Rollover Risk and Credit Spreads
in the Financial Crisis of 2008
Xing Hu∗
December 3, 2010
Abstract
This paper is an empirical investigation of the asset pricing implication of rollover
risk, the risk that a firm might not be able to refinance its due liabilities. I focus on
the long-term debt rollover risk and use the proportion of long-term debt falling due
within the year to measure a firm’s exposure to rollover risk. I find that firm-specific
rollover risk coupled with deteriorating market wide credit conditions indeed increase a
firm’s credit spreads. During the peak period of the financial crisis, namely the second
half of 2008, one-year CDS spreads for a firm that needs to refinance more than 20%
of its long-term debt are 72 basis points higher than the spreads for a similar firm
with low refinancing needs. The differences are smaller, around 32 basis points, but
still significant at the early and final stages of the financial crisis. Compared to oneyear CDS spreads, CDS spreads for longer maturities are also sensitive to rollover risk
information but at lower magnitudes throughout the financial crisis. During normal
periods, CDS spreads are mostly explained by fundamental risk while rollover risk
is not a significant determinant of credit spreads. Similar rollover risk effect is also
observed in other financial markets, including the corporate bond, stock and option
markets.
∗
Xing is from Princeton University (xinghu@princeton.edu).
1
1
Introduction
This paper is an empirical investigation of the asset pricing implication of rollover risk, the
risk that a firm might not able to refinance its due liabilities. Firms that rely heavily on
short-term debt to fund their illiquid long-term liabilities, such as financial institutions, are
particularly vulnerable to credit market conditions because of their needs to regularly tap
the credit market for external financing. The collapses of both Bear Stearns and Lehman
Brothers, for example, are directly caused by the runs of their short-term creditors in the
past financial crisis.
Another source of refinancing risk that is often overlooked in the existing literature is
the rollover risk of long-term debt. Depending on a firm’s specific debt maturity choices, its
maturing liabilities consist of both short-term debt and the expiring portion of its long-term
debt. Firms that happen to have a significant portion of their long-term obligations coming
due during credit crunch are also adversely affected by the diminishing supply of credit in the
long-term corporate debt market. The recent 2008 financial crisis represents such an episode
of credit shortage in the long-term capital market. In the US, investment grade long-term
corporate bond issuance slided 1/3 from $991B in 2007 to $664B in 2008, while issuance in
high yields dropped 2/3 from $146B in 2007 to $46B in 2008.1 At the same time, bond and
loan spreads in the secondary market were at all-time highs, pushed up the borrowing cost
of firms that can tap the credit markets. Despite anecdotal evidence that refinancing needs
might be a potential default trigger, the relationship between rollover risk and corporates’
default risk remains an empirical question. This paper comprehensively investigates the
impact of firm-specific rollover risk on asset prices by exploring various financial markets.
In this paper, I focus on the long-term debt rollover risk and use the proportion of long
term debt falling due within the year (RO) to measure a firm’s exposure to rollover risk. The
main reason is that the choice between short-term versus long-term debt has been shown
in the literature to be an endogenous decision that is related to firm characteristics such
as size and ratings.2 It is thus difficult to draw unconfounded conclusions from empirical
relationships between firms’ short-term debt position and their credit spreads. On the other
hand, the current portion of long-term debt is mainly determined by past cumulative and
non-reversible managerial decisions on a firm’s debt maturity structure and its repayment
schedule. It is then reasonable to assume that the long-term debt refinancing needs only
reflect a firm’s long-term debt rollover risk, and are not directly related to its current risk
1
Includes all non-convertible corporate debt, MTNs, and Yankee bonds, but excludes all issues with
maturities of one or less and CDs.Source: Thomson Reuters.
2
See Barclay and Smith (1995), Stohs and Mauer (1996) and Guedes and Opler (1996).
2
profiles. For the past financial crisis, although originated from the troubled residential mortgage section, the followed turmoil in the broad credit market is largely unexpected. It is hard
to imagine some firms can foresee the credit crunch and change their long-term debt maturity
structure accordingly before the crisis. Long-term debt is also commonly public-held and
hard to renegotiate on the spot, which makes it difficult for firms to adjust their long-term
debt refinancing positions as the crisis unfold. Overall, the current portion of long-term debt
provides a clean identification of a firm’s rollover risk during both normal and crisis period.
I start my analysis from the credit default swap (CDS) market, and find that firm-specific
rollover risk coupled with deteriorating market wide credit condition indeed increases a firm’s
CDS spreads. A CDS is a bilateral contract between a buyer and a seller of protection,
where the buyer makes periodic payments to the seller, and in return receives a payoff if the
underlying reference entity defaults or experiences a similar credit event. CDS spreads are
generally considered as a relatively clean measure of default risk.3
I focus first on firms with high rollover risk during the 2008 financial crisis. In other words,
firms that need to refinance more than 20% of their long-term debt in 2008. Panel (a) of
figure 5 plots the time-series of the average 1-year CDS spreads for 24 high rollover risk firms
and 24 corresponding “normal” firms with low rollove risk. The “normal” firm are similar to
the high rollover risk firms in terms of their credit qualities before the 2008 financial crisis,
with the only difference being the amount of long-term debt coming due during the 2008
crisis. As shown in the plot, the spreads for both groups are close to each other (around 50
bps) until the early fall of 2007 when concerns about troubled residential mortgage market
spreaded to financial institutions that invested heavily in asset-back securities. There is
significant co-movement between the high rollover risk firms and normal firms during the
crisis. But the CDS spreads for high rollover firms rise much faster than their matching
low rollover risk ones, and reach the first peak in March 2008 following the collapse of two
Bear-Stearns hedge funds. The differences in CDS spreads is around 150 bps and remains
stable for a short time period. Following the bankruptcy of Lehman Brothers, the CDS
spreads for both high rollover risk and normal firms continue to hike and reach the alltime high around march 2009. The spreads for the treated and control firms show signs of
converging as the financial market recoveres in late 2009. Moreover, this rollover effect on
CDS spreads can only be observed for firms that need to refinance during the crisis period.
In panel (b) of figure 5, the spreads for firms with large amount of long-term debt due in
normal periods track closely to the comparable firms with low rollover risk. This contrasting
3
The CDS quotes are obtained from CMA. Given the limited coverage of single-name CDS contracts in
the CDS market, I choose my sample period to be from 2004 to 2009 which covers a reasonable number of
corporations and the period of the late 2008 financial crisis.
3
behavior of CDS spreads for high rollover risk firms in the financial crisis and the non-crisis
periods highlights that the overall credit market condition plays an important role in the
relationship between firm-specific refinancing needs and its default risk.
I formally investigate the impact of rollover risk on credit spreads in a regression framework by analyzing a large panel of firms during the sample period of 2004-2009. Consistent
with the matching results, I find that one-year CDS spreads for high rollover risk firms are
32-72 bps wider than a similar firm with low rollover risk during the financial crisis, after
controlling a variety of firm characteristics including volatility, leverage, credit ratings, firm
and month fixed effects. The rollover effect on one-year CDS spreads is strongest during the
peak period of the financial crisis (72 bps), namely the second half year of 2008, and less
striking at the early and final stages of the crisis (around 32 bps). When moving from the
short-end to the long-end maturities, the rollover effect on CDS spreads is monotonically
decreasing during the crisis period. This is not surprising because long-term spreads reflect
average default probabilities over time. This Monotonic decreasing pattern indicates that
rollover risk mainly affects a firm’s default likelihood in the near future, while having limited power in explaining its fundamental long-term survivability. Moreover, the sensitivity
of CDS spreads to rollover risk at different maturities provides useful information on the
severity and persistence of the financial crisis. Exposure to rollover risk is not a significant
determinant of CDS spreads during normal period.
My base results demonstrate that rollover risk, as an additional channel of why firms may
default, is correctly priced in the CDS market. In normal times when credit is abundant,
refinancing expiring long-term debt is business as usual. During credit crunch, as in the
last financial crisis, default risk increase for high rollover risk firms because they have large
chunk of maturing debt and don’t have access to new capital at reasonable cost. Therefore,
rollover risk of such long-term debt captures a firm’s short-term liquidity stress rather than
its fundamental survivability. Liquidity here means a business’s ability to meet its payment
obligations, in contrast to the solvency risk which refer to the default risk due to poor
performance of fundamental asset values.4
Cross-sectionally, I find that the positive relationship between rollover risk and CDS
spreads is stronger for less profitable firms, consistent with the idea that firms are likely to
face greater liquidity squeeze if they also experience a negative shock to their cash flows.
Across time, I find that the relative contribution of rollover risk and solvency risk on credit
spreads is time varying and depend greatly on market conditions. During normal periods,
4
Notice that the liquidity risk here is different from market liquidity, which usually refers to an asset’s
ability to be traded in a market without causing a significant movement in the price and with minimum loss
of value.
4
CDS spreads are mostly explained by fundamental risk while rollover risk is not a significant
determinant of credit risk. The influence of fundamental risk on CDS spreads decreases
during crisis, in contrast to rollover risk which shows up as an important component of
credit spreads.
In addition to the CDS market, rollover effect also exists in the corporate bond market.
I construct bond spreads at short, median and long maturities for every firm in my sample
using bond transaction prices in the TRACE database. Although the results for corporate
bond spreads are weaker, they are largely in line with the results in the CDS market. This
is not surprising because bond spreads are widely known as “noisy” measure of default risk.
The general consistent results reaffirm that rollover risk is an important determinant of credit
risk.
I also explore the stock and option markets. From a theoretical point of view, both stocks
and bonds are claims on the same underlying asset value. Stock and option prices should also
reflect a firm’s specific default risk. Certain type of options, such as deep out-of-the-money
puts, offer similar downside risk protection as credit default swaps.5 In order to capture the
credit risk information conveyed in the equity and option market and put them on the same
footing with the CDS and bond spreads, I calculated synthetic CDS spreads for every firm
in my sample based on the simple Merton structural model.
Consistent with previous findings in the CDS and bond markets, firms with a higher
proportion of long-term debt due have higher synthetic CDS spreads, but only during the
financial crisis period. Aside from balance sheet information and stock prices, the key input
in the calculation of synthetic CDS spreads is stock volatility. I consider two candidates for
the equity volatility, both realized equity volatility (RV ) calculated in the stock market and
implied volatility (IV ) derived from the option market. Compared with the realized volatility
(RV ), the implied volatility (IV ) maybe a better predictor of future equity volatility but
is contaminated by the risk premiums in the option market. Thus the “risk-neutral” CDS
spreads SCDS IV , besides its loss-given-default component, also contain information about
investor’s risk aversion in the option market through the channel of implied volatilities. I
find that this risk aversion component of SCDS IV is also positively related to rollover risk
during the crisis period. It implies that investors in the option market also demand higher
risk premiums for firms that need to repay large amount of long-term debt during the crisis.
Moreover, I find that rollover risk information in the CDS market and the option market is
not redundant.
5
One more advantage of studying the stock and option markets is that the sample covers a much broader
range of firms compared with the CDS and bond market, which alleviate the concerns that my results are
driven by a few outliers.
5
This paper makes the following contribution to the finance literature. It points out
that debt maturity structure as a new determinant of firms’ credit risk. In the standard
structural models , e.g. Merton (1974), Longstaff and Schwartz (1995), Black and Cox
(1976) and Leland and Toft (1996), default is triggered when the asset value falls below
a critical level, which is either exogenously related to the liability level or endogenously
determined by equity holders to maximize their value. This type of models focus primarily
on the distance-to-default measure, which is essentially a volatility adjusted measure of firm
leverage. Debt maturity structure is usually not considered in structural models with the
exception of a recent paper by He and Wei. He and Xiong (2010) enrich the structural model
in Leland and Toft (1996) with mixed debt maturities and illiquid bond market, and show
that firm faces rollover risk because of intrinsic conflict of interest between equity and debt
holders. In their model, the illiquidity in the bond market will increase a firm’s endogenous
default boundary and cause higher default probability through the channel of debt rollover.
Partly because of limitations in theoretical models, rollover risk is largely ignored as a determinant of credit spreads in the empirical literature, including Collin-Dufresne, Goldstein,
and Martin (2001), Eom, Huang, and Helwege (2002), Campbell and Taksler (2003) and
Longstaff, Mithal, and Neis (2005) etc. My paper contributes to this part of the literature
by first documenting and quantifying the effect of rollover risk in exacerbating firms’ credit
spreads. My results show a new form of flight-to-quality phenomenon. During credit crunch,
the credit spreads for firms with high refinancing needs rise much more than those of low refinancing needs. There are only a handful empirical papers that I am aware of addressing the
relationship between rollover risk and credit spreads. Gopalan, Song, and Yerramilli (2010)
argue that rating agencies systematically underestimate rollover risk by showing firms with
heavier short-term liabilities are more likely to experience rating downgrades, and their longterm debt are traded at higher yields even after controlling for credit ratings and other known
determinants. Valenzuela (2010) uses the rollover risk proxies motivated by He and Xiong
(2010), and find that market illiquidity affects corporate bond spreads beyond a liquidity
premium through a rollover risk channel.
One criticism for Gopalan, Song, and Yerramilli (2010) and Valenzuela (2010) is that their
rollover risk measures, which base on short-term debt position, are endogenous variables.6
Ignoring potential endogeneity bias can lead to confounded conclusion about rollover risk
and credit spreads. For example, one way of reducing rollover risk is to hold excessive cash
reserves. Yet, Acharya, Davydenko, and Strebulaev (2008) find a robust positive correlation
6
Both papers try to address the potential endogeneity issue by deploying instrument variable regressions.
Gopalan, Song, and Yerramilli (2010) show that their bond spreads results are robust to using Long-Term
Debt Due/Total Debt as the main independent variable.
6
between cash and credit spreads. Their findings can be explained by the precautionary
motive of high credit risk firms for saving cash. Using instrument variables like growth
option and managerial private costs of financial distress, they find that the “exogenous”
component of cash holdings is negatively correlated with credit spreads. By exploring the
frictions in long-term debt maturity structure, our results benefit from an clean identification
of long-term debt rollover risk. Almeida, Campello, Laranjeira, and Weisbenner (2009) use
the same measure to study the real effect of the 2007 credit crisis, where the authors focus
on rollover risk and firm investment.
My paper complements recent studies of rollover risk and financial crisis. Morris and
Shin (2004) and Morris and Shin (2009) analyze rollover risk through coordination failure in
shot-term creditors. He and Xiong (2009) study a dynamic model of panic runs of creditors
triggered by fear of a firm’s future rollover risk. Acharya, Gale, and Yorulmazer (2010) show
that high rollover frequency can diminish debt capacity of risk assets. These papers have
been focused on the role played by short-term debt in exacerbating rollover risk during major
market liquidity disruptions.
The remainder of the paper is organized as follows. Section 2 describes the data and
discuss the measure for the long-term debt rollover risk. Section 3 presents the rollover
risk effects in the CDS and corporate bond markets. Section 4 investigates the credit risk
information in the stock and option market. Finally, section 5 concludes.
2
2.1
Data and Variable Construction
Data Sources
My data comes from various sources. For balance sheet information, I merge database of
COMPUSTAT North America fundamentals Annual, Rating files, FISD Mergent database.
I start from the Annual files, and delete all ADRs and firms in the regulated industry (SIC
code in range 4900 and 4939). I also drop all firms with negative or missing values for
total assets (at), fiscal year-end price (prcc f ), number of shares outstanding (csho) and
debt maturity variables (dd1, dd2, dd3, dd4, dd5, dltt). I exclude firms with total assets
below $10 million, and those who file annual report for less than 5 years during the sample
period to avoid biases coming from very small and young firms. I also apply several filters
to remove inconsistent data entries. First, I require that firm’s total asset (at) is larger
than the sum of current asset (act) and property, plant and equipment (ppent). Because
COMPUSTAT variables dd1, dd2, dd3, dd4, dd5 and dltt represent the dollar amount of
long-term debt payable in the first (second, third, fourth, fifth and beyond first) year, I
7
delete firms with negative debt maturity variables (dd1, dd2, dd3, dd4, dd5 and dltt), total
long-term debt (dd1 + dltt) larger than total assets (at), and long-term debt maturing in
more than one year (dltt) smaller than the sum of long-term debt maturing between 2 and
5 years (dd2 + dd3 + dd4 + dd5). To ensure that the firms in my sample are quality firms
that can tap the long-term debt market, I require all firms’ total assets (at) consist at least
5% long-term debt maturing beyond one year (dltt). In addition, I exclude firms not rated
by S&P and firms that don’t issue any public bond during the sample period.
CDS data is obtained from CMA via Datastream. The sample is monthly average of
daily quotes from 2004 to 2009. The bond spreads are calculated using bond transaction
prices from TRACE. The stock and option data are from CRSP and OptionMetrics.
2.2
Measure of long-term debt rollover risk
I measure a firm’s long-term debt rollover risk with the variable RO, which I define as
the proportion of the firm’s total long-term debt that is maturing within the year. RO is
calculated as the ratio between total long-term debt due within one year (COMPUSTAT
data item dd1) and total long-term debt (COMPUSTAT data item dd1 + dltt). I report the
distribution of RO, together with other related long-term debt maturity variable: dd1, longterm leverage and long-term debt maturity in table 1. There is wide cross-firm variations in
RO, with 1st decile in the range of 0.00% to 0.59%, and the 9th decile at 12.13% to 19.02%.
In particular, the 9th decile for RO is around 15% at 2008 and 2009. This assues that I can
identify a group of firms that need to refinance significant amount of long-term debt during
the financial crisis, exactly when credit supply dimishes.
It is also worth pointing out that the distribution of RO along with dd1, long-term
leverage and long-term debt maturity during the financial crisis (2008 and 2009) is similar
to the overall distribution in the years preceding the crisis. This eliminates the possibility
that firms foresee the credit crunch before the crisis and actively adapt their long-term debt
structure correspondingly. Firms also have similar long-term debt duration during the crisis,
which confirms that it is difficult to renegotiate or extend long-term debt with their creditors
as the crisis unfold. Combing all the evidence, it is reasonable to conjecture that the credit
crunch in the past financial crisis represents an exogenous negative shock to the supply of
credit in the overall credit market, which significantly increases the refinancing cost of firms
that need to rollover a large chunk of their debt.
A large body of corporate finance literature has been contributed to research in firm’s
capital structure choices such as leverage and maturity. But little is known on firm’s optimal long-term debt maturity structure. Fixing a firm’s long-term debt leverage and overall
8
maturity, it is unclear whether it should maintain a staggered or non-staggered repayment
schedule for its long-term liability. On one hand, firms might want to spread out their debt
expirations across time to avoid repaying a large amount of debt in any particular year.
On the other hand, it is more expensive to sustain a well diversified liability structure in
the presence of significant transaction costs, especially when debt maturity mismatches asset maturity. Staggered long-term debt structure also does not completely protect a firm
from rollover risk. As studied in the paper by He and Xiong (2009), fear of a firm’s future refinancing risk can lead to preemptive runs by maturing creditor in a model with a
risky fundamental value process and a staggered debt structure. I construct the normalized
Herfindalh index to quantify the degree of diversification in a firm’s long-term debt maturity
structure. The average normalized Herfindalh index for RO in my sample is 0.24, suggesting
that firms don’t usually maintain well-diversified maturity structures in my sample. Moreover, most of the discontinuity in RO comes from spikes in the numerator dd1 , not the
denominator dd1 + dltt.7
[Table 1 about here.]
3
Rollover risk and Credit spreads
3.1
Preliminary results
The key question of this paper is to investigate whether the needs to refinance a large portion
of their long-term liabilities increases a firm’s credit risk during the financial crisis. I start
my investigation by presenting the preliminary results based on the approach of matching
estimators. The test is to first identify the “treated” group, in my case the firms that need
to refinance more than 20% of their long-term debt during the crisis (e.g., 2008). I then find
“control” firms in the non-treated population that best match the “treated” ones in terms of
their credit qualities before the crisis. Inferences about the impact of rollover risk on credit
spreads are estimated as the average treatment effect for the treated (ATT), by comparing
the ex-post credit spreads of treated and control firms.
To address the heterogeneity in terms of firms’ credit quality, I use a list of variables
suggested in the credit risk literature. These covariates include both discrete variables, such
as the firm’s industry classification and its long-term S&P rating, and continuous variables
such as long-term debt leverage, equity return, equity volatility, size, and profitability. I
7
The average normalized Herfindalh index for dd1/at is 0.24, while The average normalized Herfindalh
index for (dd1 + dltt)/at is 0.01.
9
employ the bias-corrected, heteroskedasticity-consistent matching estimators as suggested
in Abadie and Imbens (2002). For every treated firm, the Abadie-Imbens estimator finds a
control firm that “exact” matches on discrete covariates and closest on continuous covariates.
The procedure includes a component to reduce bias caused by discrepancy in continuous
covariates. Because early concerns and reports about the housing market started from the
late summer of 2007, it is ambiguous whether the last quarter of 2007 is part of the past
financial crisis. I thus calculate the average CDS spreads in the first three quarters of 2007
and 2008 as the ex-ante and ex-post responses.
The matching results are summarized in table 5. Comparing the 24 treated and control
firms, panel A shows the median values for both continuous covariates and CDS spreads at 1,
5, and 10 year maturities in 2007 and 2008. Before the crisis, treated and control firms don’t
display statistically significant differences in either covariates or CDS spreads, suggesting
they have similar credit qualities in the pre-crisis period and only differ in their impending
long-term debt repayment.
I examine the rollover risk effect on credit spreads in the panel B of table 5. Before
the crisis, treated and control firms have similar CDS spreads at various maturities. The
average CDS term structure for both treated and control firms are upward sloping, with
1-year CDS spreads close to 14 bps and 10-year CDS spreads around 64-75 bps. During the
financial crisis, CDS spreads for both treated and control firms hike up, but significantly
more for treated firms. At one year maturity, CDS spreads for treated firms jump to 257
bps compared with 75 bps for control ones. The difference-in-difference estimator suggests
that CDS spreads increase by 181 bps more for firms burdened with heavy long-term debt,
relative to otherwise similar control firms. This increase is economically huge with regards to
both ex-ante and ex-post CDS spreads, and statistically significant at 95% level. The biascorrecting Abadie-Imbens estimates is lower in terms of level (169 bps), but still statistically
significant at 90% level. CDS spreads at 5 and 10 year maturities show similar pattern,
although less striking than the short-term one year CDS spreads. Notice that the term
structure of CDS spreads became much flatter during the crisis, especially for treated firms.
The time-series of the CDS spreads for treated and control firms are plotted in panel (a) of
figure 5.
[Table 2 about here.]
One important assumption for my identification strategy is that firms with heavy expiring long-term debt find it is difficult to refinance their maturing debt by tapping the external
capital market. Credit market conditions play a key role in determining whether this assumption holds. The 2008 financial crisis is an unique episode of credit crunch in which the
10
assumption is likely to hold. The results in table 5 confirm that firms’ maturity structure do
affect their CDS spreads during the financial crisis period. In the same way, the assumption
is unlikely to hold during periods of easy credit. For reference, I replicate the same testing
strategy for year 2006, defining firms that need to refinance more than 20% of their long-term
debt in 2006 as the new treatment firms, and matching them with control firms using the
same covariate matching approach. The results are summarized in table 5. Indeed, I don’t
observe the adverse effects of long-term debt maturity structure on CDS spreads in 2006,
firms in the treatment group have similar CDS spreads with the control group. The changes
in CDS spreads from 2005 to 2006 are economically small and not statistically significant.8
[Table 3 about here.]
Panel (b) of figure 5 plots the time series of average CDS spreads for the treated and
control firms which are rebalanced yearly. At the begining of every year, I choose the group
of treated firms as those which need to refinance more than 20% of their long-term debt in
the coming year, and match them with control firms in the non-treated population that have
similar credit qualities measured at the end of previous year. As seen in the plot, the spreads
for the treated and control firms are close to each other until the early fall of 2007 when
concerns about troubled residential mortgage market spread to financial institutions that
invested heavily in asset-back securities. In 2008, there is significant co-movement between
the treated and control firms during the crisis. But the CDS spreads for treated firms rise
much faster than their corresponding control ones, and reach the first peak in March 2008
following the collapse of two Bear-Stearns hedge funds and remain stable for a short time
period. The significant matching estimator results discussed above capture this difference.
Following the bankruptcy of Lehman Brothers, the CDS spreads for both treated and control
firms continue to rise until the end of the year while treated firms have higher CDS spreads
the control ones. The CDS spreads for treated and control firms are not significantly different
during 2009, partly because the turmoil in the CDS and stock market make it hard to match
firms based on their credit qualities in 2008.
[Figure 1 about here.]
The matching estimator results suggest that firms have a large amount of long-term debt
coming due have higher credit risk as reflected in their CDS spreads during the financial crisis.
However, the matching estimator results could suffer from biases coming from imperfect
controls. Start from the next session, I continue my investigation in a regression framework.
8
The results for 2005 and 2007 are similar to the results in 2006.
11
3.2
Main regression results
I first exam the relationship between CDS spreads and rollover risk as measured by the
proportion of expiring long-term debt RO by running the following cross-section regression
month by month:
Spreadsit = αt + β1,t × RO CutOf fti + γt × Xti + Industry F E.
(1)
where the subscripts i and t indicate firm and the month. The dependent variable
Spreadsit is the log of the average daily CDS spreads in the month. RO CutOf fti is a
dummy variable which takes value 1 if ROti is bigger than 0.2 and 0 otherwise. Xti denotes
variables to control for firm characteristics and insolvency risk. It includes equity returns,
volatilities, leverage, profitability, size, BM and credit ratings. The coefficient β1,t captures
the impact of liquidity risk on credit spreads. I run the regression (1) for CDS spreads at
1Y ear
5Y ear
10Y ear
maturities 1, 5 and 10 year, and plot the time-series of coefficients β1,t
, β1,t
, β1,t
in
figure 2. The figure shows that the impact of rollover risk on credit spreads is much higher
at the peak of the past financial crisis, namely the second half of 2008. It indicates that
the sensitivities of credit spreads to rollover risk is probably time-varying, and depending on
overall credit market conditions.
[Figure 2 about here.]
I formally test the idea with the panel regression setting specified below:
Spreadsit = α0 + β1 × RO CutOf fti + β2 × (RO CutOf fti ∗ Crisis Dummiest )
+ γ × Xti + F irm F E + T ime F E.
(2)
where Crisis Dummiest are indicators of whether month t falls into five periods of the
past financial crisis: second half of 2007, first half of 2008, second half of 2008, first half
of 2009 and second half of 2008. I use firm fixed effects to control for any unobservable
firm characteristics that might affect credit risk, and time fixed effects for impact of macro
conditions.
I perform panel regression tests as specified in (2) to investigate whether credit spreads
reflect rollover risk. The dependent variables are the log CDS spreads at maturities 1, 5, and
10 year, and the key independent variables are the exposure to rollover risk RO CutOf f and
the interactive variables between RO CutOf f and the crisis time dummies. I include firm
and time fixed effects, and report robust standard errors that are clustered at the individual
firm and time level. The regression results are described in table 5.
12
The first three columns of table 5 report regression results in a setting without the interaction effect between rollover risk and crisis. The positive but insignificant coefficients for
RO CutOf f indicate that exposure to rollover risk alone is not a significant determinant of
credit spreads in general. However, interesting relationship between rollover risk and credit
spreads shows up during the past financial crisis as reported in the last three columns of
table 5. The coefficient for the interaction variables (RO CutOf f ∗ Crisis Dummies) (β2 )
are mostly positive and statistical significant. it implies that firms with higher refinancing
risk have wider CDS spreads during the financial crisis, after controlling other known determinants of credit risk. The impact of rollover risk on credit spreads starts to pick up
from the second half year of 2007, strongest during the second half of 2008, then gradually
declines and becomes insignificant at the second half of 2009. This pattern is consistent with
the time line of the past financial crisis which hit its most critical stage around September
2008 following the collapse of Lehman Brothers. It indicates that credit market condition
is an important component that determines the size of the rollover risk impact on firms’
credit spreads. The coefficients are also economically significant: log one-year CDS spreads
for a firm burdened with large amount of long-term debt maturing in the crisis is 0.26 to
0.52 higher than a similar firm without refinancing needs, in comparison with average CDS
spreads of 4.67 (log) or 107 (bps) during this period. Put it another way, CDS spreads for
high rollover risk firms are 30% to 68% or 32 to 72 bps higher.
When moving from the short-end to the long-end maturities, the coefficients for (RO CutOf f ∗
Crisis Dummies) are monotonically decreasing during the crisis period. This is not surprising because long-term spreads reflect average default probabilities over time. This Monotonic
decreasing pattern indicates that rollover risk mainly affects a firm’s default likelihood in
the near future while having limited power in explaining its fundamental long-term survivability. However, the slope of the coefficients of rollover risk along the maturity horizon does
provide useful information on the severity and persistence of the financial crisis. When the
crisis first started in late 2007, the rollover risk coefficients decline quickly from around 0.31
for one year to 0.10 for 10 year CDS spreads. The coefficients are much more persistent in
the second half of 2008 with 0.52 for short-term versus 0.32 for long-term spreads. During
this period, the financial crisis was at its worst conditions and the daunting credit market
conditions were not expected to recovery any time soon. The term structure for the rollover
risk coefficients became steeper later as the financial market showed signs of improvement
later on .
The coefficients on the control variables are consistent with theoretic predictions and
empirical findings in previous work such as Collin-Dufresne, Goldstein, and Martin (2001)
13
and Campbell and Taksler (2003). Low rated firms with high leverage and high volatilities
have wider credit spreads. Firms experienced negative stock returns have higher credit
spreads. Good profitability lower firm’s credit spreads at the short-end maturity, but not
significant for long-term spreads. High growth firms, measured by BM ratios, have tighter
spreads. Bigger firms in my sample have higher spreads though not significant at the oneyear maturity. In unreported tests, I control for industry fixed effects. The results are
very similar to the base case, except that size and Book-to-market ratio are not significant
determinants of credit spreads.
Credit ratings are included in the regression as controls for heterogeneity among firm’s
credit quality. Rating agencies observe firms’ debt maturity structure, and may incorporate
rollover risk in the ratings that they assigned. My results show that the proportion of expiring
long-term debt have explanatory power in determining credit spreads above and beyond
information contained in credit ratings. It supports the wiede held view that credit ratings
are an incomplete measure of credit risk. My results are also consistent with the findings in
Gopalan, Song, and Yerramilli (2010) that rating agencies systematically underestimate the
so-called liquidity risk, which refers to the risk that firms may face difficulties in refinancing
its short-term debt in their paper.
[Table 4 about here.]
3.3
Rollover risk and profitability
To further establish that the positive relationship between expiring long-term debt and credit
spreads is through the channel of roll over, I investigate whether the credit risk effect generated by firms’ long-term debt maturity structure varies with respect to their profitableness.
The positive relationship between refinancing risk and credit spreads should be stronger for
less profitable firms during the crisis. Profitability affects rollover risk for a number of reasons. Firms that cannot refinance their existing debt has to seek other financing sources.
Both Almeida, Campello, Laranjeira, and Weisbenner (2009) and Duchin, Ozbas, and Sensoy
(2009) point out that these firms drew heavily on their least costly internal funds to mitigate the negative impact of diminishing supply in external financing. Companies that at the
same time suffer substantial losses are more likely to not have sufficient funds on hand to pay
their lenders. Additionally, concerns about the quality of the assets tend to occur when firms
experience a decline in profits. Recent models by He and Xiong (2009) and Morris and Shin
(2009) show that firms with deteriorating fundamentals are also more likely to experience
panic runs by maturing creditors. These runs are particularly devastating during financial
14
distress when it is hard to raise additional funding. To summarize in one sentence, rollover
risk and deteriorating market conditions can exacerbate the impact of negative fundamental
shocks on credit risk.
I test the above hypothesis by including an interaction variable of refinance risk and
profitability RO CutOf f ×P rof itability. This interaction variable captures the incremental
credit risk effect generated by the maturity structure of a firm that also experiences a negative
shock to its cash flows. I also let variable RO CutOf f × P rof itability interact with the
crisis time dummies because the increment effect of profitability on rollover risk is likely to
depend on overall credit market conditions. The regression results are reported in table 5. As
expected, the coefficients for the interaction variable RO CutOf f ×P rof itability are mostly
negative significant during the crisis. This confirms that the increase in credit spreads due
to refinancing risk is higher for less profitable firms during credit market disruptions. Again,
this effect is stronger for short-term CDS spreads. During normal times, the coefficient
for (RO CutOf f × P rof itability) is negative but not statistically significant. The results
support the hypothesis that rollover risk together with market conditions amplify the effect
of profitability on credit spreads. It indicates that fundamental asset risk can enter credit
spreads via an indirect channel through rollover risk, despite its’s conventional direct effect.
[Table 5 about here.]
3.4
Relative importance of rollover risk and fundamental risk during the financial crisis
My base results indicate that the contribution of rollover risk to credit spreads time-varying:
an important source during the crisis period while not substantial during normal times.
Notice that my approach is different but related to the common practice in empirical studies
where credit spreads are decomposed into the default component which reflect fundamental
asset risk, and the liquidity premium which generally arising from trading frictions in the
corporate bond market, either bond-specific or market wide aggregate illiquidity. Researchers
have found that liquidity premium is an important factor in firms’ bond spreads, especially
during credit-market crises,e.g, Longstaff, Mithal, and Neis (2005) and Bao, Pan, and Wang
(2008). Compared with corporate bond market, the CDS market is relatively much more
liquid even during the crisis period. CDS spreads are thus considered to be a “pure” measure
of default risk. However, disruptions in the broad credit market, including illiquidity in
the corporate bond market, can increase firms’ default probability through the channel of
rollover risk. It raise an interesting question about the relative importance of rollover risk
15
and fundamental risk in the determination of default risk under different market conditions.
In the setting of my base results, rollover risk variable RO CutOf f is the only variable
that interacts with the crisis dummies. To address the question that other credit risk determinants might as well affect credit spreads differently during the crisis, I augment the base
regression setting 2 with additional variables interacting with the crisis dummies. I focus
specifically on leverage and volatilities because they are the two key variables suggested by
structural models in evaluating a firm’s fundamental credit risk. Their importance have been
studied extensively in empirical literature, and are proven to be two most important and
robust determinant of credit risk. As seen in table 6, the impact of leverage and volatility
on credit spreads decrease significantly during the crisis relative to the normal periods, especially for short-term spreads. This is in contrast with the behavior of rollover risk during the
crisis. It suggests that investors in credit market concerns more about default probabilities
arising from rollover risk and discount the importance of fundamental survivability.9 This
is as expected because rollover risk reflect a more “imminent” risk faced by firms that have
heavy debt matures in near term. A firm without enough fund to repay its lenders can be
forced into bankruptcy, even when it has positive net worth or probably benign long-term
prospects. During normal times when credit market functions efficiently, the CDS spreads
are mainly explained by fundamental risk factors.
[Table 6 about here.]
3.5
Rollover risk in the corporate bond market
In this section, I repeat my base case analysis for corporate bonds. Corporate bonds share
similar credit risk exposures as credit default swaps, but are thought to have significant
non-credit risk component caused by illiquidity in the corporate bond market. Bond pricing
data is obtained from FINRA’s TRACE database. Introduced in July of 2002, TRACE
consolidates over-the-counter (“OTC”) secondary market transactions data for all eligible
corporate bonds. Bringing transparency to the corporate bond market, it represents over
99% of total U.S. corporate bond market activity in over 30, 000 securities. From Trace, I
construct daily observed bond yields from 2004 to 2009. For my sample, I filter out canceled,
corrected and special trades and also dropped cases where prices are obviously misreported.
In addition, I only use pricing data from last trades before 5PM that are at least 100k in
face value. I obtain bond characteristics that include flags for callability, putability and
9
This argument is further supported in table 5 when I use synthetic CDS spreads calculated from the
equity and option market to control for fundamental risk.
16
convertibility along with various other characteristics such as issuance, offering date and
coupons. I drop callable, putable and convertible bond and also bonds with variable coupons
from my sample. Benchmark treasury yields are obtained from Datastream. The bond yield
spreads are difference between corporate bond yields and the corresponding treasury yields
with the same maturity. For each issuer, I select three benchmark bonds to represent credit
spreads at short, median and long maturities. This is done so that I can capture the impact
of rollover risk on corporate bonds with various maturities, and make sure that the results
are not due to a couple of firms that have large number of bond issues. Specifically, I keep
one bond that is close to 1-year maturity with maturity in the range of 1 month and 4 years,
one bond that is close to 5-year maturity with maturity in the range of 3 years and 8 years,
and one bond that is close to 10-year maturity with maturity in the range of 8 years and
20 years. Finally, I aggregate the daily bond yield spreads to monthly observations. The
summary statistics for corporate bond spreads data are reported in table 7.
[Table 7 about here.]
Similar to the base regression setup in section 3.2, I investigate rollover risk effect in the
corporate bond market by putting corporate bond spreads as the main dependent variable
on the left-hand side. The results are summarized in table 8. Consistent with the results
in the CDS market, rollover risk affects bond spreads only during the crisis period, and has
larger impact on the short-end bond spreads than the long-end bond spreads. However,
the coefficients of RO CutOf f × CrisisDummies are much smaller in magnitudes and less
significant in term of t-statistics for corporate bond spreads. Log one-year bond spreads for
high rollover risk firms are 0.185 (or 25 basic points in bond spreads) higher than low rollover
risk ones during the second half year of 2008, compared to 0.53 (or 72 basis points in CDS
spreads) for log one-year CDS spreads during the same period. Because bond spreads are
widely known as “noisy” measure of default risk, it is not surprising that the rollover risk
effect is weaker for bond spreads. But the general consistent results confirm that rollover risk
coupled with adverse credit market conditions are important determinant of credit spreads.
[Table 8 about here.]
17
4
Credit risk information in the stock and option market
In this section, I investigate the credit risk information conveyed in the stock and option
markets. I use synthetic CDS spreads calculated based on the simple Merton structural
model to measure the credit risk of every firm in my sample. In this way, my results in the
stock and option markets are on the same footing with the CDS and corporate bond results
that I discussed in previous sections.
4.1
Variable Construction
I assume that the asset value Vt follows a Geometric Brownian Motion (GBM) process, then
the Merton Model implies:
dVt
= (r + λ − δ)dt + σv dWt
Vt
ln(V /K) + (r − δ − 0.5σV2 )T
√
d2 =
d1 = d2 + σV
σV T
q(t) = N (−d2)
(3)
(4)
(5)
where I approximate the book value of debt K as the sum of debt in current liabilities and
long term debt (dlc + dltt), and the asset value V as the sum of market value of equity and
the book value of debt E + K. I assume constant interest rate r and payout ratio δ for all
firms in my sample. The interest rate is set to be 3% which is close to the average 1-year
CMT yields during my sample period. δ is set to be the weighted average of coupon rate
and repurchase adjusted dividend yields in my sample (4.5%). q(t) represents risk-neutral
cumulative default probability. For asset volatility σV , although not directly observable, it
can be derived from equity volatility which can be easily inferred from either the equity or
the option market. Specifically, the Merton model implies
σE =
1
V
N (d1)σV ≈
σV
E
1 − K/V
(6)
The recovery rate R is assumed to be constant 50% for all firms in my sample. The the
synthetic 1-year CDS spreads (SCDS) can be calculated from the following equation:
SCDS
Z
0
1
−rs
(1 − q(s))e
ds = (1 − R)
Z
1
q ′ (s)e−rs ds
(7)
0
Aside from the balance sheet information about the firm’s capital structure, the key input
to the calculation of synthetic CDS spreads is σE . I consider two candidates for the equity
18
volatility, realized equity volatility (σ RV ) in the stock market and the implied volatility (σ IV )
from the option market. Compared with the realized volatility calculated using historical
equity returns, the implied volatility derived from the option market maybe a better measure
of future equity volatility but it is contaminated by the risk premiums in the option market.
The implied volatility is obtained as the standardized 3-month at-the-money (ATM) option
implied volatility from Optionmetrics, while the realized volatility is calculated using the
stock return data in CRSP.
The summary statistics are reported in table 9. Consider the same time period from 2004
to 2009, I have a much broader sample of firms comparing to the previous one I studied for
credit spreads. This is because option market covers a much broader range of firms than the
relative new CDS market. In my sample, only 1/3 of firms have liquid CDS quotes on them.
I am also able to identify significantly more firms with high rollover risk (RO > 0.2), this
reassures that my results in this paper are not driven by a few extreme outliers. The average
realized and implied volatility is around the level of 30% before the financial crisis, and
almost doubled during the financial crisis in 2008 and 2009. During normal period, implied
volatility is slightly higher than the realized volatility in the market, consistent with the
fact that implied volatility contains option market risk premia. However, this relationship
) also increases
is reversed during the year 2008. At the same time, market leverage ( K
V
significantly during the crisis, reflecting poor performance of equity market during the crisis
period.
The calculated synthetic CDS spreads show similar level and time-series dynamics as the
observed market CDS spreads. Although synthetic CDS spreads are only a fraction of the
market CDS spreads before the crisis, they quickly pick up during the crisis and account
for a large portion of the market CDS spreads. This confirms that synthetic CDS spreads,
calculated from the structural Merton model, correctly capture the market-wide increase in
default risk among US firms during the past crisis. In next session, I study whether high
rollover risk firms display higher credit risk, as measured by synthetic CDS spreads using
information in the equity and option market.
[Table 9 about here.]
4.2
Rollover risk and synthetic CDS spreads
Similar to the settings in the previous section for market CDS spreads, I run regressions
with equity volatility and synthetic CDS spreads (log) as the left-hand variables, and report
the results in table 5. The first two columns of table 5 show that both realized volatility
19
RV and synthetic CDS spreads SCDS RV increase more for firms that need to refinance a
large portion of their long-term debt during the crisis. For these high rollover risk firms,
their stocks are 11% more volatile than otherwise similar firms. The increase is economically
significant relative to average volatility of 64% in 2008, and is statistically significant for the
first half year of 2008. Synthetic CDS spreads CDS RV display similar patterns during the
crisis period, suggesting that high rollover risk firms have higher default probabilities during
the crisis. During the normal period, the coefficient for RO CutOf f is not significant.
Compared with the increase in the market CDS spreads (0.26-0.51), the coefficients here
for synthetic CDS spreads are much larger (0.56-1.02). This is because the coefficients for
the interactive variables (RO CutOf f × CrisisDummes) are estimated based on log CDS
spreads in the regressions, which are close to percentage changes in CDS spreads. Given
that synthetic CDS spreads are much lower than market CDS spreads before the crisis, it is
not surprising to find that the percentage changes in synthetic CDS spreads are larger than
the percentage changes in market CDS spreads. Similar patterns are found for the implied
volatility and its corresponding synthetic CDS spreads SCDS IV .
Option implied volatility IV contains two important components: expected future volatilities and risk premiums. The “risk-neutral” CDS spreads SCDS IV , besides its default component, may contain information about investor’s risk aversion in the option market through
the channel of implied volatilities. It is interesting to investigate whether this risk aversion
component in SCDS IV is also related to rollover risk. In the column (5) of table 5, I control the default risk component in SCDS IV by adding SCDS RV as explainable variable on
the right-hand side of the regression. Comparing with column (4) of the same table, the
coefficients on the interactive variables RO CutOf f × CrisisDummies decrease in their
magnitudes, but are still significant. This result suggests that investors in the option market
also demand higher risk premiums for firms that need to repay large amount of long-term
debt during the crisis. This effect of rollover risk on the option risk premiums is also timevarying, and not significant during normal period. One criticism for controlling the default
component of SCDS IV with SCDS RV is that implied volatility is considered as a market consensus forecast of future volatility, and maybe a better estimate for future realized
volatility relative to the slow-moving realized historical volatility. I address this concern
by controlling the default component by SCDS F RV where the synthetic CDS spreads are
calculated using the realized future volatility. The regression results in column (6) is fairly
similar, confirming that option risk premiums are also sensitive to a firm’s long-term debt
maturity structure during the crisis.10
10
The results are robust to adding interaction variables of SCDS RV and SCDS F RV with crisis dummies.
20
[Table 10 about here.]
The information content in the CDS market and the option market is not redundant.
Controlling credit risk with CDS (log) spreads on the right-hand side, table 5 shows that
rollover risk effect still shows up in the synthetic CDS spreads (SCDS IV ) during the crisis
period. The reverse is also true when regressing the CDS spreads by putting synthetic CDS
spreads (SCDS IV ) as control on the right-hand side. Moreover, the last column of table
5 reconfirms that solvency risk, as a major determinant of CDS spreads, is less important
during crises. This is consistent with the discussion in section 3.4.
[Table 11 about here.]
5
Conclusion
In this paper, I investigate the asset pricing implication of rollover risk, the risk that a
firm might not able to refinance its due liabilities. To avoid potential endogneity bias of
measuring short-term debt position directly, I focus on the long-term debt rollover risk and
use the proportion of long term debt falling due within the year (RO) to measure a firm’s
exposure to rollover risk. I find that firm-specific rollover risk coupled with deteriorating
market wide credit condition indeed increase a firm’s credit spreads. This rollover effect is
strongest during the peak period of the financial crisis, namely the second half year of 2008,
and less striking at the early and final stages of the crisis. Exposure to rollover risk is not
a significant determinant of CDS spreads during normal period. When moving from the
short-end to the long-end maturities, the rollover effect on CDS spreads is monotonically
decreasing during the crisis period. Cross-sectionally, I find that the positive relationship
between rollover risk and CDS spreads is stronger for less profitable firms, consistent with the
idea that firms are likely to face greater liquidity squeeze if they also experience a negative
shock to their cash flows. Across time, I find that the relative contribution of rollover risk
and solvency risk on default risk is time varying and depends greatly on market conditions.
During normal periods, CDS spreads are mostly explained by fundamental risk while rollover
risk is not a significant determinant of credit risk. The influence of fundamental risk on CDS
spreads decreases during crisis, in contrast to rollover risk which shows up as an important
component of credit spreads.
In addition to the CDS market, rollover effect also exists in the corporate bond market.
I construct bond spreads using bond transaction prices in the TRACE database. Although
the results for rollover risk are weaker, they are largely in line with the conclusions for the
21
CDS spreads. Rollover risk effect is also observed in the stock and option markets where
the credit spreads are measured using synthetic CDS spreads calculated from the Merton
structural model.
22
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24
Table 1: Cross-section distribution of RO,
25
KS p-Value(2008 vs 2000-2007)
KS p-Value(2009 vs 2000-2007)
#Firms 1st Decile
0.00
389
388
0.00
0.00
398
420
0.00
0.00
418
400
0.00
0.00
389
357
0.00
0.00
347
0.00
327
Median
2.11
2.92
2.46
2.51
2.29
2.39
1.87
1.99
1.86
2.49
0.518
0.726
leverage and long-term debt maturity .
dd1
at
RO(%)
Year
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
DD1
,
at
9th Decile
15.98
17.38
18.44
18.07
16.16
17.14
16.60
19.02
15.48
15.93
1st Decile
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Median
0.61
0.74
0.70
0.77
0.57
0.56
0.44
0.42
0.53
0.58
0.671
0.947
Leverage
9th Decile
3.90
5.01
4.98
4.85
4.50
3.86
4.47
4.70
4.72
4.91
1st Decile
11.19
13.08
14.03
13.18
13.28
12.96
11.09
10.92
13.26
13.47
Median
28.61
28.72
30.69
28.67
25.27
25.02
23.66
22.86
26.67
28.11
0.940
0.188
Long-Term Debt Maturity
9th Decile 1st Decile
59.94
32.49
57.75
25.09
56.65
31.11
55.75
39.06
50.66
37.04
50.79
42.30
50.63
39.18
47.71
42.37
57.32
41.29
55.61
43.44
Median
73.92
70.11
75.85
78.19
77.24
78.44
80.43
80.95
80.12
76.21
9th Decile
98.23
97.65
98.30
98.90
99.35
99.40
99.50
99.45
99.72
99.62
0.089
0.401
RO is the ratio between COMPUSTAT item dd1 and item dd1+dltt. dd1 represents total amount of long-term debt maturing
within the year. at is total assets. Leverage is long-term book leverage calculated as the ratio between total long-term debt
(dd1 + dltt) and book assets(at).
Table 2: CDS spreads for the treated and controls firms in 2007 and 2008.
Panel A: Distribution for treated and control firms in 2007.
Treated
Control
Difference
Median Test
N
Ret
24
24
0.234
0.161
0.073
0.192
σE
Size
Leverage
Profitability
CDS1
CDS5
CDSX
Medians for Treated and Control Firms in 2007
0.223
10.055
0.260
0.107
8.586
0.195
9.627
0.227
0.120
7.750
0.028
0.428
0.033
-0.014
0.836
0.122
0.958
0.085
0.217
0.476
29.358
23.351
6.008
0.394
47.763
35.253
12.510
0.170
Panel B: Diff-In-Diff and matching estimator results for changes in CDS spreads from 2007 to 2008.
1-year CDS
Treated
26
Control
Diff
Matching
Estimator(ATT)
5-year CDS
10-year CDS
2008
2007
2008-2007
2008
2007
2008-2007
2008
2007
2008-2007
256.58***
(94.44)
75.36***
(19.01)
181.21**
(90.52)
14.01***
( 3.26)
14.11***
( 4.80)
-0.10
( 3.80)
242.57***
(91.83)
61.26***
(14.40)
181.31**
(88.49)
168.76*
(94.07)
274.25***
(79.12)
133.64***
(23.95)
140.61*
(78.74)
48.70***
( 9.84)
43.44***
(11.69)
5.26
(12.60)
225.55***
(71.06)
90.21***
(13.82)
135.35*
(69.82)
128.94**
(63.81)
254.96***
(67.50)
141.97***
(23.04)
112.99
(69.05)
75.12***
(14.70)
64.39***
(14.26)
10.74
(17.14)
179.84***
(56.23)
77.58***
(11.83)
102.25*
(56.35)
101.52**
(47.40)
The treated firms are firms that have more than 20 percent long-term debt maturing within one year (i.e., 2008) and non-treated
firms are those for which the percentage of long-term debt maturing within one year is less than or equal to 20 percent. Control firms
are a subset of the non-treated firms selected as the closest match to the treated firms based on a set of firm characteristics: industry
classification, credit rating, equity return, equity volatility, size, leverage and profitability. Panel A compares the characteristics of
the treated and control firms in 2007. CDS spreads are average CDS spreads in the first three quarters of 2007. The test for a
difference in the medians of a firm characteristic across two groups is conducted by calculating the continuity-correct Pearson’s χ2
statistics, with the reported p-values. Panel B of this table present estimates of the change in average CDS spreads from the first
three quarters of 2007 to the first three quarters of 2008. ATT is the Abadie-Imbens bias corrected average treated effect matching
estimator (Matching Estimator). Heteroskedasticity-consistent standard errors are in parentheses.
Table 3: CDS spreads for the treated and controls firms in 2005 and 2006.
Panel A: Distribution for treated and control firms in 2005.
Treated
Control
Difference
Median Test
N
Ret
29
29
0.099
0.080
0.019
0.888
σE
Size
Leverage
Profitability
CDS1
CDS5
CDSX
Medians for Treated and Control Firms in 2005
0.227
9.611
0.193
0.128
12.430
0.206
9.352
0.205
0.115
8.032
0.021
0.258
-0.012
0.013
4.398
0.056
0.888
0.754
0.773
0.284
40.425
29.499
10.926
0.284
61.419
50.095
11.324
0.184
Panel B: Diff-In-Diff and matching estimator results for changes in CDS spreads from 2005 to 2006.
1-year CDS
Treated
27
Control
Diff
Matching Estimator
(ATT)
5-year CDS
10-year CDS
2006
2005
2006-2005
2006
2005
2006-2005
2006
2005
2006-2005
14.60***
( 3.66)
14.28***
( 4.55)
0.32
( 3.64)
24.98***
( 8.15)
19.97***
( 6.15)
5.00
( 7.46)
-10.37*
( 5.54)
-5.69**
( 2.41)
-4.68
( 4.28)
-4.21
( 5.72)
56.33***
(13.11)
50.38***
(12.79)
5.95
(11.59)
65.26***
(14.93)
56.45***
(14.32)
8.81
(14.41)
-8.93
( 6.12)
-6.07
( 3.74)
-2.86
( 5.66)
-2.43
( 8.86)
80.17***
(15.58)
72.07***
(14.45)
8.09
(13.56)
90.01***
(16.49)
78.08***
(15.73)
11.93
(15.70)
-9.84
( 6.58)
-6.01
( 4.37)
-3.84
( 6.48)
-3.61
( 9.50)
The treated firms are firms that have more than 20 percent long-term debt maturing within one year (i.e., 2006) and non-treated
firms are those for which the percentage of long-term debt maturing within one year is less than or equal to 20 percent. Control firms
are a subset of the non-treated firms selected as the closest match to the treated firms based on a set of firm characteristics: industry
classification, credit rating, equity return, equity volatility, size, leverage and profitability. Panel A compares the characteristics of
the treated and control firms in 2005. CDS spreads are average CDS spreads in the first three quarters of 2005. The test for a
difference in the medians of a firm characteristic across two groups is conducted by calculating the continuity-correct Pearson’s χ2
statistics, with the reported p-values. Panel B of this table present estimates of the change in average CDS spreads from the first
three quarters of 2005 to the first three quarters of 2006. ATT is the Abadie-Imbens bias corrected average treated effect matching
estimator (Matching Estimator). Heteroskedasticity-consistent standard errors are in parentheses.
Table 4: The Base regression results for CDS spreads.
Parameter
Log (CDS1)
Log (CDS5)
Log (CDSX) Log (CDS1)
RO CutOf f × (200707 − 200712)
0.048
[ 1.04]
-.140
[-5.51]
1.039
[ 7.77]
0.762
[ 2.07]
-1.01
[-2.15]
0.160
[ 1.88]
0.081
[ 2.16]
0.368
[ 8.08]
0.020
[ 0.61]
-.107
[-6.29]
0.776
[ 7.36]
0.660
[ 2.41]
-.211
[-0.62]
0.201
[ 3.14]
0.051
[ 1.81]
0.352
[10.15]
0.011
[ 0.37]
-.092
[-6.67]
0.765
[ 8.79]
0.580
[ 2.47]
-.088
[-0.30]
0.156
[ 2.73]
0.048
[ 2.00]
0.306
[ 9.81]
0.307
[ 3.00]
0.394
[ 2.56]
0.516
[ 4.25]
0.263
[ 2.91]
0.160
[ 1.39]
-.070
[-1.40]
-.137
[-5.54]
1.013
[ 7.91]
0.745
[ 2.08]
-.984
[-2.24]
0.160
[ 1.91]
0.082
[ 2.29]
0.367
[ 8.03]
18644
0.887
YES
YES
18644
0.903
YES
YES
18644
0.890
YES
YES
18644
0.888
YES
YES
RO CutOf f × (200801 − 200806)
RO CutOf f × (200807 − 200812)
RO CutOf f × (200901 − 200906)
RO CutOf f × (200907 − 200912)
RO CutOf f
Ret
Vol
Leverage
Profitability
Size
BM
Rating
NOBS
R2
Firm Dummies
Month Dummies
Log (CDS5) Log (CDSX)
0.160
[ 2.31]
0.228
[ 2.46]
0.387
[ 4.34]
0.171
[ 2.24]
0.110
[ 1.22]
-.055
[-1.50]
-.106
[-6.37]
0.757
[ 7.47]
0.652
[ 2.42]
-.197
[-0.61]
0.201
[ 3.15]
0.052
[ 1.92]
0.352
[10.03]
0.102
[ 2.02]
0.132
[ 1.71]
0.322
[ 4.04]
0.127
[ 1.79]
0.106
[ 1.32]
-.044
[-1.40]
-.091
[-6.76]
0.750
[ 8.95]
0.575
[ 2.47]
-.081
[-0.29]
0.156
[ 2.74]
0.049
[ 2.10]
0.306
[ 9.66]
18644
0.904
YES
YES
18644
0.890
YES
YES
Observations are monthly. All regressions contain month and firm fixed-effects. The dependent variables
are the log of 1, 5 and 10 year CDS spreads (in basis points). T-statistics use standard deviations clustered
by firm and month. Coefficients for RO CutOf f × CrisisDummies are highlighted in bold if they are 5%
significant.
28
Table 5: Interaction effect of refinancing risk and profitability.
Parm
Log(CDS1)
Log(CDS5)
Log(CDSX)
RO CutOf f × (200707 − 200712)
Other Controls
0.572
[ 2.49]
0.761
[ 3.43]
0.783
[ 4.42]
0.312
[ 1.73]
0.390
[ 2.12]
-1.86
[-1.61]
-3.90
[-3.57]
-3.00
[-2.76]
-.434
[-0.40]
-2.06
[-2.01]
-.021
[-0.21]
-.210
[-0.37]
omitted
0.328
[ 2.24]
0.365
[ 2.51]
0.513
[ 3.84]
0.112
[ 0.77]
0.260
[ 1.76]
-1.18
[-1.49]
-1.58
[-1.83]
-1.56
[-1.68]
0.451
[ 0.55]
-1.37
[-1.56]
0.018
[ 0.22]
-.429
[-0.84]
omitted
0.188
[ 1.79]
0.218
[ 1.87]
0.420
[ 3.58]
0.066
[ 0.50]
0.266
[ 2.12]
-.597
[-1.00]
-1.05
[-1.41]
-1.27
[-1.49]
0.475
[ 0.62]
-1.44
[-1.90]
0.022
[ 0.31]
-.406
[-0.85]
omitted
NOBS
R2
Firm Dummies
Month Dummies
18644
0.890
YES
YES
18644
0.904
YES
YES
18644
0.891
YES
YES
RO CutOf f × (200801 − 200806)
RO CutOf f × (200807 − 200812)
RO CutOf f × (200901 − 200906)
RO CutOf f × (200907 − 200912)
RO CutOf f × P rof itability × (200707 − 200712)
RO CutOf f × P rof itability × (200801 − 200806)
RO CutOf f × P rof itability × (200807 − 200812)
RO CutOf f × P rof itability × (200901 − 200906)
RO CutOf f × P rof itability × (200907 − 200912)
RO CutOf f
RO CutOf f × P rof itability
Observations are monthly. All regressions contain month and firm fixed-effects. The dependent variables
are the log of 1, 5 and 10 year CDS spreads (in basis points). T-statistics use standard deviations clustered
by firm and month. Coefficients for RO CutOf f × CrisisDummies and RO CutOf f × P rof itability ×
CrisisDummies are highlighted in bold if they are 5% significant. Other control variables include Ret, Vol,
Leverage, Size, BM and Ratings.
29
Table 6: Interaction effects of leverage and volatility with crisis.
Parameter
Log (CDS1)
RO CutOf f × (200707 − 200712)
0.307
[ 3.31]
0.324
[ 2.27]
0.494
[ 4.18]
0.262
[ 3.05]
0.125
[ 1.12]
0.542
[ 1.52]
-.555
[-1.83]
-.976
[-3.55]
-1.26
[-4.73]
-1.01
[-3.08]
RO CutOf f × (200801 − 200806)
RO CutOf f × (200807 − 200812)
RO CutOf f × (200901 − 200906)
RO CutOf f × (200907 − 200912)
Vol ×(200707 − 200712)
Vol ×(200801 − 200806)
Vol ×(200807 − 200812)
Vol ×(200901 − 200906)
Vol ×(200907 − 200912)
Log (CDS5) Log (CDSX) Log (CDS1)
0.157
[ 2.45]
0.195
[ 2.27]
0.374
[ 4.35]
0.170
[ 2.39]
0.092
[ 1.05]
0.098
[ 0.42]
-.589
[-2.74]
-.733
[-3.48]
-.922
[-4.38]
-.635
[-2.49]
0.101
[ 2.08]
0.117
[ 1.59]
0.314
[ 4.03]
0.132
[ 1.95]
0.100
[ 1.27]
0.071
[ 0.37]
-.291
[-1.55]
-.332
[-1.77]
-.476
[-2.51]
-.219
[-0.91]
Leverage ×(200707 − 200712)
0.296
[ 2.90]
0.374
[ 2.42]
0.490
[ 4.02]
0.192
[ 2.00]
0.141
[ 1.23]
0.153
[ 2.21]
0.216
[ 2.33]
0.370
[ 4.15]
0.120
[ 1.52]
0.101
[ 1.11]
0.099
[ 1.96]
0.125
[ 1.62]
0.312
[ 3.91]
0.095
[ 1.30]
0.105
[ 1.30]
-.091
[-0.59]
-.495
[-2.77]
-.691
[-3.35]
-.905
[-4.28]
-.322
[-1.67]
-.049
[-1.35]
0.800
[ 8.00]
0.959
[ 3.40]
omitted
0.016
[ 0.13]
-.211
[-1.46]
-.371
[-2.06]
-.561
[-2.77]
-.078
[-0.42]
-.042
[-1.32]
0.776
[ 9.42]
0.725
[ 2.95]
omitted
18644
0.905
YES
YES
18644
0.891
YES
YES
Other Controls
-.069
[-1.40]
2.017
[ 7.71]
0.787
[ 2.25]
omitted
-.054
[-1.53]
1.501
[ 7.29]
0.675
[ 2.57]
omitted
-.045
[-1.41]
1.103
[ 6.03]
0.587
[ 2.54]
omitted
-.345
[-1.54]
-.795
[-2.99]
-1.07
[-3.73]
-1.31
[-5.01]
-.590
[-2.40]
-.060
[-1.21]
1.074
[ 8.45]
1.245
[ 3.33]
omitted
NOBS
R2
Firm Dummies
Month Dummies
18644
0.891
YES
YES
18644
0.906
YES
YES
18644
0.891
YES
YES
18644
0.890
YES
YES
Leverage ×(200801 − 200806)
Leverage ×(200807 − 200812)
Leverage ×(200901 − 200906)
Leverage ×(200907 − 200912)
RO CutOf f
Vol
Leverage
Log (CDS5) Log (CDSX)
Observations are monthly. All regressions contain month and firm fixed-effects. The dependent variables
are the log of 1, 5 and 10 year CDS spreads (in basis points). T-statistics use standard deviations clustered
by firm and month. Coefficients for RO CutOf f × CrisisDummies, V ol × CrisisDummies, Leverage ×
CrisisDummies are highlighted in bold if they are 5% significant. Other control variables include Ret,
Profitability, Size, BM and Ratings.
30
Table 7: Summary Statistics for Bond Spreads
1-Year Bonds 5-Year Bonds 10-Year Bonds
Mean Std Mean Std Mean
Std
RO
Spreads (log)
Ret
Vol
Leverage
Profitability
Size
BM
Rating
Coupon(%)
Age
YTM
0.08
4.83
0.13
0.35
0.28
0.13
9.55
0.48
3.78
6.17
5.69
1.81
0.10
1.04
1.03
0.29
0.16
0.08
1.38
0.61
1.01
1.56
3.07
1.07
# Obs
12785
# Firms
415
# Firms(RO > 0.2) 2008
39
# Firms(RO > 0.2) 2009
34
0.07
5.13
0.14
0.35
0.30
0.13
9.39
0.49
3.95
6.42
3.95
5.17
15314
449
39
28
0.09
0.91
1.05
0.29
0.17
0.08
1.40
0.60
1.12
1.56
2.75
1.21
0.08
5.18
0.11
0.34
0.28
0.13
9.67
0.48
3.72
6.25
3.39
10.14
0.10
0.74
0.97
0.28
0.14
0.07
1.40
0.50
0.97
1.18
4.51
2.45
9927
343
29
23
Observations are monthly. RO is the ratio between COMPUSTAT item dd1 and item
dd1 + dltt. Spreads are calculated as the log of the yield differences(in basis points) between
bond yields and treasury yields with the same maturity. Ret and Vol are annualized stock
return and volatility calculated using daily stock return data in the previous 180 days.
Leverage is long-term book leverage calculated as the ratio between total long-term debt
(dd1 + dltt) and book assets(at). Profitability is defined as the earnings before interest
(ebitda) normalized by total assets (at). Size is the log of total assets (at). BM is book value
of equity (ceq) divided by market value of equity at the end of last fiscal year (prccf × csho).
Rating is S&P long-term rating for the bond issuer, coded as 1 for AAA and 9 for C with
intermediate ratings coded. Age is the number of years since a bond’s issuance and YTM is
the number of years to a bond’s maturity. For each issuer, I keep one bond that is close to
1-year maturity with maturity in the range of 1 month and 4 years, one bond that is close
to 5-year maturity with maturity in the range of 3 years and 8 years, and one bond that is
close to 10-year maturity with maturity in the range of 8 years and 20 years.
31
Table 8: Regression Results for Bond Spreads.
1-Year Bond
5-Year Bond
10-Year Bond
1-Year Bond
5-Year Bond
10-Year Bond
0.095
[ 2.79]
0.133
[ 3.00]
0.160
[ 2.66]
0.016
[ 0.27]
0.058
[ 0.72]
-.029
[-1.56]
-.019
[-4.08]
0.401
[ 9.58]
-.761
[-3.08]
-.022
[-0.56]
0.060
[ 2.16]
0.351
[ 2.46]
0.046
[ 4.44]
0.008
[ 1.89]
0.044
[ 7.13]
0.188
[ 8.09]
0.042
[ 1.11]
0.085
[ 1.67]
0.058
[ 1.02]
-.021
[-0.54]
0.072
[ 1.29]
-.032
[-1.58]
-.011
[-1.93]
0.386
[ 8.78]
-1.02
[-3.82]
-.068
[-2.29]
0.063
[ 2.55]
0.529
[ 3.77]
0.072
[ 7.35]
0.003
[ 1.14]
0.018
[ 4.47]
0.154
[ 6.82]
15314
0.945
YES
YES
9927
0.947
YES
YES
RO CutOf f × (200707 − 200712)
0.051
[ 2.21]
-.020
[-3.07]
0.518
[ 7.75]
-.917
[-3.09]
-.079
[-1.55]
0.059
[ 1.30]
0.112
[ 0.58]
0.012
[ 1.06]
0.020
[ 4.59]
0.077
[ 7.45]
0.211
[ 6.26]
0.009
[ 0.47]
-.019
[-4.08]
0.405
[ 9.69]
-.758
[-3.05]
-.022
[-0.57]
0.060
[ 2.15]
0.354
[ 2.45]
0.047
[ 4.53]
0.008
[ 1.86]
0.044
[ 7.17]
0.188
[ 8.07]
-.012
[-0.64]
-.011
[-1.97]
0.387
[ 8.85]
-1.01
[-3.77]
-.066
[-2.23]
0.064
[ 2.53]
0.525
[ 3.72]
0.072
[ 7.51]
0.003
[ 1.17]
0.018
[ 4.46]
0.154
[ 6.86]
0.114
[ 2.30]
0.091
[ 1.69]
0.185
[ 2.22]
-.019
[-0.29]
0.026
[ 0.32]
0.020
[ 0.78]
-.020
[-2.97]
0.512
[ 7.74]
-.905
[-3.11]
-.079
[-1.57]
0.060
[ 1.33]
0.105
[ 0.55]
0.012
[ 1.00]
0.020
[ 4.66]
0.078
[ 7.54]
0.212
[ 6.25]
12785
0.913
YES
YES
15314
0.945
YES
YES
9927
0.947
YES
YES
12785
0.913
YES
YES
RO CutOf f × (200801 − 200806)
RO CutOf f × (200807 − 200812)
RO CutOf f × (200901 − 200906)
RO CutOf f × (200907 − 200912)
RO CutOf f
Ret
Vol
Profitability
Size
BM
Leverage
Coupon
AGE
YTM
Rating
NOBS
R2
Firm Dummies
Month Dummies
Observations are monthly. All regressions contain month and firm fixed-effects. The dependent variables are
the log of bond spreads (in basis points). T-statistics use standard deviations clustered by firm and month.
Coefficients for RO CutOf f × CrisisDummies are highlighted in bold if they are 5% significant.
32
Table 9: Summary statistics for synthetic CDS spreads.
33
#Firms
RO > 0.2
#Firms
wCDS
RO
σ RV
σ IV
610
53
65
8137
676
68
172
2006
8152
670
62
227
2007
8657
713
74
248
2008
8540
710
62
246
2009
6206
697
59
254
0.07
( 0.10)
0.07
( 0.10)
0.07
( 0.11)
0.07
( 0.11)
0.06
( 0.10)
0.06
( 0.09)
0.30
( 0.18)
0.28
( 0.15)
0.29
( 0.17)
0.32
( 0.20)
0.64
( 0.48)
0.61
( 0.40)
0.33
( 0.16)
0.32
( 0.15)
0.33
( 0.15)
0.35
( 0.18)
0.58
( 0.32)
0.61
( 0.30)
Year
#Obs #Firms
2004
7701
2005
MktLev SCDS RV
0.30
( 0.20)
0.28
( 0.19)
0.28
( 0.19)
0.28
( 0.20)
0.35
( 0.23)
0.42
( 0.23)
17.88
(149.4)
9.95
(71.39)
10.44
(85.05)
24.24
(137.3)
254.3
(498.3)
262.0
(468.1)
SCDS IV
CDS1
SCDS RV
(log)
SCDS IV
(log)
CDS1
(log)
19.08
(142.6)
11.27
(57.75)
13.85
(74.49)
29.31
(147.3)
192.4
(397.5)
243.6
(420.1)
37.65
(67.55)
35.13
(112.1)
26.16
(68.47)
41.02
(120.1)
230.5
(515.9)
376.4
(867.1)
0.50
( 1.33)
0.35
( 1.11)
0.37
( 1.12)
0.55
( 1.45)
2.68
( 2.82)
3.00
( 2.79)
0.58
( 1.42)
0.48
( 1.28)
0.50
( 1.32)
0.66
( 1.59)
2.54
( 2.68)
3.16
( 2.69)
2.85
( 1.17)
2.67
( 1.09)
2.43
( 1.10)
2.68
( 1.24)
4.41
( 1.34)
5.02
( 1.26)
Observations are monthly. σ RV is the realized equity volatility calculated using daily stock returns in the previous 3 month. σ IV is the
3-month at-the-money (ATM) option implied volatility obtained from Optionmetrics. MktLev is market leverage calculated using K
V , where
K is the book value of debt (Compustat item dlc + dltt), and the asset value V is sum of market value of equity and the book value of debt.
SCDS RV (SCDS IV ) is synthetic CDS spreads calculated based on Merton model using σ RV (σ IV ) as input for equity volatility. CDS1 is
one-year CDS spreads obtained via Datastream from CMA.
Table 10: Rollover risk in the option market.
Variable
σ RV
SCDS RV
σ IV
SCDS IV
SCDS IV
SCDS IV
RO CutOf f × (200707 − 200712)
0.060
[ 2.36]
0.116
[ 2.57]
0.114
[ 1.77]
0.145
[ 1.68]
0.023
[ 0.71]
-.006
[-0.40]
0.563
[ 2.17]
1.019
[ 2.42]
0.738
[ 2.19]
0.502
[ 0.97]
0.333
[ 0.71]
-.005
[-0.04]
0.049
[ 2.38]
0.088
[ 2.51]
0.109
[ 2.55]
0.091
[ 1.48]
0.050
[ 1.51]
-.001
[-0.08]
0.640
[ 2.32]
1.043
[ 2.48]
0.919
[ 2.55]
0.623
[ 1.24]
0.523
[ 1.09]
-.047
[-0.38]
0.196
[ 2.14]
0.239
[ 2.32]
0.337
[ 3.43]
0.227
[ 1.35]
0.260
[ 1.76]
-.043
[-1.06]
0.229
[ 7.78]
0.128
[ 1.75]
0.277
[ 2.73]
0.315
[ 1.99]
0.286
[ 1.48]
0.346
[ 1.95]
-.034
[-0.76]
RO CutOf f × (200801 − 200806)
RO CutOf f × (200807 − 200812)
RO CutOf f × (200901 − 200906)
RO CutOf f × (200907 − 200912)
RO CutOf f
SCDS RV
SCDS F RV
Profitability
Size
BM
Rating
NOBS
R2
Months
Firm
-.265
[-2.78]
-.005
[-1.31]
-0.001
[-0.31]
0.063
[ 6.93]
-2.35
[-3.06]
0.068
[ 2.19]
-0.001
[-0.05]
0.520
[ 7.16]
-.253
[-3.09]
-.006
[-1.67]
-0.001
[-0.18]
0.068
[ 8.80]
-2.59
[-3.28]
0.094
[ 2.84]
-0.001
[-0.36]
0.639
[ 7.77]
0.789
[48.24]
-.740
[-2.85]
0.040
[ 3.26]
-.000
[-1.28]
0.247
[ 8.05]
0.745
[38.83]
-.823
[-2.83]
0.043
[ 3.34]
-.000
[-1.01]
37884
0.554
YES
YES
37884
0.557
YES
YES
37884
0.629
YES
YES
37884
0.549
YES
YES
37884
0.834
YES
YES
37884
0.809
YES
YES
Observations are monthly. All regressions contain time and firm fixed-effects. T-statistics use standard-errors
clustered by firm and month. SCDS RV and SCDS IV are log synthetic CDS spreads calculated using σ RV
and σ IV as inputs for equity volatility. SCDS F RV is log synthetic CDS spreads calculated using realized
future volatility in the next 3 months. Coefficients for RO CutOf f × CrisisDummies are highlighted in
bold if they are 5% significant.
34
Table 11: Information content in the option and CDS market.
Variable
SCDS IV
CDS1
CDS1
RO CutOf f × (200707 − 200712)
0.259
[ 1.19]
0.986
[ 2.77]
0.815
[ 2.28]
0.862
[ 2.20]
0.580
[ 2.06]
0.254
[ 2.70]
0.451
[ 2.34]
0.547
[ 4.02]
0.246
[ 2.33]
0.106
[ 0.94]
0.181
[ 2.41]
0.387
[ 2.17]
0.564
[ 3.88]
0.300
[ 2.73]
0.122
[ 1.06]
0.051
[ 1.01]
-.081
[-1.75]
-.134
[-3.36]
-.174
[-4.27]
-.152
[-3.52]
RO CutOf f × (200801 − 200806)
RO CutOf f × (200807 − 200812)
RO CutOf f × (200901 − 200906)
RO CutOf f × (200907 − 200912)
SCDS IV × (200707 − 200712)
SCDS IV × (200801 − 200806)
SCDS IV × (200807 − 200812)
SCDS IV × (200901 − 200906)
SCDS IV × (200907 − 200912)
CDS1
0.555
[ 6.70]
SCDS IV
RO CutOf f
Other Controls
-.088
[-0.78]
Omitted
NOBS
R2
Months
Firm
13729
0.767
YES
YES
0.150
0.257
[ 7.27]
[ 6.21]
-.022
-.013
[-0.36]
[-0.21]
Omitted Omitted
13729
0.891
YES
YES
13729
0.895
YES
YES
Observations are monthly. All regressions contain time and firm fixed-effects. T-statistics use
standard-errors clustered by firm and month. SCDS IV is log synthetic CDS spreads calculated using
σ IV as input for equity volatility. CDS1 is log one-year CDS sperads. Coefficients for RO CutOf f ×
CrisisDummies are highlighted in bold if they are 5% significant. Other control variables include
profitability, size, BM and Ratings.
35
Figure 1: The time-series of CDS spreads for treatment and control firms.
1000
treated firms
control firms
03/09
900
800
1−year CDS spreads(bps)
700
600
500
400
03/08
300
200
09/07
100
0
01/07
01/08
01/09
Date
(a)
800
treated firms
control firms
700
01/09
1−year CDS spreads(bps)
600
500
400
300
200
100
0
01/05
09/07
01/06
01/07
01/08
01/09
Date
36(b)
In panel (a), treated firms are firms that need to refinance during the crisis year 2008. Control firms are chosen
in the non-treatment population based on their credit qualities in 2007. In panel (b), treatment and control firms
are rebalanced at the begining of every year. Treatment firms are definied as those that need to refinance more
than 20% of their long-term debt in the coming year. Control firms are chosen in the non-treatment population
based on the covariate matching appraoch described in the text.
Figure 2: The time-series of cross-section regression coefficients by regression CDS spreads
(log) on RO CutOf f .
0.4
beta 1Year
1
beta 5Year
1
0.3
beta 10Year
1
0.2
0.1
0
−0.1
−0.2
−0.3
2005
2006
2007
37
2008
2009