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Uncertainty and Covenant Design
in Private and Public Debt
Sandrine Docgne Penlap∗
January 9, 2018
ABSTRACT
I use a forward-looking and exogenous measure of output price uncertainty to investigate the
effect of uncertainty on covenants design in private and public debt of firms in the U.S. oil and
gas upstream sector. I show that private lenders are more likely to use performance covenants
to deal with high level of uncertainty at contract initiation. I also show that bondholders
free-ride on the monitoring efforts of private lenders in order to reduce renegotiation costs.
This paper contributes to our understanding on the role that financial covenants play in
incomplete contracting and how their usage differ in public and private debt.
∗Sandrine Docgne Penlap is at the University of Houston. Email: [email protected]
1 Introduction
The question of how borrowers and creditors deal with uncertainty in debt contracts still
remains largely unexplored in empirical studies. The incomplete contract theory predicts
that renegotiations will be frequent and accounting information will be used to assign control
rights (Aghion and Bolton, 1992; Christensen et al., 2016). Accounting information plays an
important role in the resolution of contracting incompleteness because they are contractible
signals that allocate control rights in different states (Chava and Roberts, 2008). Although
many studies have looked at the relationship between covenants and renegotiations (Rajan
and Winton, 1995; Chava and Roberts, 2008; Garleanu and Zwiebel, 2009; Christensen and
Nikolaev, 2012), empirical tests between the presence of covenants and uncertainty are largely
unexplored. How does uncertainty affect the design of covenants in public and private debt?
Is there a difference between bondholders and private lenders choice of covenants when faced
with higher uncertainty at contract initiation? If so, what could explain the difference?
Few empirical studies examine the design the relationship between covenants and uncer-
tainty. While the agency theory implies that the likelihood of covenants restricting man-
agerial actions will increase with cash flow volatility, it does not provide a rational for the
existence of financial covenant which are not based on managers’ actions but on account-
ing signals. One study look at the role that uncertainty plays in the likelihood of contract
renegotiation. Using the volatility of stock returns as proxy for uncertainty, (Roberts, 2015)
shows that firms with higher uncertainty are more likely to have their private debt contract
renegotiated. Making a similar argument as in this paper, Demerjian (2017) look at the rela-
tionship between financial covenants and various measure of uncertainty at the firm, industry
and macroeconomic level. His results show that the intensity of financial covenants increase
with uncertainty at the firm and industry levels but not at the macroeconomic levels.
Focusing on firms in the U.S. upstream oil and gas sector, I am able to use a forward
looking and exogenous measure of output price uncertainty to empirically test the effect that
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high level of uncertainty on the future value of an investment has on the choice and design of
covenants. By using the options on crude oil futures, I can derive a measure of output price
uncertainty, Price Uncertainty, that is more in line with creditors expectation and a better
proxy for uncertainty surrounding future cash flow. Given that any firm in this sector is
unlikely to affect crude oil prices through its investment decisions, the uncertainty measure
can be considered exogenous, which helps with the identification of the effect.
To study the effect of uncertainty on covenant design in private loans, I classify financial
covenants in two groups following Christensen and Nikolaev (2012). Performance covenants
are covenants that are formulated in terms of income statement information and Capital
covenants are covenants that are defined in terms of balance sheet information. Christensen
and Nikolaev (2012) show that these two groups of financial covenants mitigate agency
conflicts through different mechanisms. I find that on average Price Uncertainty is positively
correlated with Performance covenants but has no effect Capital covenants. On average
creditors are more likely to include performance covenants and to increase their number
per loan package when uncertainty is high. These results are consistent with the results
of Christensen and Nikolaev (2012) who finds a positive relation between the likelihood
of renegotiation and performance covenants, but not with capital covenants, and with the
findings of Demerjian (2017), who also look at the effect of uncertainty on financial covenants.
These results provide more evidence on the role and purpose of covenants in an incomplete
contracting framework.
In public bonds, I find that on average Price Uncertainty has a negative effect on the
likelihood of including financial covenants in the bond contracts. This effect persist even
after controlling for the futures price. One explanation for this negative relationship is
that bondholders design debt contracts to minimize renegotiation costs when uncertainty is
high because of the high cost of renegotiation in public debt Garleanu and Zwiebel (2009).
However, one possible trade-off to this strategy is that there will be less monitoring which
can increase managerial moral hazard since covenants provide an incentive to monitor Park
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(2000). I argue that bondholders are able to reduce the usage of financial covenants when
uncertainty is high without giving up monitoring by free-riding on the monitoring efforts of
private lenders. After controlling for the presence of financial covenants in the firms private
debt, I find that the joint effect with uncertainty on the likelihood of including financial
covenants in the public debt of those firms is a strongly negative. On the other, firms that
do not have financial covenants in their private loans at the time of bond issuance are more
likely to include financial covenants in their bond indenture.
This paper contributes to the literature on the role of covenants in public and private
debt contracts. It provides support for the role that covenants play in resolving inefficiencies
that arise from the incompleteness of contracts. Several studies examine the role of covenants
in private loans as channels for the allocation of state contingent control rights (Rajan and
Winton, 1995; Chava and Roberts, 2008; Garleanu and Zwiebel, 2009). To my knowledge,
this is the first paper to look at the effect of uncertainty on the choice of covenants in
public bonds. This paper is also the first to test whether bondholders take into account the
covenants present in firms’ private loan when designing restrictions in the bond indenture.
The findings of this test are consistent with the literature on delegated monitoring between
different type of investors in an incomplete contracting framework (Fama, 1990; Diamond,
1991; Rajan, 1992). This study expands our understanding of the various role and purpose
of covenants in different type of debt.
2 Motivation and Hypotheses
2.1 Theoretical framework
To understand the link between uncertainty and financial covenants it is important to
first understand the role that accounting information plays in the incomplete contracting
framework. The seminal work by Coase (1937) introduced the argument that uncertainty
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about the future create a need for a decision power to coordinate economic transactions and
justify the existence of the firm. Building on this work, many researchers have explore the
opportunistic behavior that stems from the inability to write complete contracts. The central
point of the theory of incomplete contracts is that contracts between lenders and borrowers
are always incomplete because either it is prohibitively costly to write down all contingencies
or some information that would affect the terms of the contract are impossible to know or
predict (Aghion and Bolton, 1992; Christensen et al., 2016). Within this framework, tension
arises because the incompleteness of contract creates room for opportunistic behavior ex-post
(Rajan, 1992; Dewatripont and Maskin, 1995). The threat of post-contractual opportunism
affects both parties ability to enter into a contract. To resolve the issue, contracting parties
can agree on the state-contingent allocation of control rights to limit opportunistic behaviors
(Grossman and Hart, 1986; Hart and Moore, 1990). Accounting information are an essential
tool because they are contractible signals that allow for the efficient allocation of control
rights.
2.2 Financial covenants and uncertainty
Several studies have examined the role of accounting-based covenants as mechanisms for
state-contingent allocation of control rights (Rajan and Winton, 1995; Chava and Roberts,
2008; Garleanu and Zwiebel, 2009). However, this view does not explain why contracts
are regularly renegotiated (Hart and Moore, 1988; Roberts, 2015), and why violations of
covenants are often followed by waiver (Berlin and Mester, 1992; Beneish and Press, 1993).
What these studies show is that one role of covenants is to allow for renegotiation. Roberts
(2015) posits that the role of renegotiation is to dynamically complete contracts. Thus when
facing contractual incompleteness, creditors use covenants as tool to allow them to force
renegotiation. Demerjian (2017) develops an analytical framework in which the source of
incompleteness in uncertainty about the future state of the world rather than information
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asymmetry or moral hazard. The entrepreneur and the investor have the same initial in-
formation set, the missing information could affect the loan contract term, and a future
signal will reveal the information to both parties at the same time. The solution to the
issue is a contract that allow the contracting parties to renegotiate the contract terms upon
receiving the signal. The signal can be written in terms of financial covenants that are easy
to monitor. Financial covenants facilitate contracting under contractual incompleteness by
increasing the scope of renegotiation, which in turn reduce the likelihood of opportunistic
behavior.
The notion that covenants in private loans are tools for renegotiation have ample support
in the literature. Berlin and Mester (1992) show that covenants are set more tightly initially
because they can be relaxed at the lenders discretion during renegotiation. They also show
that firms with higher ex-ante credit risk are also more likely to have contract that are
renegotiated. Dichev and Skinner (2002) find that private lenders use tightly set covenants
as “trip wires”. As a result, technical violations occur often and are not necessarily linked to
financial distress. Christensen and Nikolaev (2012) show that the use of financial covenants
is positively associated with the likelihood of renegotiation. Roberts (2015) shows that
renegotiation occurs very frequently in bank loans and that the pricing, maturity, amount,
and covenants are significantly modified each time. While these studies offer support for
the incomplete contract theory, they do not provide direct evidence on the link between
covenants and the sources of contractual incompleteness.
One source of contractual incompleteness that can be empirically examine is output
price uncertainty. When facing high uncertainty about expected output price, covenants
are contractual provisions that give creditors the ability to initiate renegotiation by shifting
control rights. Without such provisions, the lenders would have no recourse if the information
reveal that the value of the investment is less than expected at contract initiation. However,
if the information signal is such that the firm is in technical default, then creditors can
force renegotiation to obtain better terms under the threat of recalling the loan. Empirical
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analysis of covenant violations and renegotiations show that the renegotiated terms are
usually favorable to the creditors (Beneish and Press, 1993; Roberts and Sufi, 2009). One
implication of the incomplete contract theory then is that private debt contracts will include
more and tighter financial covenants as uncertainty increases.
Hypothesis 1 The likelihood of inclusion and the intensity of financial covenants will be
positively related to uncertainty.
3 Data and Sample Characteristics
3.1 Data sources
I obtain firm financial information from Compustat Quarterly files. The analysis focuses
on U.S. firms in the upstream oil & gas sector. I restrict the sample to US-based firms and
require that all four industry classification codes provided by Compustat match in order to
include a firm in the sample (Doshi et al., 2017). More specifically, I require the National
American Industry Classification System (NAICS) to equal 211111, the Standard Industry
Classification (SIC ) to equal 1311, S&P Industry Sector Code (SPCINDCD) to equal 380,
and the Global Industry Classification Sector Code (GSECTOR) to equal 10.
I obtain daily data on options and futures on crude oil from the Commodity Research
Bureau for the period of January 2, 1990 to May 30, 2014. The bond data comes from
the 2016 Fixed Investment Securities Database (FISD) and is restricted to bond issued in
U.S dollars by non-financial U.S. domiciled firms between 1990 and 2014 with non missing
covenant information. I match the bond data to firms’ financial using 6-digit cusip. The
loan data comes from the LPC/Dealscan database and include details on private loans issued
between 1990 and 2012. I match the loan data to the Compustat data using the file from
Chava and Roberts (2008) updated on August 31, 2012. Dealscan provide information on
facilities, which are individual loans, and on packages, which are groups of facilities issued
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under the same loan agreement. Since covenants apply to all facilities in a package, I run
my analysis on private loans at the package level. I exclude all observations for which any
of the regression variables is missing. The final bond sample has 249 bonds issued by 57
firms. The final loan sample has 897 packages or deals, issued by 150 firms. I winsorize firm
characteristics at the 1% and 99% levels to alleviate the effect of outliers.
3.2 Key variables
The main dependent variables are financial covenants in private and public debt agree-
ments. Financial covenants are covenants that specify a threshold of an accounting-based
metric that the borrower must maintain. Financial covenants in public bonds are mostly
restrictions fixed charge coverage (19.28%), with a small portion on net worth (2.41%). More
detail on bond covenants is provided in Appendix A. Private loans have restrictions on a
wider variety of accounting metrics. I classify financial covenants in private debt in two cate-
gories following Christensen and Nikolaev (2012). Performance covenants are formulated in
terms of current performance ratios and include restrictions on cash interest coverage, debt
service coverage, ebitda, fixed charge coverage, interest coverage, debt to ebitda, and senior
debt to ebitda. Capital covenants are defined in terms of the source and use of capital and
include restrictions on current ratio, debt to equity, loan to value, debt to tangible net worth,
leverage, net worth, tangible net worth, and capital expenditure. More detail loan covenants
is provided in Appendix B. Christensen and Nikolaev (2012) show that these two groups of fi-
nancial covenants address agency conflicts via two mechanisms. Performance covenants serve
as channel for the allocation of state-contingent control rights, while capital covenants are
tools to align debtholder and shareholder interest. They show that performance covenants,
but not capital covenants, are positively related to the frequency of contract renegotiations.
As such, I expect that creditors are more likely to rely on the use of performance covenants
when uncertainty is high.
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The key independent variable is Price Uncertainty, which proxy for the forward-looking
measure of uncertainty related to the price of oil one year ahead at contract initiation. I
measure oil price volatility using the Bjerksund-Stensland (2002) approximation. I exclude
options contracts with price less than 1 cent, and those violating standard no-arbitrage
conditions. I interpolate or extrapolate the risk-neutral volatility only if the nearest available
maturity is equal to at least 70% of the desired maturity of 365-day. Price uncertainty for a
given day is the average of the two nearest the money calls and the two nearest the money
puts and correspond to the oil price volatility at the one year horizon on the day the loan
contract is originated.
3.3 Descriptive statistics
I present descriptive data on the sample of private and public debt in Tables 1 and 2
respectively. Panel A in Table 1 present summary statistics of firms that issue private loans.
The number of lenders in a syndication for each deal varies widely, with an average of 8
and a median of 5 lenders per package. On average, loans are issued primarily for corporate
purposes (36%) or debt repayment (25%). The average maturity of a loan is 4 years. The
size of the firms in the oil and gas upstream is highly skewed; the average size of firms in
the private loan sample is almost 5 times the median size. 53% of firms have a credit rating.
Panel B of Table 1 present the proportion of firms that issue loans with covenants, and
Panel C shows the number of performance and capital covenants per package. Performance
(capital) covenants are present 41% (45%) of deals. Most packages with covenants include
restrictions on 1 or 2 accounting ratios, with an average of 1.34 performance and 1.36 capital
covenants per package. Other loan covenants include sweeps that require the firm to repay a
specify proportion of the loan conditional on the specify event such as asset sale or security
issuance. Most packages also include restrictions on dividend payments (85%). A package
is considered secured if at least one facility in the package is secured. 73% of deals in the
8
sample are secured.
Panel A of Table 2 gives summary statistics for the public bond sample. Firms that issue
public debt are larger than firms that issue private debt and relatively less skewed. In all
regressions, I use the natural logarithm of total book value of assets (Size) to proxy for the
size of the firm. Firms that issue public debt are also more likely to have a credit rating.
Public debt has longer maturity, with an average of 14 and a median of 10 years. Panel B of
Tables 2 shows the number of bonds in the sample that are issued with covenants. Almost
20% of bonds contains a financial covenant. The most frequent covenants are restrictions
on merger and acquisition and sale of assets (85%). Other covenants include restrictions on
risky investment, dividend payments, additional debt, sale leaseback, negative pledge, event
risk, and poison put covenants.
In both sample, the mean and the median of Price Uncertainty is equal to 0.27. I use
the price of crude oil futures contract with maturity closest to one year (Futures Price) to
proxy for crude oil price. Futures Prices varies between $16 to $103 per barrel in the private
loan sample, and to $114 per barrel in the public bond sample. Price Uncertainty is strongly
positively correlated with Futures Price in both sample with correlation coefficient of 0.45
in loans and 0.47 in bonds. The strong correlation is not surprising, but the level suggest
that these two measures are not simply proxy for each other. I estimate regressions with
and without Futures Price to further isolate the effect of price uncertainty.
4 Primary Results
To examine the effect that output price uncertainty has on the design of debt covenants,
I run several regressions to estimate the likelihood of financial covenant inclusion and their
intensity. I estimate the following regression.
Yi,t = f(α + βUncertaintyt + ΓXi,t−1 + εi,t) (1)
9
To estimate the likelihood of covenant inclusion, I use a probit regression where in equation 1
Yi,t = 1 if the covenant is included in the loan and 0 otherwise. To estimate the intensity of
covenants, I use a poisson regression where Yi,t is the number of financial covenants in the
loan. Xi,t−1 represents important firms, loan, and macroeconomic characteristics that have
been shown to affect the choice and design of debt covenants.
Following the previous empirical work (Chava et al., 2010; Christensen and Nikolaev,
2012; Reisel, 2014; Bradley and Roberts, 2015), I control for the following firm’s Size, growth
options (LogMTB), Leverage, asset tangibility (PPE/Assets), profitability (EBITDA/As-
sets). I also control for the loan’s maturity (LogMATURITY ) and amount (Loan/Assets),
and for term structure of interest rates (Term Spread) which is the difference in the yields
on the 10-year and 1-year treasury bonds at the time of loan initiation. For the covenant
on private loans, I control for additional variables: whether or not the firm had a long-term
S&P credit rating (Rated) at the time the debt is originated; whether or not the firm has
a Performance Pricing option attached to it, the Number of Lenders per package, and the
purpose of the loan. From an empirical standpoint, there is not enough variations in the
bond sample to control for other features in the regression such as whether the firm has a
credit rating or not, and whether the bond is putable, callable, or convertible.
4.1 Private loans
Tables 3 and 4 present the regression results for the probability of inclusion (probit) and
the intensity (poisson) of performance covenants in private loans. The t-stat in parenthesis
are clustered at the borrower level. In column (1), the regression omits Futures Price,
the number of lenders, and the deal purpose. The positive and significant coefficient on
Price Uncertainty indicates that the likelihood of inclusion and the number of performance
covenants in private loan contracts increase when uncertainty is high. The significance persist
even after controlling for crude oil prices (column(2)) and for the number of lenders and the
10
purpose of the loan. The effect is economically significant.The average Price Uncertainty
in the private loan sample is 0.27 with a standard deviation of 0.07. Using the coefficient
estimates from column(3) which includes all control variables, a one standard deviation shift
in Price Uncertainty leads to an increase of 4% in the likelihood of covenant including and
to 0.064 more performance covenants. Given an average of 1.34 performance covenants
per packages, this corresponds to an increase of 4.8%. The number of lenders and the
purpose of the deal have only a small effect on the number of performance covenants. The
presence of performance pricing option is strongly positively linked to the likelihood of having
performance covenants in the loan contract.
Tables 5 and 6 give the regression results for capital covenants. After controlling for all
the variables, Price Uncertainty has no effect on the choice and number of capital covenants.
These findings are consistent with the view that capital covenants serve to limit moral haz-
ard by aligning the interest of sharelholders with that of creditors. In contrast, performance
covenants act more as trip-wires and are positively associated with the likelihood of renego-
tiation (Christensen and Nikolaev, 2012). The number of lenders and the purpose of the loan
have strong positive impact on the choice and number of capital covenants. The positive and
significant sign on Futures Price suggest that private lenders are more likely to add financial
covenants in loans to firms in the oil and gas upstream sector when the 1-year future price
of crude oil is high. However, this effect is more significant for performance covenants.
Overall these results suggests that private lenders rely on the use of performance covenants
to address high uncertainty at contract initiation. These findings are consistent with previous
studies on the role of financial covenants in an incomplete contracting framework.
4.2 Public bonds
Table 7 reports the results of the probit regression for the likelihood of including financial
covenants in public indentures. The coefficient estimate of Price Uncertainty is negative and
11
significant at the 1% level after controlling for 1-year future price of crude oil. Contrary to
what was expected, these results suggest that bondholders are less likely to include financial
covenants in bond contracts with firms in the oil and gas upstream sector when uncertainty is
high. One possible explanation is that bondholders are designing debt contract to minimize
renegotiation costs when uncertainty is high. However, while financial covenants increase
the likelihood of renegotiation ex-post, they also increase the incentive to monitor. Without
effective monitoring, managerial moral hazard might go undetected until it is too late. If
bondholders are designing contract to minimize renegotiation costs, one trade-off is that they
will also decrease the incentive to monitor, which can be just as costly. I argue in the next
section that free-ride on the monitoring efforts of bankers in order to minimize renegotiation
costs.
5 Public Bonds and Delegated Monitoring
5.1 Hypothesis
The incomplete contract theory implies that bondholders face a trade-off when contract-
ing under uncertainty because renegotiation are costlier and less likely in public debt (Smith
and Warner, 1979; Denis and Mihov, 2003; Bharath et al., 2008). On one hand they need to
write contracts that minimize the likelihood of renegotiation to avoid forcing into bankruptcy
an otherwise financially healthy firm that is simply going through a period of temporary low
earnings because of a volatile environment (Garleanu and Zwiebel, 2009; Dichev and Skinner,
2002). The deterioration in the financial ratio in this case is not due to managerial misbe-
havior, and bondholders would like to give management time to make necessary adjustment
without going through costly bankruptcy. This can be achieved by not including financial
covenants in the bond indenture. On the other hand, uncertainty exacerbates agency con-
flicts and increases the need for monitoring. Financial covenants have been shown to provide
12
incentives to monitor the firm’s performance since the ability to acquire the information nec-
essary to prove whether or not the covenant has been violated is conditional on the level of
monitoring (Park, 2000; Rajan and Winton, 1995). If bondholders are writing contracts that
minimize the likelihood of renegotiation, does that mean they are relinquishing the ability
to monitor the firms in times when monitoring is much needed? I argue that bondholders
are able to limit financial covenants when uncertainty is high because they are free-riding
on the monitoring of private lenders.
The free-riding problem has been highlighted in the literature on several levels. Grossman
and Hart (1980) highlight the free-rider problem among equity investors. They argue that
if one investor want to take over an improve the firm, all other small investors will attempt
to free-ride if they believe that the improvement will occur. Lummer and McConnell (1989)
argue that bank loans revisions provide significant information to capital markets. Fama
(1985) posits that bank’s lending decisions which are public become credible signals about the
credit worthiness of the firm and reduce the monitoring costs of other creditors. Beatty et al.
(2012) argue that bondholders use cross-acceleration provisions to delegate monitoring to
banks because of the high costs of renegotiation. In this paper I hypothesize that bondholders
also delegate monitoring to banks when contracting under uncertainty by relying on the
banks use of financial covenants as credible signals. This argument is consistent with previous
research on incomplete contracting and delegated monitoring (Fama, 1990; Diamond, 1991;
Rajan, 1992).
The free-rider problem can generally cause markets to collapse. However, several studies
point out that in certain circumstances, some market participants with sufficient stakes can
monitor even if other stakeholders are likely to free-ride on their monitoring efforts. In the
equity market, Grossman and Hart (1980) show that a takeover that would improve the value
of the firm is likely to fail because of the free-rider problem. However, Shleifer and Vishny
(1986) shows that a large shareholder with sufficient stakes will always do some monitoring
and can solve the free-rider problem in the case of a monitor who wants to take over.
13
In the debt market, Diamond (1984) develops a model in which the free-rider problem or
duplicate monitoring efforts can reduce the benefits of monitoring, and show that delegating
monitoring to a financial intermediaries can solve the problem. Fama (1990) argues that
delegating monitoring to specialists lower contracting costs and allows other stakeholders
to specialize in other contracting problems more in line with their expertise. When the
specialists have a strong interest in the firm’s default risk, their monitoring activities can
be credible signals and help avoid duplicate monitoring costs. By delegating monitoring,
contract costs are lowered.
Park (2000) develop a model in which the borrower moral hazard problem is severe and
monitoring is required to deter borrower’s opportunistic behavior. His model shows that it
can be optimal for the firm if public lenders delegate monitoring to senior private lenders.
When the private loan include a covenant and violation does not automatically results in
liquidation, it can be optimal for the firm that public lenders delegate some of their moni-
toring to private lenders. Doing so reduces the likelihood that the firm will be prematurely
liquidated. Berlin and Loeys (1988) build a model that shows that when bond covenants
would lead to too many mistakes, a banker with the right incentives can provide monitoring
and choose an efficient default policy. Thus when writing contract under uncertainty, bond-
holders are more likely to rely on signals from private lenders to decide whether or not to
include financial covenants in the contract. By relying on the private lenders signal, they
can avoid duplicate monitoring efforts and reduce contracting costs.
Hypothesis 2 The likelihood of including financial covenants in bond contract when uncer-
tainty is high is negatively related to the presence of financial covenants in the firm’s private
debt.
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5.2 Results
To test the delegated monitoring hypothesis, I redo the regression in equation (1) for the
bond sample while controlling for the presence of financial covenants in the firm private loan.
First, I identify firms that appears in both private and public debt sample. Then I create
a dummy variable that equals 1 if the firm has a private loan with a financial covenant at
the time the public bond is originated. Table 8 shows the results of the regression for the
delegated monitoring hypothesis where equation (1) is augmented with a dummy variable
and an interaction terms to capture the effect of the presence of financial covenants in private
loan on the inclusion of financial covenants in public bond when uncertainty is high. The
first column shows the joint effect of uncertainty and performance covenants, and the second
column the joint effect of uncertainty and capital covenants.
The results of column(1) show that the presence of of performance covenants in the
firm’s private loan has no effect on bondholders decision to include financial covenants in
the bond contract. However, firms without capital covenants in their private loan contracts
are more likely to have financial covenants in the bond indenture, while firms with a capital
covenant are significantly less likely to have such provisions. These results are consistent with
the hypothesis that a degree of coordination between bondholders and private lenders exist
when designing debt contract. Christensen and Nikolaev (2012) show that capital covenants
are more likely to be used when accounting signals are poorly correlated with credit risk. In
a highly volatile environment, firms are more likely to violate financial covenants without
being financially distressed. Given the high costs of inefficient transfer of control rights,
bondholders minimize renegotiation costs by relying on bank’s signal.
15
6 Other Covenants and Debt Features
6.1 Other covenants
Tables 9 and 10 show results for the likelihood of including other type covenants in
the private or public debt contract, respectively. A private loan package is said to have
Collateral restrictions if at least one facility in the package is secured. Sweeps are covenants
that require the firm to repay a specify percentage of the loan conditional on a specific
event. Dividend restrictions are covenants that impose restrictions on the firm’s ability to
pay dividend. I test the effect of uncertainty on the likelihood of including these covenants in
the private loan contract. The results in Table 9 show that on average, collateral requirement,
dividend restrictions, asset sweep, debt sweep, and equity sweep are not correlated with Price
Uncertainty. However, creditors ar more likely to require collateral from firms in the oil and
gas upstream sector when the price of crude oil is high.
Appendix A provides more descriptions on bond covenants. Price Uncertainty has no
effect on other bond covenants except for event-risk covenants and poison control put provi-
sions. Table 10 shows that bondholders are less likely to include event-risk provisions such
cross-acceleration covenants in the bond indenture when uncertainty is high. This result is
consistent with Beatty et al. (2012) who show that the use of cross-acceleration provisions
is less likely when the likelihood and cost of cascading defaults is higher. As uncertainty in-
crease, private lenders rely on financial covenants violation to renegotiate contracts ex-post.
Consistent with the delegated monitoring hypothesis, bondholders can reduce renegotiations
costs by relaxing the use of event-risk provisions in the bond contract. Table 10 also shows
that when Price Uncertainty high, bondholders are more likely to include poison put pro-
visions in the bond indenture. This suggests that bondholders are concerned with hostile
takeover when the business environment is more volatile.
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6.2 Maturity and performance pricing
I also test the effect of Price Uncertainty on debt maturity and performance pricing.
Performance pricing are provisions in private loans that make the interest rate contingent on
accounting ratios or credit ratings. By including pre-negotiated interest rates that are linked
to financial performance, creditors and borrowers can reduce ex-post renegotiation costs.
Thus when uncertainty is high, creditors can use a performance pricing grid to negotiate
on the interest rate ex-ante. Table 11 shows that Price Uncertainty is positively correlated
with the use of performance pricing in private loans. This suggest that performance pricing
is also a channel through which creditors and borrowers address uncertainty at contract
initiation. The table also shows that debt maturity is strongly negatively associated with
Price Uncertainty, suggesting that greater uncertainty is associated with shorter loans.
7 Conclusion
I examine the usage of financial covenants in private and public debt contracts when
uncertainty is high. By focusing on firms in the U.S. oil and gas upstream, I am able to use
options on crude oil futures to derive a forward-looking and reasonably exogenous measure
of output price uncertainty to analyze the causative effect of price uncertainty on covenant
design. I find that the effect of uncertainty differs between public and private debt. Private
lenders are more likely to increase financial covenants, and more specifically performance
covenants which are based on firm’s current performance ratios, when uncertainty at contract
initiation is high. Bondholders, are more likely to decrease the use of financial covenants.
Upon further analysis, I show that the decrease of financial covenants in bond contracts is
conditional on the firm’s having financial covenants in an existing private loan. This analysis
highlight the difference in the usage of covenant private and public debt. It also shed more
light on the important role that banks play as delegated monitor.
17
References
Aghion, Philippe, and Patrick Bolton, 1992, An incomplete contract approach to financialcontracting, Review of Economic Studies 59, 473–494.
Beatty, Anne, Scott Liao, and Joseph Weber, 2012, Evidence on the determinants andeconomic consequences of delegated monitoring, Journal of Accounting and Economics53, 555–576.
Beneish, Messod D., and Eric Press, 1993, Costs of Technical Violation of Accounting-BasedDebt Covenants, The Accounting Review 68, 233–257.
Berlin, Mitchell, and Jan Loeys, 1988, Bond Covenants and Delegaed Monitoring, Journalof Finance 43, 397–412.
Berlin, Mitchell, and Loretta J. Mester, 1992, Debt Covenants and Renegotiation, Journalof Financial Intermediation 2, 95–133.
Bharath, Sreedhar T., Jayanthi Sunder, and Shyam V. Sunder, 2008, Accounting Qualityand Debt Contracting, Accounting Review 83, 1–28.
Bradley, Michael H., and Michael R Roberts, 2015, The Structure and Pricing of CorporateDebt Covenants, The Quarterly Journal of Finance 5, 1550001.
Chava, Sudheer, Praveen Kumar, and Arthur Warga, 2010, Managerial Agency and BondCovenants, The Review of Financial Studies 23, 1120–1148.
Chava, Sudheer, and Michael R. Roberts, 2008, How does financing impact investment? therole of debt covenants, Journal of Finance 63, 2085–2121.
Christensen, Hans B., and Valeri V. Nikolaev, 2012, Capital versus performance covenantsin debt contracts, Journal of Accounting Research 50, 75–116.
Christensen, Hans B., Valeri V. Nikolaev, and Regina Wittenberg-Moerman, 2016, Account-ing Information in Financial Contracting: The Incomplete Contract Theory Perspective,Journal of Accounting Research 54, 397–435.
Coase, R. H., 1937, The Nature of the Firm, Economica 4, 386–405.
Demerjian, Peter R., 2017, Uncertainty and debt covenants, Review of Accounting Studies1–42.
Denis, David J., and Vassil T. Mihov, 2003, The choice among bank debt, non-bank privatedebt, and public debt: Evidence from new corporate borrowings, Journal of FinancialEconomics 70, 3–28.
Dewatripont, M., and Eric S. Maskin, 1995, Contractual Contingencies and Renegotiation,RAND Journal of Economics 26, 704–719.
18
Diamond, Douglas, 1984, Financial intermediation and delegated monitoring, Review ofEconomic Studies 51, 393–414.
Diamond, Douglas W, 1991, Monitoring and Reputation: The Choice between Bank Loansand Directly Placed Debt, Journal of Political Economy 99, 689–721.
Dichev, I D, and D J Skinner, 2002, Large-sample evidence on the debt covenant hypothesis,Journal of Accounting Research 40, 1091–1123.
Doshi, Hitesh, Praveen Kumar, and Vijay Yerramilli, 2017, Uncertainty, Capital Investment,and Risk Management, Management Science 1–18.
Fama, Eugene F., 1985, What’s different about banks?, Journal of Monetary Economics 15,29–39.
Fama, Eugene F., 1990, Contract Costs and Financing Decisions, The Journal of Business63, S71.
Garleanu, Nicolae, and Jeffrey Zwiebel, 2009, Design and renegotiation of debt covenants,Review of Financial Studies 22, 749–781.
Grossman, Sanford J, and Oliver D Hart, 1980, Takeover Bids , The Free-Rider Problem , andthe Theory of the Corporation Author ( s ): Sanford J . Grossman and Oliver D . HartPublished by : RAND Corporation Stable URL : http://www.jstor.org/stable/3003400Accessed : 28-04-2016 14 : 45 UTC Your use o, The Bell Journal of Economics 11, 42–64.
Grossman, Sanford J., and Oliver D Hart, 1986, The Costs and Benefits of Ownership: ATheory of Vertical and Lateral Integration, Journal of Political Economy 94, 691–719.
Hart, Oliver, and John Moore, 1988, Incomplete Contracts and Renegotiation, Econometrica56, 755–785.
Hart, Oliver, and John Moore, 1990, Property Rights and the Nature of the Firm, Journalof Political Economy 98, 1119–1158.
Lummer, Scott L., and John J. McConnell, 1989, Further evidence on the bank lendingprocess and the capital-market response to bank loan agreements, Journal of FinancialEconomics 25, 99–122.
Park, Cheol, 2000, Monitoring and structure of debt contracts, Journal of Finance 55, 2157–2195.
Rajan, Raghuram, 1992, Insiders and Outsiders: The Choice between Informed and Arm’sLength Debt, The Journal of Finance 47, 1367–1400.
Rajan, Raghuram, and Andrew Winton, 1995, Covenants and Collateral as Incentives toMonitor, The Journal of Finance 50, 1113–1146.
19
Reisel, Natalia, 2014, On the value of restrictive covenants: Empirical investigation of publicbond issues, Journal of Corporate Finance 27, 251–268.
Roberts, Michael R., 2015, The role of dynamic renegotiation and asymmetric informationin financial contracting, Journal of Financial Economics 116, 61–81.
Roberts, Michael R., and Amir Sufi, 2009, Renegotiation of financial contracts: Evidencefrom private credit agreements, Journal of Financial Economics 93, 159–184.
Shleifer, Andrei, and Robert W. Vishny, 1986, Large Shareholders and Corporate Control,Journal of Political Economy 94, 461–488.
Smith, Clifford W., and Jerold B. Warner, 1979, On financial contracting, Journal of Finan-cial Economics 7, 117–161.
20
Appendix A: Classification of bond covenants
Bond Covenant in FISD Description in FISD
Financial Covenants
MAINTENANCE NET WORTH Issuer must maintain a minimum specified net worth.
FIXED CHARGE COVERAGE IS Issuer is required to have a ratio of earnings available forfixed charges, of at least a minimum specified level.
FIXED CHARGE COVERAGE SUB Subsidiaries are required to maintain a minimum ratio ofnet income to fixed charges.
Investment Restrictions
AFTER ACQUIRED PROPERTY CLAUSE Property acquired after the sale of current debt issues willbe included in the current issuer’s mortgage.
TRANSACTION AFFILIATES Issuer is restricted in certain business dealings with its sub-sidiaries.
SUBSIDIARY REDESIGNATION Indicates if restricted subsidiaries may be reclassified as anunrestricted subsidiaries.
ASSET SALE CLAUSE Covenant requiring the issuer to use net proceeds from thesale of certain assets to redeem the bonds at par or at apremium.
SALE XFER ASSETS UNRESTRICTED Issuer must use proceeds from sale of subsidiaries’ assets toreduce debt.
STOCK TRANSFER SALE DISP Restricts the issuer from transferring, selling, or disposingof it’s own common stock or the common stock of a sub-sidiary.
INVESTMENTS Restricts issuer’s investment policy to prevent risky invest-ments.
INVESTMENTS UNRESTRICTED SUBS Restricts subsidiaries’ investments.
Merger Restrictions
CONSOLIDATION MERGER A consolidation or merger of the issuer with another entityis restricted.
SALE ASSETS Restrictions on the ability of an issuer to sell assets or re-strictions on the issuer’s use of the proceeds from the saleof assets.
Dividend Restrictions
DIVIDENDS RELATED PAYMENTS IS Payments made to shareholders or other entities may belimited to a certain percentage of net income or some otherratio.
DIVIDENDS RELATED PAYMENTS SUB Limits the subsidiaries’ payment of dividends to a certainpercentage of net income or some other ratio.
RESTRICTED PAYMENTS Restricts issuer’s freedom to make payments (other thandividend related payments) to shareholders and others.
21
Bond Covenant in FISD Description in FISD
Additional Debt Restrictions
FUNDED DEBT IS Restricts issuer from issuing additional funded debt.
INDEBTEDNESS IS Restricts user from incurring additional debt with limits onabsolute dollar amount of debt outstanding or percentagetotal capital.
FUNDED DEBT SUB Restricts issuer’s subsidiaries from issuing additionalfunded debt.
INDEBTEDNESS SUB Restricts the total indebtedness of the subsidiaries.
SENIOR DEBT ISSUANCE Restricts issuer to the amount of senior debt it may issuein the future.
SUBORDINATED DEBT ISSUANCE Restricts issuance of junior or subordinated debt.
NET EARNINGS TEST ISSUANCE To issue additional debt the issuer must have achieved ormaintained certain profitability levels.
LEVERAGE TEST IS Restricts total-indebtedness of the issuer.
LEVERAGE TEST SUB Limits subsidiaries’ leverage.
BORROWING RESTRICTED Indicates subsidiaries are restricted from borrowing, exceptfrom parent.
Sale Leaseback Restrictions
SALES LEASEBACK IS Restricts issuer to the type or amount of property used ina sale leaseback transaction and may restrict its use of theproceeds of the sale.
SALES LEASEBACK SUB Restricts subsidiaries from selling then leasing back assetsthat provide security for the debtholder.
Negative Pledge Restrictions
NEGATIVE PLEDGE COVENANT The issuer cannot issue secured debt unless it secures thecurrent issue on a pari passu basis.
Event-risk Restrictions
CROSS ACCELERATION A bondholder protective covenant that allows the holder toaccelerate their debt, if any other debt of the organizationhas been accelerated due to an event of default.
CROSS DEFAULT A bondholder protective covenant that will activate anevent of default in their issue, if an event of default hasoccurred under any other debt of the company.
RATING DECLINE TRIGGER PUT A decline in the credit rating of the issuer (or issue) triggersa bondholder put provision.
DECLINING NET WORTH If issuer’s net worth (as defined) falls below minimum level,certain bond provisions are triggered.
Poison Put Restrictions
CHANGE CONTROL PUT PROVISIONS Upon a change of control in the issuer, bondholders havethe option of selling the issue back to the issuer.
22
Appendix B: Description of loan covenants
Performance Covenants : Cash interest coverage ratio, Debt service coverage ratio, Level of
EBITDA, Fixed charge coverage ratio, Interest coverage ratio, Debt to EBITDA ratio,
Senior debt to EBITDA ratio
Capital Covenants : Current ratio, Debt to equity ratio, Loan to value ratio, Debt to
tangible net worth ratio, Leverage ratio, Net worth and tangible net worth requirement,
Level of Capital expenditure.
Asset Sales sweep: The percentage amount of net proceeds a company receives from an
asset sale that must be used to pay down any outstanding loan balance.
Debt Sales sweep: The percentage amount of net proceeds a company receives from the
issuance of debt that must be used to pay down any outstanding loan balance.
Equity Sales sweep: The percentage amount of net proceeds a company receives from the
issuance of equity that must be used to pay down any outstanding loan balance.
Dividend restrictions : A y/n flag indicating whether or the borrower is restricted from
paying dividends to its shareholders.
Collateral restrictions : At least one facility in the loan package is secured.
Appendix C: Variable Definitions
• Price Uncertainty : The at-the-money implied volatility at 365-day maturity
estimated using options on crude oil futures.
• Futures Price: Price of the crude oil futures contract closest to 1-year maturity.
• Size: Natural logarithm of total assets.
• MTB (Market to book): Ratio of the sum of the market value of equity and the book
value of interest-bearing debt to the sum of book values of equity and
23
interest-bearing debt.
• Leverage: Sum of debt in current liabilities and long-term debt divided by assets.
• PPE/Assets : Ratio of net property, plant and equipment to total assets.
• EBITDA/Assets :Ratio of operating before depreciation to total assets.
• Maturity is the number of years between the offering date and the maturity date.
• Loan amount/Assets is the ratio of the loan offering amount to total assets.
• Term Spread is the difference between the 10-year and 1-year treasury bonds.
• Performance pricing : A dummy variable that equals 1 if at least one facility in the
package has a performance pricing grid attached to it.
• Rated : A dummy variable that equals 1 if the company has a S&P long-term credit
rating , and equals 0 otherwise.
• Number of lenders is the difference between the 10-year and 1-year treasury bonds.
• Deal purpose: Type of purpose the deal was issued for.
24
Table 1: Descriptive Statistics Private Loans
Panel A: Summary Statistics
Mean Median Std. P25 P75
Oil market and macro characteristics:
Price Uncertainty 0.27 0.27 0.07 0.21 0.31
Futures Price 36.30 21.80 27.51 19.09 46.46
Term Spread 1.30 0.97 1.07 0.42 2.29
Firm characteristics:
Assets ($ Millions) 2,851.85 572.34 6,379.71 150.72 2,315.75
MTB 1.72 1.52 0.79 1.20 2.02
Leverage 0.35 0.33 0.19 0.23 0.45
PPE/Assets 0.81 0.84 0.12 0.76 0.89
EBITDA/Assets 0.04 0.04 0.04 0.03 0.06
Rated 0.53 0.00 0.00 0.00 0.00
Loan characteristics:
Maturity (years) 4.10 4.00 2.26 2.92 5.00
Loan amount/ Assets 0.51 0.31 1.32 0.13 0.58
Performance Pricing 0.44 0.00 0.00 0.00 0.00
Number of lenders 8.43 5.00 9.79 2.00 11.00
Deal Purpose
Acquis. line 0.09 0.00 0.00 0.00 0.00
Corp. purposes 0.36 0.00 0.00 0.00 0.00
Debt Repay. 0.25 0.00 0.00 0.00 0.00
Takeover 0.07 0.00 0.00 0.00 0.00
Work. cap. 0.14 0.00 0.00 0.00 0.00
Other 0.08 0.00 0.00 0.00 0.00
Panel B: Frequency of Covenants
N Percentage
Performance covenants 368 41.03
Capital covenants 409 45.60
Asset Sweep 93 18.53
Debt Sweep 46 9.18
Equity Sweep 44 8.80
Dividend restrictions 453 85.31
Collateral restrictions 482 73.03
Panel C: Financial Covenants Intensity
Intensity Performance Capital
covenants (%) covenants (%)
0 58.97 54.4
1 28.99 30.77
2 10.37 13.38
3 1.56 1.45
5 0.11
Panel A presents summary statistics for the regressions variables in the loan sample. Panel B provides thefrequency of covenants present in loans, and Panel C shows the number of performance and capital covenantsper package. The sample include loans issued from 1990 to 2012 by U.S. firms in the Oil & Gas upstreamsector. Loan covenants are defined in Appendix B and regression variables are defined in Appendix C.
25
Table 2: Descriptive Statistics Public Bonds
Panel A: Summary Statistics
Mean Median Std. P25 P75
Oil market and macro characteristics:
Price Uncertainty 0.27 0.27 0.08 0.20 0.31
Futures Price 52.43 27.09 35.75 19.65 86.71
Term Spread 1.59 1.78 1.08 0.67 2.51
Firm characteristics:
Assets ($ Millions) 11,440.60 4,297.23 14,334.96 1,716.69 16,589.84
MTB 1.77 1.60 0.68 1.31 2.06
Leverage 0.32 0.32 0.14 0.23 0.39
PPE/Assets 0.83 0.86 0.08 0.77 0.89
EBITDA/Assets 0.04 0.04 0.04 0.03 0.05
Rated 0.90 0.00 0.00 0.00 0.00
Bond characteristics:
Maturity (years) 14.14 10.02 13.89 8.01 12.00
Loan amount/ Assets 0.10 0.06 0.13 0.03 0.11
Panel B: Frequency of Covenants
N Percentage
Financial covenants 49 19.68
Investment restrictions 68 27.31
Merger restrictions 211 84.74
Dividend restrictions 69 27.71
Additional Debt restrictions 65 26.1
Sale leaseback restrictions 121 48.59
Negative Pledge restrictions 210 84.34
Event risk restrictions 190 76.31
Poison put restrictions 103 41.37
Panel A presents summary statistics for the regressions variables in the public bond sample. Panel B providesthe frequency of covenants present in bonds. The sample include public bonds issued from 1990 to 2014 byU.S. firms in the Oil & Gas upstream sector. Bond covenants are defined in Appendix A and regressionvariables are defined in Appendix C.
26
Table 3: Probit of performance covenants inclusion in loans
(1) (2) (3)
Estimate t-stat Estimate t-stat Estimate t-stat
Price Uncertainty 3.29∗∗∗ (4.56) 1.46∗∗ (2.16) 1.60∗∗ (2.36)
Futures Price 0.02∗∗∗ (4.67) 0.02∗∗∗ (4.93)
Size −0.14∗∗∗ (−2.62) −0.25∗∗∗ (−4.55) −0.25∗∗∗ (−4.39)
LogMTB −0.22 (−1.28) −0.31∗ (−1.89) −0.31∗ (−1.78)
Leverage 0.48 (1.52) 0.74∗∗ (2.26) 0.69∗∗ (2.11)
PPE/Assets 0.59 (1.24) 0.65 (1.40) 0.62 (1.31)
EBITDA/Assets 1.33 (1.16) 1.34 (1.06) 1.49 (1.12)
LogMATURITY 0.02 (0.23) −0.02 (−0.27) −0.07∗∗ (−0.77)
Loan/ Assets −0.01 (−0.35) −0.04 (−0.90) −0.05 (−0.71)
Term Spread −0.08 (−1.64) −0.13∗∗∗ (−2.81) −0.11∗∗ (−2.09)
Performance Pricing 1.33∗∗∗ (10.99) 1.41∗∗∗ (10.92) 1.27∗∗∗ (9.03)
Rated −0.11 (−0.64) −0.07 (−0.40) −0.14 (−0.83)
Number of Lenders 0.01 (1.50)
Deal Purpose
Acquis. line 0.23 (0.71)
Corp. purposes −0.15 (−0.46)
Debt Repay. 0.58∗ (1.88)
Takeover 0.45 (1.34)
Work. cap. 0.48 (1.41)
Intercept −1.31∗∗∗ (−2.58) −0.72 (−1.51) −1.04 (−1.63)
R2 0.21 0.25 0.28
N 897 897 897
covenant = yes 368 368 368
This table presents the results of the probit regression for performance covenant inclusion in privateloans. Covenants in loans are described in Appendix B. Regression variables are described in Appendix C.Standard errors are adjusted for clustering at the firm level. t-stats are provided in parentheses. ***, **,* indicates statistical significance at the 1%, 5%, and 10% respectively.
27
Table 4: Poisson of performance covenants intensity in loans
(1) (2) (3)
Estimate t-stat Estimate t-stat Estimate t-stat
Price Uncertainty 3.05∗∗∗ (5.20) 1.60∗∗∗ (2.77) 1.62∗∗∗ (2.97)
Futures Price 0.01∗∗∗ (4.53) 0.01∗∗∗ (5.10)
Size −0.08 (−1.30) −0.15∗∗ (−2.51) −0.14∗∗ (−2.47)
LogMTB −0.19 (−1.30) −0.30∗∗ (−2.29) −0.32∗∗ (−2.54)
Leverage 0.72∗∗∗ (2.76) 0.81∗∗∗ (3.04) 0.79∗∗∗ (2.99)
PPE/Assets 0.39 (0.67) 0.50 (0.97) 0.50 (1.04)
EBITDA/Assets 0.76 (0.69) 0.71 (0.59) 0.88 (0.76)
LogMATURITY 0.07 (0.72) 0.05 (0.55) 0.01 (0.10)
Loan/ Assets −0.02 (−0.51) −0.07 (−1.15) −0.07 (−1.03)
Term Spread −0.01 (−0.18) −0.05 (−1.03) −0.01 (−0.26)
Performance Pricing 1.14∗∗∗ (8.19) 1.14∗∗∗ (8.26) 0.96∗∗∗ (6.60)
Rated −0.21 (−1.15) −0.17 (−0.99) −0.25 (−1.49)
Number of Lenders 0.01∗ (1.88)
Deal Purpose
Acquis. line 0.38 (1.06)
Corp. purposes 0.01 (0.03)
Debt Repay. 0.59∗ (1.75)
Takeover 0.69∗ (1.88)
Work. cap. 0.60∗ (1.69)
Intercept −2.08∗∗∗ (−4.04) −1.61∗∗∗ (−3.12) −2.04∗∗∗ (−3.39)
R2 0.12 0.14 0.16
N 897 897 897
This table presents the results of the poisson regression for performance covenant intensity in privateloans. Covenants in loans are described in Appendix B. Regression variables are described in Appendix C.Standard errors are adjusted for clustering at the firm level. t-stats are provided in parentheses. ***, **,* indicates statistical significance at the 1%, 5%, and 10% respectively.
28
Table 5: Probit of capital covenants inclusion in loans
(1) (2) (3)
Estimate t-stat Estimate t-stat Estimate t-stat
Price Uncertainty 1.79∗∗∗ (2.63) 0.82 (1.12) 1.09 (1.60)
Futures Price 0.01∗∗ (1.95) 0.01∗∗ (2.33)
Size −0.10∗∗ (−2.14) −0.15∗∗∗ (−2.64) −0.14∗∗ (−2.27)
LogMTB −0.43∗∗∗ (−2.78) −0.48∗∗∗ (−3.18) −0.48∗∗∗ (−3.01)
Leverage −0.49 (−1.54) −0.38 (−1.16) −0.58∗ (−1.71)
PPE/Assets 0.36 (0.68) 0.39 (0.70) 0.37 (0.65)
EBITDA/Assets −0.27 (−0.21) −0.33 (−0.23) −0.24 (−0.15)
LogMATURITY −0.06 (−0.71) −0.09 (−1.14) −0.19∗∗ (−2.51)
Loan/ Assets 0.02 (0.55) 0.01 (0.28) 0.01 (0.36)
Term Spread −0.12∗∗ (−2.44) −0.14∗∗∗ (−2.93) −0.11∗∗ (−2.25)
Performance Pricing 1.70∗∗∗ (12.99) 1.72∗∗∗ (12.92) 1.56∗∗∗ (10.97)
Rated 0.06 (0.34) 0.09 (0.48) 0.02 (0.08)
Number of Lenders 0.02∗∗ (2.54)
Deal Purpose
Acquis. line 0.64∗ (1.67)
Corp. purposes 0.27 (0.76)
Debt Repay. 1.16∗∗∗ (2.92)
Takeover 0.73∗ (1.70)
Work. cap. 1.00∗∗∗ (2.71)
Intercept −0.46 (−0.87) −0.16 (−0.29) −0.94 (−1.35)
R2 0.29 0.29 0.35
N 897 897 897
covenant = yes 409 409 409
This table presents the results of the probit regression for capital covenant inclusion in private loans.Covenants in loans are described in Appendix B. Regression variables are described in Appendix C. Stan-dard errors are adjusted for clustering at the firm level. t-stats are provided in parentheses. ***, **, *indicates statistical significance at the 1%, 5%, and 10% respectively.
29
Table 6: Poisson of capital covenants intensity in loans
(1) (2) (3)
Estimate t-stat Estimate t-stat Estimate t-stat
Price Uncertainty 0.50 (1.00) 0.21 (0.37) 0.55 (1.02)
Futures Price 0.002 (0.98) 0.004∗ (1.71)
Size −0.17∗∗∗ −4.16 −0.18∗∗∗ (−3.96) −0.17∗∗∗ (−3.66)
LogMTB −0.38∗∗∗ (−3.41) −0.39∗∗∗ (−3.56) −0.38∗∗∗ (−3.61)
Leverage −0.38 (−1.48) −0.35 (−1.34) −0.46∗ (−1.79)
PPE/Assets 0.28 (0.62) 0.30 (0.66) 0.23 (0.54)
EBITDA/Assets 0.27 (0.28) 0.25 (0.26) 0.50 (0.52)
LogMATURITY −0.04 (−0.64) −0.05 (−0.76) −0.11∗ (−1.85)
Loan/ Assets −0.03 (−0.92) −0.04 (−1.06) −0.02 (−0.49)
Term Spread −0.14∗∗∗ (−4.26) −0.14∗∗∗ (−4.36) −0.10∗∗∗ (−2.81)
Performance Pricing 1.41∗∗∗ (12.60) 1.41∗∗∗ (12.65) 1.23∗∗∗ (10.44)
Rated 0.05 (0.39) 0.06 (0.43) 0.01 (0.07)
Number of Lenders 0.02∗∗∗ (3.82)
Deal Purpose
Acquis. line 0.71∗∗ (2.33)
Corp. purposes 0.30 (0.99)
Debt Repay. 1.00∗∗∗ (3.40)
Takeover 0.65∗∗ (2.01)
Work. cap. 0.79∗∗∗ (2.68)
Intercept −0.14 (−0.30) −0.06 (−0.13) −0.84 (−1.56)
R2 0.17 0.17 0.19
N 897 897 897
This table presents the results of the poisson regression for capital covenant intensity in private loans.Covenants in loans are described in Appendix B. Regression variables are described in Appendix C. Stan-dard errors are adjusted for clustering at the firm level. t-stats are provided in parentheses. ***, **, *indicates statistical significance at the 1%, 5%, and 10% respectively.
30
Table 7: Probit of financial covenants inclusion in bonds
(1) (2)
Estimate t-stat Estimate t-stat
Price Uncertainty −3.85∗ (−1.76) −9.38∗∗∗ (−3.00)
Futures Price 0.05∗∗∗ (5.99)
Size 0.43∗∗∗ (3.56) −0.29 (−1.16)
LogMTB −0.57 (−0.85) −0.97∗∗ (−1.97)
Leverage −1.64 (−1.54) 0.07 (0.04)
PPE/Assets 0.10 (0.06) −2.07 (−1.22)
EBITDA/Assets −1.16 (−0.38) −5.47∗∗ (−2.24)
LogMATURITY −0.14∗ (−1.91) −0.12 (−0.80)
Loan/ Assets 2.42∗∗ (2.37) −2.97 (−0.87)
Term Spread 0.29∗∗∗ (3.41) 0.13 (1.20)
Intercept −3.29∗ (−1.90) 3.64 (1.13)
R2 0.22 0.51
N 249 249
covenant = yes 49 49
This table presents the results of the probit regression for financial covenant inclusion inpublic bonds. Covenants in bonds are described in Appendix A. Regression variables aredescribed in Appendix C. Standard errors are adjusted for clustering at the firm level.t-stats are provided in parentheses. ***, **, * indicates statistical significance at the 1%,5%, and 10% respectively.
31
Table 8: Bond Covenants, Uncertainty and Delegated monitoring
Financial covenants (in bond)
(1) (2)
Estimate t-stat Estimate t-stat
Price Uncertainty −10.42∗∗∗ (−2.69) 4.93∗∗ (2.51)
Perf. Cov. (in loan) −0.82 (−0.45)
Perf. Cov.* Uncertainty 2.24 (0.29)
Cap. Cov. (in loan) 3.37∗∗∗ (3.76)
Cap. Cov.* Uncertainty −16.16∗∗∗ (−4.85)
Futures Price 0.05∗∗∗ (5.28) 0.05∗∗∗ (6.35)
Size −0.33 (−1.17) −0.35 (−1.22)
LogMTB −1.00∗ (−1.82) −0.94∗ (−1.89)
Leverage 0.19 (0.14) 0.05 (0.03)
PPE/Assets −1.88 (−1.11) −0.40 (−0.23)
EBITDA/Assets −6.13∗∗ (−2.28) −6.91∗∗∗ (−2.83)
LogMATURITY −0.14 (−1.14) −0.15 (−0.94)
Loan/ Assets −3.17 (−0.93) −3.45 (−0.83)
Term Spread 0.11 (0.86) 0.21∗ (1.91)
Intercept 4.12 (1.32) −0.26 (−0.08)
R2 0.51 0.55
N 248 248
Bond cov. = yes 49 49
Cov. (in loan)=yes 93 160
This table presents the results of the probit regression for financial covenant inclusion in publicbonds. Covenants in bonds and loans are described in Appendices A and B. Regression variablesare described in Appendix C. Standard errors are adjusted for clustering at the firm level. t-statsare provided in parentheses. ***, **, * indicates statistical significance at the 1%, 5%, and 10%respectively.
32
Tab
le9:
Oth
erlo
anco
venan
tsan
dunce
rtai
nty
Col
late
ral
rest
rict
ion
sD
ivid
end
rest
rict
ion
sA
sset
Sw
eep
Deb
tSw
eep
Equ
ity
Sw
eep
Est
imat
et-
stat
Est
imate
t-st
at
Est
imate
t-st
at
Est
imate
t-st
at
Est
imate
t-st
at
Pri
ceU
nce
rtai
nty
−1.
15(−
0.84
)0.7
5(0.4
8)
−1.5
6(−
1.1
7)
1.76
(1.2
1)
0.21
(0.1
3)
Fu
ture
sP
rice
0.03
∗∗∗
5.64
0.0
11.2
1−
0.0
01
−0.2
5−
0.0
04
(−0.8
0)
−0.0
01
(−0.
18)
Siz
e−
0.65
∗∗∗
(−5.
99)
−0.2
1∗∗
(−2.0
4)
0.0
1(0.2
2)
0.21∗∗
(2.2
3)
0.26∗∗
(2.5
1)
Log
MT
B−
0.66
∗∗∗
(−3.
31)
−0.4
5∗∗
(−2.1
0)
−0.1
6(−
0.8
5)
−0.4
3∗
(−1.7
3)
−0.5
2∗
(−1.8
0)
Lev
erag
e1.
48∗∗
∗(2.9
0)0.5
8(1.1
4)
0.8
2∗
(1.6
6)
0.76
(1.1
6)
1.54∗∗
∗(2.5
9)
PP
E/A
sset
s−
0.30
(−0.4
6)0.0
5(0.0
7)
−0.4
6(−
0.6
3)
0.82
(0.9
3)
0.43
(0.5
7)
EB
ITD
A/A
sset
s1.
14(0.7
7)0.0
4(0.0
2)
1.1
6(0.6
1)
−0.6
6(−
0.3
3)
2.84
(1.0
7)
Log
MA
TU
RIT
Y−
0.03
(−0.2
5)0.1
5(0.9
4)
−0.1
7(−
1.0
4)
−0.2
1∗
(−1.6
5)
−0.4
6∗∗
∗(−
3.6
2)
Loa
n/
Ass
ets
−0.
03(−
0.96
)0.0
3(0.1
9)
−0.1
0(−
1.0
6)
−0.0
5(−
0.3
2)
−0.1
4(−
0.7
1)
Ter
mS
pre
ad0.
10∗
(1.6
8)−
0.0
4(−
0.5
1)
−0.0
3(−
0.4
2)
−0.0
6(−
0.5
7)
−0.2
3∗∗
∗(−
2.5
9)
Per
form
ance
Pri
cin
g0.
03(0.1
8)−
0.0
6(−
0.3
1)
−0.0
1(−
0.0
5)
−0.1
3(−
0.6
1)
0.07
(0.3
2)
Rat
ed0.1
1(0.4
5)0.4
1∗
(1.7
7)
0.0
6(0.2
0)
−0.3
6(−
1.0
5)
−0.9
3∗∗
∗(−
3.8
9)
Nu
mb
erof
Len
der
s0.
002
(0.2
7)0.0
0(0.3
9)
0.0
1(0.7
0)
0.00
(0.1
0)
0.00
(−0.2
4)
DealPurpose
Acq
uis
.li
ne
0.06
(0.1
8)0.1
1(0.1
9)
−0.1
8(−
0.4
7)
0.05
(0.1
0)
−0.3
8(−
0.6
8)
Cor
p.
pu
rpos
es−
0.40
(−1.
42)
−0.4
3(−
0.9
0)
−0.4
1(−
1.0
0)
−0.6
1(−
1.3
4)
−0.7
6(−
1.5
4)
Deb
tR
epay
.−
0.40
(−1.
63)
−0.3
8(−
0.8
0)
−0.4
3(−
1.2
7)
−0.7
9(−
1.6
2)
−0.6
8(−
1.28)
Tak
eove
r−
0.10
(−0.
34)
−0.0
3(−
0.0
8)
0.2
4(0.6
3)
0.48
(1.0
6)
0.56
(1.0
5)
Wor
k.
cap
.0.1
5(0.5
1)−
0.1
5(−
0.2
8)
−0.2
7(−
0.7
0)
−0.3
6(−
0.7
5)
−0.7
7(−
1.49)
Inte
rcep
t4.1
8∗∗∗
(4.4
6)1.9
4∗∗
(2.0
5)
0.0
5(0.0
6)
−2.8
3∗∗
∗(−
2.7
4)
−2.2
4∗∗
(−2.2
3)
R2
0.35
0.0
70.
06
0.1
50.2
3
N66
0531
502
501
500
coven
ant
=yes
482
453
93
46
44
Th
ista
ble
pre
sents
the
resu
lts
ofth
ep
rob
itre
gre
ssio
nfo
roth
erlo
an
coven
ant
incl
usi
on
inp
riva
telo
an
s.C
oven
ants
inlo
an
sare
des
crib
edin
Ap
pen
dix
B.
Reg
ress
ion
vari
able
sare
des
crib
edin
Ap
pen
dix
C.
Sta
nd
ard
erro
rsare
ad
just
edfo
rcl
ust
erin
gat
the
firm
leve
l.t-
stats
are
pro
vid
edin
par
enth
eses
.**
*,**
,*
ind
icat
esst
ati
stic
al
sign
ifica
nce
at
the
1%
,5%
,an
d10%
resp
ecti
vel
y.
33
Tab
le10:
Oth
erb
ond
cove
nan
tsan
dunce
rtai
nty
Investm
entrestrictions
Merger
restrictions
Dividen
drestrictions
AdditionalDeb
trestrictions
Estim
ate
t-stat
Estim
ate
t-stat
Estim
ate
t-stat
Estim
ate
t-stat
Price
Uncertainty
1.59
(1.08)
−1.28
(−0.93)
0.90
(0.55)
0.27
(0.15)
Futu
resPrice
0.02∗
(1.74)
0.01∗∗
(2.08)
0.02∗∗
(2.57)
0.02∗∗
∗(2.99)
Size
−0.55∗∗
(−2.42)
−0.21
(−0.69)
−0.95∗∗
∗(−
4.41)
−0.81∗∗
∗(−
4.53)
LogMTB
−0.40
(−0.69)
−0.56
(−1.08)
−1.08∗
(−1.94)
−0.81∗
(−1.79)
Lev
erage
1.61
(1.50)
4.42∗∗
(2.28)
1.97
(1.31)
0.45
(0.32)
PPE/Assets
2.14
(1.50)
−4.23∗
(−1.65)
2.17
(1.32)
1.48
(1.10)
EBIT
DA/Assets
−3.72∗
(−1.79)
−2.93
(−1.17)
−3.21
(−1.31)
−3.42
(−1.53)
LogMATURIT
Y−0.07
(−0.34)
0.22
(1.24)
−0.42
(−1.84)
−0.20
(−1.29)
Loan/Assets
2.52
(1.54)
−0.20
(−0.07)
0.20
(0.20)
0.63
(0.96)
Term
Spread
−0.25∗∗
∗(−
2.86)
−0.30∗∗
(−2.22)
−0.03
(−0.23)
−0.20∗∗
(−2.11)
Intercep
t0.96
(0.39)
5.21∗
(1.79)
4.93∗∗
(2.25)
4.53∗∗
∗(2.73)
R2
0.34
0.19
0.46
0.39
N249
249
249
249
coven
ant=
yes
68
211
69
65
Sale
leaseback
restrictions
Neg
ativePledgerestrictions
Even
trisk
restrictions
Poisonputrestrictions
Estim
ate
t-stat
Estim
ate
t-stat
Estim
ate
t-stat
Estim
ate
t-stat
Price
Uncertainty
−2.38
(−1.35)
−1.53
(−0.96)
−4.88∗∗
∗(−
2.65)
3.61∗∗
∗(2.61)
Futu
resPrice
−0.01
(−0.81)
−0.01
(−1.43)
0.02∗∗
∗(2.58)
0.03∗∗
∗(3.14)
Size
0.24
(1.21)
0.37∗∗
∗(2.71)
−0.21
(−1.17)
−0.60∗
(−1.73)
LogMTB
−0.38
(−0.93)
0.45
(1.27)
0.32
(0.66)
−1.88∗∗
(−2.49)
Lev
erage
1.37
(1.05)
−0.64
(−0.68)
0.32
(0.21)
1.05
(0.47)
PPE/Assets
0.91
(0.39)
−0.47
(−0.30)
0.81
(0.48)
4.89∗
(1.83)
EBIT
DA/Assets
9.68
(1.46)
−12.26
(−1.10)
−11.80∗
(−1.85)
4.84
(1.27)
LogMATURIT
Y−0.01
(−0.04)
−0.18
(−1.47)
−0.01
(−0.06)
0.63∗∗
∗(4.78)
Loan/Assets
−1.29
(−1.22)
−0.35
(−0.27)
1.39
(0.75)
11.22∗∗
∗(4.13)
Term
Spread
−0.03
(−0.27)
−0.07
(−0.48)
0.05
(0.39)
−0.16
(−1.86)
Intercep
t−2.35
(−0.90)
0.19
(0.08)
2.22
(0.96)
−3.76
(−0.89)
R2
0.10
0.14
0.16
0.50
N249
249
249
249
coven
ant=
yes
121
210
190
103
Th
ista
ble
pre
sents
the
resu
lts
ofth
ep
rob
itre
gre
ssio
nfo
roth
erb
on
dco
ven
ant
incl
usi
on
inpu
bli
cb
on
ds.
Cov
enants
inlo
an
sare
des
crib
edin
Ap
pen
dix
B.
Reg
ress
ion
vari
ab
les
are
des
crib
edin
Ap
pen
dix
C.
Sta
nd
ard
erro
rsare
ad
just
edfo
rcl
ust
erin
gat
the
firm
leve
l.t-
stat
sar
ep
rovid
edin
par
enth
eses
.***,
**,
*in
dic
ate
sst
ati
stic
al
sign
ifica
nce
at
the
1%
,5%
,an
d10%
resp
ecti
vely
.
34
Table 11: Maturity, performance pricing, and uncertainty
Performance Pricing (loan) Loan Maturity Bond Maturity
Parm Estimate t-stat Estimate t-stat Estimate t-stat
Price Uncertainty 1.80∗∗∗ (2.63) −1.37∗∗∗ (−3.94) −1.31∗∗ (−2.14)
Futures Price 0.002 (0.78) 0.004∗∗∗ (2.90) −0.003∗∗ (−2.38)
Size 0.10∗ (1.71) −0.02 (−0.75) 0.03∗ (0.64)
LogMTB 0.11 (0.77) 0.02 (0.27) 0.20 (1.23)
Leverage −0.22 (−0.77) 0.01 (0.09) −0.79∗∗ (−2.27)
PPE/Assets 1.46∗∗∗ (2.84) 0.34∗ (1.75) 0.49 (1.17)
EBITDA/Assets 4.34∗∗ (2.37) −0.01 (−0.01) −0.67 (−0.63)
LogMATURITY −0.03 (−0.39)
Loan/ Assets 0.06 (0.90) −0.01 (−0.46) −0.51∗∗ (−2.21)
Term Spread −0.13∗∗ (−2.53) −0.07∗∗∗ (−3.13) −0.08∗ (−1.74)
Performance Pricing −0.02 (−0.33)
Rated −0.27∗ (−1.65) 0.01 (0.21)
Number of Lenders 0.05∗∗∗ (3.28) 0.01∗∗∗ (3.62)
Deal Purpose
Acquis. line 0.63∗∗ (2.04) 0.09 (0.56)
Corp. purposes 0.12 (0.48) 0.13 (0.83)
Debt Repay. 0.89∗∗∗ (3.27) 0.32∗∗ (2.17)
Takeover 0.94∗∗∗ (2.81) 0.04 (0.25)
Work. cap. 0.80∗∗∗ (2.76) 0.12 (0.80)
Intercept −3.32∗∗∗ (−5.62) 1.16∗∗∗ (4.55) 2.64∗∗∗ (5.51)
R2 0.19 0.07 0.12
N 897 897 249
covenant = yes 392
This table presents the results of the probit regression for performance pricing inclusion in private loans,and the OLS regressions for loan and bond maturity. All variables are described in Appendix C. Standarderrors are adjusted for clustering at the firm level. t-stats are provided in parentheses. ***, **, * indicatesstatistical significance at the 1%, 5%, and 10% respectively.
35
