Guaranteed Corporate Bonds

Fang Chen, Jing-Zhi Huang, Zhenzhen Sun, Tong Yu∗

March 2014

∗Chen is at the College of Business, University of New Haven. Huang is at the Smeal College of Business,Pennsylvania State University. Sun is at the School of Business, Siena College. Yu is at the College of BusinessAdministration, University of Rhode Island. Emails: fchen@newhaven.edu, jxh56@psu.edu, zsun@siena.edu,and tongyu@uri.edu. All errors are our own. Comments are welcome.

Guaranteed Corporate Bonds

Abstract

This paper investigates the use of guarantee by corporate bond issuers. Guarantees provide

a protection to corporate bondholders to cover their losses in the event of default, and have

been widely used in the corporate bond market since late 90s. However, this important

financing method has received very little attention in the academic literature. Using a large

sample of new issues of corporate bonds over the period 1993–2012, we present empirical

evidence that guarantees indeed enhance bond credit quality. Specifically, given the same

issuer rating, the average rating of bonds issued with guarantees significantly exceed that of

bonds not issued with guarantees. Further, this positive impact of guarantees on bond rating

concentrates on issuers with BB or BBB ratings. Yet, guarantees do not have a significant

impact on bond yields. We also find that firms using guarantees tend to be more financial

constrained, subject to more severe agency problem, and have lower firm ratings and higher

default probabilities, in comparison to non-guaranteed bond issuers.

1 Introduction

It is known that many corporate debt covenants are used to protect bondholders from default

risk. One recent development in the corporate bond market is the use of guarantees provided

by firms themselves when they issue corporate bonds, a new kind of covenants introduced

in mid 1990s. For instance, based on the Mergent Corporate Bond Database, nearly 40

percent of corporate bonds (in terms of issuance amount) issued in 2009 are embedded

with guarantees. guarantees provide investors a protection through an internal or a third-

party arrangement which secures the payments when issuers are at default. Owing to its

significant role in the financial market during the 2007-09 crisis, the use of guarantees, also

known as a common type of credit enhancements, catches a wide attention among academia

and practitioners (e.g., Demyanyk and Hemert, 2011; Dou, Liu, Richardson, and Vyas,

2012). However, the spotlight so far has been on the packaging of credit enhancements

with commercial loans, mortgage- and asset-backed security sector, and municipal bonds.1

The use of guarantees in the corporate bond sector has received very little attention in the

literature.

Using a large sample of corporate bond issuance from 1993 through 2012, we empirically

analyze the impacts and determinants of the use of guarantees. Based on our finding, there

is a striking firm rating difference between bond issuers using and not using guarantees—

roughly two thirds of guarantee users have either “high” speculative ratings (i.e., S&P’s BB+,

BB, and BB- ratings) or “low” investment grade grades (S&P’s BBB+, BBB, BBB-) while

two thirds of issuers not using guarantees receive median (A+, A, A-) or low investment

grades. This is consistent with the conventional wisdom that bonds with relatively poor

credit worth may be inclined to issue securities with guarantees.

Investors of guaranteed bonds are protected due to the presence of the third party to

make payments when the issuers default. Such an institutional arrangement of guaranteed

bonds gives rise to the first set of questions of the study: do guarantees help to improve

1On the applications in mortgage backed securities, see Adelson (2003), Ashcraft and Santos, 2009; Griffinand Tang (2012); Arora, Gandhi and Longstaff (2012). For the applications in municipal bonds, see Braswell,Nosari and Browning (1982), Kidwell, Sorensen and Wachowicz (1987), Nanda and Singh (2004).

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the ratings of bond issues? and similarly, do guarantees help reduce bond yields? To ad-

dress these questions, we perform regression analysis on bond ratings and yields at issuance

for a sample including bonds with and without guarantees. In the rating regression, the

dependent variable of the regression is the bond rating at issuance. The key explanatory

variables is the interaction between a dummy indicating whether the issue is a guaranteed

bond and the issuer’s rating. The setup allows us to compare the average rating of bonds

packed with guarantees with the rating of non guaranteed bonds holding issuers’ ratings

constant. Following the literature (e.g., Ashcraft, Goldsmith-Pinkham, and Vickery, 2009),

we additionally control for firm size, tangible asset, debt level and bond characteristics. The

result reveals a positive role of guarantees – the average rating of guaranteed bonds is 10%

higher than that of non-guaranteed bonds in terms of the numerical expression of rating.

The further examination shows that the most significant rating enhancement occurs within

the firms whose ratings are close to the default, and the second significant effect concentrates

on the firms that have ratings close to the investment and junk grade cut-off. Compared

with the credit ratings of non-guaranteed bond issuers, guaranteed bond issuers on average

are poorer in their corporate financial ratings.

One may expect the improved ratings of guaranteed bonds result in lower yields of these

bonds. However, this is not true based on our comparison analysis that compares the initial

yields between guaranteed and non-guaranteed bonds. Different from the result of rating

regressions, we find that guarantee doesn’t have any significant impact on yield in general.

We further dig into this issue by analyzing the guarantee effect on bond yields conditioned

on corporate financial ratings. We interact a bond’s guarantee use and the issuer’s rating

and see how the variable affects bond yields. The result shows guarantee generally has no

significant impact on bond yields except when the firm rating is BBB-, a rating at the edge

of investment grade. Even worse, when firm ratings are at CC (a default rating) or AAA

(the highest credit rating), the yields of guaranteed bonds are higher than yields of non-

guaranteed. Investors, or more precisely underwriters, do not take the guarantee as positive

signal of the bond’s credit prospect.

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Why just a subset of firms using guaranteed bonds? This is our second question. It is

known that guarantees are arranged internally – either subsidiaries (the super majority case)

or parent companies provide guarantee to bond investors. Such an arrangement gives rise

to an easily conceived reason for the restrained use of guarantees: non-guaranteed bonds

may be issued by firms not having any subsidiary or not associated with a parent firm. This

conjecture is however quickly dismissed – in our sample, roughly 85 percent of firms have

a parent or subsidiaries. An alternative explanation is that financing cost of guaranteed

bonds could be high. One must recognize that the direct guarantee fee is trivial given that

guarantees are provided internally. However, guarantees occupy the resources of guarantors

in two ways. First, guarantees provided by guarantees typically are unconditional. The

secured guarantee use various collateral provided by the guarantors. Moreover, the covenants

of debt with guarantees normally have restrictions on the guarantors. The covenants may

restrict the guarantors to pay dividends, making loans or transferring any of their property

to the issuers or between subsidiary guarantors.

The third question of the study is what factors drive corporate use of guarantees. We

hypothesize that the primary force to be financial constraints faced by corporates. Based on

Myers and Majluf (1984)’s pecking order theory, internal capital is least costly and a firm

has to raise external capital, debt financing is preferred relative to equity financing as debt

financing is less expensive. Financial constraints, however, hurt a firm’s capacity to raise

debt from investors (e.g., Kaplan and Zingales, 1997, Lamont, Polk and Saa-Requejo, 2001,

and Baker, Stein and Wurgler, 2002). This will lower firms’ ability to achieve their optimal

investments. guarantees, which offer a guarantee to bondholders when the firm cannot afford

their debt payments, improve corporate credit worthiness thus enable these firms to raise

debt.

Corporates do not have the equal access to investment opportunities. For firms having

poor investments opportunities, the purpose of using guarantees could lie on the managerial

agency problems. Jensen (1986) argued that the managers may increase the size of the firms

for their own perquisite, i.e., the management’s “empire-building” tendency. For instance,

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a firm may adopt a new project or acquire another regardless of the return (Stulz, 1990;

Titman, Wei, and Xie, 2004; Cooper, Gulen, and Schill, 2008). As noted in the litera-

ture, a consequence of the agency conflict (e.g., in the firms with high free cash flow and

low growth opportunities) is the overinvestment (e.g., Opler and Titman, 1993). Aligned

with the agency conflict explanation, the use of the subsidiaries/parents guarantee may be

a consequence of the empire-building incentive of issuers rather than investing in positive

net present value (NPV) investment opportunities. Issuers use the resource of their sub-

sidiaries/parents to achieve this goal. Under this explanation, firms with poorly aligned

interest between management and shareholders are more likely to use guarantees.

We perform logistic regressions to look into the determinants of bond issuers’ guarantee

uses. Regarding the financial constraints theory, firm ratings are negatively associated with

the use of guarantees. Our finding also reveals that firms with less collateral (proxied by

cash and tangible assets) have a higher chance to use guarantees. The results shows a strong

evidence that firms with financial constraints tend to use guarantee to increase debt capacity.

Regarding the influence of agency problems on the use of guarantees, we find that high

degree of agency problem (as proxied by high free cash cash/low growth opportunities) are

positively associated with the use of guarantees. When we use different proxies for of growth

opportunities, we find consistent results. The finding supports the idea that the use of

subsidiaries do not align with the growth opportunities of the parents. Instead, the use of

guarantees may be a consequence of empire-building of the parents or ”corporate socialism”

among the subsidiaries. Moreover, since overinvestment is more likely to occur in firms

with low grow opportunities (e.g., Jensen, 1986; Lang and Litzenberger, 1989), the positive

relation between the guarantee and high free cash cash/low growth opportunities implies an

overinvestment problem in firms using guarantees.

It is interesting to note that some issuers alternatively use guaranteed and non-guaranteed

bonds. For the purpose of shedding light on this cause, we separate the cases that firms

mix their guarantee uses and that firms persistently use guarantees and run a multinomial

regression. The dependent variable is a categorical variables taking three values: it is 0 if

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the firm issues non-guaranteed bonds only; it is 1 if the firm issues a mix of non-guaranteed

bonds and guaranteed bonds; and it is 2 if the firm issues guaranteed bonds only. We find

that the lower firm rating, higher agency problems, less cash, less tangible, larger size, and

less debt increase the odds of issuing a mix of non-guaranteed and guaranteed bonds than

that of issuing guaranteed bonds only.

Finally, as a robustness check on the agency problems explanation, we conduct sub-

sample analysis. Chang and Mayers (1992) and Masulis, Wang and Xie (2009) find that

managerial voting power is positively associated with the agency problem. Based on this

finding, we partition the sample into three even-sized groups based on managerial voting

power. Dummy variable highMgtVote equal one if the issuer is in the highest managerial

voting rights group and zero otherwise. The intuition with higher managerial voting rights

indicates a weaker corporate governance and therefore stronger agency problems. We are

likely to observe a stronger tendency of guarantee uses in the high managerial voting rights

group than that in the low managerial voting rights group. We run the logistic regression on

the use of guarantee. The coefficient of highMgtVote is positive and significant. The results

of the subsamples regression yield further supports to the hypothesis that firms with more

agency problem are more likely to use guarantees.

The remainder of the paper is organized as follows. Section 2 provides the institutional

background of guarantees used in the corporate bond market. In Section 3, we introduce the

hypotheses. Section 4 describes our sample and data used in the sample. Section 5 presents

empirical findings. Section 6 concludes.

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2 Institutional Background on Credit Guarantee for

Corporate Bonds

Being a potential means to alleviate credit risk of a corporate bond, in recent years a sig-

nificant proportion of corporate bonds are issued with guaranties.2. The ratio of corporate

bonds with guaranties increased from 2% in 1990 to 26% in 2010 in terms of dollar value.

Overall, corporate bonds have been sold for USD16,711 billion (par value) during the period

of 1993 to 2012. Of bonds issued in the same period, 17% (USD2,853 billion), by dollar

value, were issued with guaranties.

Guaranties protect bondholders from defaults through providing the guarantee to the

payment of principal and interest payments of the underlying bonds in the event of issuer

defaults. Based on the Mergent database, firms employ three major types of credit enhance-

ments: i) guarantee, ii) bond insurance, and iii) letter of credit (LOC). They respectively

account for 96%, 3% and 1% of the total credit enhancement over the entire sample period

from 1993 to 2012. The distinction across a guarantee, bond insurance, and letter of credit

are obvious. Parent/subsidiary guarantee is an internal arrangement which requires a fil-

ing to SEC, while there is no such requirement for insurance or letter of credits which is

treated as an external contract. Because basically all bond insurances have an AAA rating,

it is common for the insured bonds to have an AAA rating. By contrast, bonds with par-

ent/subsidiary guarantee have their ratings varying from AAA as the highest to D as the

lowest. We focus on guaranties in this analysis because the super majority of guaranties are

provided through corporate guaranties (96% among guaranties).

There are several distinct features associated with corporate guaranties. First, guar-

anties are typically arranged internally where guarantors are either parent companies or

subsidiaries. In our sample, the majority of the guarantors (90%) in our sample are the sub-

sidiaries of the issuers. When the parent debt is guaranteed, the full consolidated financial

statements of the parent company (together with the subsidiaries) are used in the rating

2A recent suvey shows that the majority of over 1,100 risk managers from major international corproatesconsider credit risk as one of the most important risk exposures (Bodnar, et al., 2011

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determination. See e.g., DBRS Criteria (2010) for the reference. For instance, MGM Mirage

issued a bond using all its domestic subsidiaries as the guarantors . The aggregate par value

of the bond issuance is USD225,000,000.

Second, internally arranged parent/subsidiary guaranties typically involve a low cost. An

explicit expense of the transaction is the cost of filing to Securities and Exchange Commis-

sions (SEC), which is not materially high. An additional cost is the guarantee cost. Since

it is considered as an arm’s-length transaction, subsidiary guarantee fee is typically nominal

and seldom paid by parent companies or subsidiaries.

Third, there are indirect costs associated with guarantee uses. A parent firm and its

subsidiaries are distinct legal entities. Without guarantee, if the subsidiary is at default, its

debtholders have not recourse to the parent unless the parent is involved in some wrong-doing

(Thomson, 1991). However, with a guarantee from the parent, the subsidiary debt holders

have recourse to its parent guarantors should the subsidiary default. In practice, most

of the guaranties to the public issuers were conducted jointly by most or all its domestic

subsidiaries. By law, the guaranties are senior obligation of the guarantor, ranked equally

with all other existing and future senior debt of the guarantors in right of payment. It is

also a common practice to contain the covenants in the indenture to limit the payments of

the subsidiaries or parent companies (whoever the guarantor is), such as dividend payout,

shares repurchase or making early principle payment prior to the schedule, among other

arrangements. Consequently, this arrangement helps to reduce credit risk of guaranteed

bonds.

Fourth, it is possible for a subsidiary to achieve a higher rating than that of a parent or

a consolidated family if it is insulated. Generally, the rating of a subsidiary can exceed that

of the parent firm by up to three notches (Standard & Poor’s, 2000).3 When credit rating

of the guarantor is downgraded, the rating agency will reevaluate the bond and adjust the

rating.

Finally, there is no evidence that the use of guarantee lowers the yields to maturity at

3Insolation means parent companies may be prevented or restricted from accessing the resources of thesubsidiaries. For specific insulation factors, see Standard & Poor’s (2000).

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bond issuance. For example, the Teck Resources Ltd. issued six corporate bonds in 2011 and

2012, three with guaranties (issued in 2012) and three without guaranties (issued in 2011).

The yield spreads (the difference in the yields of the corporate bond and the treasury bonds

with corresponding maturities) of guaranteed bonds are respectively 195bps, 235bps, 285bps

while those with guaranties are 150bps, 165bps, and 190bps.4

3 Hypotheses

We propose the hypotheses in the section. The first empirical question is how guaranties

affect bond ratings at issuance? As noted in Section 2, guaranteed bondholders have recourse

to the guarantors in the event of the issuers’ default. As a result, it is likely that guarantee

reduces the credit risk of the guaranteed bonds. We postulate the following hypothesis.

H1. Guarantee has an positive impact on bond rating.

Retaining investment grades is a critical condition for bond issues. There is a signifi-

cant drop in bond yields when bond ratings rise from non-investment grades to investment

grades. Institutional investors typically are subject to regulatory scrutiny when investing

in junk bonds (see, e.g, Ellul, Jotikasthira, and Lundblad, 2011). The higher possibility of

staying at investment grade or increasing from junk to investment grade, the more potential

benefit from guarantee. This reasoning leads to the following hypothesis:

H1a. The positive rating effect of guarantee is highest when firm ratings are closer to the

cutoffs of investment and noninvestment grades.

Firms with financial constraints have difficulty of accessing the capital because of their

4This does not necessarily indicate that yield spreads of guaranteed bonds are higher as in the examplehere all guaranteed bonds are issued in 2012, a period that the average yield spread is greater than that of2011.

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low creditworthiness. These firms either cannot attract enough investors or have to pay a

high price. Credit ratings is an important factor in the requirement by several regulations

on financial institutions’ and other intermediaries’ investments in bonds. For example, regu-

lations restrict banks from investment in speculative-grade bonds since 1936 (e.g., Partnoy,

1999; West, 1973). In 1989, savings and loans were required to completely liquidate their

speculative-grade bonds by 1994 (e.g., Kisgen, 2006). Finally, pension fund guidelines often

prevent bond investments from speculative-grade bonds (e.g., Boot, Milbourn, and Schmeits,

2003).

Since the main benefit of guarantee is to increase the creditworthiness of debt to lower

the cost, firms with financial constraints can benefit from the creditworthiness increase, re-

gardless their heterogeneous characteristics. However, firm specific value like rating, size,

dividend, collateral and rating reflect the firm’s financial constraint situation and have been

widely used as the proxy for financial constraints. In general firms with financial constraints

are more motivated to use guarantee to reduce the financing cost. We therefore postulate

the following hypothesis.

H2. Firms with more financial constraints are more likely to use guarantee.

The puzzle is what drives these firms with financial constraints to use guarantee to

increase their rating and thus debt capacity? Does guarantee align with the best interest of

the parent firms? To answer these questions, we offer two possible explanations. The first

possible reason is the agency problem. If agency problem is strong, then the firm may take

negative NPV project or forfeit positive NPV project. Our paper is on debt issuance, thus

the focus is on the former type of agency problem which leads to overinvestment problem.

Tobin’s Q (the ratio of the market value of assets to the current replacement cost of those

assets) has been used in the literature (e.g. Lang and Litzenberger (1989)) as a proxy for

investment opportunities, with the notion that the managers of a firm with poor investment

opportunities (“low” Q) are likely to overinvest or waste their stockholders’ cash. Using Q as

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a proxy for overinvestment, Lang and Litzenberger (1989) find that returns around dividend

change announcements are significantly more positive for firms with Q less than one than for

firms with Q greater than one. Investment may be the consequence of the empire-building

of parents rather than following the investment opportunities (e.g., Jensen, 1986).

Moreover, in the specific parent-subsidiary corporate structure, the agency problem may

act through another channel. Scharfstein and Stein (2000) argue that a two-tier agency

problem, stemming from misaligned incentives at parents and at divisions, is necessary for

“corporate socialism” in internal capital allocation. Kolasinski (2009) finds the main rea-

son for subsidiary to issue debt is protecting themselves from the ”corporate socialism” and

”poaching” problems in internal capital markets. On other hand, parents’ use of the sub-

sidiary guarantee may be a consequence of empire-building preference of parents rather than

following the investment opportunities (e.g., Jensen, 1986). In both cases, the agency prob-

lem of the parent spurs the use of guarantee regardless the low growth opportunities. We

therefore postulate the following hypothesis:

H3. Firms with more agency problem are more likely to use guarantee.

Another possible reason of guarantee use is the tax benefit from increased debt level.

An optimal debt structure is to max the marginal tax benefit. While firms are near their

debt capacity but do not reach their desired debt structure, they can use external resources

like subsidiary guarantee to expand the debt capacity. Therefore, when examining a firm’s

motivation of using guarantee, it is important to consider not only the firm’s ability to access

the capital, but also its preferred degree of debt capacity.

The more the marginal tax benefit from increased debt capacity, the more likely the firm

will use guarantee. This reasoning leads to the following empirical hypothesis:

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4 Sample and Data

4.1 Sample

Our bond data comes from the Mergent Fixed Income Securities Database (FISD). FISD

provides the information of public bonds by both public and private issuers. We select

U.S. corporate bonds including US corporate debenture, US corporate MTN, asset-backed

security and other US corporate bonds. We exclude the US corporate convertible bonds and

preferred stocks. FISD shows whether the corporate bonds are issued with one of the credit

enhancements: guarantee, letter of credit (LOC) or insurance.

Our data sample period is from 1993 to 2012. In this period, there are 14876 US cor-

porate bonds issued with one of the three credit enhancements, including 14407 guarantees,

466 insurance, 3 LOC. We further identify the guarantors of the guarantees. FISD provides

the guarantor information in two ways. First, FISD directly lists most guarantors as ”Sub-

sidiaries”. Second, FISD list the parent of the issuers. If the parent id matches the guarantor

id, the bond is guaranteed by the parent of the issuer. Among 14407 guarantees, we find

7196 parent guarantors, 3702 subsidiaries guarantor besides 3509 unknown guarantors. The

paper investigates the public issuers only. We limit the data to the issuers that are listed

at the time of bond issuance by matching the CUSIP of the issuers in FISD with that in

CRSP. Also for the rest of the unknown guarantors, we manually search their SEC filings.

The guarantors information are disclosed in the 424B, S-4, 8-k, 10-Q or 10-K. From 1993

to 2012, there are 2278 guaranteed bonds issued by public firms, including 2012 bonds with

subsidiaries guarantees, 130 bonds with parents’ guarantees and 144 bonds with insurance.

For the purpose of comparison, the sample includes both bonds with guarantee and those

without guarantee. Follow the convention on bond literature, we exclude financial firms (SIC

codes 6000-6999) and regulated utility firms (SIC codes 4900-4999). From FISD, we obtain

the bond issuance information such as bond maturity, initial bond yield, bond ratings, the

indicators for callable bonds, putable bonds, and secured bonds, and so on. Then we merge

sample bonds with the Compustat by the issuer ID. We obtain firm characteristics data

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from the Compustat including total assets, cash, operating income, tangible assets, total

debt, free cash flow, and sales.

Firm ratings come from Compusta and are the most recent S&P long term issuer ratings

before bond issuance. Bond ratings are provided by FISD. We first use S&P rating. If S&P

rating is missing, we use Moody’s rating. If both S&P and Moody’s ratings are missing, we

use Fitch’s rating. We delete firms with missing data on total assets, bond ratings and firm

ratings. After the screening, the sample includes 8321 corporate bonds issued by 2214 public

firms from 1993 to 2012. Among them, 794 bonds were offered with guarantee and 7527

bonds without guarantee. Figure 1 provides a detailed depiction of the number of bonds

with guarantee issued by publicly listed companies.

Since all the guarantees are internal arrangement either by the parents or the subsidiaries,

it is natural to examine whether the issuer has the parent or the subsidiaries. We use Capital

IQ to search the information manually. Capital IQ collects companies’ information from their

SEC filings and the information includes the parent and the subsidiaries of the companies in

its corporate tree section. Among 2214 public firms, we identify 1035 firms with subsidiaries

only, 347 firms with parents only, 424 firms with both subsidiaries and parents, 409 firms

without both subsidiaries and parents.

We first provide a distribution of guaranteed bonds in the sample in Table 1. We find

that the percentage of guaranteed bonds in the sample is about 12% in terms of both the

number of bonds and the aggregate value of bonds. We divide the sample firms into public

firms and private firms. In terms of the number of bonds, the percentage of guaranteed bonds

issued by public firms are higher than that issued by private firms (13.02% vs. 10.79%). The

percentage of aggregate value of bonds issued by public firms is on average 8.50% with the

range between 1.19% in 1994 and 20.09% in 2009. However, the average percentage of the

aggregate value of bonds issued by private firms is 15.76% with the range between 0.78% in

1993 and 41.54% in 2009. In sum, public firms have more guaranteed bonds but less bond

value than private firms.

Secondly, we plot the time-series percentage of guaranteed bonds in terms of the aggregate

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value and the total number of issues by private and public firms together and present the plot

in Figure 2. In Panel A, the percentage of guaranteed bonds in terms of the aggregate value

increases over time and peaks in 2009. Panel B shows that the percentage of guaranteed

bonds in terms of the number of issues varies and peaks in 2009 as well. The average

percentages of guaranteed bonds are about 12% in terms of both the aggregate value and

the number of issues. This finding is consistent with that in Table 1.

Finally, we divide sample bonds into seven groups based on the issuers’ ratings. We

assign number 1 to group 1 if firms’ ratings are AAA, AA+, AA, or AA-; number 2 to group

2 if firms’ ratings are A+, A, or A-; number 3 to group 3 if firms’ ratings are BBB+, BBB,

or BBB-; number 4 to group 4 if firms’ ratings are BB+, BB, or BB-; number 5 to group 5

if firms’ ratings are B+, B, or B-; number 6 to group 6 if firms’ ratings are CCC+, CCC, or

CCC-; and number 7 if firms’ ratings are CC, C, or D. We then compute the percentage of the

aggregate value in each group for guaranteed bonds and non-guaranteed bonds respectively.

The plot is presented in Figure 3. We find that most of guaranteed bonds are issued by

firms with the ratings of BB+, BB, or BB- while most of non-guaranteed bonds are issued

by firms with the ratings of A+, A, or A-. It suggests that firms with lower ratings are more

likely to issue bonds packed with guarantee.

4.2 Data and Summary Statistics

We provide descriptive statistics of guaranteed bonds and non-guaranteed bonds as well as

their issuers’ characteristics in Table 2. The variables are computed as of the year end before

the debt offerings. The financial data is from Compustat and stock data is from CRSP.

We find a dramatic difference between guaranteed bonds and non-guaranteed bonds in

Panel A of Table 2. For instance, non-guaranteed bonds have higher firm ratings than

guaranteed bonds (17.79 vs. 16.30). In terms of the size, they are similar (8.40 vs. 8.34).

The mean (median) market-to-book of firms with guaranteed bonds is 1.53 (1.41) and this

value increases to 1.87 (1.53) for firms without guaranteed bonds. The results show that

the firms with guarantee have a lower market to book than those without guarantee. Two

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proxies of growth opportunities, P/E and sales growth, have a different result in two samples.

the mean and median P/E of the guarantee firms are significantly lower than those of non-

guarantee firms. In contrast, sales growth for guarantee firms is significantly higher for the

guarantee firms that for the non-guarantee firms. The implication of this difference is that

P/E and sales growth may capture a different aspect of growth opportunities. Considering

this possibility, we estimate the regression for the high growth and the low growth samples

using P/E and sales growth respectively. The S&P long-term domestic debt rating for

guarantee firms is lower than that for non-guarantee firms.

As shown by Table 2, bond-issuing firms with guarantee tend to have a higher debt level,

lower ROA, lower market-to-book, lower P/E, less free cash flow, lower long-term credit

rating by S&P, higher sales growth than those without guarantee. Except free cash flow and

inventory, all the mean differences are statistically significant.

Panel B of Table 2 shows that the correlation matrix of variables used in the analysis.

Firm ratings are positively correlated with firms’ size, operation profits, bonds’ par value,

free cash flow, rating distance, and market to book ratio but negatively correlated with the

marginal tax rate, and sales growth. For instance, the correlation between firm ratings and

firm size is 0.66.

5 Empirical Results

5.1 Effect of Guarantee on Bond Ratings

In this section, we first examine the impact of guarantee on the bonds rating at issuance.

The dependent variable is bond rating at issuance. The regression specifications are specified

as follows.

Bond rating = α0 + α1Guarantee + α2Firm variable + α3Bond variable + ε (1)

where Guarantee is the indicator variable equal to one if the bond is guaranteed bond and

zero otherwise. We include a set of firm variables and bond variables as the control variables.

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Firm variables are firm rating (FirmRat), firm size (Size), the percentage of tangible assets

in the total assets (Tangible), profit (Profit) and debt ratio (Debt) (e.g., Kovner and Wei,

2012). The bond variables include bond maturity (Mat), bond issuance amount (Par),the

dummy variable for secured bonds (Secure), the dummy variable for callable bonds (Call),

and the dummy variable for putable bonds (Put). Finally, we add the industry dummies

and control for the fixed year effect.

The regression results are reported in Table 3. In column (1), we include Guarantee only.

The coefficient on Guarantee is significantly negative, suggesting that the low rating or low

quality of Guarantee firms or negative impact of Guarantee. In column (2), we control for

the firm rating. The coefficient of guarantee dummy turns to positive and significant at 1%

level. We add firm variables in column (3) and include both firm and bond variables in last

column. We find that The coefficient of Guarantee is positive and significant at 5% level.

The result confirms a positive relation between guarantee and bond rating. On average,

guarantee increases bond rating by 0.110. The coefficients of firm ratings, firm size, profit,

par value, and the dummy for secured bonds are all significantly positive. The coefficient of

debt ratio and the dummy for putable bonds is significantly negative. Overall, the finding

shows a positive impact of guarantee on bond ratings and therefore provide strong support

to our hypothesis H1.

We next examine whether the impact of guarantee on bond rating is conditional on the

firm rating.To answer this question, we perform the regressions of the guaranty and firm

ratings on the change in bond ratings relative to firm ratings. The dependant variable is

the bond rating at issuance. Then we identify the firm ratings in which there are both

guaranteed bonds and non-guaranteed bonds. The firm ratings meeting the criteria are

AAA, BBB+, BBB, BBB-, BB+, BB, BB-, B+, B, B-, CCC+, CCC and CC. We interact

the guarantee dummy with the firm rating dummies respectively. We also control for the

firm rating, other firm variables and bond variables as in the regressions in Table 3. The

regression specifications are specified as follows.

15

Bond rating = α0 + α1guarantee*Firm Rating + α2Firm variable + α3Bond variable + ε (2)

The dependent variable is bond rating at issuance. guarantee*Firm Rating is one when

the bond is guaranteed and the firm rating is the specified rating. Other independent vari-

ables are the same as defined in the regression of bond rating in Table 3. The result is

reported in Table 4. The coefficient of firm rating is positive and confirms the positive

impact of firm rating on bond rating.When the firm rating is close to the default rating,

the coefficient on guarantee*CCC is 2.708 and the coefficient on guarantee*CC is 5.221 and

significant at 1% level. The coefficient of guarantee*BBB is 0.273. When the firm rating is

close to the investment and non-investment grade cut-off, the coefficient of guarantee*BBB-

is 0.305 and the coefficient of guarantee*BB+ is 0.498. They are all significant at 1% level.

While the firm rating is AAA, the coefficient of guarantee*AAA is -0.508 and significant.

The coefficients for the interaction of Guarantee with other firm rating are not significant.

In sum, the positive impact of guarantee concentrates on the firms with ratings either

close to default or the split of investment and non-investment grade, with the largest positive

impact when firm ratings are close to default. There are not positive influence of guarantee

on bond ratings at other firm ratings. The result provide evidence that impact of guarantee

on bond rating is conditional on the firm rating and support hypothesis H1b.

5.2 Effect of Guarantee on Bond Yield at Issuance

In this section, we examine the impact of guarantee on the bonds at issuance. The dependent

variable is bond offering yield spread which is the difference in the yields of the corporate

bond and the treasury bonds with corresponding maturities.The regression specifications are

specified as follows.

Yield Spread = α0 + α1guarantee + α2Firm variable + α3Bond variable + ε (3)

The independent variables are the same as defined in the regression of bond rating in

Table 3. The results are presented in Table 5. In Column (1), we add the dummy variable

16

Guarantee first. The coefficient is not significant. Then we add more firm rating, other

firm variables and bond variables into column (2) to (4) and find the coefficient of dummy

variable Guarantee is not significant in all specifications. Contract to the result of rating

regressions, we find that guarantee doesn’t have any significant impact on yield in general.

To further explore this issue, we perform the regression conditioned on each firm rating

level. Specifically, we add the key dummy variable as in the rating regression to indicate

the interaction between a guarantee dummy and the issuer’s rating dummy. Similar to the

regression of bond rating, the firm ratings AAA, BBB+, BBB, BBB-, BB+, BB, BB-, B+,

B, B-, CCC+, CCC and CC are selected because there are both guaranteed bonds and

non-guarantee bonds at these firm ratings. The regression specifications are specified as

follows.

Yield Spread = α0 + α1guarantee*Firm rating + α2Firm variable + α3Bond variable + ε (4)

The dependent variable is bond yield spread at issuance. guarantee*Firm rating is one

when the bond is guaranteed and the firm rating is the specified rating. Other independent

variables are the same as defined in the regression of bond rating in Table 3.

The result is reported in Table 6. The coefficient of guarantee*BBB- is -0.497 and suf-

ficient at 1% level. The result shows guarantee lowers the yield spread significantly when

the firm rating is at BBB-. While firm rating is at CC, B+, BB, or AAA, the coefficients

are positive and significant and thus the guaranty increases the yield spread significantly

instead at these firm ratings. For firms at most of the ratings, we do not detect a significant

yield spread difference between two types of bonds. The result reveals the fact that investors

interprets the information of guarantee on bonds conditioned on issuer’s ratings. Specifically,

investors, or more precisely underwriters, do not take the guarantee as positive signal of the

bond’s credit prospect in general.

17

5.3 The Determinants of the Use of Guarantees

5.3.1 Bond-level Analysis

In this section, we examine the determinants of the use of the guarantee. The special

arrangement of subsidiary guarantee requires a firm to have subsidiaries to obtain guarantee.

As presented in Panel A of Table 2, guarantee firms have the largest number of domestic

subsidiaries (39) at rating range (’BBB+’,’BBB’,’BBB-’) while non-guarantee firms have the

largest number of domestic subsidiaries (31) at rating range (’B+’,’B’,’B-’). On average,

guaranteed bonds have 45 domestic subsidiaries and non-guarantee firms have 26 domestic

subsidiaries. The difference is not significant. The appearing question is why non-guarantee

firms do not use subsidiary guarantee while they could, given the increase of bond rating

from guarantee?

To answer this question, we perform logistic regressions to examine the determinants of

firms that consider guaranteed bonds or non-guaranteed bonds (Y). For each bond, firms

either consider guarantee (Y=1) or don’t consider guarantee (Y=0). We consider a set of

factors in a vector x to explain the decision. We model the probability that a firm uses

guarantee as a probit function:

Pr(Y ∗) = Φ(β′X) (5)

where Y* is not observable while we can observe y, Φ(.) denotes the standard normal distri-

bution, X is a set of variables explaining bond issuers’ propensity to use guarantee (discussed

below). The set of parameters β′ reflects the impact of changes in on the probability.

In the setting of probit, we have:

Y =

1, if Y ∗ > 0;

0, if Y ∗ <= 0.(6)

X includes Guarantee dummy and two group of variables: financial constraints and agency

problem. Guarantee is the dummy variable if the bond is packed with guarantee and zero

otherwise.

18

The independent variables are in two groups. The first group of variables proxy for

financial constraint consists of firm ratings FirmRat, firm size Size, cash ratio Cash, tangible

assets Tangible and debt ratio Debt. The second group of variables proxy for agency problem

contains High FCF*Low MB, High FCF*Low SG and High FCF*Low PE. We control for

Moody’s yield spread for corporate bonds, bond maturity, bond issuance amount at issuance,

the dummies for callable bonds, putable bonds, and secured bonds.

The key measure for financial constraints is the credit quality. In this study, credit

quality is measured by firm rating. A firm’s rating is its Standard & Poor’s long-term

issuer rating as recorded in Compustat and reflects a firm’s absolute creditworthiness. We

expect the higher the firm’s rating, the more likely the firm uses guarantee. Therefore, both

coefficients are expected to have positive signs. In addition, we control for the collateral of

the firm. Bernanke and Campbell (1988) advocate that cash and PPE variables can be used

to measure a firm’s collateral for external capital. The profitability is also a typical proxy

for debt capacity in the corporate finance literature. We expect collateral to decrease the

use of guarantee. Specifically, we expect a negative sign of their coefficients.

Size is also a standard measure of financial constraints (e.g., Gilchrist and Himmelberg,

1995; Erickson and Whited,2000; Almeida and Campello, 2007; Li and Zhan, 2010). Small

firms have more information asymmetry than larger firms and lack collateral to back up

their borrowing. Consequently, small firms have limit access to debt market and have more

financial constraints (Whited, 1992). Therefore smaller size firms are more likely to use guar-

antee. However, smaller size firms are less likely to have strong subsidiaries as guarantors.

The sign of the coefficient can be either positive or negative. The net effect is a empirical

test question. Tangible is proxy for collateral.

The agency problem is measured by the indicator of a combination of high free cash flow

and low growth opportunities. In the agency literature, it has been well established that the

higher free cash and lower growth opportunities increase the agency problem (Jenson, 1986;

Opler and Titman, 1993). Following Kolasinski (2009) and Custodio, Ferreira and Laureano

(2013), we use market-to-book ratio as a measure for growth opportunities. Alternative,

19

McConnell and Servaes (1995) use sales growth and P/E ratio as the measures for growth

opportunities.

We divide the sample into high and low free cash flow groups and growth opportunities

group respectively. For the firms in the high free cash flow and low growth opportunities

group, we define it as the firms more likely to have agency problem. As stated in previous

section, agency problem may play an important role in the decision of guarantee use. It

seems reasonable to assume the sign of the coefficients of number of subsidiaries and agency

problems proxies is positive. All data are in the fiscal year before the debt issuance.

We use a set of control variables. Par value (Par) is the total offering amount in $

millions. Time to maturity (Mat) is the maturity of each bond in years. Secured dummy

(Secure) is equal to one if the bond is secured. Call dummy (Call) and Put dummy (Put)

are dummy variables equal to one if the bond has a call or put provision. Moody’s yield

spread for corporate bond is used to control for the timing of bond issuance by a firm. We

use the average monthly yield of Moody’s AAA and Baa bonds.The lower the yield of bonds

in the market, the more likely the firms use guarantee to increase their debt capacity. We

expect a negative sigh for the yield variable.

To control for time-varying macroeconomic factors and industry specific factors, we also

include the fixed year effect and fixed industry effect. To overcome the potential bias in

standard errors from the correlation between firms, we follow Kolasinski’s (2009) approach

in his research of determinants of subsidiary debt. Specifically, Robust standard errors are

clustered at the firm and year level.

The results are reported in Table 7. In column (1), we include the major financial

constraint proxy firm rating and market yield spread. The coefficient of FirmRat is -0.011

and significant at 1% level. As a major measure of financial constraint, the result shows

that firms with more financial constraints are more likely to use guarantee. In column

(2) , we include other financial constraints variables Size, Cash, Tangible and Debt and

bonds variables. The coefficient of Size) is positive and significant. The finding reveals that

the larger firms are more likely use guarantee. The coefficients of (Cash and Tangible are

20

negative and Debt is positive and they are all statistically significant. Overall, the results of

the financial constraint variables confirm that an increase of financial constraint is associated

with an higher possibility of using guarantee. The results support hypothesis H1.

In column (3), we include both firm rating, other financial constraints variables and bond

variables. The result is consistent with the results in column (1) and column (2).

In column (4) - column (6), we add three agency problem proxy variables HighCF*LowMB,

HighCF*LowSG, HighCF*LowPE respectively. The coefficient of High FCF*Low MB is

0.036 and significant at 5% level. When we use the alternative proxy for growth opportu-

nities, the coefficients for High FCF*Low SG is 0.039 and High FCF*Low PE is 0.045 and

significant at 1% level. The results indicate that the more agency problem a firm has, the

more likely it uses guarantee. Our finding supports hypothesis H3.

5.3.2 Multinomial Logistic Regressions on guarantee Uses: Firm-level Analysis

For firms using credit enhancements, there are actual two options. One is to issue a mix

of guaranteed bonds and non-guaranteed bonds and another one is to issue guaranteed

bonds only. In this section, we investigate the determinants of the firms’ choice between

issuing guaranteed bonds only and issuing a mix of guaranteed and non-guaranteed bonds.

The probability that uses the issuance strategy is estimated using a multinomial logistic

regression model. The multinomial logistic model extended to include fixed effects can be

written as follows:

Log(P (Ym = 1, 2)

P (Yi = 0)) = αm +

K∑k=1

βmkXik + ε (7)

The three categories of the dependent variable Y are:

0: if the firm issues non-guaranteed bonds only in year k.

1: if the firm issues both non-guaranteed bonds and guaranteed bonds in year k.

2: if the firm issues guaranteed bonds only in year k.

21

In this multinomial logistic regression model, the baseline group is Yi = 0. X is a vector

of independent variables that includes: subsidiary number, market yield of corporate bonds,

firm rating, size, cash, tangible, debt, marginal and the indicator of agency problem High

FCF*low MB. The result is reported in Table 8.

The second column is the regression results for Yi = 1 in which the dependant variable is

the ratio of using mixed strategy to issuing non-guaranteed bonds only and the third column

is the regression results for Yi = 2 in which the dependant variable is the ratio of using pure

strategy to issuing non-guaranteed bonds only.

The coefficients of the firm rating are -0.321 in the mixed strategy group while -0.163

in the pure strategy group. In other words, holding other variables at a fixed value, with a

one-unit decrease in the firm rating, the log odds of using the mixed strategy (j = 1) over

using non-guaranteed bonds (j = 0) increase 0.321, while the log odds of using the pure

strategy (j = 2) over using non-guaranteed bonds (j = 0) is 0.163.

The coefficients of the High FCF*low MB variable in the mixed strategy group and in

the pure strategy group are not significant.

The coefficients of the cash variable are -4.234 in the mixed strategy group and -2.992

in the pure strategy group. The coefficients of the tangible variable are -1.049 in the mixed

strategy group and -0.699 in the pure strategy group. All these coefficients are significant at

1% level. The coefficient of Size is 0.567 in the mixed strategy group and 0.138 in the pure

strategy group and both are significant at 1%level.

In sum, the lower firm rating, less cash, less tangible, larger size increase the odds of

using mixed strategy more than that of using pure strategy.

5.4 Subsample Analysis

The analysis confirms that agency problem has a positive influence on the use of guaranties.

Because the corporate governance is negatively associated with the agency problem, a rea-

sonable expectation is that the influence of agency problem on the use of guarantee will be

22

affected by the corporate governance 5. In the this section, we examine the conjecture.

5.4.1 Effect of Managerial Voting Power

Chang and Mayers (1992) examine employee stock ownership plan announcements and find

the evidence of more agency problem from increased managerial voting power. Masulis,

Wang and Xie (2009) use a example of U.S. dual-class companies to examine how divergence

between managerial voting and cash flow rights affects managers’ private benefit. Their

empirical results support that the managers with more voting power are more prone to

purse private benefit. We therefore postulate that firms with more managerial voting power

are more likely to use guaranties.

We examine the impact of the managerial voting power on the use of guaranties. We

use the percentage of the voting shares owned by the managers to proxy the managerial

voting power. The subsample is an intersection of the whole sample and the data with

available percentage of the voting shares from RiskMetrics. There are 5,247 observations in

the subsample.The sample is then divided into 3 groups based on the managerial percent

control of voting power. We assign bonds issuance in the largest percentage group to high

managerial voting power subsample and bonds issuance in the smallest percentage group to

the low managerial voting power subsample.

The logistic regression runs in the subsample with available managerial voting power

data. The dependent variable is the dummy variable for guarantee use. The independent

variables are a dummy variable HighMgtVote which equals to one if the firm is in the high

managerial voting power subsample and zero otherwise. Other independent variables include

financial constraints variables FirmRate, Size, Cash, Tangible and Debt and bond variables

Mat, Par, Secure, Call and Put. The results are reported in the Table 9.

In the first column, we include dummy variable HighMgtVote and financial constraints

variables FirmRate, Size, Cash, Tangible. The coefficient of HighMgtVote is positive and

5The widely used Gindex for corporate governance is available from 1990 to 2006. Our sample is from

1993 to 2012. The subsample with GIndex loses two thirds of the observations. Therefore we do not use

GIndex in this analysis.

23

significant at 1% level. In the second column, we include dummy variable HighMgtVote

and bond variables Mat, pat, Secure, Call and Put. The coefficients of HighMgtVote is still

positive and significant at 1% level. In the third column, we include dummy variable High-

MgtVote , financial constraints variables and bond variables. The coefficients of HighMgtVote

is 0.059 and significant at 1% level. The result shows that in the firms with weaker corpo-

rate governance have higher chance to use guarantee. Since weaker corporate governance is

associated with more agency problem, the finding substantiates hypothesis 3.

6 Conclusions

Recently a significant portion of newly issued corporate bonds in the U.S. have embedded

guaranties. For instance, such bonds account for nearly 40 percent of new issues in 2009 in

terms of issuance amount. Yet, there is very little research on this important development in

the corporate bond market over the past decade or so.This study investigates the widespread

phenomenon of using subsidiaries/parents guarantee as credit enhancements for public firms

from 1993 to 2012.

We find that the majority of the guarantee for public firms are subsidiary/parent guar-

antee. We show that the impact of guarantee concentrates on bond rating increase. On

average, guarantee increases the bond rating by 10%. Further, this positive impact of guar-

anties on bond rating concentrates on firms with BB or BBB ratings. Besides, the yield

spread at issuance is lowered by guarantee only when issuer has BBB- rating. We further

exploit the determinants of guarantee use. We also find that firms using guarantee tend

to be more financial constrained, subject to more severe agency problem, and have lower

firm ratings and higher default probabilities, relative to firms issuing regular bonds (without

guaranties). Robust analysis reveals a stronger tendency of the use of guarantee in firms

with weak corporate governance.

We also examine the determinants of firms’ choice between issuing a mix of guarantee and

non-guarantee bonds and issuing guarantee bonds only. The result shows that the lower firm

24

rating, higher agency problem, less cash, less tangible, larger size and less debt increase the

odds of issuing a mix of non-guarantee and guarantee bonds than that of issuing guarantee

bonds only.

Our findings suggest that the use of guarantee may not be in the best interest of firms

and their subsidiaries/parents since it does not align with the firm growth opportunities.

Instead, bundling the resources of the subsidiaries/parents as collateral for issuers’ debt

leaves a room for “corporate socialism” and “poaching” problems in a parent-subsidiary

corporate structure. Our findings call for the need to examine the valuation effect of the use

of guarantee on corporate bonds.

25

Appendix A: Main Variables Used in the Analysis

In this appendix we describe in detail the definitions of variables used in our empirical analysis. We winsorizethe top and bottom 1 percentile observations for the variables.

Variable Definition

guarantee It is an indicator variable for a bond equal to one if the bond is issued with creditenhancement and zero otherwise

FirmRat It is the most recent S&P long term issuer rating before issuance for a firmMktYld It is the market yield from the Moody’s corporate bond yield in the month before issuanceSize It is the logarithm of total assetsCash It is the ratio of the sum of cash, short-term investments and receivables to total assetsTangible It is the ratio of the property, plant and equipment to total assetsProfit It is the ratio of the operating income before depreciation to total assetsPar It is the logarithm of total offering amount in millions dollarsMat It is the logarithm of the bond maturity in yearsSecure It is an indicator for a bond equal to one if the bond is secured and zero otherwiseCall It is an indicator for a bond equal to one if the bond has a call provisionPut It is an indicator for a bond equal to one if the bond has a put provisionDebt It is the ratio of total debt to total assetsTax It is the marginal tax rateIyld It is the yield to maturity for a bond at the time of issuanceExpense It is the difference between the price that the issuer received for its securities and the price

investors paid for themFCF It is the free cash flow computed as the EBITDA minus the sum of XINT, TXT, DVC,

and DVP and then divided by total assetMB It is the ratio of market value to book value computed as the total assets minus total

equity plus market capitalization and then divided by total assetsSG It is the sales growth rate computed as the year end sales minus the sales in prior year

divided by the sales in prior yearPE It is the ratio of market value to operating income before depreciation

26

References

Almeida, H. and M. Campello, 2007, Financial Constraints, Asset Tangibility, and Corporate In-vestment, Review of Financial Studies 20, 1429–1460.

Arora, Navneet, Priyank Gandhib, and Francis A. Longstaffb, 2012, Counterparty credit risk andthe credit default swap market, Journal of Financial Economics, 103(2), 280-293.

Ashcraft, Adam, Paul Goldsmith-Pinkham, and James Vickery, 2009, MBS Ratings and the Mort-gage Credit Boom, Working paper, Federal Reserve Bank, New York.

Baker, M., J. Stein and J. Wurgler, 2003, When does the market matter? Stock priguaranties andthe investment of equity-dependent firms, Quarterly Journal of Economics 118, 969-1006.

Bernanke, Ben S., and John Y. Campbell. 1988, Is There A Corporate Debt Crisis? BrookingsPapers on Economic Activity 1, 1988, 83-125.

Bodnar, Gordon M., John Graham, Campbell R. Harvey and Richard C. Marston, 2011, Managingrisk management, Working paper, Duke University.

Bolton, Patrick, Hui Chen, and Neng Wang, 2013, A dynamic tradeoff theory for financially con-strained firms, Working paper, Columbia University.

Boot, Arnoud W. A., Todd T. Milbourn, and Anjolein Schmeits, 2006, Credit ratings as coordina-tion mechanisms, Review of Financial Studies, 19(1), 81-118.

Cantor, Richard, and Frank Packer, 1996, Determinants and impact of sovereign credit ratings,Economic Policy Review, Federal Reserve Bank of New York, issue Oct, pages 37-53.

Chang, Saeyoung, and David Mayers, 1992, Managerial vote ownership and shareholder wealth,Journal of Financial Economics, 38, 211-240.

Chiang, Chia-Chien, and Yung-Ming Shiu, 2008, Determinants of Financial Strength Ratings:Evidence from the Lloyds Market, Working paper.

Chunga, Kee H. and Hao Zhanga, 2011, Corporate governance and institutional ownership, Journalof Financial and Quantitative Analysis, 46, 247-273.

Cooper, M.J., Gulen, H., Schill, M.J., 2008. Asset growth and the cross section of stock returns.Journal of Finance 63, 1609–1651.

Custodio, Claudia, Ferreira, Miguel A. & Laureano, Luis, 2013, Why are US firms using moreshort-term debt?, Journal of Financial Economics, 108(1), 182-212.

DBRS Criteria, 2010, Rating Parent/Holding Companies and Their Subsidaries.

Demyanyk, Y., and O. Van Hemert. 2011. Understanding the Subprime Mortgage Crisis. Reviewof Financial Studies 24:1848-80.

Dou, Yiwei, Yanju Liu, Gordon Richardson, and Dushyantkumar Vyas, 2012, The risk-relevance ofsecuritizations during the recent financial crisis, Working paper.

Ellul, Andrew, Chotibhak Jotikasthira, and Christian T. Lundblad (2011), Pressure and Fire Salesin the Corporate Bond Market, Journal of Financial Economics, 101, 596-620.

Erickson, Timothy, and Toni M. Whited, 2000, Measurement Error and the Relationship betweenInvestment and q, Journal of Political Economy 108(5), 1027-1057.

Gilchrist, Simon, and Charles P. Himmelberg, 1995, Evidence on the role of cash flow for investment,Journal of Monetary Economics, 36, 541–572.

27

Graham, John R., 1996, Proxies for the corporate marginal tax rate, Journal of Financial Eco-nomics 42, 187-221.

Graham, John R., 2000, How Big Are the Tax Benefits of Debt?, Journal of Finance 55, 1901-1941.

Graham, John R., and Alan Tucker, 2006, Tax Shelters and Corporate Debt Policy, Journal ofFinancial Economics 81, 563-594.

Jensen, Michael C., 1986, Agency cost of free cash flow, corporate finance, and takeovers, AmericanEconomic Review, 76(2), 323-329

Kaplan, S. N., and L. Zingales, 1997, Do investment-cash flow sensitivities provide useful measuresof financing constraints? Quarterly Journal of Economics 112, 169-215.

Kisgen, Darren J., 2006, Credit ratings and capital structure, Journal of Finance, 61(3), 1035-1072.

Kolasinski, Adam C., 2009, Subsidiary debt, capital structure and internal capital markets, Journalof Financial Economics, 94 (2009) 327-343.

Kovner, Anna, and Chenyang Wei, 2012. The private premium in public bonds, Staff Reports 553,Federal Reserve Bank of New York.

Lamont, O. C. Polk, and T. Saa-Requejo, 2001, Financial constraints and stock returns, Journalof Finance 14(2), 529-554.

Lando, David. 2004, Credit Risk Modeling Theory and Applications. Princeton, NJ: PrincetonUniversity Press.

Lang, L.H.P. and Litzenberger, R.H.,1989, Dividend Announcements, Journal of Financial Eco-nomics, 24, 181–191.

Li, Dongmei, and Lu Zhang, 2010, Does q-theory with investment frictions explain anomalies inthe cross-section of returns? Journal of Financial Economics 98 (2), 297-314

Masulis, Ronald W. and Cong Wang, 2009, Agency problems at dual-class companies, Journal ofFinance, 64, 1697-1727.

McConnell, John J., Henri Servaes, 1995, Equity ownership and the two faguaranties of debt,Journalof Financial Economics, 39, 131-157

Nanda, Vikram and Rajdeep Singh, 2004, Bond insurance: what is special about munis? Journalof Finance, 59(5), 2253-2279

Opler, Tim and Titman, Sheridan, 1993, The determinants of leveraged buyout activity: Free cashflow vs. financial distress costs, Journal of Finance 48, 1985-1999.

Partnoy, Frank, 1999, The Siskel and Ebert of financial markets: Two thumbs down for the creditrating agencies, Washington University Law Quarterly, 77, 619-715

Prowse, Stephen, 1990, Institutional investment patterns and corporate financial behavior in theUnited States and Japan, Journal of Financial Economics, 27, 43-66.

Scharfstein, D., and J. Stein, 2000, The dark side of internal capital markets: Divisional rent-seekingand inefficient investment,Journal of Finance, 55, 2537-2264

Standard & Poor’s, 2000, Parent and Subsidiary Ratings, Ratings Direct.

Thomson, Robert, 1991, Piercing the Corporate Veil: An Empirical Study. Cornell Law Review76, 1036-1065.

Titman, Sheridan, K. C. John Wei, Feixue Xie, 2004, Capital investments and stock returns, Journalof Financial and Quantitative Analysis, 39(4), 677–700.

28

West, Richard R., 1973, Bond ratings, bond yields and financial regulation: Some findings, Journalof Law and Economics, 16, 159-168.

Whited, Toni M., 1992, Debt, liquidity constraints, and corporate investment: Evidence from paneldata, Journal of Finance, 47(4), 1425-1460.

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Table 1: Statistics for Corporate Bonds with guarantee

This table reports the numbers and aggregate value of corporate bonds and such bonds with guaranteeincluded in the Fixed Income Securities Database (FISD) for each year from 1993 to 2012. Summarystatistics are reported separately for private and public firms. Also shown are the portion of credit enhancedbonds in the sample and the percentage of aggregate value of such bonds for both private and public firms.In the last row, the number of unique corporate bonds and the average of aggregate value of corporate bondsare reported.

Number of bonds Aggregate value of bonds (in Million $)

Public firms Private firms Public firms Private firms

Year Total CE CE/Total Total CE CE/Total Total CE CE/Total Total CE CE/Total(%) (%) (%) (%)

1993 824 76 9.22 2,429 145 5.97 126.68 2.87 2.26 172.53 1.35 0.781994 302 8 2.65 2,486 63 2.53 50.32 0.60 1.19 115.82 1.68 1.451995 487 19 3.90 3,909 84 2.15 75.14 1.36 1.80 175.11 2.89 1.651996 563 27 4.80 2,546 57 2.24 109.98 6.71 6.10 175.53 8.70 4.961997 705 34 4.82 2,998 167 5.57 141.51 4.85 3.43 253.43 33.53 13.231998 873 48 5.50 3,280 241 7.35 214.33 9.97 4.65 409.79 50.40 12.301999 583 34 5.83 3,470 201 5.79 201.93 8.41 4.16 489.86 56.21 11.482000 499 29 5.81 3,040 160 5.26 225.08 8.71 3.87 599.57 92.91 15.502001 756 40 5.29 3,293 265 8.05 343.08 16.59 4.83 646.14 66.95 10.362002 608 35 5.76 3,704 377 10.18 229.92 15.70 6.83 530.12 48.20 9.092003 911 45 4.94 5,596 356 6.36 301.59 16.55 5.49 617.64 57.06 9.242004 701 43 6.13 6,254 549 8.78 232.93 10.84 4.65 670.33 51.94 7.752005 586 31 5.29 6,204 865 13.94 227.90 10.66 4.68 663.48 59.34 8.942006 701 61 8.70 6,900 1,076 15.59 323.63 32.43 10.02 1,119.37 96.14 8.592007 1,092 60 5.49 9,322 1,429 15.33 401.82 27.34 6.80 1,018.56 92.22 9.052008 635 48 7.56 8,614 1,290 14.98 283.82 35.68 12.57 852.67 187.06 21.942009 689 112 16.26 5,698 981 17.22 361.33 72.58 20.09 1,211.30 503.20 41.542010 691 98 14.18 8,594 761 8.86 369.23 49.44 13.39 708.93 156.67 22.102011 629 60 9.54 9,736 627 6.44 364.06 35.29 9.69 689.11 150.93 21.902012 837 105 12.54 11,289 519 4.60 447.30 61.13 13.67 858.69 170.15 19.82Unique 17,372 2,261 116,582 12,576Avg 13.02 10.79 251.58 21.38 8.50 598.90 94.38 15.76

30

Table 2: Cross-sectional Distributions of Variables

Panel A of the table reports the cross-sectional mean, median, and standard deviation of the main variablesused in the analysis for guaranteed bonds and non-guaranteed bonds respectively. They include the numberof subsidiaries of a firm (NSub), firm rating (FirmRat), market yield (MktYld), the logarithm of total assets(Size), cash ratio, the ratio of PPE to total assets (Tangible), profit, the logarithm of par value (Par), thelogarithm of bond maturity (Mat), firm leverage (Debt), marginal tax rate (Tax), number of firms’ sub-sidiaries (NSub), free cash flow (FCF), market to book ratio (MB), sales growth rate (SG), and PE ratio(PE). More detailed explanations on the variables are provided in the Appendix A. We obtain each statisticeach year, then take the average over time. Panel B reports the correlations among variables. We computethe correlations in each year and then take the average over time. The sample period is from 1993 to 2012.

Panel A: Statistics

Credit-enhanced bonds Non-credit-enhanced bonds

Mean Median Std Dev Mean Median Std Dev

NSub 45 22 59 26 7 48FirmRat 16.30 16.03 2.36 17.79 17.98 3.52Size 8.40 8.38 1.49 8.34 8.37 1.60MktYld 6.64 6.65 0.26 6.67 6.69 0.29Cash 0.16 0.13 0.10 0.22 0.19 0.15Tangible 0.55 0.48 0.40 0.63 0.56 0.39Profit 0.13 0.13 0.07 0.14 0.14 0.09Par 5.67 5.60 0.68 5.64 5.61 0.73Mat 2.37 2.33 0.53 2.27 2.24 0.63Debt 0.40 0.40 0.16 0.33 0.30 0.20Tax 0.18 0.21 0.15 0.21 0.29 0.15FCF 0.10 0.08 0.14 0.06 0.09 0.32MB 1.53 1.41 0.60 1.87 1.53 1.06SG -0.78 -0.88 0.31 -0.81 -0.90 0.37PE 8.13 6.86 5.04 9.07 6.85 8.68

Panel B: Correlation

FirmRat Size MktYld Cash Tangible Profit Par Mat Debt Tax NSub FCF MB SG

Size 0.66MktYld 0.01 0.06Cash 0.05 -0.22 -0.05Tangible -0.03 0.04 0.02 -0.43Profit 0.41 0.19 -0.02 -0.12 0.22Par 0.30 0.60 0.05 -0.09 -0.04 0.14Mat 0.10 0.11 -0.03 -0.03 0.07 0.08 0.08Debt -0.02 0.10 0.02 -0.11 -0.09 -0.05 0.09 0.02Tax -0.43 -0.15 0.02 -0.21 0.09 -0.18 -0.08 -0.06 0.07NSub 0.35 0.23 0.03 -0.04 0.02 0.33 0.11 0.07 0.02 -0.24FCF 0.18 0.20 0.00 -0.25 0.21 0.59 0.11 0.09 0.04 -0.17 0.20MB 0.30 -0.13 -0.05 0.29 -0.15 0.20 0.07 -0.03 -0.08 -0.10 0.01 -0.13SG -0.21 -0.18 -0.02 0.05 -0.10 -0.12 -0.03 -0.04 0.05 0.04 -0.09 -0.11 0.12PE 0.06 -0.12 -0.05 0.18 -0.21 -0.20 0.03 -0.02 -0.01 -0.22 -0.09 -0.09 0.45 0.19

31

Table 3: Effect of Credit Enhancements on Bond Rating

This table reports the results of the impact of credit enhancements on bond ratings. The dependent variableis bond rating. The independent variables include guarantee, firm characteristics (FirmRat, Size, Tangible,Profit, Debt), and bond characteristics (Par, Mat, and three dummies for callable bonds (Call), putablebonds (Put), and secured bonds (Secure)). All data are in the fiscal year before the debt issuance. Thedefinitions of these variables are provided in the Appendix A. We consider industry dummies and fixed-yeareffect in the regressions. Robust standard errors are clustered at the industry and year level. t-statistics arepresented in parentheses. ***, **, and * represents the significance at the 1%, 5%, and 10% level, respec-tively. The sample period is from 1993 to 2012.

(1) (2) (3) (4)

Intercept 18.05*** -1.23*** -1.68*** -1.53***(109.04) (-8.26) (-12.05) (-8.30)

Guarantee -1.732*** 0.146*** 0.109** 0.110**(8.98) (2.90) (2.17) (2.29)

Firm VariablesFirmRat 1.065*** 1.001*** 1.009***

(122.95) (60.94) (61.05)Size 0.164*** 0.189***

(7.14) (6.94)Tangible 0.050 0.029

(1.08) (0.62)Profit 0.718** 0.770***

(2.44) (2.68)Debt -0.351*** -0.414***

(-3.22) (-3.92)Bond VariablesPar 0.086**

(-2.57)Mat 0.025

(1.47)Secure 1.099***

(6.96)Call -0.035

(-1.10)Put -0.108

(-1.57)Industry dummies Yes Yes Yes YesFixed year effect Yes Yes Yes YesN 6477 6477 6366 6366R2 0.049 0.935 0.938 0.939

32

Table 4: Effect of Credit Enhancements on Bond Rating: Further Tests Conditioned onIssuer Rating

This table reports the results of the impact of credit enhancements and issuer rating on bond ratings. Thedependent variable is bond rating. The independent variables include thirteen interactive dummies variablesguarantee*firm rating. Guaranteed bonds exist when firm rating is AAA, BBB+, BBB, BBB-, BB+, BB,BB-, B+, B, B-, CCC+, CCC and CC. For each of these firm rating, guarantee*firm rating equals to one ifthe firm has this rating and the bond is guaranteed bond and zero otherwise. The control variables are firmand bond characteristics are as defined in Table 3. We consider industry dummies and fixed-year effect inthe regressions. Robust standard errors are clustered at the industry and year level.t-statistics are presentedin parentheses. ***, **, and * represents the significance at the 1%, 5%, and 10% level, respectively. Thesample period is from 1993 to 2012.

33

(1) (2) (3)

Intercept -1.502*** -1.858*** -1.692***(-4.34) (-5.17) (-4.68)

FirmRat 1.068*** 1.010*** 1.015***(120.93) (61.77) (60.52)

Guarantee*AAA -1.180** -0.639*** -0.580***(-2.19) (-4.76) (-4.83)

Guarantee*BBB+ 0.267*** 0.107 0.110(2.68) (1.07) (1.10)

Guarantee*BBB 0.304*** 0.260*** 0.273***(3.94) (3.30) (3.41)

Guarantee*BBB- 0.326*** 0.284*** 0.305***(5.22) (4.64) (4.80)

Guarantee*BB+ 0.549*** 0.520*** 0.498***(4.68) (4.44) (5.43)

Guarantee*BB -0.140 -0.173 -0.155(-0.98) (-1.21) (-1.09)

Guarantee*BB- -0.464*** -0.474*** -0.418***(-4.41) (-4.57) (-4.05)

Guarantee*B+ -0.348** -0.282 -0.267(-2.05) (-1.54) (-1.46)

Guarantee*B 0.222 0.202 0.184(0.93) (0.79) (0.71)

Guarantee*B- 0.432 0.208 0.019(1.42) (0.63) (0.07)

Guarantee*CCC+ 1.108 0.859 0.626(1.60) (1.33) (1.23)

Guarantee*CCC 3.952*** 3.484*** 2.708***(36.37) (21.12) (14.20)

Guarantee*CC 6.387*** 6.017*** 5.221***(3.88) (3.67) (3.23)

Firm VariablesSize 0.149*** 0.178**

(6.53) (6.51)Tangible 0.052 0.035

(1.10) (0.75)Profit 0.542* 0.614**

(1.90) (2.19)Debt -0.306*** -0.365***

(-2.87) (-3.53)Bond VariablesPar

Mat

Secure

Call

Put

Industry dummies Yes Yes YesFixed year effect Yes Yes YesN 6477 6366 6366R2 0.9375 0.939 0.9413

34

Table 5: Effect of Credit Enhancements on Bond Offering Yield Spread

This table reports the results of the impact of credit enhancements on bond offering yield spread. The de-pendent variable is bond offering yield spread which is the difference in the yields of the corporate bond andthe treasury bonds with corresponding maturities. The control variables are firm and bond characteristics asdefined in Table 3. All data are in the fiscal year before the debt issuance. The definitions of these variablesare provided in the Appendix A. We consider industry dummies and fixed-year effect in the regressions.Robust standard errors are clustered at the industry and year level. t-statistics are presented in parentheses.***, **, and * represents the significance at the 1%, 5%, and 10% level, respectively. The sample period isfrom 1993 to 2012.

(1) (2) (3) (4)

Intercept 5.251*** -11.334*** 10.589*** 8.307***(44.40) (63.74) (37.54) (26.80)

guarantee 0.508*** 0.017 0.102 0.081(4.59) (0.19) (1.13) (1.03)

Firm VariablesFirmRat -0.367*** -0.305*** -0.291***

(-38.49) (-19.98) (-21.30)Size -0.095*** -0.137***

(-3.48) (-4.95)Tangible 0.369*** 0.213***

(5.36) (3.40)Profit -0.732 0.739

(-1.44) (-1.61)Debt 0.899*** 0.716***

(4.35) (3.92)Bond VariablesPar 0.091**

(2.28)Mat 0.730***

(22.43)Secure 1.913***

(10.51)Call 0.542***

(8.12)Put -3.844***

(-27.34)Industry dummies Yes Yes Yes YesFixed year effect Yes Yes Yes YesN 6235 6235 6117 6117R2 0.237 0.4481 0.4560 0.5812

35

Table 6: Effect of Credit Enhancements on Offering Yield Spread: Further Tests Conditionedon Issuer Rating

This table reports the results of the impact of credit enhancements and issuer rating on bond offering yieldspread. The dependent variable is bond offering yield spread which is the difference in the yields of thecorporate bond and the treasury bonds with corresponding maturities. The independent variables includethirteen interactive dummies variables guarantee*firm rating as defined in table 4. The control variablesare firm and bond characteristics similar to those in Table 3. We consider industry dummies and fixed-yeareffect in the regressions. Robust standard errors are clustered at the industry and year level.t-statistics arepresented in parentheses. ***, **, and * represents the significance at the 1%, 5%, and 10% level, respec-tively. The sample period is from 1993 to 2012.

36

(1) (2) (3)

Intercept 11.86*** 11.251*** 8.86***(23.69) (19.04) (16.98)

FirmRat -0.362*** -0.299*** -0.286***(-36.65) (-19.53) (-20.88)

Guarantee*AAA 1.243*** 1.271*** 1.143**(7.30) (9.71) (5.24)

Guarantee*BBB+ 0.042 0.291 0.081(0.17) (1.09) (0.30)

Guarantee*BBB -0.053 0.058 -0.071(-0.28) (0.28) (-0.38)

Guarantee*BBB- -0.557*** -0.505*** -0.494***(-3.41) (-3.01) (-3.47)

Guarantee*BB+ -0.121 -0.013 -0.100(-0.71) (-0.07) (-0.56)

Guarantee*BB 0.253 0.294 0.445**(0.96) (1.14) (2.08)

Guarantee*BB- 0.161 0.239 0.327(0.65) (0.94) (1.50)

Guarantee*B+ 0.722** 0.748** 0.825***(2.01) (2.25) (2.67)

Guarantee*B 0.211 0.061 0.477(0.34) (0.10) (0.90)

Guarantee*B- 0.095 0.083 0.235(0.15) (0.14) (0.68)

Guarantee*CCC+ 1.356 1.510* 1.320(1.57) (1.70) (1.30)

Guarantee*CCC 2.100*** 2.325*** 0.872(3.26) (3.40) (1.22)

Guarantee*CC 2.884*** 3.363*** 1.807***(18.46) (19.65) (8.02)

Firm VariablesSize -0.100*** -0.136***

(-3.63) (-4.88)Tangible 0.361*** 0.197***

(5.23) (3.15)Profit -0.767 -0.705

(-1.57) (-1.54)Debt 0.902*** 0.696***

(4.36) (3.81)Bond VariablesPar 0.091**

(2.28)Mat 0.722***

(22.63)Secure 1.855***

(9.93)Call 0.551***

(8.30)Put -3.853***

(-27.32)Industry dummies Yes Yes YesFixed year effect Yes Yes YesN 6235 6117 6117R2 0.4512 0.4594 0.5840

37

Table 7: Determinants of Debt Credit Enhancements: Bond-Level Logistic Regressions

This table shows the results of two-way cluster regressions using the sample bonds issued by public firmswith subsidiaries. The dependent variable is guarantee for bonds, an indicator variable equal to one if it isissued with guarantee and zero otherwise. The basic independent variables include NSub, MktYld, Mat, andFirmRat. In addition, we include the financial constraints variables (Size, Cash, Tangible) and tax benefitvariables (Debt, Tax) in columns (1) and (2) and then add add the different measures of agency problem(HighCF*LowMB, HighCF*LowSG, HighCF*LowPE) in columns (3)-(5) respectively. We divide the firmsinto high free cash flow and low free cash flow groups based on FCF. We also divide the firms into high growthopportunities and low opportunities groups based on MB, SG and PE respectively. A firm is classified as lowif it is below the median and as high if it is above the median of a given firm characteristic. HighCF*LowMB(HighCF*LowSG, HighCF*LowPE) is a dummy variable for a firm equal to one if the firm is in the highFCF group and low MB (SG, PE) group and zero otherwise. All specifications include industry fixed effectand year fixed effect. We also control for offering value, secured, callable and putable of the bonds. Robuststandard errors are clustered at the firm and year level. t-statistics are presented in parentheses. ***, **,and * mean significant at the 1%, 5%, and 10% level, respectively.

38

(1) (2) (3) (4) (5) (6)

Intercept 0.384*** 0.219** 0.324*** 0.307 0.035*** 0.313(4.60) (2.21) (3.35) (3.14) (3.68) (3.23)

MktYld 0.014 0.016 0.018 0.018 0.016 0.019(0.89) (1.03) (1.17) (1.23) (1.05) (1.24)

FirmRat -0.011*** -0.017*** -0.017*** -0.019*** -0.017***(-7.16) (-6.94) (-7.02) (-7.43) (-7.05)

Firm VariablesSize -0.008* 0.015*** 0.016*** 0.016*** 0.015***

(-1.75) (2.57) (2.73) (2.87) (2.61)Cash -0.299*** -0.281*** -0.281*** -0.290*** -0.288***

(-6.64) (-6.34) (-6.38) (-6.58) (-6.52)Tangible -0.096*** -0.091*** -0.097*** -0.100*** -0.106***

(-4.99) (-4.81) (-5.18) (-5.25) (-5.42)Debt 0.100*** -0.002 0.006 -0.001 0.005

(3.58) (-0.08) (0.20) (-0.002) (0.15)Bond VariablesMat 0.032*** 0.036*** 0.036*** 0.037*** 0.036***

(5.25) (5.58) (5.86) (5.98) (5.89)Par 0.013 0.009 0.009 0.008 0.009

(1.50) (1.08) (1.09) (0.93) (1.15)Secure 0.042 0.008 0.009 0.007 0.011

(1.23) (0.24) (0.29) (0.23) (0.33)Call -0.011 -0.016 -0.015 -0.017 -0.017

(-0.097) (-1.35) (-1.50) (-1.45) (-1.39)Put -0.022 -0.025 -0.025 -0.026 -0.022

(-1.22) (-1.39) (-1.36) (-1.42) (-1.23)Agency Problem VariablesHigh FCF/Low MB 0.036**

(2.26)High FCF/Low SG 0.039***

(2.84)High FCF/Low PE 0.045***

(3.07)Fixed Firm Effect Yes Yes Yes Yes Yes YesFixed YearEffect Yes Yes Yes Yes Yes Yes# of Observation 5961 5854 5854 5854 5854 5854Pseudo R2 0.069 0.079 0.094 0.097 0.097 0.098

39

Table 8: Multinomial Logistic Regression Results for the Determinants of Debt CreditEnhancements

The probability that uses guarantee for bonds is estimated using a multinomial logistic regression model.The independent variable is j where j equals 0 when the firm issues non-guaranteed bonds in a year, 1 whenthe firm issues both guaranteed and non-guaranteed bonds in a year, 2 when the firm issues only guaranteedbonds in a year. All data are in the fiscal year before the debt issuance. The baseline group for j is j=0. Allspecifications include industry fixed effect and year fixed effect. Robust standard errors are clustered at thefirm and year level. t-statistics are presented in parentheses. ***, **, and * mean significant at the 1%, 5%,and 10% level, respectively.

Guaranteed and non-Guaranteed Bonds Guaranteed Bonds Only

Intercept -1.888 -0.481(0.483) (0.626)

MktYld -0.215 0.139(0.617) (0.331)

FirmRat -0.321*** -0.163***(<0001) (<0001)

Firm VariablesSize 0.567*** 0.1379**

(0.0004) (0.0434)Cash -4.234*** -2.992***

(0.004) (<0001)Tangible -1.049*** -0.699***

(0.0094) (<0001)Debt 0.129 0.405

(0.8787) (0.2550)Bond VariablesMat -0.182 0.397***

(0.5915) (0.0015)Par 0.091 0.059

(0.7454) (0.6107)Secure 0.215 -0.375

(0.7251) (0.2100)Call -0.324 0.056

(0.3815) (0.7271)Put 0.619 -0.708**

(0.3120) (0.0231)Agency Problem VariablesHigh FCF/Low MB 0.578 0.234

(0.119) (0.114)Fixed Industry EffectFixed YearEffect# of Observation 3771 3771Likelihood Ratio 451.114 451.114Pseudo R2 0.113 0.113

40

Table 9: Impacts of Managerial Voting Power

This table shows the results of the regressions for one pairs of subsamples: high or low managerial votingpower subsamples. The dependent variable is guarantee for bonds, an indicator variable equal to one ifit is issued with guarantee and zero otherwise. We first sort firms into 3 groups based on the managerialpercent control of voting power. Then we assign bonds issuance in the largest percentage group to thehigh managerial voting power subsample and bonds issuance in the smallest percentage group to the lowmanagerial voting power subsample. We include industry fixed effect and year fixed effect and also controlfor offering value, secured, callable and putable of the bonds. Robust standard errors are clustered at thefirm and year level. at-statistics are presented in parentheses. ***, **, and * mean significant at the 1%,5%, and 10% level, respectively.

(1) (2) (3)

Intercept 0.490*** 0.234* 0.298***(3.95) (1.74) (2.22)

MktYld 0.015 0.021 0.018(0.75) (1.11) (0.94)

FirmRat -0.018*** -0.018*** -0.174***(-5.32) (-6.60) (-5.30)

Firm VariablesSize 0.015* 0.005

(1.94) (0.66)Cash -0.276*** -0.281***

(-4.32) (-4.41)Tangible -0.077*** -0.081***

(-2.81) (-2.97)Debt 0.061 0.070

(1.14) (1.31)Bond VariablesMat 0.035*** 0.035***

(4.22) (4.23)Par 0.033*** 0.029**

(2.85) (2.41)Secure -0.090** -0.086**

(-2.09) (-1.99)Call -0.003 0.005

(-0.21) (0.36)Put -0.078*** -0.069***

(-3.75) (-3.42)Managerial Voting PowerHighMgtVote 0.058*** 0.066*** 0.059***

(3.03) (3.53) (3.12)Fixed Firm Effect Yes Yes YesFixed Year Effect Yes Yes Yes# of Observation 3177 3231 3177Pseudo R2 0.123 0.121 0.1326

41

Figure 1: Guarantors of Credit Enhanced Bonds

This figure shows the number of guaranteed corporate bonds and that of guaranteed bonds with differenttypes of guarantors. The two main types of guarantors are external and internal guarantors. Withinthe guaranteed bonds with internal guarantors, there are bonds with parent guarantors and subsidiaryguarantors.

The number of CE bonds: 2,278

CE bonds with external guarantors (bond insurance): 141

CE bonds with internal guarantors: 2,137

CE bonds with parent guarantors: 109 CE bonds with subsidiary guarantors: 2,014

42

Figure 2: Statistics of Credit Enhanced Corporate Bonds

This figure shows the the percentage of guaranteed corporate bonds issued by private and public firmstogether in the total corporate bonds in terms of the number of issues and the market value of issues.Each year, we sort corporate bonds into guaranteed bonds and non-guaranteed bonds and then computethe percentage of guaranteed bonds value in all bonds and the percentage of guaranteed bonds number inall bonds. Panel A shows the percentage of guaranteed bonds value in all bonds and Panel B shows thepercentage of guaranteed bonds number in all bonds. The sample period is from 1993 to 2012.

1994 1996 1998 2000 2002 2004 2006 2008 2010 20120

20%

40%

Panel A: Percentage of CE-Bond Value in All Bonds

1994 1996 1998 2000 2002 2004 2006 2008 2010 20120

10%

20%Panel B: Pecentage of CE-Bond Number in All Bonds

43

Figure 3: Distribution of Corporate Bond Issuers

This figure plots the distributions of corporate bond issues across different rating groups. Panel A is for theissuers of guaranteed bonds (credit enhanced bonds) and Panel B is for the issuers of non-guaranteed bonds.R=1 if the firm rating is in (’AAA’,’AA+’,’AA’,’AA-’); R=2 when the firm rating is in (’A+’,’A’,’A-’); R=3when the firm rating is in (’BBB+’,’BBB’,’BBB-’); R=4 if the firm rating in (’BB+’,’BB’,’BB-’); R=5 if thefirm rating in (’B+’,’B’,’B-’); R=6 if the firm rating in (’CCC+’,’CCC’,’CCC-’); R=7 if the firm rating in(’CC’,’C’,’D’). The sample period is from 1993 to 2012.

1 2 3 4 5 6 70

20%

40%

Panel A: CE Bond Issuers

1 2 3 4 5 6 70

20%

40%

Panel B: Non-CE Bond Issuers

44