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Corporate Yield Spreads and Bond Liquidity

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Abstract

We find that liquidity is priced in corporate yield spreads. Using a battery of liquidity measures covering over 4,000 corporate bonds and spanning both investment grade and speculative categories, we find that more illiquid bonds earn higher yield spreads, and an improvement in liquidity causes a significant reduction in yield spreads. These results hold after controlling for common bond-specific, firm-specific, and macroeconomic variables, and are robust to issuers' fixed effect and potential endogeneity bias. Our findings justify the concern in the default risk literature that neither the level nor the dynamic of yield spreads can be fully explained by default risk determinants. Copyright 2007 by The American Finance Association.
Corporate Yield Spreads and Bond Liquidity
Long Chen
Department of Finance
Michigan State University
chen@bus.msu.edu
(517) 353-2955
David A. Lesmond
A.B. Freeman School of Business
Tulane University
dlesmond@tulane.edu
(504) 865-5665
Jason Wei
Rotman School of Management
University of Toronto
wei@Rotman.Utoronto.Ca
(416) 287-7332
April 21, 2005
This paper is based on an earlier version entitled “An Indirect Estimate of Transaction Costs for
Corporate Bonds.” We thank conference participants at the 2001 FMA and 2003 AFA meetings, and the 14th
Annual Conference on Finance and Accounting at Indiana University. We also thank seminar participants
at Beijing University, Louisiana State University, Michigan State University, the University of New Orleans,
and York University for their helpful comments. Sincere thanks go to (especially) Yakov Amihud, Laurence
Booth, Kirt Butler, Melanie Cao, John Hull, Raymond Kan, Madhu Kalimipalli, Tom McMcurdy, Gordon
Roberts, Chester Spatt, Yisong Tian, Alan White, and Xiaoyun Yu for their constructive comments. We
wish to thank Andre Haris, Lozan Bakayatov, and Davron Yakubov for their excellent data collection efforts.
In addition, we thank the financial assistance of the Social Sciences and Humanities Research Council of
Canada. All errors remain the responsibility of the authors.
Corporate Yield Spreads and Bond Liquidity
Abstract
We examine whether liquidity is priced in corporate yield spreads. Using a battery
of liquidity measures covering over 4000 corporate bonds and spanning investment grade
and speculative categories, we find that more illiquid bonds earn higher yield spreads; and
that an improvement of liquidity causes a significant reduction in yield spreads. These
results hold after controlling for common bond-specific, firm-specific, and macroeconomic
variables, and are robust to issuers’ fixed effect and potential endogeneity bias. Our finding
mitigates the concern in the default risk literature that neither the level nor the dynamic
of yield spreads can be fully explained by default risk determinants, and suggests that
liquidity plays an important role in corporate bond valuation.
Introduction
A number of recent studies (Collin-Dufresne, Goldstein, and Martin, 2001, and Huang and
Huang, 2003) indicate that neither levels nor changes in the yield spread of corporate bonds
over treasury bonds can be fully explained by credit risk determinants proposed by structural
form models. Illiquidity is acknowledged as a possible explanation for the failure of these models
to more properly capture the yield spread variation (Longstaff, Mithal, and Neis, 2004). Yet
much of the current literature abstracts from liquidity’s influence (Elton, Gruber, Agrawal,
and Mann, 2001), focuses on aggregate liquidity proxies (Grinblatt, 1995, Duffie and Singleton,
1997, Collin-Dufresne et al., 2001, and Campbell and Taksler, 2003) or simply assumes that the
unexplained portion of the yield spread is liquidity based (Duffee, 1999). This paper attempts
to fill this void by comprehensively assessing bond-specific liquidity for a broad spectrum of
corporate investment grade and speculative grade bonds and by examining the association
between bond-specific liquidity estimates and corporate bond yield spreads.
The notion that investors demand a liquidity premium for illiquid securities dates back to
Amihud and Mendelson (1986). Lo, Mamaysky, and Wang (2004) further argue that liquidity
costs inhibit the frequency of trading. Because investors cannot continuously hedge their risk,
they demand an ex-ante risk premium by lowering security prices. Therefore, for the same
promised cash flows, less liquid bonds will be traded less frequently, have lower prices, and
exhibit higher yield spreads. Thus, the theoretical prior is that liquidity is expected to be
priced in yield spreads. We investigate bond-specific liquidity effects on the yield spread using
three separate liquidity measures. These include the bid-ask spread, the liquidity proxy of zero
1
returns, and a liquidity estimator based on a model variant of Lesmond, Ogden, and Trzcinka
(1999). We find that liquidity is indeed priced in both levels and changes of the yield spread.
Contemporaneous studies by Longstaff et al. (2004) and Ericsson and Renault (2002) also
relate corporate bond liquidity to yield spreads. However, Longstaff et al. (2004) focus only on
68 issuers that have liquid default-swap trading data, leaving some doubt as to the generality
of the results for the larger universe of corporate bonds. Ericsson and Renault (2002) focus on
a theoretical model and they simply use a new issue dummy as their empirical bond-specific
liquidity measure. However, this liquidity proxy will not shed light on the liquidity difference
spanning corporate bonds, nor provide liquidity measures for more mature bonds. We provide
extensive bond-specific liquidity measures for over 4000 corporate bonds spanning investment
and speculative grade categories over a nine year period allowing for a more comprehensive
assessment of the relation between liquidity and yield spreads.
Historically, the lack of credible information on spread prices1or bond quotes has been
a major impediment in the analysis of liquidity (Goodhart and O’Hara, 1997) and liquidity’s
impact on yield spreads. We employ Bloomberg and Datastream to provide our three liquidity
estimates. Among them, the bid-ask spread is arguably the most demonstrable measure of
liquidity costs, while the percentage of zero returns is increasingly used as a liquidity proxy in
1Liquidity’s importance is well recognized by academics, regulators, and bond traders. Arthur Levitt, as the
Chairman of the Securities and Exchange Commission, notes that “the sad truth is that investors in the corporate
bond market do not enjoy the same access to information as a car buyer or a home buyer or, I dare say, a fruit
buyer. Improving transparency is a top priority for us” (Wall Street Journal, 9/10/1998). Greg Ip of the Wall
Street Journal notes that “the bond market’s biggest worry these days isn’t default or interest rates. It’s illiquidity
that is crippling the very workings of the market” (Wall Street Journal, 10/19/1998). Reflecting on bond liquidity
concerns, the NASD has recently instituted TRACE (Trade Reporting and Compliance Engine) which provides
real-time quote estimates for 4200 corporate bond issues (Wall Street Journal, 3/14/2003).
2
a host of empirical studies.2Despite the clear intuition surrounding the zero return proxy, it is
a noisy measure of liquidity, since it is the combination of a zero return and the simultaneous
movement of bond price determinants that more properly estimates liquidity costs, not the lack
of price changes per se.
To more properly capture this notion, we employ the limited dependent variable model
proposed by Lesmond, Ogden, and Trzcinka (1999) (hereafter, LOT) to obtain an alternative
liquidity estimate.3The premise of the LOT model is that, while the true value of the bond
is driven by many stochastic factors, measured prices will reflect new information only if the
information value of the marginal trader exceeds the total liquidity costs. This implies that a
liquidity cost threshold exists for each bond, which is equivalent to the minimum information
value for a trade. Within the liquidity cost threshold, the probability of observing a zero return
is higher than outside the liquidity cost threshold. We use a maximum likelihood method to
jointly estimate the risk factors related to market-wide information and the upper and lower
liquidity thresholds that, taken as a whole, represent round-trip liquidity costs.
We find a significant association between corporate bond liquidity and the yield spread with
each of the three liquidity measures. Depending on the liquidity measure, liquidity alone can
explain as much as 7% of the cross-sectional variation in bond yields for investment grade bonds,
2Theoretically, it is well known that in the presence of transaction costs, investors will trade infrequently
(Constantinides, 1986), and thus the magnitude of the proportion of zero returns is representative of illiquidity.
Empirically, this measure has been found to be an effective liquidity measure in the U.S. equity market (e.g.,
Lesmond, Schill, and Zhou, 2004) and in the emerging market for equities where the lack of liquidity-related
information remains a challenge (e.g., Bekaert, Lundblad, and Campbell, 2003.)
3Lesmond et al. (1999) and Lesmond (2004) find that this method works well for equity markets, as evidenced
by an 80% correlation between the LOT liquidity estimate and the bid-ask spread plus commissions. The proposed
LOT model does not rely on the use of bid-ask spread prices; instead, it uses only daily closing returns to estimate
liquidity costs. This method is also a natural extension of Glosten and Milgrom (1985), who illustrate that trades
will occur when the information value exceeds the transaction costs defined by the bid-ask spread.
3
and 22% for speculative grade bonds. Using the bid-ask spread as the measure, we find that
one basis point increase in bid-ask spread is related to 0.42 basis point increase in the yield
spread for investment grade bonds, and 2.30 basis point increase for speculative grade bonds.
Using either the bid-ask spread or the LOT estimate, the liquidity effect remains significant
even after we control for general yield spread factors such as credit rating, maturity, and the
amount outstanding; the tax effect (Elton et al., 2001); the equity volatility (Merton, 1974,
and Campbell and Taksler, 2003); the accounting variables of Campbell and Taksler; and the
macroeconomic variables of Collin-Dufresne et al. (2001). The results are robust to issuer fixed
effects and potential endogeneity in yield spreads, liquidity, and credit ratings. The results
extend to the zero return liquidity proxy, but are most robust for investment grade bonds.
Extending the study to changes in yield spreads, we again find a liquidity influence. Under all
three liquidity measures, an increase in illiquidity is significantly and positively associated with
an increase in yield spreads regardless of controlling for changes in credit rating, macro-economic
influences, or firm-specific factors.
This paper contributes to the growing debate over bond market liquidity and corporate
yields. First, in the credit risk literature, it is common to assume that the yield spread, as
a whole, represents default risk. Practitioners frequently draw conclusions regarding default
probability from yield spreads. Our findings imply that this approach is inappropriate, as the
liquidity component in the yield spread is not directly related to default risk. Our results also
mitigate the concern that the yield spread overstates the default probability (e.g., Elton et al.,
2001, and Huang and Huang, 2003). Additionally, the high consistency between the traditional
4
model-independent measure, the bid-ask spread, and the model-dependent measure, the LOT
estimate, suggests that the latter, making use of return data only, can be an effective tool
in bond liquidity studies. This is particularly meaningful for illiquid bonds where the lack of
liquidity-related information is common.
The paper is organized as follows. Section 1 introduces the liquidity measures and their
summary statistics. Section 2 presents model validation tests, the consistency among the
liquidity measures, and initial tests on the relation between liquidity and the yield spread.
Section 3 studies the relation between liquidity levels and yield spread levels. Section 4 presents
test results of changes in liquidity and changes in the yield spread. Section 5 concludes.
1. Liquidity Measures
The literature provides a menu of measures for estimating liquidity. The most demonstrable
measure is the bid-ask spread, but the spread is not always available for all bonds or for all
time periods. This is especially true for thinly traded bonds or off-the-run bonds. Additionally,
because our data is hand-collected, our quote information is gathered only on a quarterly basis
resulting in a less precise measure of liquidity. This is especially true if only a single quarterly
quote is available for the bond over an annual trading period.
Lesmond et al. (1999) introduce an alternative indirect method for estimating liquidity based
on the occurrence of zero returns. Bekaert et al. (2004) show that zero returns themselves are
a reasonable liquidity proxy. The LOT measure is a comprehensive estimate of liquidity by
including the spread and other costs that may impinge on informed trade, such as commission
costs, opportunity costs, and price impact costs. The maintained hypothesis is that the marginal
5
trader will trade only if the value of the information exceeds the marginal costs. If trading costs
are sizeable, Lesmond et al. (1999) argue that zero return days will occur more frequently
because new information must accumulate longer, on average, before informed trade affects
price. They show that the LOT estimate is a more accurate measure of the underlying liquidity
costs than is the percentage of zero returns because the LOT measure extracts more information
from the return generating process.
A potential theoretical drawback of the LOT model is that it requires a return generating
model for bonds which the literature has yet to definitively prescribe. A practical limitation
is that the LOT model requires some zero returns to estimate liquidity’s effect on the price.
For on-the-run bonds or bonds offered mid-year, the sequence of prices may not reveal any zero
returns invalidating the LOT estimate. Conversely, too many zero returns (i.e. greater than
85% over the estimation period) also makes this measure inestimable. However, both the zero
return liquidity proxy and the LOT liquidity measure are presumed to be positively related to
the bid-ask spread.
Because of the strengths and weaknesses of each measure, we employ all three estimators to
determine the relation between corporate bond yield spreads and liquidity. This will not only
increase robustness, but also shed light on the relative power of each liquidity measure. If we
find that all three liquidity measures lead to consistent inferences, then we can take comfort in
using the other two measures in situations where the bid-ask spread is not available. Bekaert et
al. (2003) is a case in point when they study the equity liquidity in emerging markets.
6
1.1 The bid-ask spread
Data on the quarterly bid-ask quotes are hand-collected from the Bloomberg Terminals. Most
quotes are available only from 2000 to 2003. For each quarter, we calculate the proportional
spread as the ask minus the bid divided by the average bid and ask price. The bond-year’s
proportional bid-ask spread is then calculated as the average of the quarterly proportional
spreads. To include as many bonds as possible, we compute the annual proportional spread as
long as there is at least one quarterly quote for the year. The bid-ask quotes recorded are the
Bloomberg Generic Quote which reflects the consensus quotes among market participants.
1.2 The percentage zeros and the LOT model
The LOT measure of informed trading utilizes only daily bond returns to estimate bond-level
liquidity costs. The effect of liquidity is observable through the incidence of zero returns.
Datastream is used to provide prices, which, in turn, uses Merrill Lynch as the data source
for the price across all market makers for the bond. This feature will underestimate the number
of zero returns for each bond issue as the probability of observing a zero return is decreasing
with increasing numbers of market makers. Given that our model is predicated on days with no
price changes, we will understate our estimate of bond-specific liquidity costs, biasing against
our liquidity hypothesis. We choose the start date of 1995 since daily prices are more regularly
available through Datastream only after 1995. The data span a nine-year period ending in 2003.
We record the clean, non-matrix price of each bond on a daily basis, deleting prices that
deviate more than 50% from the prior day’s price. We separate the data into bond-years; that is,
7
using daily data for each bond within each year, we jointly estimate the bond’s return generating
function and liquidity costs applicable to that year. This allows time-series variations in the
bond liquidity estimates to be adequately represented.
To price corporate bonds, we extend the Lesmond et al. (1999) methodology to a two-factor
model. (Appendix A shows the theoretical basis for this approach.) The two factors are the
interest rate and the equity market return, reflecting the fact that a corporate bond is a hybrid
between a risk free bond and equity. Following Jarrow (1978), we scale all risk coefficients by
duration to obtain stable estimation coefficients. The return generating process is then given
as:
R
j,t =βj1Durationj,t Rft +βj2Durationj,t ∆S&P Indext+j,t.(1)
The term R
j,t represents the unobserved “true” bond return for bond j and day t that investors
would bid given zero transaction costs. Rft is the daily change in the ten-year, risk-free interest
rate. Following Cornell and Green (1991), ∆S&P Index is the daily return in the Standard &
Poor’s 500 index.4
Amihud and Mendelson (1986, 1987) develop a framework in which the intrinsic value of a
firm differs from its observed value. Amihud and Mendelson (1986) attribute this difference to a
liquidity premium that requires higher cost assets to be priced lower to compensate investors for
liquidity costs. Extending Amihud and Mendelson (1986) to fixed income securities, liquidity
effects on bond returns can be stated as:
Rj,t =R
j,t αi,j,(2)
4We also estimated the model using the Fama-French (1993) bond factors in the objective function. The results
are largely invariant to this specification.
8
where Rj,t is the measured return, α2,j is the effective buy side cost, and α1,j is the effective
sell side cost for bond j. Thus, the desired return and the measured return are related, but
only after taking transaction costs into account. The effect of liquidity on bond prices is then
modeled by combining the objective function with the liquidity constraint as:
R
j,t =βj1Durationj,t Rft +βj2Durationj,t ∆S&P Indext+j,t.(3)
where:
Rj,t =R
j,t α1,j if R
j,t
1,j and α1,j <0
Rj,t =0 if α1,j R
j,t α2,j
Rj,t =R
j,t α2jif R
j,t
2,j and α2,j >0
The resulting log-likelihood function is stated as:
LnL =
X
1
Ln 1
(2πσ2
j)1/2X
1
1
2σ2
j
(Rj+α1,j βj1Durationj,t Rft βj2Durationj,t ∆S&P Indext)2
+X
2
Ln 1
(2πσ2
j)1/2X
2
1
2σ2
j
(Rj+α2,j βj1Durationj,t Rft βj2Durationj,t ∆S&P Indext)2
+X
0
Ln(Φ2,j Φ1,j),(4)
where Φi,j represents the cumulative distribution function for each bond-year evaluated at (αi,j
βj1Durationj,t Rft βj2Durationj,t ∆S&P Indext)j.P1(region 1) represents the negative
nonzero measured returns, P2(region 2) represents the positive nonzero measured returns, and
P0(region 0) represents the zero measured returns. Maddala (1983) and Lesmond et al. (1999)
outline the estimation procedure.
For purposes of liquidity estimation, we focus only on the α2,j and α1,j estimates. Taken in
difference form, α2,j α1,j, represents the liquidity effects on bond returns related to round-trip
transaction costs.
9
Implicitly, our model assumes that information motivates trade in bonds and that
information is efficiently impounded into bond prices. This assumption finds support from
Hotchkiss and Ronen (2002) who conclude that the informational efficiency of bond prices is
similar to that of the underlying equity. The marginal trader is assumed to assess the value
of information before deciding to trade relative to the expected liquidity costs. The marginal
trader with the highest net difference between the value of information and transaction costs will
drive price movements.5We do not impose any particular assumptions on whether the marginal
investor possesses public or private information; rather, we assume that prices should rationally
reflect the costs of trade relative to the information value of the trade. Unanticipated public
information, noise trades, or trades of idiosyncratic nature will not be priced in a rational asset
pricing framework and will only be captured, on average, in the error term.
1.3 Yield spreads and corporate information
We examine over 4000 U.S. corporate bonds. Datastream is used to provide yield spreads and
bond characteristics. We use the Fixed Income Securities Database to provide up-to-date credit
ratings for each bond, and when unavailable we use Standard and Poor’s rating on Datastream.
We delete bonds not rated by either S&P or the Fixed Income Securities Database. Finally, we
use the Compustat Annual Industrial database to collect all firm-level data for both active and
inactive firms to minimize any survivorship bias in the liquidity determinant and yield spread
regressions.6Each variable is collected in the year prior to the yield spread measurement. The
5The LOT model is consistent with the Kyle (1985) model. Specifically, Kyle assumes that the market maker is
risk neutral and allows for the market being composed of three trader types: informed, uninformed, and the market
maker. The LOT model is predicated on trades made by the marginal trader who could be informed, uninformed,
or even the market maker.
6We collect the operating income after depreciation (item 178) and the interest expense (item 15) to determine
10
equity volatility is estimated using 252 daily returns (from the CRSP file) for the year prior to
the bond liquidity estimate. The bond volatility is estimated similarly using bond prices.
2. Preliminary Findings
2.1 Summary statistics
Table 1 contains the summary statistics segregated by maturity levels and credit ratings.
Within each panel there are two sets. The first set relates all the bond information for a
matching sample of zero returns and the LOT estimate, while the second presents information
for a matching sample of zero returns, the LOT estimate and the bid-ask spread. Several
observations are apparent. First, liquidity costs are demonstrably higher for speculative grade
bonds than for investment grade bonds. In particular, we observe a significant increase in
the percentage of zero returns and the size of the LOT estimate of liquidity while moving
from investment grade to speculative grade bonds. This is matched with a similar increase in
the bid-ask spread.7Not surprisingly, yield spreads also increase markedly across these bond
categories. For the matched sample of all three liquidity measures, the trend of each liquidity
measure appears to match the underlying credit rating. Namely, for investment grade bonds,
moving from AA bonds to BBB bonds we observe increasing transaction costs. However, for
speculative grade bonds, the trend of increasing liquidity costs with decreasing credit worthiness,
the pre-tax interest coverage. For the operating income to sales we collect the firm’s operating income before
depreciation (item 13) divided by the net sales (item 12). We use two definitions for debt: total long-term debt
(item 9) divided by total assets (item 6), and total long-term debt plus debt in current liabilities (item 34) plus
short-term borrowings (item 104) divided by total liabilities (item 181) plus market capitalization.
7We emphasize that, while the general trends are similar, the LOT liquidity costs do not necessarily need to
agree with the bid-ask spread in magnitude. The LOT estimates are derived from the investors’ trading decision,
which incorporates all relevant liquidity related costs. The marginal traders’ reservation price will then reflect all
these relevant costs which could include commission costs, credit spread costs, and search costs, in addition to the
bid-ask spread.
11
is only observed for the LOT measure and the bid-ask spread. The percentage of zero returns
appears to be a weaker proxy for liquidity.
Second, liquidity costs increase moving from short to long maturity bonds, consistent with an
investment horizon argument offered by Amihud and Mendelson (1991) or the return volatility
arguments of Chakravarty and Sarkar (1999).
Finally, yield spreads generally increase (decrease) with maturity for investment
(speculative) grade bonds. Merton (1974) shows that corporate yield spreads can either increase
or decrease with maturity depending on the risk of the firm. Investmentgrade issuers face upward
sloping yield spreads while speculative grade issuers face flat or downward sloping yield spreads.
Helwege and Turner (1999) find that within the same speculative credit rating category, the
safer firms tend to issue longer term bonds, which causes the average yield spread to decline
with maturity.
2.2 Model validation
Even though the proportion of zero returns and the LOT estimate both stem from the
premise that liquidity costs inhibit trade, the LOT estimate is a less noisy measure because
it incorporates the covariation between the zero returns and the market movement of the
bond price determinants. To verify this point, we first perform a model specification check,
by investigating whether the LOT model helps to recover intuitive beta coefficients on the
systematic risk factors. These coefficients are then compared to a naive asset pricing model
without liquidity cost considerations.
12
If the model is correctly specified, we would expect several patterns to appear. First, the
interest rate coefficient should be negative. However, moving from high-grade to low-grade
bonds, this relationship is expected to become weaker (Schultz, 2001). Second, the equity
return coefficient should be positive for low-grade bonds (Cornell and Green, 1991). Intuitively,
a positive equity return, signaling an improvement in the firm’s business operation, will have a
positive effect on the bond return. However, the effect of the equity return on high-grade bonds
is not clear. On the one hand, a positive equity return might increase bond prices, as in the
low-grade bond case. On the other hand, the positive equity return might be caused by capital
flows from the corporate bond market into the equity market, in which case a negative return
on corporate bonds is expected.8
The estimation results are summarized in Panel A of Table 2. A comparison of the LOT
results with those of the naive OLS model provides a clear indication of the influence that zero
returns have on the estimation results. The LOT model’s interest rate estimates are mostly
negative and significant, while the interest rate influence is decreasing with decreasing bond
ratings, all as expected. In sharp contrast, the naive OLS model produces interest rate estimates
that are largely insignificant from zero. In addition, the interest rate effect has no apparent trend
with bond rating, contrary to common beliefs.
The falloff in interest rate influence for the LOT model is offset by a concomitant increase
in the S&P500 equity return influence, especially for speculative grade bonds. Also evident is
8Kwan (1996) finds a positive equity return coefficient for investment grade bonds. Cornell and Green (1991)
find that, when both the interest rate and the S&P500 equity return are considered, the sign of equity return
coefficient changes from positive to negative for the period 1977 to 1989.
13
the switch in sign for the S&P500 coefficient from investment grade to speculative grade bonds.
This would indicate that signaling effects prevail in the case of speculative grade bonds, while
substitution effects prevail for investment grade bonds. Similar, but more muted patterns are
apparent for the naive OLS model’s estimates.
2.3 Bid-ask spread tests
We provide further evidence on the consistency of the three liquidity measures. In particular,
we regress the bid-ask spread separately on the other two liquidity measures controlling for other
liquidity determinants as follows:
Bid-Askit =η0+η1Liquidityit +η2Maturityit +η3Ageit +η4Amount Outstandingit
+η5Bond Ratingit +η6Bond Volatilityi+η7Bond Rating Dummy + t
The subscript “it” refers to bond i and year t. Liquidity refers to either the proportion of
zero returns or the LOT estimate. The liquidity determinants are chosen according to Garbade
and Silber (1979), Sarig and Warga (1989), Chakravarty and Sarkar (1999), Stoll (2000), Schultz
(2001), and Brandt and Kavajecz (2003). Bond rating proxies for default risk. For the overall
regressions, bond ratings are assigned a cardinal scale ranging from one for AAA rated bonds
to seven for CCC to D rated bonds. Panel B of Table 2 presents the results.
For investment grade bonds, the LOT liquidity estimate alone explains 6.39% of the
cross-sectional variation in the bid-ask spread, while the percentage of zero returns explains
6.82% of the cross-sectional variation in the bid-ask spread. In comparison, Schultz (2001)
reports an R2of 3.43% in regressions on all microstructure trading cost determinants for
investment grade bonds. Both the LOT estimate and the percentage of zero returns remain
14
positively and significantly related to the bid-ask spread when other variables are included.9
Similar results can be seen for speculative bonds, but only for the LOT model estimate. In
particular, the proportion of zeros is insignificant without the control variables, but becomes
significant after including them in the regression. The percentage of zero returns appears to
suffer more from specification error bias than does the LOT measure. This is to be expected
given that the LOT measure extracts more information than is provided by the percentage of
zero returns.
2.4 Initial yield spread and liquidity tests
We now test the relation between the yield spread and the three liquidity estimates. To
provide a consistent comparison we match the bid-ask spread sample to the available liquidity
estimates. As shown in Panel C of Table 2, for investment grade bonds all three liquidity
estimates are positively and significantly associated with the underlying yield spread. The LOT
measure and the bid-ask spread provide almost identical power in explaining the cross-sectional
variation in the yield spread, with a reported R2of approximately 7.3%. The percentage of zero
returns explains almost 6% of the cross-sectional variation in the yield spread.
For speculative bonds, only the bid-ask spread and the LOT measure are significantly
associated with the underlying yield spread. The LOT measure explains 7.39% of the
cross-sectional variation in the yield spread, while the bid-ask spread explains only 0.86% of
the cross-sectional variation in the yield spread.
9Although not reported, we also include the log scaled equity volatility in the regression and find it to be
insignificantly associated with the bid-ask spread.
15
3. Liquidity Effects on Yield Spread Levels
Many theoretical models (e.g., Amihud and Mendelson, 1986) predict that investors demand
higher expected returns for less liquid assets to compensate for the liquidity risk. This implies
that, for the same cash flows in the future, less liquid assets will have lower prices. Because bond
yield is a promised yield given known cash flows, the lower prices of less liquid bonds lead to
higher bond yields and higher yield spreads, ceteris paribus. We test this theoretical prediction
by investigating whether various liquidity proxies can explain yield spread levels.
3.1 Regression tests of liquidity estimates and other yield spread determinants
The following regression is specified with the yield spread as the dependent variable and the
various yield spread determinants as independent variables:
Yield Spreadit =η0+η1Liquidityit +η2Maturityit +η3Amount Outstandingit
+η4Couponit +η5Treasury Ratet+η610Yr-2Yr Treasury Ratet+η7EuroDollart+η8Volatilityit
+η9Bond Ratingt+η10PreTax Coverage Dummyit +η11Operating Income/Salesit
+η12Debt/Assetsit +η13Debt/Capitalizationit +t
The subscript “it” refers to bond i and year t. Liquidity refers to the bid-ask spread, the
proportion of zero returns, or the LOT estimate. The choice of yield spread determinants is
largely based on Elton et al. (2001) and Campbell and Taksler (2003).10 We measure the
incremental influence of the pretax coverage using the procedure outlined in Blume, Lim, and
MacKinlay (1998). In addition, we include three macroeconomic variables associated with the
yield spread. These are the one-year Treasury rate, the difference between the 10-year and
10 We exclude the additional equity market index considered by Campbell and Taksler (2003) because of potential
endogeneity problems given that the LOT estimate includes the market return from the S&P500 index.
16
2-year Treasury rates that describes the slope of the yield curve, and the difference between
the 30-day Eurodollar and 3-month Treasury bill rate that controls for other potential liquidity
effects on corporate bonds relative to Treasury bonds.
We present two separate regressions for each liquidity estimate. The first uses only the
bond-specific information yielding a larger sample, while the second incorporates the corporate
and market information yielding a smaller sample. The sample for each liquidity measure differs
due to the estimation limitations for each measure. The percentage of zero returns is the most
comprehensive sample because it only requires the daily bond prices. As a practical matter,
The LOT sample comprises more off-the-run bonds than does the bid-ask spread sample, but
the bid-ask spread sample comprises more on-the-run bonds than does the LOT sample.
The most telling finding is the consistent significance of the liquidity variable regardless
of the specification used to define liquidity, regardless of the specification used for the yield
spread determinants, or regardless of investment grade or speculative grade categories. All
these liquidity measures are positively related to the yield spread in all scenarios, for both
investment grade and speculative grade bonds, even after we control for extensive bond-specific,
firm-specific, and macroeconomic variables. The liquidity coefficients are highly significant (at
1%) in every scenario, supporting our theoretical prior that liquidity is priced in the yield
spreads.
The interpretation of the magnitude of the liquidity influence varies depending on the
liquidity measure. For investment grade bonds, the LOT measure would predict an incremental
0.21 basis point increase in the yield spread for a one basis point increase in liquidity costs,
17
while the bid-ask spread would predict an incremental 0.42 basis point increase in the yield
spread for a one basis point increase in the bid-ask spread. The coefficient for bond rating is
20 basis points, regardless of the LOT measure or the bid-ask spread liquidity measure, which
means that for each grade drop in bond rating (e.g., from BBB+ to BBB), the yield spread will
increase by 20 basis points. The incremental effect of maturity on the yield spread is significant,
but only one basis point.
For speculative grade bonds, focusing on the full accounting variable regressions, the LOT
measure would predict an incremental 0.82 basis point increase in the yield spread for a one
basis point increase in liquidity costs, while the bid-ask spread would predict an incremental
2.29 basis point increase in the yield spread for a one basis point increase in the bid-ask spread.
It may be noted that the maturity coefficient is generally positive for investment grade
bonds and negative for speculative grade bonds. For investment grade bonds, longer maturities
are often noted to be associated with increased yield spreads (Campbell and Taksler, 2003),
consistent with the positive sign for maturity. For speculative grade bonds, Helwege and Turner
(1999) argue that better quality firms are able to issue bonds with longer maturity, causing a
negative relation between the yield spread and maturity for these bonds.
At the bottom of Table 3 we report the regression of yield spread on each liquidity measure
alone using the full sample whenever the measure is available. For each measure, we also
regress the yield spread on bond rating alone for that sample as a comparison. For investment
grade bonds, liquidity alone explains from 2.12%, for the bid-ask spread, to 7.57%, for the LOT
estimate, of the cross-sectional variation in the yield spread. In comparison, for the same sample,
18
bond rating alone explains 15.20% of the cross-sectional variation of yield spread for the LOT
sample and 20.12% for the bid-ask spread sample. For speculative bonds, the bid-ask spread
alone explains 7.49% of the yield spread variation while the LOT liquidity measure explains
21.83% of the yield spread variation.
3.2 Issuer fixed-effects regressions
We perform an issuer fixed-effects regression to control for issuer influences on yields because
a small set of companies may dominate the bond market. For instance, Ford Motor Company’s
bonds comprise almost 10% of the entire bond market. As in the levels regression tests, we
use separate samples for each liquidity measure to allow for different bond characteristics. This
results in approximately 1100 issuers for investment grade bonds and 220 issuers for speculative
grade bonds. However, approximately only 300 issuers have complete accounting information for
investment grade bonds and approximately only 90 issuers have complete accounting information
for speculative grade bonds. Table 4 presents the results.
We observe the same, consistent result using either the bid-ask spread or the LOT liquidity
estimate. Liquidity is positively and significantly associated with the yield spread regardless of
bond grade, even after controlling for bond-specific, firm-specific, and macroeconomic variables.
The coefficients are highly significant at 1%. Note that the proportion of zero returns is
significantly positive (at 1%) for both investment grade bonds and for speculative grade bonds
when the accounting variables are not included, but is only significant at 10% when the firm-level
variables are included. In other words, while all liquidity measures lead to the same conclusion,
the case for the proportion of zero returns is slightly weaker, consistent with the notion that the
19
proportion of zero returns is a relatively noisy measure of liquidity.
3.3 Simultaneous equation regressions
Every liquidity measure, whether based on observable bid-ask spreads or new estimable
measures, could contain information about the credit quality of a bond, and thus could affect
the yield through the credit-risk channel. This would make it difficult to interpret the main
results purely in terms of liquidity costs. Much of the liquidity costs are due to adverse selection
under asymmetric information. For a typical corporate bond, asymmetric information on its
credit quality (rather than on interest rate) is the main reason for adverse selection costs.
Intuitively, one expects that bonds with lower credit quality should have a more severe adverse
selection problem, ceteris paribus. So higher liquidity costs could mean a lower credit quality,
which should lead to higher yield spreads. In addition, Campbell and Taksler (2003) note
that bond ratings may be contemporaneously incorporating the observed firm-level accounting
characteristics. Rating agencies may also absorb market information through the observed yield
spread as well as macro-economic information when assigning a credit rating.
To control for the potential endogeneity problems arising from the contemporaneous
measurement of the yield spread, liquidity costs, and the credit rating, we perform a simultaneous
regression using three equations representing each of the potentially endogenous variables. The
system of equations is stated as follows:
20
Yield Spreadit =η0+η1Liquidityit +η2Maturityit +η3Couponit
+η4Treasury Ratet+η510Yr -2Yr Treasury Ratet+η6EuroDollart+η7Volatilityit
+η8Credit Ratingt+η9PreTax Coverage Dummyit +η10Operating Income/Salesit
+η11Debt/Assetsit +η12Debt/Capitalizationit +t
Liquidityit =η0+η1Maturityit +η2Ageit +η3Amount Outstandingit
+η4Credit Ratingit +η5Bond Volatilityit +η6Yield Spreadit +t
Credit Ratingit =η0+η1Treasury Rateit +η210Yr -2Yr Treasury Ratet
+η3PreTax Coverage Dummyit +η4Operating Income/Salesit +η5Debt/Assetsit
+η6Debt/Capitalizationit +η7Yield Spreadit +t
The results are presented in Table 5. As is shown, the potential endogeneity bias does
not affect the relation between liquidity and the yield spread for either investment grade or
speculative grade bonds. The LOT liquidity estimate and the bid-ask spread liquidity measure
remain significant at the 1% level for investment grade bonds, and they remain significant at
the 5% level for speculative grade bonds. The percentage of zero returns is significant at the 5%
level for investment grade bonds, but is insignificant for speculative grade bonds. We conclude
that liquidity is priced in yield spreads even after the potential endogeneity bias is controlled;
and that, again, the proportion of zero returns appears to be a less powerful liquidity measure.
4. Liquidity Effects on the Yield Spread Changes
We conduct regression tests to study whether issue-specific liquidity changes are a
determinant of yield spread changes. This test offers a glimpse into how the dynamics of liquidity
are incorporated into yield spread changes. Econometrically, differencing the time-series removes
autocorrelative influences that may cause spurious results due to time-series trends.
21
4.1 Regression tests of changes in liquidity and yield spread determinants
We include a list of independent variables used in Collin-Dufresne et al. (2001) and Campbell
and Taksler (2003). Unlike the levels specification, we use the unscaled pretax coverage because
of the differencing operation. In addition, unlike Collin-Dufresne et al., we directly control for
the default probability by using the changes experienced each year in the credit ratings for each
bond. We believe this is a better control than using the change in the forward jump rate in the
option market. The regression is stated as:
∆(Yield Spread)i=γ0+γ1∆(Liquidity)i+γ2∆(S&P Rating)i+γ3∆(σE)i+γ4∆(Treasury Rate)i
+γ5∆(10 yr - 2 yr Treasure Rate)i+γ6∆(30 Day EuroDollar Rate)i
+γ7∆(PreTax Interest Coverage)i+γ8∆(Operating Income/Sales)i
+γ9∆(LT Debt/Assets)i+γ10∆(Total Debt/Market Cap)i+
where, ∆ represents the first difference in each variable, for each bond i. The results are presented
in Table 6.
As expected, a deterioration of bond quality (rating) is related to a significant increase in the
yield spread. Similarly, a rise in interest rates leads to a reduction in the yield spread, especially
for investment grade bonds (Duffee, 1998, and Longstaff and Schwartz, 1995). However, even
after controlling for this and other factors, changes in liquidity are highly associated with changes
in the yield spread, especially for the bid-ask spread and the LOT estimate. This is the case for
both investment grade and speculative grade bond categories.
Adding the macro-variables and firm-specific accounting variables increases the explanatory
power, but not at the expense of the liquidity variable which remains significant. The
22
conclusive result in Table 6 is the positive, significant coefficient for the liquidity change
variable. Liquidity changes remain significantly associated with yield spread changes regardless
of including bond-specific, firm-specific, or macro-level variables.
Economically, for investment grade bonds, a one basis point increase in LOT liquidity costs
over time results in a 0.12 basis point increase in the yield spread, while a one basis point increase
in the bid-ask spread over time results in a 0.29 basis point increase in the yield spread. The
corresponding impact for speculative grade bonds are 0.61 basis points (LOT liquidity costs)
and 2.46 basis points (bid-ask spread). Note that the coefficients for the liquidity variables are
broadly consistent with those of Table 3.
At the bottom of Table 6 we report the regression of the change in the yield spread on
the change in each liquidity measure alone using the full sample whenever the measure is
available. For each measure, we also regress the change in the yield spread on the change
in bond rating alone for that sample as a comparison. For investment grade bonds, the changes
in the percentage of zero returns and the LOT liquidity measure explain more than 2.0% of the
cross-sectional variation in the change of yield spread. For speculative bonds, changes in the
LOT liquidity measure alone explains 16.89% of changes in the yield spread, while changes in
the percentage zeros alone explains 5.97% of changes in the yield spread. Changes in the bid-ask
spread alone have a relatively lower explanatory power for both categories of bonds.
23
4.2 Simultaneous regression tests
To control for the potential endogeneity bias, we run a simultaneous system regression
with the yield spread change and the change in each of the liquidity measure (respectively) as
the endogenous variables. Unlike the levels test, we do not endogenize bond rating changes
because changes in the credit rating are infrequent within the sample period, causing a limited
dependent variable problem in the simultaneous system regression. Additionally, the equations
for the bid-ask spread sample for speculative grade bonds were unidentified because of linear
dependence between the macroeconomic variables. We therefore remove the Eurodollar variable
to allow for system identification. Moreover, changes in maturity, age, and coupon are also
excluded. The resulting simultaneous regression for yield spread changes is specified as:
∆(Yield Spread)i=η0+η1∆(Liquidity)i+η2∆(Treasury Rate)i+η3∆(10Yr -2Yr Treasury Rate)i
+η4∆(EuroDollar)i+η5∆(Volatility)i+η6∆(Credit Rating)i+η7∆(PreTax Coverage)i
+η8∆(Operating Income/Sales)i+η9∆(Debt/Assetsi)+η10∆(Debt/Capitalization)i+
∆(Liquidity)it =η0+η1∆(Credit Rating)i+η2∆(Bond Volatility)i+η3∆(Yield Spread)i+
The results, presented in Table 7, can be summarized as follows. For the bid-ask spread or
the LOT estimates, an increase of liquidity costs causes a significantly positive increase in yield
spreads, for both the investment grade and the speculative grade bonds. For the proportion
of zero returns, an increase of liquidity costs causes a significant increase in yield spreads for
investment grade bonds, but not for speculative bonds. The results concerning the percentage of
zero returns for the speculative grade bonds are consistent with the levels tests. On the whole,
Table 7 indicates that our tests on changes in liquidity are robust to potential endogeneity bias.
24
5. Conclusions
We examine the association between corporate bond liquidity and yield spreads. To ensure
robustness, we adopt two model-independent liquidity measures (the bid-ask spread and the
proportion of zero returns) and a liquidity estimate from a model developed by Lesmond et
al. (1999). We provide additional robustness checks to ensure that our liquidity measures are
intuitively consistent and empirically sound.
We find that liquidity is a key determinant in yield spreads. This is found both in yield
spread levels and changes over time. Liquidity is priced in yield spreads regardless of controlling
for issuer fixed effects, potential simultaneity bias between credit ratings, liquidity, and yield
spreads, or the commonly used yield spread determinants adopted by Campbell and Taksler
(2003) and yield spread change determinants of Collin-Dufresne et al. (2001). The liquidity
effects are apparent for both the investment grade and speculative grade bonds.
Our study adds to the literature in the following sense. Many prior studies simply assume
that liquidity is a second order effect, or totally neglect its impact given the difficulties in
estimating liquidity costs. The observation that bond yields exhibit significant liquidity effects
alters our view of bond pricing and risk estimation. It mitigates the concern that the yield spread
is “too high” to be explained by default risk alone (Elton et al., 2001 and Huang and Huang,
2003). Our results imply that the liquidity-related component must be taken into account when
examining yield spreads.
25
Bibliography
1. Amihud, Y., and H. Mendelson, 1986, “Asset pricing and the bid-ask spread,” Journal of
Financial Economics, 17, 223-249.
2. Amihud, Y., and H. Mendelson, 1987, “Trading mechanisms and stock returns: An empirical
investigation,” Journal of Finance, 42, 533-553.
3. Amihud, Y., and H. Mendelson, 1991, “Liquidity, maturity, and the yields on U.S. treasury
securities,” Journal of Finance, 46, 1411-1425.
4. Bekaert, G., H. Campbell, and C. Lundblad, 2003, “Liquidity and expected returns: Lessons
from emerging markets,” Working Paper, Duke University.
5. Blume, M. F. Lim, and C. MacKinlay, 1998, “The declining credit quality of U.S. corporate
debt: Myth or Reality,” Journal of Finance, 53, 1389-1413.
6. Brandt, M., and K. Kavajecz, 2003, “Price discovery in the U.S. treasury market: The
impact on order flow and liquidity on the yield curve,” Working Paper, Duke University
7. Campbell, J., and G. Taksler, 2003, “Equity volatility and corporate bond yields,” Journal
of Finance, 58, 2321-2349.
8. Chakravarty, S., and A. Sarkar, 1999, “Liquidity in fixed income markets: A comparison of
the bid-ask spread in corporate, government, and municipal bond markets,” Working Paper,
Federal Reserve Bank of New York.
9. Collin-Dufresne, P., R. Goldstein, and S. Martin, 2001, “The determinants of credit spread
changes,” Journal of Finance, 56, 2177-2207.
10. Constantinides, G. 1986, “Capital market equilibrium with transactions costs,” Journal of
Political Economy, 94, 842-862.
11. Cornell, B., and K. Green, 1991, “The investment performance of low-grade bond funds,”
26
Journal of Finance, 46, 29-48.
12. Duffee, G., 1998, “The relation between treasury yields and corporate bond yield spreads,”
Journal of Finance, 53, 2225-2241.
13. Duffee, G., 1999, “Estimating the price of default risk,” Review of Financial Studies, 12,
197-266.
14. Duffie, D., and K. Singleton, 1997, “An econometric model of the term structure of
interest-rate swap yields,” Journal of Finance, 53, 2225-2241.
15. Elton, E., M. Gruber, D. Agrawal, and D. Mann, 2001, “Explaining the rate spread on
corporate bonds,” Journal of Finance, 56, 247-277.
16. Ericsson, J., and O. Renault, 2002, “Liquidity and Credit Risk,” Working Paper, McGill
University.
17. Fama, E., and K. French, 1993, “Common risk factors in the returns on stocks and bonds,”
Journal of Financial Economics, 33, 3-56.
18. Garbade, D., and W. Silber, 1979, “Structural organization of secondary markets: Clearing
frequency, dealing activity, and liquidity risk,” Journal of Finance, 34, 577-593.
19. Glosten, L., and P. Milgrom, 1985, “Bid, ask, and transaction prices in a specialist market
with heterogeneously informed traders,” Journal of Financial Economics, 2, 24-38.
20. Goodhart, C., and M. O’Hara, 1997, “High frequency data in financial markets: Issues and
applications,” Journal of Empirical Finance, 4, 73-114.
21. Grinblatt, M., 1995, “An analytical solution for interest-rate swap spreads,” Working Paper,
UCLA, Anderson Graduate School of Management.
22. Helwege, J., and C. Turner, 1999, “The slope of the credit yield curve for speculative-grade
issuers,” Journal of Finance, 54, 1869-1884.
27
23. Hotchkiss, E., and T. Ronen, 2002, “The informational efficiency of the corporate bond
market: An intraday analysis,” Review of Financial Studies, 15, 1326-1354.
24. Huang, J., and M. Huang, 2003, “How much of the Corporate-Treasury yield spread is due
to credit risk,” Working Paper, Stanford University.
25. Jarrow, R., 1978, “The relationship between yield, risk, and the return on corporate bonds,”
Journal of Finance, 33, 1235-1240.
26. Kwan, S., 1996, “Firm-specific information and the correlation between individual stocks
and bonds,” Journal of Financial Economics, 40, 63-80.
27. Kyle, A., “Continuous auctions and insider trading,” Econometrica, 53, 1315-1335.
28. Lesmond, D., J. Ogden, and C. Trzcinka, 1999, “A new estimate of transaction costs,”
Review of Financial Studies, 12, 1113-1141.
29. Lesmond, D., M. Schill, and C. Zhou, 2004, “The illusory nature of momentum profits,”
Journal of Financial Economics, 71, 349-380.
30. Lesmond, D., 2004, “Liquidity of emerging markets,” Journal of Financial Economics,
Forthcoming.
31. Lo, A., H. Mamaysky, and J. Wang, 2001, “Asset prices and trading volume under fixed
transaction costs,” Working Paper, MIT.
32. Longstaff, F., S. Mithal, and E. Neis, 2004, “Corporate yield spreads: Default risk or
liquidity? New evidence from the credit-default swap market,” Forthcoming, Journal of
Finance
33. Longstaff, F., and E. Schwartz, 1995, “A simple approach to valuing risky fixed and floating
rate debt,” Journal of Finance, 50, 789-820.
34. Maddala, G., 1983, “Limited dependent and quantitative variables in econometrics,”
28
Cambridge University Press, Cambridge, Mass.
35. Merton, R., 1974, “On the pricing of corporate debt: The risk structure of interest rates,”
Journal of Finance, 29, 449-470.
36. Sarig, O., and A. Warga, 1989, “Bond price data and bond market liquidity,” Journal of
Financial and Quantitative Analysis, 24, 367-378.
37. Schultz, P., 2001, “Corporate bond trading costs: A peek behind the curtain,” Journal of
Finance, 56, 677-698.
38. Scruggs, H., 1998, “Resolving the puzzling intertemporal relation between the market risk
premium and conditional market variance: A two-factor approach,” Journal of Finance, 53,
575-603.
39. Stoll, H., 2000, “Friction,” Journal of Finance, 46, 1479-1514.
40. White, H., 1980, “A heteroskedasticity-consistent covariance matrix estimator and a direct
test for heteroskedasticity,” Econometrica, 48, 817-838.
29
Appendix A: The Return Generating Function
The bond price, Bt, by definition, is:
Bt=
Tt
X
n=Ttk
Cern +Aer(Tt),
where T is the maturity, k+1 is the number of coupon payments remaining, C is the half-year
coupon payment rate, A is the face value of debt, and r is the yield to maturity for k+1 coupon
payments remaining. We assume that rtfollows some unspecified stochastic process. By Ito’s
lemma we have:
dBt=∂B
∂r dr +∂B
∂t dt +1
2
2B
∂r2Λtdt, (A1)
where Λtis the square of the diffusion coefficients of rtprocess. If rtis a multivariate process,
then Λtshould also include the covariance terms. Therefore, from Equation (A1):
∂B
∂r =DtBt,(A2)
where Dtis the bond’s duration. We can rewrite Equation (A2) as:
dBt
Bt=Dtdr +∂B
∂t +1
22B
∂r2Λt
Btdt. (A3)
Barring arbitrage, there exists some state price density process, Λt, such that:
dΛt=µΛ,tdt +σT
Λtt.
In equilibrium, the risky bond return should satisfy:
EtdBt
Btrtdt =covtdBt
Bt,dΛt
Λt=Dtcovtrt,dΛt
Λt,(A4)
where covt(dPt/Pt,dΛt/Λt) is the instantaneous conditional covariance, and rt=µΛ,t/Λtis the
risk-free rate. We obtain the second equality above by using Equation (A3). Following the
discrete time empirical literature, we further assume that the state price density is a linear
function of both market equity return and long-term risk free bond return (e.g., Scruggs, 1998).
This implies that:
EtdBt
Bt=rtdt +Dt×γ1×covtrt,dBl,t
Bl,t +Dt×γ2×covt(rt,dMt
Mt),(A5)
30
where, γi,i=1,2 is the price of risk associated with the respective state variable, dBl,t/Bl,t is
long-term bond return, and dMt/Mtis market equity return. In the empirical implementation,
we will make the two adjustments. First, we only measure the proportional daily price change
in dBt/Btand we will not consider daily accrued interest. The last condition means we will
consider only clean prices. In summary, bond price changes will only be driven by long-term risk
free bond returns and equity returns. We also assume the conditional covariances are constant.11
This leads to the following specification for equation (1) in the text:
R
j,t =βj1×Durationj,t ×Rl,t +βj2×Durationj,t ×∆S&P Indext+j,t ,(1)
where Rj,t is the daily return for bond jthat investors would bid given zero transaction costs,
Durationj,t is the bond’s duration, and ∆S&P Indextis daily S&P equity return. Durationj,t ×
Rl,t is the proportional bond return of a long-term risk free bond adjusted by the duration of
the risky bond. The scaling of the market sensitivities by duration is consistent with Jarrow
(1978).
11 This can be justified for two reasons. First, for each bond we split daily bond prices into separate years and
estimate beta coefficients within the year. The coefficients can thus be treated as conditionally constant. Second,
we assume that changes in duration for each bond within the year will capture some of the variation in beta
coefficients.
31
Table 1
Corporate Bond Summary Statistics
We present liquidity and yield spread statistics for non-callable corporate bonds from 1995 to 2003 by three
maturity categories. %Zeros is the percentage of zero returns for a given year adjusted for missing prices. LOT refers
to the modified Lesmond et al. (1999) model’s liquidity estimate. The bid-ask is the proportional spread derived
from quarterly quotes from Bloomberg. To assign bond ratings, we use the Fixed Income Securities Database, and,
when unavailable, the Standard & Poor’s credit rating from Datastream. The yield spread is the difference between
the bond yield and the yield of a comparable maturity treasury bond as determined from Datastream. Two separate
samples for each maturity classification are presented. The first sample is restricted to only bonds with available
LOT estimates, while the second sample is restricted to only bonds with available bid-ask spreads. bp stands for
basis points and N stands for the sample size.
Short Maturity (1-7 years)
Liquidity & S&P Credit Ranking
Yield Spreads AAA AA A BBB BB B CCC to D
Zeros (%) 5.93 4.10 3.88 8.43 40.63 44.71 46.31
LOT (bp) 7.88 9.63 10.51 34.99 201.45 458.86 933.06
Yield Spread (bp) 84.06 96.91 129.34 252.09 575.58 1213.43 3949.55
N 87 336 1162 1234 333 167 119
Zeros (%) 3.20 3.35 3.33 7.80 42.77 44.00 51.09
LOT (bp) 5.83 8.18 9.82 34.40 191.23 335.63 868.59
Bid-Ask (bp) 24.51 26.02 25.82 31.01 54.26 58.76 77.00
Yield Spread (bp) 71.43 95.05 118.92 235.41 549.88 1247.23 3559.09
N 56 285 972 775 178 72 22
Medium Maturity (7-15 years)
Liquidity & S&P Credit Ranking
Yield Spreads AAA AA A BBB BB B CCC to D
Zeros (%) 9.79 12.59 10.61 11.94 36.99 38.71 34.96
LOT (bp) 24.28 47.26 57.74 70.29 259.34 342.50 941.84
Yield Spread (bp) 82.44 146.24 177.68 277.45 566.53 947.14 2887.47
N 49 120 539 730 152 78 44
Zeros (%) 10.36 8.34 6.62 8.91 42.40 38.96 18.04
LOT (bp) 25.00 36.17 36.82 51.45 266.11 272.96 282.84
Bid-Ask (bp) 49.52 36.57 38.20 44.22 54.65 60.44 180.35
Yield Spread (bp) 70.65 129.02 154.19 251.68 497.45 863.71 1619.04
N 37 67 386 394 76 32 9
Long Maturity (15-40 years)
Liquidity & S&P Credit Ranking
Yield Spreads AAA AA A BBB BB B CCC to D
Zeros (%) 7.53 9.75 10.39 8.68 29.13 31.67 41.00
LOT (bp) 59.34 83.65 79.40 66.57 252.14 284.81 1023.18
Yield Spread (bp) 133.81 152.25 183.76 242.16 437.69 681.44 2047.11
N 49 189 674 929 112 48 48
Zeros (%) 7.28 8.27 7.79 8.00 32.36 37.25 35.14
LOT (bp) 76.81 75.60 56.97 58.57 281.56 245.78 328.25
Bid-Ask (bp) 51.65 52.68 54.76 58.62 73.56 82.47 86.75
Yield Spread (bp) 113.65 142.83 172.21 236.89 457.97 623.45 2192.41
N 27 110 410 494 62 14 8
Table 2
Liquidity Measure Tests
Panel A reports coefficients on the risk free rate factor, βTBond , and the equity market return factor, βEquity , from, respectively,
a nave OLS model and the modified Lesmond et al. (1999) model (LOT). The interest rate factor is expected to be negative for all
bonds while the equity factor can be either positive or negative for investment grade bonds but positive for speculative grade bonds. N is
the sample size for each bond rating. %Zeros is the percentage of zero returns for a given year adjusted for missing prices. LOT is the
liquidity estimate from the Lesmond et al. (1999) model. Panel B reports the regression of the bid-ask spread on the other two liquidity
measures, the percentage of zero returns, and the LOT estimate, and control for other liquidity determinants. Age and maturity are in
years referenced from the year the bond was issued or its maturity date relative to the year being analyzed. The amount outstanding
is the dollar amount of the bond that has not been redeemed and is log scaled. The bond volatility is log scaled. The bond ratings are
numbered from one to 10 for investment grade bonds (S&P ratings, AAA to BBB-) and from one to 12 for speculative grade bonds (S&P
ratings, BB+ to D). Panel C reports an OLS regression of the yield spread on each liquidity measure for a matched sample using the
bid-ask as a basis. White’s (1980) t-statistics are in parentheses. An * denotes significance at the 1% level, while a denotes significance
at the 5% level.
Panel A: LOT Model and the Naive Model Coefficient Estimates
S&P Limited Dependent Variable Model Naive Model
Rating N % Zeros LOT βTBond βEquity βTBond βEquity
Investment AAA 185 7.37* 0.0026* -0.9077* -0.0072* -0.0515 -0.0055*
Grade AA 645 7.34* 0.0038* -0.9127* -0.0084* 0.0068* -0.0064*
A 2395 7.29* 0.0041* -0.9395* -0.0090* -0.0223 -0.0068*
BBB 2893 9.40* 0.0054* -0.9047* -0.0069* -0.0487 -0.0040
Speculative BB 597 37.55* 0.0225* -0.5332* -0.0001 -0.0450 -0.0019
Grade B 293 40.97* 0.0399* 0.4424 0.0171-0.1261 0.0127*
CCC to D 211 42.74* 0.0955* -0.1976 0.0885* -0.0662 0.0341
Panel B: Regression of the Bid-Ask Spread on Liquidity Measures
Variable Investment Grade Bonds Speculative Grade Bonds
Intercept 0.0112* 0.0059* 0.0032* 0.0051* 0.0049* 0.0043 0.0054* 0.0069
(103.18) (5.31) (129.10) (6.50) (26.06) (0.99) (26.96) (1.74)
LOT 0.0442* 0.0233* 0.0440* 0.0297*
(9.81) (5.65) (5.16) (3.50)
% Zeros 0.0037* 0.0027* 0.0007 0.0017*
(15.03) (11.73) (1.81) (3.80)
Maturity 0.0001* 0.0001* 0.0001* 0.0001*
(10.59) (12.92) (4.02) (5.46)
Age -0.0000 0.0000 0.0001 0.0001
(0.34) (1.25) (1.67) (0.89)
Ln(Amt. Outstanding) 0.0001 0.0001-0.0000 -0.0001
(0.60) (2.34) (0.14) (0.16)
Ln(Bond Volatility) 0.0004* 0.0003* 0.0001 0.0002*
(12.15) (14.61) (1.47) (3.05)
Bond Rating 0.0001 0.0001 0.00020.0003*
(1.38) (1.49) (2.30) (2.81)
Sample Size 3970 6040 421 525
% Adjusted R26.39 25.34 6.82 25.23 6.45 15.02 0.35 15.03
Panel C: Regression of the Yield Spread on Liquidity Measures
Investment Grade Bonds Speculative Grade Bonds
Variable Bid-Ask LOT % Zero Bid-Ask LOT % Zero
Coefficient 1.8246* 0.3181* 0.0239* 1.73960.7804* 0.0014
(17.70) (17.65) (15.76) (2.16) (5.88) (1.87)
N3970 421
% Adjusted R27.29 7.26 5.87 0.86 7.39 1.72
Table 3
Yield Spread Determinants and Liquidity Tests
The yield spread determinants are based on bond-specific effects (bond rating, amount outstanding, and maturity in years), macroe-
conomic variables (One-year Treasury note rate (T-Note), the difference between the 10-year and 2-year Treasury rates (Term Slope),
and the 30-day Eurodollar rate minus the 3-month T-Bill Rate (Eurodollar)), and firm-specific operating characteristics (pre-tax interest
coverage, operating income to sales, long-term debt to assets, and total debt to capitalization). The pretax interest coverage is further
grouped into one of four categories according to Blume et al. (1998). σEis the equity volatility for each issuer. Investment grade bonds
are numbered from one (AAA rated bonds) to 10 (BBB- rated bonds) Speculative grade bonds are numbered from one (BB+ rated bonds)
to 12 (D rated bonds). The liquidity cost estimates are based on the modified LOT model, the percent zero returns, and the bid-ask
spread. White’s (1980) t-statistics are presented in parentheses. The last partition is a univariate regression of the yield spread on either
liquidity or credit rating alone using only the bond-specific sample for each liquidity measure. An * or a signifies significance at the 1%
or 5% level, respectively.
Variable Investment Grade Bonds Speculative Grade Bonds
Intercept 0.1573* 0.1251 0.0696* 0.0902* 0.1737* 0.14080.56960.53970.3232* 0.2784* 0.9056* 0.8242*
(15.03) (11.52) (9.97) (8.76) (12.25) (14.08) (2.00) (1.98) (2.81) (3.23) (2.99) (2.88)
LOT 0.5122* 0.2166* 1.6757* 0.8213*
(10.71) (10.03) (7.82) (2.95)
Bid-Ask 0.4362* 0.4200* 2.7266* 2.2957*
(5.65) (5.20) (4.99) (4.69)
% Zeros 0.0255* 0.0138* 0.0600* 0.0521*
(12.94) (8.84) (5.25) (3.10)
Maturity 0.0001 0.0001* 0.0001* 0.0001* 0.0001* 0.0001* -0.0024* -0.0021* -0.0009* -0.0106* -0.0027* -0.0032*
(1.57) (5.28) (5.40) (5.89) (7.25) (7.44) (4.62) (3.92) (5.75) (4.03) (5.61) (4.93)
Amount 0.0001 -0.0005 0.0003 -0.0008-0.0001 -0.0007* -0.0071* 0.0005 -0.0004 0.0011 -0.00640.0023
(0.34) (1.33) (0.92) (2.48) (0.75) (2.66) (3.08) (0.34) (0.17) (0.28) (2.25) (0.78)
Coupon 0.2074* 0.1141* 0.0013* 0.0009* 0.1828* 0.1078* 0.4895 0.44410.0034* 0.0027* 1.3981* 1.2975
(10.30) (6.28) (11.09) (5.48) (12.45) (7.36) (1.52) (2.13) (3.69) (3.67) (3.42) (2.24)
T-Note -2.4595* -1.5603* -1.1035* -0.9273* -2.5467* -1.7185* -9.5597-9.4014-4.0206* -3.3823* -14.0365* -13.771*
(17.84) (15.68) (13.09) (9.83) (22.00) (15.20) (2.40) (2.43) (2.72) (4.26) (3.18) (4.43)
Term Slope -5.5080* -3.8658* -2.9417* -2.6337* -5.9552* -4.3546* -14.0822 -19.6601-10.0332* -10.7150* -28.8487* -34.342*
(18.01) (15.71) (14.92) (11.25) (23.43) (16.71) (1.58) (2.21) (2.99) (5.25) (2.97) (3.69)
EuroDollar -0.0774* -0.0677* -0.0560* -0.0541* -0.0889* -0.0751* 0.0328 -0.1369 -0.2440* -0.3370* -0.4291* -0.3924
(16.42) (15.31) (14.02) (13.92) (23.61) (19.13) (0.25) (0.98) (4.11) (5.15) (3.01) (2.45)
Credit 0.0021* 0.0020* 0.0023* 0.0023* 0.0021* 0.0021* 0.0247* 0.0249* 0.0092* 0.0091* 0.0372* 0.0366*
Rating (26.96) (20.14) (32.54) (24.35) (34.62) (22.55) (9.61) (6.45) (8.67) (6.85) (13.25) (9.29)
σE3.4437* 5.2107* 4.1768* 4.2847 2.854616.8334*
(4.99) (6.85) (5.32) (1.92) (2.27) (3.86)
PreTax D1 -0.0012* -0.0015* -0.0010* 0.0091* -0.0014 0.0037
(5.93) (6.85) (5.32) (2.79) (0.93) (0.73)
PreTax D2 0.0005* 0.0006* 0.0005* -0.0103 0.0043-0.0051
(3.56) (4.83) (4.33) (1.46) (2.12) (0.85)
PreTax D3 0.0001 0.00020.0001 0.0060 0.0047 0.0138*
(1.21) (2.24) (1.59) (0.75) (1.78) (2.66)
PreTax D4 -0.0001* -0.0001* -0.0001 0.0014 -0.00670.0273
(2.57) (4.52) (1.65) (0.12) (2.52) (1.16)
Oper. Inc. -0.0069* -0.0080* -0.0079* -0.0185 -0.0277* -0.0900
to Sales (4.80) (6.34) (5.29) (1.38) (2.81) (1.10)
LT Debt -0.0057* -0.0061* -0.00470.0165 -0.0147 -0.0295
to Assets (3.22) (3.83) (2.56) (0.48) (0.90) (0.32)
Total Debt 0.00490.00360.0073* 0.0519 0.0748* 0.0185*
to Cap. (2.53) (2.19) (4.29) (0.78) (4.24) (4.29)
N5838 2176 6035 2374 8802 3257 1041 461 583 288 1413 606
% Adj. R229.19 41.84 25.44 47.91 25.47 46.02 37.04 44.30 32.04 58.36 33.40 44.09
LOT Rating Bid-Ask Rating % Zeros Rating LOT Rating Bid-Ask Rating % Zeros Rating
Coefficient 0.4233* 0.0028* 0.5536* 0.0025* 0.0183* 0.0026* 2.4233* 0.0318* 3.3941* 0.0106* 0.1158* 0.0379*
(9.67) (29.82) (6.36) (32.67) (10.96) (35.00) (9.07) (9.89) (4.77) (10.17) (7.14) (12.35)
% Adj. R27.57 15.20 2.12 20.12 3.95 15.08 21.83 24.00 7.49 25.12 3.02 28.21
Table 4
Fixed Effects: Yield Spread Determinant Tests
The yield spread determinants are based on bond-specific effects (bond rating, amount outstanding, and maturity in years), macroe-
conomic variables (one-year Treasury note rate (T-note), the difference between the 10-year and 2-year Treasury rates (Term Slope),
and the 30-day Eurodollar rate minus the 3-month T-Bill Rate (Eurodollar)), and firm-specific operating characteristics (pre-tax interest
coverage, operating income to sale, long-term debt to assets, and total debt to capitalization). The pretax interest coverage is further
grouped into one of four categories according to Blume et al. (1998). σEis the equity volatility for each issuer. Investment grade bonds
are numbered from one (AAA rated bonds) to 10 (BBB- rated bonds) Speculative grade bonds are numbered from one (BB+ rated bonds)
to 12 (D rated bonds). The liquidity cost proxies include the LOT estimate, the percentage of zero returns, and the bid-ask spread. The
issuer is the fixed effect. The issuer fixed effects test is reported by the F-test. An * or a signifies significance at the 1% or 5% level,
respectively.
Variable Investment Grade Bonds Speculative Grade Bonds
Intercept 0.1111* 0.1194* 0.1086* 0.0932* 0.1219* 0.1053* 0.77430.2208 0.3588* 0.4188* 1.0571* 0.0360
(12.15) (14.11) (14.93) (10.25) (10.98) (13.78) (2.55) (0.61) (2.87) (3.78) (3.47) (0.85)
LOT 0.2118* 0.1078* 2.2897* 0.8764*
(13.12) (9.73) (13.80) (4.31)
Bid-Ask 0.1703* 0.2019* 1.4963* 1.5890*
(5.72) (5.15) (4.75) (4.17)
% Zeros 0.0119* 0.0044* 0.0981* 0.0482
(13.01) (5.75) (5.01) (1.72)
Maturity 0.0001* 0.0001* 0.0002* 0.0001* 0.0001* 0.0002* -0.0029* -0.0029* -0.0011* -0.0011* -0.0022* -0.0028*
(5.60) (12.15) (12.51) (12.45) (9.98) (14.75) (4.84) (4.22) (7.85) (7.18) (3.94) (3.73)
Amount -0.0006-0.0004 -0.0001 -0.0002 -0.0005* -0.0003 -0.0058 -0.0026 -0.0064* -0.00410.0038 0.0087
(2.26) (1.86) (0.60) (0.84) (2.72) (1.63) (1.48) (0.79) (2.93) (1.95) (1.00) (2.20)
Coupon 0.0658* 0.0705* 0.0005* 0.0005* 0.0629* 0.0667* 0.6891 1.6274* 0.0013 0.0002 0.6689 1.5849*
(3.84) (5.32) (5.10) (3.84) (4.83) (6.03) (1.28) (3.09) (1.00) (0.17) (1.31) (2.62)
T-Note Rate -1.5298* -1.2470* -1.1839* -0.9141* -1.6169* -1.1972* -13.6442* -5.8177 -2.4538 -5.3023* -19.0025* -10.352
(17.02) (18.48) (15.85) (10.96) (19.69) (17.02) (3.48) (1.18) (1.35) (3.29) (4.24) (1.78)
Term Slope -3.6526* -3.3459* -3.2950* -2.6766* -3.9435* -3.2680* -20.8069-13.1133 -7.5091-13.7416* -39.3252* -24.667
(17.60) (21.05) (19.81) (14.15) (21.16) (20.08) (2.42) (1.20) (2.04) (4.18) (4.59) (1.92)
Eurodollar -0.0570* -0.0602* -0.0685* -0.0545* -0.0637* -0.0591* -0.2086 -0.2031 -0.2571* -0.2533* -0.6623* -0.3158
(15.95) (20.94) (25.95) (17.22) (21.74) (21.90) (1.57) (1.20) (7.64) (6.39) (5.37) (1.62)
Credit 0.0035* 0.0008* 0.0009* 0.0008* 0.0031* 0.0012* 0.0048 0.0250* 0.0123* 0.0123* 0.0300* 0.0423*
Rating (16.98) (3.87) (5.92) (3.75) (19.03) (6.28) (1.25) (6.52) (10.37) (9.80) (8.36) (9.67)
σE1.1266* 2.2140* 1.8186* -1.5774 -2.9956* 6.8704
(3.89) (5.40) (6.80) (0.44) (3.21) (1.72)
Pre-Tax D1 -0.0013* -0.0010* -0.0009* 0.0098 0.00350.0007
(7.67) (5.42) (5.82) (1.52) (2.34) (0.10)
Pre-Tax D2 0.00030.0005* 0.0004* -0.0045 0.0007 0.0028
(1.96) (3.55) (3.70) (0.29) (0.26) (0.23)
Pre-Tax D3 0.0001 0.0000 0.0001 0.0336 0.0076 0.0057
(0.47) (0.27) (1.11) (0.66) (1.26) (0.30)
Pre-Tax D4 -0.0000 0.0000 -0.0000 -0.0578 -0.0077 -0.0099
(0.15) (0.42) (0.37) (0.62) (1.26) (0.36)
Oper. Inc. -0.0022 -0.0068* -0.0039 -0.0274 -0.0713* 0.0024
to Sales (1.00) (3.10) (1.92) (0.69) (3.42) (0.46)
LT Debt -0.0004 -0.0047 0.0005 0.06180.0812* 0.0037
to Assets (0.15) (1.42) (0.19) (2.02) (2.73) (0.09)
Total Debt 0.0187* 0.0182* 0.0186* 0.1041 0.0732* -0.0094
to Cap. (6.79) (6.33) (7.60) (1.25) (2.79) (0.12)
Sample Size 5838 2176 6035 2374 8802 3257 1041 461 583 288 1413 606
Issuers 1124 336 1019 306 1235 367 263 96 179 76 294 106
F-Statistic 6.67* 14.01* 14.06* 14.12* 8.74* 15.80 3.06* 2.10* 11.11* 9.65* 3.67* 2.90*
Table 5
Simultaneous Regressions
Simultaneous regressions are presented using three liquidity measures, the LOT estimate, the percentage of zero returns, and the
bid-ask spread. The liquidity instrumental variablesare the bond return volatility, the amount outstanding, the age and maturity in years,
and bond rating. σBand σE, refer to bond volatility and equity volatility, respectively. The amount outstanding and bond volatility are
log scaled. The instrumental variables of the yield spread include bond-specific effects (bond rating, amount outstanding, and maturity
in years), macroeconomic variables (one-year T-note rate, the difference between the 10-year and two-year Treasury rates, and the 30-day
Eurodollar rate over 3-month T-bill rate), and firm-specific accounting variables (pre-tax interest coverage, operating income to sales,
long-term debt to assets, and total debt to capitalization), and equity market effects (equity volatility). The instrumental variables of
bond rating are the bond age and firm-specific accounting variables. An * denotes 1% significance while a denotes 5% significance.
Investment Grade
Instrumental Yield Credit Yield Credit Yield Credit
Variable Spread Bid-Ask Rating Spread LOT Rating Spread %Zeros Rating
Intercept 0.1661* 0.0150* 8.4213* 0.2398* 0.1476* 8.1414* 0.7819 1.1697* 7.9553*
(2.77) (5.05) (6.52) (5.10) (13.84) (8.39) (1.87) (10.46) (9.12)
Liquidity 7.0442* 0.9975* 0.2126
(4.23) (7.44) (1.96)
Maturity -0.0003 0.00010.0001 -0.0004* 0.0001 -0.0028*
(1.94) (2.19) (1.29) (9.23) (1.72) (5.88)
Coupon -0.0009 -0.0504 -0.1899
(1.14) (0.66) (1.93)
T-Note -2.5455* -49.4529-3.9712* -35.4179-11.8048-34.8361
(3.41) (2.35) (6.55) (2.32) (1.96) (2.50)
Term Slope -6.7878* -86.3815-8.6125* -61.5634-26.2885-60.4370
(3.82) (2.08) (6.93) (0.31) (2.00) (2.17)
Eurodollar -0.1121* -0.0810* -0.2727
(4.36) (6.79) (2.19)
σE2.0849 1.6004 -5.2688
(0.92) (1.88) (1.43)
Credit Rating 0.00570.0003* 0.0056* 0.0018* 0.0038* 0.0396*
(2.34) (3.18) (2.65) (4.90) (3.94) (9.54)
PreTax D1 -0.0001 0.0414 -0.0003 0.0140 0.0016 0.0382
(1.44) (1.15) (0.81) (0.39) (1.10) (1.29)
PreTax D2 0.0015 -0.2862* 0.0015-0.2921* 0.0012 -0.2964*
(1.66) (9.67) (2.19) (9.52) (1.03) (11.98)
PreTax D3 0.0005 -0.1152* 0.0004 -0.1012* 0.0001 -0.1031*
(1.02) (5.48) (1.51) (4.20) (1.03) (5.56)
PreTax D4 -0.0002 0.0337* -0.0001 0.0298* 0.0001 0.0226*
(0.81) (3.67) (0.99) (2.82) (0.31) (2.88)
Operating Income 0.0002 -1.4810* 0.0051 -1.7060* 0.0079 -1.4883*
(0.03) (5.84) (1.02) (6.41) (1.21) (6.93)
Long Term Debt -0.02823.9493* -0.0189 4.0813 -0.0188 3.6651*
to Assets (2.40) (12.85) (1.90) (12.76) (1.28) (14.21)
Total Debt 0.0026 -0.1889 0.0077-0.4549 0.0202-0.4472
to Cap. (0.44) (0.58) (2.17) (1.34) (2.53) (1.59)
Ln(σB) 0.0008* 0.0080* 0.0633*
(5.44) (17.22) (12.76)
Ln(Amt. Outstanding) -0.0002 -0.0026* -0.0250*
(1.80) (5.79) (5.53)
Bond Age 0.0001-0.0001 -0.0001
(2.45) (0.58) (0.04)
Yield Spread -0.0784* 63.4919* -0.7430* 50.2688* -9.9378* 56.7310*
(3.24) (11.34) (9.11) (8.78) (10.83) (12.14)
Sample Size 2374 2176 3257
Adjusted R25.23 5.48 40.05 17.77 13.80 36.88 3.45 5.88 36.45
Table 5: Continued
Simultaneous Regressions
Speculative Grade
Instrumental Yield Credit Yield Credit Yield Credit
Variable Spread Bid-Ask Rating Spread LOT Rating Spread %Zeros Rating
Intercept 0.0139 0.0077 4.9369 0.1265 0.0568* -8.1870 0.4540* -0.7170* -2.8820
(0.04) (0.78) (0.75) (0.30) (1.06) (1.64) (0.66) (3.02) (0.51)
Liquidity 12.13081.8432-0.1944
(1.95) (1.96) (0.83)
Maturity -0.00270.0001* -0.0024* 0.0008 -0.0039* 0.0041
(2.14) (2.72) (3.89) (1.85) (3.37) (2.07)
Coupon 0.00640.2784 1.5665*
(2.35) (1.24) (2.76)
T-Note 0.4380 -49.1239 -4.5914 192.8104-4.3845 80.9381
(0.08) (0.45) (0.82) (2.35) (0.58) (0.87)
Term Slope -5.7854 -66.7110 -5.8488 375.1280-16.1279 165.3930
(0.51) (0.31) (0.39) (2.29) (1.00) (0.89)
Eurodollar -0.55360.0981 -0.5329
(2.65) (0.36) (2.11)
σE5.9458 -0.4133 17.8417
(1.32) (0.17) (1.97)
Credit Rating 0.0321* 0.0018* 0.0211-0.0024 0.0397* 0.0657*
(3.68) (4.70) (2.04) (0.79) (3.85) (3.10)
PreTax D1 0.0090-0.3952* 0.0081 -0.3802* -0.0017 -0.1887
(2.14) (6.20) (1.80) (4.38) (0.20) (1.98)
PreTax D2 0.0073 0.0611 0.0078 -0.0868* -0.0170 -0.0784
(1.06) (0.49) (0.73) (0.36) (0.85) (0.41)
PreTax D3 -0.0067 -0.1693 -0.0192 -0.0994 0.0194 -0.0460
(0.64) (0.92) (0.81) (0.21) (0.97) (0.17)
PreTax D4 -0.0031 0.2538 0.0345 0.1380 -0.0190 0.1594
(0.26) (1.11) (0.88) (0.18) (0.80) (1.79)
Operating Income 0.1101 -0.5159 -0.0045 -0.8189 0.0410 -1.3637
(1.90) (0.97) (0.15) (1.45) (0.69) (1.79)
Long Term Debt -0.1915* 2.4873* 0.0490 -0.1068 -0.2921* 3.2336*
to Assets (2.84) (3.17) (1.50) (0.20) (3.81) (2.89)
Total Debt 0.1994* -3.1155* -0.0372 -1.2718 0.1443-1.4239
to Cap. (3.77) (4.00) (0.59) (1.47) (1.96) (1.45)
Ln(σB) 0.0004 0.0024 -0.0992*
(1.30) (0.65) (6.38)
Ln(Amt. Outstanding) -0.0001 -0.0012 -0.0006
(0.11) (1.06) (0.09)
Bond Age -0.0002-0.0004 -0.0057
(2.28) (1.03) (1.56)
Yield Spread -0.0312 24.3250* 0.3014* 22.3214* 0.1582 15.5033*
(1.59) (9.38) (3.45) (16.65) (0.56) (14.32)
Sample Size 288 461 606
Adjusted R228.81 22.17 58.57 46.46 22.84 47.82 35.31 6.08 36.87
Table 6
Yield Spread Change Determinants and Liquidity Tests
The yield spread change determinants are based on bond-specific effects, macroeconomic effects, and firm-specific operating charac-
teristics. Annual changes in all variables are examined for the 1995-2003 period. The liquidity cost proxies include the LOT estimate,
the percentage of zero returns, and the bid-ask spread. We use a cardinal scale for all bonds, regardless of whether they are investment
grade or speculative grade bonds, ranging from one for AAA bonds to 22 for D rated bonds. The firm-specific operating characteristics
are pre-tax interest coverage, operating income to sales, long-term debt to assets, and total debt to capitalization. σEis equity volatility.
T-Note Rate is the one-year Treasury rate. Term-Slope is the difference between the 10 year and 2-year Treasury rates. Eurodollar refers
to the difference between the 30-day Eurodollar rate and the 3-month T-Bill rate. The last partition is a univariate regression of the yield
spread on either liquidity or credit rating alone using only the available bond-specific sample for each liquidity measure. White’s (1980)
t-statistics are presented in parentheses. An * denotes 1% significance while a denotes 5% significance.
Variable Investment Grade Bonds Speculative Grade Bonds
Intercept -0.0001 -0.0004 -0.0005* -0.0006* -0.0014* -0.0006 0.0327 0.0213 0.0003 0.0080 -0.0103* -0.0006
(0.49) (1.34) (2.81) (2.80) (5.49) (1.83) (2.55) (1.31) (0.08) (1.06) (10.21) (0.39)
∆(LOT) 0.1885* 0.1239* 1.5153* 0.6068*
(5.92) (4.01) (3.56) (4.17)
∆(%Zero) 0.0286* 0.01340.0172 0.0369
(6.02) (3.51) (1.81) (2.11)
∆(Bid-Ask) 0.18730.29092.16362.4613
(2.22) (2.34) (2.44) (2.57)
∆(Credit 0.0015* 0.0011* 0.0018* 0.0015* 0.0007* 0.0014* 0.0123 0.0293* 0.0098* 0.0089* 0.0181* 0.0197*
Rating) (2.64) (2.91) (4.48) (5.10) (4.25) (4.33) (1.90) (4.99) (5.75) (3.03) (6.72) (6.17)
∆(T-Note) -0.9791* -0.7426* -1.1166* -0.6589* -0.9203* -0.4271* -8.9819 -6.9540 -4.2911-2.6598 -0.2080 0.4007
(13.04) (9.69) (11.04) (7.48) (12.31) (4.18) (2.29) (1.65) (2.43) (0.56) (0.46) (0.56)
∆(Term -2.6012* -2.2579* -2.7856* -1.9930* -2.5691* -1.5795* -18.153-20.057-10.728* 9.8818 -1.1486 -3.3099
Slope) (15.60) (13.90) (13.24) (11.41) (16.15) (7.72) (2.22) (2.40) (3.12) (0.99) (1.17) (1.69)
∆(Euro- -0.0538* -0.0473* -0.0504* -0.0397* -0.0601* -0.0385* -0.2534 -0.2927-0.1999* -0.2396* -0.2001* -0.2808*
Dollar) (16.93) (12.62) (9.40) (15.28) (23.95) (11.13) (2.41) (2.30) (7.18) (3.37) (3.96) (4.87)
∆(σE) -0.3013 -0.5490 -1.7774 -3.0106 -2.16672.2830
(0.36) (0.73) (1.77) (1.79) (1.94) (1.21)
∆(PreTax 0.0000 0.0001 0.0001 -0.0001 -0.0005 -0.0000
Interest) (0.08) (1.02) (1.75) (0.10) (1.08) (0.02)
∆(Operating -0.0107* -0.0115* -0.0181* 0.0044 -0.0192 -0.0393
Income) (2.57) (4.01) (3.12) (0.16) (1.55) (0.75)
∆(LT Debt 0.0094 0.0037 0.0189* 0.0437 0.0027 0.0709
to Assets) (1.80) (0.97) (3.02) (1.25) (0.09) (0.97)
∆(Debt to 0.0083 0.0075-0.0057 0.0674 0.0407 0.0349
Capit.) (1.67) (2.08) (0.92) (1.38) (1.54) (0.90)
Sample Size 2646 985 5170 1842 2914 1164 451 198 477 195 188 103
Adj R2(%) 11.93 21.19 9.10 17.32 16.06 18.34 17.49 63.26 18.98 33.05 34.17 50.43
Liquidity 0.1847 0.0276* 0.2270* 1.7739* 0.0527* 0.9534
Alone (6.19) (5.95) (2.57) (4.74) (5.57) (2.00)
% Adj. R22.80 2.32 0.19 16.89 5.97 0.75
Credit Risk 0.00180.0023* 0.0007* 0.0250* 0.0095* 0.0178*
Alone (2.53) (4.36) (3.13) (3.56) (4.67) (6.84)
% Adj. R21.03 1.19 0.34 4.30 5.39 19.65
Table 7
Simultaneous Regressions: Yield Spread Changes
Simultaneous regressions are presented using three liquidity measures: the LOT estimate, the percentage of zero returns, and the
bid-ask spread. The liquidity instrumental variables are the bond return volatility and bond rating. σBand σE, refer to bond volatility
and equity volatility, respectively. The instrumental variables of the yield spread are bond rating, macroeconomic variables (one-year
Treasury note rate, the difference between the 10-year and two-year Treasury rates, and the 30-day Eurodollar rate over the 3-month
T-bill rate), firm-specific accounting variables (pre-tax interest coverage, operating income to sales, long-term debt to assets, and total
debt to capitalization), and equity market effects (equity volatility). An * denotes 1% significance while a denotes 5% significance.
Investment Grade Speculative Grade
Instrumental Yield Yield Yield Yield Yield Yield
Variable Spread LOT Spread %Zeros Spread Bid-Ask Spread LOT Spread %Zeros Spread Bid-Ask
Intercept 0.0006 -0.00050.0001 -0.0080* -0.0027 -0.00010.0169 -0.0119* 0.1634 -0.0090 -0.27850.0002
(1.33) (2.32) (0.10) (4.77) (1.16) (2.45) (0.80) (4.71) (0.43) (1.06) (2.55) (0.43)
∆(LOT) 0.7608* 1.6039
(8.03) (2.55)
∆(%Zero) 0.2785* 2.2042
(5.20) (0.44)
∆(Bid-Ask) 19.080312.4693*
(2.51) (2.90)
∆(T-Note) -0.5787* -3.6513* -241.617 -9.5024 71.448 1.1050
(3.97) (5.48) (1.90) (1.24) (0.42) (1.74)
∆(Term -1.8475* -7.7999* -2.3671 -16.522 131.721 -428.795
Slope) (5.92) (5.95) (1.44) (1.08) (0.39) (1.68)
∆(Euro- -0.0299* -0.0575* 0.0230 0.1190 1.3221
Dollar) (5.76) (6.99) (0.71) (0.59) (0.36)
∆(σE) -0.1831 0.2952 3.3901 -5.9277* -39.873 -2.7684
(0.44) (0.38) (0.93) (3.49) (0.45) (0.99)
∆(Credit 0.0013* -0.0004 0.0021* -0.0008 0.0005 -0.0000 -0.0022 0.0053-0.0231 0.02480.0183* 0.0000
Rating) (3.16) (1.10) (3.39) (0.33) (0.34) (0.38) (0.39) (2.02) (0.31) (2.12) (3.35) (1.24)
∆(PreTax 0.0000 -0.0000 -0.0001 0.00330.0042 -0.0015
Interest) (0.09) (0.23) (0.28) (2.24) (0.36) (1.44)
∆(Operating -0.0165* -0.0102 -0.0068 0.12870.1280 0.1304
Income) (4.79) (1.79) (0.49) (2.45) (0.35) (1.51)
∆(Debt to 0.0115-0.0107 0.0123 0.1187 0.1484 -0.0291
Assets) (2.34) (1.29) (0.54) (1.91) (0.36) (0.30)
∆(Debt to 0.0050 0.0057 0.0029 -0.0067 -0.1256 0.0139
Capit.) (1.06) (0.77) (0.14) (0.11) (0.28) (0.22)
∆(Ln(σB)) 27.3013* 138.250* -0.0816 5.4303 -22.2797 2.1237
(8.33) (5.38) (0.08) (0.97) (0.88) (1.03)
∆(Yield 0.1109 -1.0143 0.0596* 0.1847 0.3965 -0.0205
Spread) (1.35) (1.65) (3.06) (1.28) (0.55) (0.68)
Sample Size 985 1842 1164 198 195 103
Adjusted R218.92 12.20 3.63 1.83 0.60 1.46 20.36 11.33 -4.98 3.17 26.59 -0.22
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