industries with signi�cant volatility of the underlying commodity

price are natural candidates for studying the determinants of hedging intensity. The oil and

gas industry is an excellent example. Figure 1 indicates that (during 2000-2002) crude oil

and natural gas were almost three times as volatile as gold, and almost twice as volatile as

the S&P 500 index. Thus, understanding the determinants of hedging in this industry is an

important endeavor.

Independent oil and gas exploration and production �rms (E&P �rms) explore and pro-

duce crude oil and natural gas and sell it on an �as is� basis at the wellhead. They sell

their output as it is extracted from the reservoir with no other downstream services so that

their earnings are directly governed by the commodity prices. Hence, their pro�tability,

performance, and default probabilities are very sensitive to oil and gas price volatility. In

contrast to the E&P �rms, the integrated companies encompass many layers of the indus-

try value-chain. Consequently, their pro�tability is not as closely tied to commodity price

�uctuations as is the case with the E&P �rms. In addition, integrated �rms are involved

20

in both upstream and downstream businesses and they are often naturally hedged while

E&P companies are not.17 Moreover, by its very nature, the exploration and production

business is highly capital intensive, positive-NPV investment opportunities are lumpy and

arise sometimes unexpectedly; capital shortfalls in these periods can have long-term costs.

Growth opportunities for integrated companies, on the other hand, arise from many di¤er-

ent and relatively uncorrelated sources, and these �rms, being larger, have better access to

capital and money markets than the E&P �rms do. For all these reasons, the independent

E&P �rms have a greater intrinsic demand for hedging compared to the integrated �rms.

4.2 Sample Construction

Because complete hedging data were not available prior to 1996, our sample selection begins

with all the E&P �rms given in Hoovers Online (www.hoovers.com) as of January 1996.

Hedging activities in the oil and gas industry have a relatively short horizon; we therefore

obtain data on derivative positions on a quarterly basis, the smallest interval for which data

are publicly available. We obtained the data on �rm-speci�c characteristics from Compustat,

Execucomp, CRSP, and Capital IQ. The data on �rms�hedged positions were hand-collected

from 10-K and 10-Q forms �led by listed companies with the SEC.

We required �rms to be represented on COMPUSTAT and have at least 10 years of

hedging data. We eliminated �rms that went out of business, or were merged or acquired,

or had missing observations, or had a mismatch between their �scal years and the calendar

quarters. Our �nal sample comprises of 41 E&P companies, each with complete hedging

17For example, the price of crude oil, which is an input in the re�ning process, is highly positively correlatedwith the prices of cracked products, such as heating oil and unleaded gasoline.

21

data for a minimum of 40 quarters and a maximum of 52 quarters; this sample yields a

dynamic panel data of 2087 �rm-quarters for a sample period from the beginning of 1996

through the end of 2008.18

5 Empirical Test Design

In this section, we describe our measure of hedging intensity and the proxies for managerial

entrenchment. We also specify our control variables and the econometric methodology.

5.1 Measuring Hedging Intensity

The recent literature measures hedging intensity by the delta percentage (Tufano, 1996)

that incorporates the e¤ects of local (commodity) price movements on the value of a hedge

portfolio. However, our sample �rms typically produce and hedge in both oil and natural

gas derivative markets. Therefore, we compute a weighted average portfolio of the deltas of

all the positions in both markets, the weights being the �rm�s relative proportion of oil and

natural gas production. We call this the weighted average delta percentage (WADP).

It is generally infeasible to determine the exact date on which the company took the

hedge position. We, therefore, adopt the convention that �rms open the hedge one quarter

ahead of time. For example, the hedge position in the �rst quarter of 1996 was opened on

the �rst business day of October 1995 etc. The example below, based on the actual hedging

data from a sample �rm, clari�es our calculation of the WADP.

18The data on management compensation and corporate governance is annual rather than quarterly be-cause EXECUCOMP and governance related databases do not typically provide quarterly data.

22

5.1.1 Calculating the WADP: An Example

Figure 2 displays the derivative positions entered by the Clayton Williams Energy Company

Inc. (Ticker: CWEI) in the third quarter of 2003 (3Q03), as reported in the 10-Q form �led

by the company for this quarter. The �rm entered into a gas swap contract for 4Q03 for 1720

MMcf. Since CWEI executes a producer swap, which pays o¤ $1 for $1 when the market

drops below a swap contract price of $3.80 per MMBtu, the delta for the swap contract is

-1.0. The product of the delta and contract volume for this swap contract, therefore, equals

to -1720, so that the e¤ective hedged volume from the swap contract is 1720 MMcf.

Furthermore, CWEI purchased a producer collar, which is a combination of buying a

put option and selling a call option simultaneously, for 4Q03. This creates a short �nancial

position, providing a hedge for the company, which has a natural long physical position. If

in 4Q03 the market drops below the put exercise price of $4.50/MMBtu, then CWEI will

exercise its put option to receive a �oor price and when the market price rises above the call

strike price of $7.04/MMBtu, the call option counter-party will exercise the call. The net

e¤ect of this hedge is a �xed price range of $4.50/MMBtu to $7.04/MMBtu during 4Q03

for CWEI. The deltas of the long put and the short call are -0.059 and -0.348, respectively.

Multiplying the covered production volume by the delta of the instrument yields the amount

of natural gas e¤ectively hedged during 4Q03 via the collar transaction: 939 MMcf.

This example, which is based on real data, emphasizes the importance of the use of the

portfolio delta to derive the e¤ectively hedged volume in any given quarter. Here, CWEI

hedged 2310 MMcf of natural gas production during 4Q03 via a collar position (taken in

3Q03), of which only 939 was e¤ectively hedged. Combined with the e¤ective natural gas

23

production volume hedged by the swap contract (i.e., 1720 MMcf), the total natural gas

volume of 4Q03 production e¤ectively hedged (in 3Q03) was 2659 MMcf (as no other natural

gas derivative position was adopted during 3Q03). As for crude oil, CWEI entered into a

swap contract for 4Q03 of 80 MBbl (million Barrels). Since the delta of this producer swap

contract is -1.0, the total oil volume of 4Q03 production is also 80 MMbl because no other

derivative positions in oil were taken in 3Q03.

Once we have the e¤ective hedged volume of oil and gas, we can calculate the WADP. In

4Q03, the total natural gas and oil production of CWEI were 4952 MMcf and 352 MBbl, re-

spectively. Under the assumption that the observed production in 4Q03 equals the expected

production (in 3Q03), CWEI e¤ectively hedged 31.92% of the expected gas production and

52.74% of the expected oil production.19 But in 4Q03, 70.1% of CWEI�s total production

is gas and the rest (29.9%) is crude oil, assuming the industry standard that 1 Bbl of oil

has the same heat content as 6 Mcf of gas. The WADP for 3Q03 derived from using the

percentage of oil and gas production as weights is therefore 44.44%. This is also our measure

of the hedging intensity chosen in this quarter.

5.2 Sample WADP: Frequency Distribution and Time Trends

Figure 3 provides a frequency (quintal) distribution of the WADPs in our sample of 2087

�rm-quarters. In about 20% of the sample, the WADP was zero, but in the top quintal the

19The assumption that the realized production on average equals the expected production is very reason-able. Over time, E&P �rms have developed models that very precisely predict the production rates of oiland gas over time; these schedules are �seasoned� for expected weather conditions. Typically, deviationsfrom the production schedules occur for truly unanticipated events such as major accidents.

24

WADP exceeded 40%. The mean WADP is 21% with a median of 13%.20

During our sample period of 1996-2008, oil and natural gas prices experienced a high

degree of variation. Panel A of Figure 4, which plots the monthly price levels of these two

commodities, shows a sharp run-up in commodity prices from the beginning of 2006 through

the third quarter of 2008. Crude oil prices in 2008 dollars almost doubled between mid-2006

and August 2008, when they peaked at $140 Bbl and then crashed and fell by over two-thirds

in less than two months. Panel B of Figure 4, which plots the six-month trailing volatility

(normalized by the mean), indicates that there was a run-up in volatility as well from the

middle of 2006 through 2008.

Figure 5 plots the mean WADPs of sample �rms for each quarter in the sample period.

The mean WADP appears to follow a mean reverting process with an upward drift between

1996 and 2006. However, there is a steep rise in the mean WADPs from mid-2006 onwards,

coinciding with the sharp increase in commodity price levels and volatility (cf. Figure 4).

The deltas maintain an upward trend and reach a local maximum in 2001, following a similar

pattern in crude prices. This is consistent with the theory, since deltas rise as options get

deeper in-the-money. Conversely, the deltas fall steeply following the collapse in crude prices

in the fourth quarter of 2001. But crude prices started a sharp upward swing in the second

half of 2002, culminating in a local maximum in 2003. Thereafter, crude prices declined

brie�y, and follow a mean reverting pattern till 2005, when they start to rise again. The

deltas follow a pattern similar to the underlying commodity prices during our sample period.

20By comparison, in Tufano�s (1996) sample of 48 gold mining �rms during 1992-1994 about 15% of the�rms had zero deltas, and the mean delta was 25.6%.

25

5.3 Explanatory Variables

Table 1 de�nes the variables we use in our empirical analysis. WADP is the dependent vari-

able and the rest are the explanatory variables that we now brie�y motivate and summarize.

5.3.1 Baseline Variables

By construction, our theoretical framework is stylized to allow us to focus on the e¤ects

of entrenchment on managerial hedging. In particular, we exclude the role of the following

variables on corporate hedging:

Firm Size: The association between �rm size and hedging properly relates to the asset

size of the �rm. Hence, we use the total assets of the �rm (SIZE). However, for robustness,

we also use only the �xed assets of the �rm.

Leverage: In the usual way, we measure leverage through the debt-to-capital ratio, i.e.,

the ratio of the total debt to assets (LEVERAGE).

Marginal Tax Rate: We estimate the marginal tax rate from the quarterly �nancial

statements using the simulation estimates of Graham and Mills (2008).21

We refer to these as the baseline variables for expositional convenience because they have

been analyzed in the existing literature on corporate hedging.22 Testing the e¤ects of these

baseline variables on our unique dynamic panel data is of independent interest.

21Graham and Mills (2008) show that this method closely approximates the results from simulations basedon actual tax data.22See Fenn, Post and Sharpe (1997) and Stulz (2003). We do not include the role of managerial equity

ownership here because we will consider this variable from the perspective of our model below.

26

5.3.2 Free Cash Flows and Risk

We use the standard de�nition of free cash �ows (FCSHFLW ). To control for any size e¤ects,

we normalize the free cash �ows with the total assets of the �rm (NFCSHFLW ). We examine

the serial correlation in free cash �ows (as assumed in our model) by estimating an AR(4)

model. Untabulated results indicate signi�cant serial correlation with the coe¢ cients of

quarters t� 1; t� 2; and t� 4 being positive and highly signi�cant.

In our theoretical model, the manager chooses the hedging intensity given the free cash

�ows (C0). In light of the serial correlation in the normalized free cash �ows, we use the

average of the normalized free cash �ows over the previous 4 quarters (LAGNFCSHFLW ).

Unlevered Beta: Financial theory posits that the �rm�s intrinsic cash �ow risk (i.e., the

risk emanating from its business) should be measured by the unlevered (or asset) beta. We

use the unlevered beta for each quarter, which is derived from the equity beta in the standard

way (see Table 1) (UNLEVBETA).

5.3.3 Managerial Entrenchment

There is a developing literature that examines empirically the e¤ects of managerial entrench-

ment on various aspects of corporate �nancial policies (Berger et al, 1997; Hu and Kumar,

2004; Chava et al., 2010). We follow this literature in our choice of the proxies for managerial

entrenchment. The principal entrenchment-related variables we use are:

Long CEO Tenure: CEO tenure is positively associated with entrenchment. Organiza-

tional theorists argue that tenure and CEO�s internal power are positively related (Finkel-

stein and Hambrick, 1989) and greater power makes the CEO less vulnerable to disciplining

27

by outsiders. Moreover, Murphy (1986) argues that CEOs nearing retirement have greater

moral hazard because they have a shorter career horizon and the disciplining power of repu-

tation is relatively weak. We measure CEO tenure as the number of years the current CEO

has been in that position (CEOTENURE).

CEO Change: The positive association between CEO tenure and the level of entrench-

ment implies, in particular, that a CEO�s power and entrenchment should be at a relative

minimum when she or he is transitioning into the position. We, therefore, use a dummy vari-

able to identify the year when a new CEO assumes the post; i.e., the value of this variable

is 1 in the year of a CEO change, and zero otherwise (CEOCHNG).

Board Independence: A high proportion of outside directors is often considered to enhance

the quality of corporate governance (e.g., Fama and Jensen,1983). Indeed, in the last decade

regulatory bodies (e.g., the SEC) and stock exchanges (e.g., NYSE) have instituted reforms

to encourage board independence.23 Consistent with the existing literature, we de�ne outside

directors as those that are not direct family members or relatives of the CEO, and are not

current or former employees. Rather than using arbitrary thresholds, we use the percentage

of the board that is composed of independent directors (BOARDINDEP) during the year.

Duality: Jensen (1993) and Boyd (1994) argue that CEO �duality�(where the CEO is

also the chairman of the board) diminishes the independence and e¤ectiveness of the board

and increases CEO power. We use a dummy variable to identify whether a CEO is also a

chairman of the board (DUALITY ).

23We note, however, that the literature �nds an ambiguous relationship between board independence and�rm performance both theoretically (e.g., Kumar and Sivaramakrishnan, 2008) and empirically (e.g., Bhagatand Black, 2003).

28

Interlocking Relationships: CEO entrenchment and power also manifests itself through

a board structure that may be especially pliant to CEO interests. In particular, the SEC

requires disclosure in the �Compensation Committee Interlocks and Insider Participation�

section of the proxy for an o¢ cer of the �rm when either of the following situations are

true: the o¢ cer serves on the compensation committee of the board that makes his or her

compensation decisions or the o¢ cer serves on the board (and possibly compensation com-

mittee) of another company that has an executive o¢ cer serving on the board (and possibly)

compensation committee of the o¢ cer�s company. We use a dummy variable to identify if

the CEO made a disclosure of interlocking relationships during the year (INTERLOCK ).

Outside Blockholder Ownership: Large external shareholders reduce the agency risk from

entrenchment, because such shareholders have greater incentives to monitor the manage-

ment (Shleifer and Vishny, 1986). We use the percentage of �rm equity held by outside

blockholders during the year (EXTBLCKPCT ).24

CEO Equity Ownership: We use the value of the restricted stock held by the CEO

(CEOEQT ) during the year.25

Finally, PRICEVOLAT is an indicator variable to identify the high price volatility phase

of mid-2006-mid-2008.

In Table 2, we present summary statistics for the covariates de�ned above.

24These data are available for 1996-2001 only (see Dlugosz et al., 2006).25Our model uses the manager�s equity ownership in the �rm, which is most closely approximated by the

(value of) the restricted stock holdings. We note that executive stock options, which we do not model, mayhave a very di¤erent e¤ect on management�s hedging demand compared to the e¤ects of equity ownership(Smith and Stulz, 1985; Tufano, 1996).

29

5.4 Empirical Hypotheses

We �rst specify a �baseline hypothesis�that summarizes the refutable predictions from the

literature on the e¤ects of the baseline variables on the demand for hedging.

Hypothesis 1 The WADP is positively related to �rms�(i) leverage, (ii) earnings risk (iii)

total assets, and (iv) the marginal income tax rate and (iv) deferred taxes.

We next express the empirical implications of our model in terms of the empirical mea-

sures and proxies that we have speci�ed above. Propositions 5 and 6 imply that:

Hypothesis 2 The WADP is negatively related to the lagged normalized free cash �ows, but

positively related to the unlevered beta.

Next, Proposition 4 implies that:

Hypothesis 3 The WADP is positively related to the value of the CEO�s equity ownership.

Our main hypothesis, based on Theorem 1 � regarding the positive relationship between

hedging intensity and the level of managerial entrenchment � is:

Hypothesis 4 The WADP is positively related to (i) the length of the CEO�s tenure, (ii)

duality, (iii) and the presence of interlocking relationships. However, the WADP is negatively

related to (iv) incidence of CEO change and (v) board independence.

5.5 Econometric Methodology

Our cross-sectional time-series data constitutes a (strongly balanced) panel dataset, and we

use dynamic panel linear regressions (see Baltagi, 1995). For the reason mentioned above,

30

we use �rm �xed e¤ects that also allow for the in�uence of omitted (but time-invariant)

�rm-speci�c variables. We also allow for the possibility that variations in the economic en-

vironment over time may commonly impact the hedging intensity choice of all the �rms.

Subscripting �rms by i and quarters by t, our econometric model is:

WADPit = Xit�+ �t + �i + "it (15)

where Xit is a matrix of explanatory variables (where the �rst entry in each row is a 1), �

is a vector of unknown parameters to be estimated, �t is a common time-factor for t; �i is

�rm-speci�c �xed (or time-invariant) factor, and "it is a �rm- and time-speci�c error term

(see Arellano and Honoré, 2001). We check the robustness of our results using the Arellano

and Bond (1991) approach for instrumental variables estimation employing the generalized

method of moments (GMM), which is robust to heteroscedasticity and correlation in the

error terms (see Almeida et al., 2010).

6 Results

6.1 Baseline Regressions: Tests of Hypothesis 1

Model 3.1 in Table 3 presents the results of using as explanatory variables only the baseline

variables (cf. Hypothesis 1). In that sense, this regression is similar to that used in the

literature. We �nd that �rm leverage has a highly signi�cant and positive e¤ect on hedg-

ing intensity, a result that is consistent with the theories that emphasize the reduction of

�nancial contracting costs as a motivation for hedging (e.g., Smith and Stulz, 1985; Froot

31

et al., 1993). However, previous empirical studies have typically been unable to �nd a sig-

ni�cant association between leverage and hedging intensity (e.g., Tufano, 1996) or hedging

activity (e.g., Nance et al., 1993). One exception is Haushalter (2000) who �nds a signi�cant

association in his sample of oil and gas industry �rms from 1992-1994.

We also �nd that the unlevered beta, as a measure of intrinsic cash �ow risk, is also a

highly signi�cant and positive determinant of hedging intensity. Again, this �nding is con-

sistent with theories of corporate risk management that emphasize the reduction of �nancial

contracting costs as a motive for hedging. But most previous empirical studies have either

not examined the role of the unlevered beta or have been unable to �nd a signi�cant e¤ect.

The signi�cant e¤ect of this theoretically justi�able proxy for intrinsic cash �ow risk on

hedging intensity (in this baseline regression) is therefore of independent interest. Finally,

results from model 3.1 indicate that the progressivity of corporate income tax has a signi�-

cant impact on hedging intensity, which is consistent with the arguments of Smith and Stulz

(1985) and Graham and Smith (1999). However, �rms size, which is arguably related to

the ability to hedge (Haushalter, 2000) does not appear to play a signi�cant role in hedging

intensity, once we control for the other factors.

In sum, the results of model 3.1, based on detailed information on the derivative positions

of E&P �rms in the riskiest part of the oil and gas industry over a relatively long period,

indicate substantial support for the view that �rms hedge to reduce �nancial contracting

costs. And except for the �rm size, the results support Hypothesis 1.

In the 12 years covered by our sample, there is substantial variation in the underlying

commodity prices; in particular, the mid-2006-mid-2008 period saw exceptionally high price

32

volatility (cf. Figure 4). In models 3.2 and 3.3, we examine whether the high volatility phase

had an independent e¤ect on �rms�choice of hedging intensity, when we do not or do control

for intrinsic cash �ow risk, respectively. While the results in model 3.2 suggest that �rms

signi�cantly increased hedging intensity, the results in model 3.3 indicate otherwise, once we

control for the cash �ow risk as measured by the unlevered beta. We note, however, that

the unlevered beta is time-varying in our analysis. The data indeed show an upward trend

in the computed unlevered betas during the mid-2006-mid-2008 period.26 Taken together,

models 3.2 and 3.3 suggest that the hedging intensity was higher during the high commodity

price volatility phase, if we were to hold �xed the leverage and the unlevered beta.

6.2 The Role of (Available) Free Cash Flows

Table 4 examines the e¤ects of free cash �ow availability on hedging intensity (cf. Hypothesis

2). In model 4.1, we �nd that, consistent with the Hypothesis, lagged (normalized) free

cash �ows have a negative in�uence on hedging intensity, and this e¤ect is signi�cant at

conventional levels.

In model 4.2, we examine the role of free cash �ow when controlling for the baseline

explanatory variables (that we use in Table 3). The e¤ect of lagged free cash �ows remains

negative and, interestingly, their in�uence gains in statistical signi�cance (with a p-value <

0.01) once we control for the baseline covariates. Comparing model 3.1 with model 4.2, we

�nd that leverage, unlevered beta, and the marginal tax rate continue to have a signi�cantly

26The upward trend in the unlevered beta is possibly due to the fact that the equity betas of the sample�rms rose sharply during this time of high commodity price levels and volatility, while the marginal tax ratesand debt-to-equity ratios did not change as fast.

33

positive e¤ect on hedging intensity. We conclude from the results of models 4.1 and 4.2

that free cash �ow availability is a signi�cant determinant of hedging intensity and, in our

sample, the e¤ect is consistent with a theoretical model where managerial moral hazard for

investment is a major driver for corporate risk management. Finally, as in Table 3, model

4.3 indicates that the exceptionally high commodity price volatility did not have a signi�cant

independent e¤ect on the extent of risk management when we control for the other factors.

6.3 The Role of Managerial Entrenchment

In Tables 5 and 6, we present the main empirical results of our study. Table 5 examines

only the e¤ects of the managerial entrenchment related factors whereas in Table 6 we also

include the baseline variables.

6.3.1 The E¤ects of Managerial Entrenchment

In Table 5, model 5.1 examines the e¤ect of CEO equity ownership; model 5.2 studies the

e¤ects of the length of CEO tenure and CEO change; and model 5.3 adds variables related

to the structure of the board. Finally, model 5.4 includes all the above variables as well

as the ownership of outside blockholders. In this way, we can discern the relative e¤ects of

variables grouped in terms of the various aspects of CEO entrenchment and power.

Consistent with Hypothesis 3, model 5.1 indicates that hedging intensity is signi�cantly

positively related to the value of the CEO�s equity ownership. Taken by itself, this result

complements Tufano (1996), although the motivation and role of equity incentives in our

model is very di¤erent from the managerial risk-aversion argument given in the literature.

34

Next, the results of model 5.2 indicate that CEO tenure has a highly signi�cant e¤ect on

hedging intensity, along the directions speci�ed in Hypothesis 4. The e¤ect of the length

of CEO tenure on hedging intensity is signi�cantly positive, while hedging intensity drops

appreciably when there is change in the CEO; the latter e¤ect is especially signi�cant.

Model 5.3 includes the major variables related to the strength (or lack thereof) of CEO

power and internal governance of top-management. We �nd that, ceteris paribus, board

independence has a signi�cantly negative impact on the hedging intensity while �rms choose

higher hedging intensity when the CEO is involved in interlocking relationships.27 Recent

literature has theoretically and empirically questioned the relationship between board inde-

pendence and �rm performance (Bhagat and Black, 2001; Kumar and Sivaramakrishnan,

2008). However, even this part of the literature acknowledges that while independent di-

rectors are likely monitor the CEO intensively, this may not translate into superior �rm

performance. In our model, any factor that raises the CEO�s costs of exploiting entrench-

ment for personal agendas should be negatively related to hedging intensity, other things

held �xed. The results of model 5.3 appear to con�rm this prediction. We note also that

(in model 5.3), the e¤ects of CEO equity ownership and CEO change on hedging intensity

remain highly signi�cant, while the in�uence of the length of CEO tenure drops somewhat

in signi�cance when we control for the board related variables.

In model 5.4, we examine the e¤ects of the strength of external governance.28 Consistent

27All the CEOs in our Execucomp sub-sample were also chairs of the board for each quarter. Hence, theDUALITY variable was dropped.28As noted above, data on the ownership of external blockholders is only available for 1996-2001, resulting

in signi�cantly lower number of observations. We drop the CEOEQUITY variable for this regression becausethere were relatively few �rm-quarters with data on both CEO equity ownership and external blockholderownership.

35

with Hypothesis 4, the strength of external governance, measured by the equity ownership

of external blockholders, has a signi�cantly negative in�uence on hedging intensity, other

things held �xed. Moreover, controlling for external governance strength appears to modify

the e¤ects of the internal governance and CEO power related variables. While the e¤ects of

board independence and CEO change remain highly signi�cant, the e¤ects of interlocking

relationships and the length of CEO tenure drop in signi�cance (with the latter no longer

being signi�cant). These results are empirical con�rmations of the importance of external

governance by large shareholders (Shleifer and Vishny, 1986; Cyert et al., 2002). In partic-

ular, strong external governance appears to reduce signi�cantly the ability of long-serving

CEOs to choose risk-management policies for their self-interest.

6.3.2 The E¤ects of Managerial Entrenchment and Baseline Variables

In Table 6, we examine the e¤ects of the managerial entrenchment related variables and the

baseline variables simultaneously.29 Model 6.1 includes the variables used in Hypothesis 1 and

Hypothesis 4. We �nd that CEO change, board independence, and interlocking relationships

of the CEO remain highly signi�cant. Amongst the baseline variables, leverage remains the

most highly signi�cant and tax progressivity is also signi�cant. Interestingly, the unlevered

beta is no longer signi�cant. This pattern is repeated in model 6.2, where we also include

the e¤ects of CEO equity ownership, which remains positive and signi�cant (consistent with

Hypothesis 3). One interpretation of the change in the role of the unlevered beta is that

the e¤ect of cash �ow risk on corporate hedging is related to managerial entrenchment;

29We do not include external blockholder ownership because doing so restricts the testing sample to 2001.

36

this risk matters more to highly entrenched managers, and once we control for the level

of entrenchment, cash �ow risk no longer has a signi�cant independent e¤ect. A similar

interpretation appears applicable to the role of cash �ow availability, as seen in model 6.3.

Overall, we �nd strong empirical support for the main prediction of our theoretical analy-

sis, namely, that more entrenched managers will have a greater demand for hedging, other

things held �xed.

6.4 Robustness

As a check of the robustness of our results, we apply the Arellano and Bond (1991) instru-

mental variables estimator using GMM, i.e., the ABB-GMM estimator of Almeida et al.

(2010). Speci�cally, we use lagged values of the WADP (i.e., WADPit�1) as an instrument

for the �rst-di¤erenced equation for �rm i in period t:

Table 7 demonstrates the results (with respect to the most comprehensive speci�cations).

These results are very similar to those shown in Table 6. In particular, the e¤ects of the

managerial entrenchment related variables remain highly signi�cant. The baseline variable

leverage remains the most signi�cant in�uence on hedging intensity, while the marginal

tax rate loses signi�cance relative to Table 6. Following Arellano and Bond (1991), we also

obtained the AB-GMM estimates using all the orthogonality conditions and with all available

lags of WADP as instrumental variables; the results were similar.

37

7 Summary and Conclusions

Hedging and risk management by corporations continue to generate intense interest. While

the literature provides a variety of theoretical reasons why hedging may raise �rm value,

the empirical literature generates ambiguous results on the relationship between hedging

and value. Our analysis departs from the value-maximization motivations for hedging and

focuses on the demand for hedging by self-interested entrenched managers. Our theoreti-

cal model analyzes the optimal hedging demand of entrenched managers with moral hazard

for overinvestment when there are external �nancing constraints. We �nd that the avoid-

ance of �nancial distress costs and managerial risk aversion are not necessary for hedging

and risk management by corporations. Moreover, we present novel refutable predictions on

the determinants of �rms�hedging intensity, in particular, the positive role of managerial

entrenchment and the negative role of available cash �ows.

We test our model with a unique hand-collected quarterly dataset on the hedged positions

of a sample of independent oil and gas �rms during 1996-2008. We �nd strong empirical

support for the main prediction of our theoretical model: More entrenched managers have a

greater demand for hedging, when controlling for other factors emphasized in the literature.

Indeed, for our sample, entrenchment related factors are most (statistically) signi�cantly as-

sociated with the demand for hedging by companies. We also con�rm that hedging intensity

is negatively related to available cash �ows and positively related to management�s equity

ownership. Extending the tests of our model to other industries is an important topic for

future research.

38

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43

Appendix

Proof of Proposition 1: We have (cf. (9)):

Eh�(C1) C0; �0

i=

I�1ZIe1

(C1 � Ie1)!(C1;�1; �21)dC1 + (I

�1 � Ie1)

1ZI�1

!(C1;�1; �21)dC1 (16)

Applying the generalized Fundamental Theorem of Calculus on (16), we compute

@Eh�(C1) C0; �0

i@

=@I�1@ (I�1 � Ie1)[!(I

�1 ;�1; �

21)� !(I�1 ;�1; �

21)] +

@I�1@

h1� �(I�1 ;�1; �21)

i=

@I�1@

h1� �(I�1 ;�1; �21)

i> 0

(because @I�1@

> 0 since I�1 �(��2)

2

4(R� ��0

)2). Similarly,

@E

"�(C1) C0;�0

#@�0

< 0 because @I�1@�0

< 0 and

@E

"�(C1) C0;�0

#@R

< 0 because @I�1@R

< 0: Q.E.D.

Proof of Proposition 2: It is apparent from (10) that V1(C1 ) is strictly increasing over

0 < C1 < 1, with a point of non-di¤erentiability at C1 = I�1 : Furthermore, the concavity

characterization given in the statement of the proposition are straightforwardly veri�ed by

computing the second derivative with respect to C1 . Q.E.D.

Proof of Proposition 3: Using (10), direct computation yields

V 01(C1 ) =

8>><>>:��0��22pC1+ 0 < C1 < I�1

��0R I�1 � C1 <1(17)

44

An application of the Fundamental Theorem of Calculus again yields that

1Z0

V 01(C1 )!(C1;�1; �

21)dC1 is

strictly increasing in : Q.E.D.

Proof of Theorem 1: We rewrite the optimality condition (13) in the form:

� =

1Z0

V1(C1 )

�(��2")

@!(C1;�1; �21)

@�21� Rg0(��0)

@!(C1;�1; �21)

@�1

�dC1 = 0 (18)

In the usual way, we apply the implicit function theorem to (18) to conclude that, if ��0 is a

relative maximum, then for any parameter x; sign(@��0

@x) = sign(@�

@x).

Now, explicating the dependence of C1 on �0, i.e., C1 = c1 + (c0 � g(�0))R (from (3))

and noting that

d!(C1;�1; �21)

d�0= (��2")

@!(C1;�1; �21)

@�21� Rg0(�0)

@!(C1;�1; �21)

@�1(19)

we can apply the integration by parts and re-write (18) as,

� = �1Z0

V 01(C1 )(

@C1@�0

)!(C1;�1; �21)dC1 (20)

= Rg0(��0)

1Z0

V 01(C1 )!(C1;�1; �

21)dC1 (21)

where, in (20) we have substituted @C1@�0

= �Rg0(��0) to obtain (21). Hence, it follows from

Proposition 3 that @�@

> 0 and therefore that @��0@

> 0: Q.E.D.

45

Proof of Proposition 4: Using (21), we note that

@�

@�0= Rg0(��0)

@

24 1Z0

V 01(C1 )!(C1;�1; �

21)dC1

35@�0

(22)

But using (17), we compute,

�(C1) �@V 0

1(C1 )

@�0=

8>><>>:���22pC1

0 < C1 < I�1

�R I�1 � C1 <1(23)

(23) implies that �(C1) > 0 for 0 < C1 < 1: It follows then from (22) and the implicit

function theorem that @��0@�0

> 0 Q.E.D.

Proof of Proposition 4: Note that C0 impacts the distribution of C1 only through the

mean �1 = C0(�+R)� g(�0)R, so that@�1@C0

= �+R: Again, using (21), we get

@�

@C0= Rg0(��0)

@

24 1Z0

V 01(C1 )!(C1;�1; �

21)dC1

35@C0

(24)

But from Proposition 2, V 01(C1 ) is strictly decreasing on 0 < C1 < I�1 and it is non-increasing

on I�1 � C1 < 1: And because @�1@C0

= � + R > 0, it follows from (24) and �rst order

stochastic dominance that @�@C0

< 0: Hence, @��0

@C0< 0 if there is an interior solution for ��0, i.e.,

g(��0) < C0. Q.E.D.

Proof of Proposition 5: (17) implies that V 01(C1 ) is convex in C1 for 0 < C1 < I�1 . (21)

and second order stochastic dominance then imply that @�@�2"

> 0; so that @��0@�2"

> 0: Q.E.D.

46

Table 1

Variable Definitions

This table presents the definitions of the variables used in our empirical analysis. We hand collect

quarterly data on hedging positions from 10-Q and 10-K reports, and also obtain quarterly data from

CRSP, COMPUSTAT, and CAPITALIQ. We obtain annual data on CEO compensation and board

of directors from EXECUCOMP. The data on blockholder holdings is available for 1996-2001from

Dlugosz et al. (2006) through the Wharton Research Database. The marginal tax rates are computed

according to the simulation model of Graham and Mills (2008). (We identify quarterly data by “Q”

and annual data by “A”). Our sample period is from 1996:Q1 through 2008:Q4.

Variable Definition

WADP Weighted Average of Natural Gas and Oil Delta Percentage (Q)

SIZE Total assets in $ million (Q)

LEVERAGE Ratio of total debt to total assets (Q)

MTAXRATE Estimated Marginal Tax Rate (Q)

FCSHFLW Free cash flows in $ million (Q)

= Operating cash flows – capital expenditures –

change in net working capital

NFCSHFLW Ratio of free cash flows to total assets (Q)

LAGNFCSHFLW Average of the FCSHFLAS over the previous four quarters (Q)

UNLVBETA Unlevered Beta (Q)

= Equity/((1 – mtaxrate) . (total debt/market value of equity))

CEOTENURE Number of years the current CEO has been in the position (A)

CEOCHNG Identifies if the CEO changed during the year (A)

BOARDINDEP Percentage of directors on the board that are independent (A)

DUALITY Identifies if CEO is also Chair of the board (A)

INTERLOCK Identifies if CEO is involved in interlocking relationships (A)

CEOEQT Value ($ million) of the CEO’s restricted stock holdings (A)

EXTBLCKPCT Percentage of firm’s equity held by external blockholders (A)

PRICEVOLAT Identifies if high price volatility period (2006-2008) (A)

TABLE 2

Summary Statistics

This table presents summary statistics of the variable used in our empirical analysis. These variables are

defined in Table 1.

Mean Median Std. dev. Min. Max.

WADP

0.210

0.130

0.276

-0.025

4.126

SIZE

3249.89

564.535

7400.52

10.00

60176.00

LEVERAGE

0.264

0.255

0.179

0.000

1.084

MTAXRATE

0.292

0.34

0.114

0.00

0.34

FCSHFLW 47.130 -0.51 24.75 -835.342 14790.289

NFCSHFLW -0.022 0.002 0.160 -0.99 1.497

UNLVBETA

0.330

0.257

0.511

-6.881

6.463

CEOTENURE

8.875

7.000

7.716

0

39

BOARDINDEP

0.769

0.800

0.156

0.167

1.000

CEOEQT

26.376

5.912

5.74

0.000

443.311

EXTBLCKPCT 20.901 20.7 13.877 0.000 55.4

Table 3

Determinants of Hedging Intensity: Baseline Regressions

This table presents an analysis of the determinants of firms’ hedging intensity based on factors identified in the

literature (see Hypothesis 1). We report the results of estimating Equation (14). The independent variables (except

for the interaction terms) are specified in Hypothesis 1. We use a hand-collected dataset on the quarterly positions in

oil and natural gas derivative markets of 41 public (but non-integrated) firms in the exploration and production

segment of the oil and gas industry from 1996 through 2008. The t-statistics are reported in the parentheses.

Asterisks indicate significance at 1% (***), 5% (**) and 10% (*) levels, respectively. We also provide the expected

signs for the coefficients based on Hypothesis 1.

Dependent Variable = Weighted Average Delta

(Percent)

Independent Variable

Predicted

Sign Model 3.1 Model 3.2 Model 3.3

Intercept 0.156***

0.178***

0.156***

(10.86) (14.45) (10.85)

LEVERAGE (+) 0.00317***

0.00267***

0.00325***

(3.31) (4.45) (3.40)

SIZE (+) -2.26e-05 -2.15e-05 -2.80e-05

(-1.01) (-1.56) (-1.21) UNLVBETA (+) 0.0247

*** 0.0241

***

(2.77) (2.61)

MTAXRATE (+) 0.099**

| 0.075**

| 0.099**

|

(2.15) (1.88) (2.13)

PRICEVOLAT (+) 0.0378***

0.237

(3.43) (0.84)

UNLVBETA*PRICEVOLAT (+) -0.005

(0.03)

R2 (%)

2.64 2.64 2.7

Fixed Effects

Yes

Yes Yes

Observations

1946 1946 1946

Table 4

Determinants of Hedging Intensity: Effects of Free Cash Flows

This table presents the empirical analysis of the effects of (lagged) free cash flows on firms’ hedging intensity (see

Hypothesis 2). We report the results of estimating Equation (14). We use a hand-collected dataset on the quarterly

positions in oil and natural gas derivative markets of 41 public (but non-integrated) firms in the exploration and

production segment of the oil and gas industry from 1996 through 2008. The t-statistics are reported in the

parentheses. Asterisks indicate significance at 1% (***), 5% (**) and 10% (*) levels, respectively. We also provide

the expected signs for the coefficients based on Hypotheses 1 and 2.

Dependent Variable = Weighted Average Delta

(Percent)

Independent Variable

Predicted

Sign Model 4.1 Model 4.2 Model 4.3

Intercept 0.225***

0.16***

0.159***

(53.3) (11.09) (11.09)

LAGNFCSHLW (-) -0.104* -0.223

*** -0.228

***

(-1.68) (-3.14) (-3.19)

LEVERAGE (+) 0.00305***

0.00311***

(3.16) (3.20)

SIZE (+) -1.86e-05 -2.32e-05

(-0.82) (-1.00) UNLVBETA (+) 0.021

** | 0.021

** |

(2.13) (2.10)

MTAXRATE (+) 0.108**

| 0.108**

|

(2.43) (2.41)

PRICEVOLAT (+) 0.017

(0.61)

UNLVBETA*PRICEVOLAT (+) 3.68e-03

(0.1)

R2 (%)

0.2 3.4 3.4

Fixed Effects

Yes

Yes Yes

Observations

1799 1799 1799

Table 5

Determinants of Hedging Intensity: Effects of Managerial Entrenchment (I)

This table presents the empirical analysis of the effects of the level of managerial entrenchment on firms’ hedging

intensity (see Hypotheses 3 and 4). We report the results of estimating Equation (14). The independent variables are

specified in Hypotheses 3 and 4. We use a hand-collected dataset on the quarterly positions in oil and natural gas

derivative markets of 41 public (but non-integrated) firms in the exploration and production segment of the oil and

gas industry from 1996 through 2008. The t-statistics are reported in the parentheses. Asterisks indicate significance

at 1% (***), 5% (**) and 10% (*) levels, respectively. We also provide the expected signs for the coefficients based

on Hypotheses 3 and 4.

Dependent Variable = Weighted Average Delta

(Percent)

Independent

Variable

Predicted

Sign Model 5.1 Model 5.2 Model 5.3 Model 5.4

Intercept 0.246***

0.252***

0.90***

1.07***

(27.44) (27.44) (8.59) (8.02)

CEOEQT (+) 9.11e-05***

9.75e-05***

(3.44) (3.84)

CEOTENURE (+) 0.0016**

0.009* 0.001

(1.91) (1.21) (0.74)

CEOCHNG (-) -0.442***

-0.148***

-0.161***

(-8.57) (-8.83) (-7.78) BOARDINDEP (-) -0.855

*** -1.05

***

(-6.42) (-7.78) INTERLOCK (+) 0.113

*** | 0.058

* |

(3.11) (1.61)

EXTBLKPCT (-) -0.002*

(-1.86)

R2 (%)

1.12 6.75 12.27 13.71

Fixed Effects

Yes

Yes

Yes Yes

Observations

1168 1274 1086 366

Table 6

Determinants of Hedging Intensity: Effects of Managerial Entrenchment (II)

This table presents the empirical analysis of the effects of the level of managerial entrenchment on firms’ hedging

intensity when controlling for other determinants of hedging (see Table 3). We report the results of estimating

Equation (14). The independent variables are specified in Hypotheses 1- 4. We use a hand-collected dataset on the

quarterly positions in oil and natural gas derivative markets of 41 public (but non-integrated) firms in the exploration

and production segment of the oil and gas industry from 1996 through 2008. The t-statistics are reported in the

parentheses. Asterisks indicate significance at 1% (***), 5% (**) and 10% (*) levels, respectively. We also provide

the expected signs for the coefficients based on Hypotheses 1-4.

Dependent Variable =

Weighted Average Delta

(Percent)

Independent

Variable

Predicted

Sign Model 6.1 Model 6.2 Model 6.3

Intercept 1.533***

1.559***

1.653***

(8.66) (8.71) (8.91)

CEOEQT (+) 1.36e-05***

1.12e-05***

(4.11) (3.50)

CEOTENURE (+) 4.0e-03 3.0e-03 8.0e-03

(0.38) (0.28) (0.76)

CEOCHNG (-) -0.163***

-0.154***

-0.156***

(-8.93) (-8.21) (-8.56) BOARDINDEP (-) -0.174

*** -0.178

*** -0.189

***

(-7.77) (-7.86) (-8.04) INTERLOCK (+) 0.115

*** | 0.111

*** | 0.161

*** |

(2.92) (2.82) (3.81)

LEVERAGE (+) 0.00028***

0.00028***

0.00029***

(6.52) (6.40) (6.53)

UNLVBETA (+) 0.005 0.011 0.012

(0.31) (0.77) (0.76)

MTAXRATE (+) 0.133**

| 0.112**

| 0.123**

| (2.28) (1.91) (2.09)

LAGNFCSHLW -0.083

(-1.28)

R2 (%)

18.5 19.8 22.2

Fixed Effects

Yes

Yes

Yes

Observations

991 897 821

Table 7

GMM Estimation of Determinants of Hedging Intensity

This table presents the empirical analysis of the effects of the level of managerial entrenchment and other financial

variables on firms’ hedging intensity (the independent variables are specified in Hypotheses 1-4). We report the

results of estimating Equation (14) with the Arellano and Bond (1991) instrumental variables estimator using the

generalized method of moments (GMM). We use a hand-collected dataset on the quarterly positions in oil and

natural gas derivative markets of 41 public (but non-integrated) firms in the exploration and production segment of

the oil and gas industry from 1996 through 2008. The z-statistics are reported in the parentheses. Asterisks indicate

significance at 1% (***), 5% (**) and 10% (*) levels, respectively. We also provide the expected signs for the

coefficients based on Hypotheses 1-4.

Dependent Variable =

Weighted Average Delta

(Percent)

Independent

Variable

Predicted

Sign Model 7.1 Model 7.2 Model 7.3

Intercept 0.318***

0.857***

0.930***

(3.82) (5.08) (5.36)

CEOEQT (+) 1.42e-05 1.86e-05

(1.51) (1.31)

CEOTENURE (+) 0.007 0.002 0.001

(0.87) (0.26) (0.13)

CEOCHNG (-) -0.0817***

-0.093***

-0.095***

(-6.03) (-6.08) (-6.06) BOARDINDEP (-) -0.902

*** -0.991

*** -1.076

***

(-4.93) (-4.86) (-4.93) INTERLOCK (+) 0.087

** | 0.075

** | 0.081

** |

(2.13) (2.07) (2.20)

LEVERAGE (+) 0.00015***

0.00016***

(3.86) (3.96)

UNLVBETA (+) 0.011 0.014

(0.77) (1.17)

MTAXRATE (+) 0.017 | 0.013 | (0.68) (0.88)

LAGNFCSHLW -0.104

(-1.13)

Wald ChiSquare

Stat.

998.46***

877.46***

752.64***

Observations

1039 851 735

Figure 1: This figure exhibits the different levels of historical annualized volatility of various commodities from

historical daily prices of selected commodities during the period January 2000 to December 2002.

119%

76%

49%

28%

21%17% 16%

13%

1%0%

20%

40%

60%

80%

100%

120%

140%

PJM West

Power

Natural

Gas

Crude Oil S&P 500 Wheat Gold Sugar #14 French

Franc

1-m LIBOR

Vo

lati

lity

(%

)

Figure 2 This figure presents the derivative positions entered by the Clayton Williams Energy Company Inc.

(Ticker: CWEI) in the third quarter of 2003 as reported in the 10Q filings by the company.

Clayton Williams Energy Inc. (CWEI) Gas Delta Percentage

Gas Production

(MMcf)

% Gas Production

Oil Delta Percentage

Oil Production

(MBbl)

% Oil Production

Weighted Average Delta %

Contracts Entered in 3Q03 53.70% 4,952 70.10% 22.73% 352 29.90% 44.44%

Type of Contract Contract Price

($/MMBtu)

Contract Quantity

(MMBtu/day)

Contract Quantity

(MMcf/Qtr)

Delta Delta-MMcf Equivalent Position

(MMcf/Qtr)

Gas Production

(MMcf)

Gas Swap (4Q03) 3.80 1,720 -1.000 (1,720) (2,659) (4Q03)4,952

Gas Collar Buy Put (4Q03) 4.50 2,310 -0.059 (136) Gas Collar Sell Call (4Q03) 7.04 2,310 -0.348 (803)

Type of Contract Contract Price

($/Bbl)

Contract Quantity (Bbl/day)

Contract Quantity

(MBbl/Qtr)

Delta Delta-MBbl Equivalent Position

(MBbl/Qtr)

Oil Production

(MBbl)

Oil Swap (4Q03) 24.20 80 -1.000 (80) (80) (4Q03)352

Figure 3 This figure presents the frequency density function of the weighted average delta (%), a firm-wide

measure of hedging intensity, from a sample of 41 public and non-integrated firms in the exploration and

production segment

Figure 4 (Panel A) This figure presents the monthly crude oil and well-head natural gas prices from

January 1996 through December 1998 in constant 2008 U.S. Dollars. For comparability, the natural gas

prices have been rescaled by the ratio of the commodity prices in January 1996.

Data Source: U.S. Energy Information Administration

0

50

100

150

200

250

Jan

-19

96

Au

g-1

99

6

Mar

-19

97

Oct

-19

97

May

-19

98

Dec

-19

98

Jul-

19

99

Feb

-20

00

Sep

-20

00

Ap

r-2

00

1

No

v-2

00

1

Jun

-20

02

Jan

-20

03

Au

g-2

00

3

Mar

-20

04

Oct

-20

04

May

-20

05

Dec

-20

05

Jul-

20

06

Feb

-20

07

Sep

-20

07

Ap

r-2

00

8

No

v-2

00

8

20

08

U.S

. Do

llars

Oil and Natural Gas Prices

Oil

Natural Gas

Figure 4 (Panel B) This figure presents the six-month trailing moving average volatility (normalized by

the mean) of monthly spot crude and well-head natural gas prices from January 1996 through December

1998.

Data Source: U.S. Energy Information Administration

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Jan

-19

96

Jul-

19

96

Jan

-19

97

Jul-

19

97

Jan

-19

98

Jul-

19

98

Jan

-19

99

Jul-

19

99

Jan

-20

00

Jul-

20

00

Jan

-20

01

Jul-

20

01

Jan

-20

02

Jul-

20

02

Jan

-20

03

Jul-

20

03

Jan

-20

04

Jul-

20

04

Jan

-20

05

Jul-

20

05

Jan

-20

06

Jul-

20

06

Jan

-20

07

Jul-

20

07

Jan

-20

08

Jul-

20

08

Vo

lati

lity

Oil & Natural Gas Volatility

Oil

Natural Gas

Figure 5 This figure plots the mean weighted average delta (%), a firm-wide measure of hedging intensity,

for each quarter from 1996:1 through 2008:4 of a sample of 41 public and non-integrated firms in the

exploration and production segment

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

19

96

-1

19

96

-3

19

97

-1

19

97

-3

19

98

-1

19

98

-3

19

99

-1

19

99

-3

20

00

-1

20

00

-3

20

01

-1

20

01

-3

20

02

-1

20

02

-3

20

03

-1

20

03

-3

20

04

-1

20

04

-3

20

05

-1

20

05

-3

20

06

-1

20

06

-3

20

07

-1

20

07

-3

20

08

-1

20

08

-3

MEA

N W

AD

P