Liquidity Premium in the Eye of the Beholder: An Analysis of the

Clientele Effect in the Corporate Bond Market

Jing-Zhi Huang, Zhenzhen Sun, Tong Yao, and Tong Yu∗

April 2013

∗We thank Jens Dick-Nielsen, Marco Rossi, and participants at the 2013 MFA Meetings (Chicago) forhelpful comments and suggestions. Huang is from the Smeal College of Business, Pennsylvania State Uni-versity. Email: jxh56@psu.edu. Sun is from School of Business, Siena College. Email: zsun@siena.edu. Yaois from Henry B. Tippie College of Business, University of Iowa. Email: tong-yao@uiowa.edu. Yu is fromCollege of Business and Administration, University of Rhode Island. Email: tongyu@uri.edu.

Liquidity Premium in the Eye of the Beholder:

An Analysis of the Clientele Effect in the Corporate Bond Market

Abstract

This paper examines how liquidity and the heterogeneous liquidity preference of investors

interact to affect asset pricing, known as the liquidity clientele effect. We use detailed cor-

porate bond holdings by insurance firms as well as measures of corporate bond liquidity to

quantify investors’ liquidity preference. We find a wide dispersion of liquidity preference

across investors. Such liquidity preferences persist over time and, importantly, are related

to characteristics associated with investment horizons. Further, among corporate bonds

heavily held by investors with strong preference for liquidity, there is a strong liquidity pre-

mium effect—namely, bonds with higher illiquidity command higher yield spreads; however,

the liquidity premium is substantially lower among bonds heavily held by investors with

a penchant for illiquidity. This finding presents support to the liquidity clientele effect, a

long-standing conjecture in the asset pricing theory of liquidity.

1 Introduction

The liquidity premium—the compensation demanded by investors for holding illiquid securities—

has been well documented in various sectors of the financial market and, in particular, in

the corporate bond market. Several empirical studies have reported a significant liquidity

component in corporate bond yield spreads (see, e.g., Longstaff, Mithal, and Neis, 2005;

Chen, Lesmond, and Wei 2007; Bao, Pan, and Wang, 2011; Dick-Nielsen, Feldhutter, and

Lando, 2012; Friewald, Jankowitsch, and Subrahmanyam, 2012). A growing literature has

argued that liquidity may help explain the “credit spread puzzle” (Huang and Huang, 2012)

and/or changes in the yield spread (Collin-Dufresne, Goldstein, and Martin, 2001).1

In this study we examine the related phenomenon of “liquidity clientele”—namely, due

perhaps to heterogenous investment horizons, some investors may require less compensation

for holding illiquid securities and prefer holding more illiquid securities given the same level

of compensation, while other investors may prefer the opposite. When illiquid securities

predominantly attract investors with low liquidity preference, the liquidity premium on these

securities may be attenuated.

The idea of liquidity clientele can be traced to at least Amihud and Mendelson (1986)

and Constantinides (1986). The former study introduces the notion of exogenous liquid-

ity clienteles and shows that when investors have different investment horizons, investors

with longer horizons tend to hold more illiquid securities. Specifically, in the Amihud and

Mendelson (1986) model, the liquidity premium is a concave function of trading cost. Con-

stantinides (1986) shows that in a multi-period model with endogenous investment horizon,

investors would reduce their trading frequency in response to high trading cost. As a result,

they hold illiquid securities for much longer than they do on liquid securities. Furthermore,

his calibration results indicate that trading cost only has a second-order effect on security

1The “credit spread puzzle” here refers to the stylized fact that structural models can explain only a smallportion of investment-grade corporate bond yield spreads once the models are calibrated to be consistentwith historical default rates and losses, and the equity risk premium. Collin-Dufresne et al. (2001) documentthat proxies for credit risk explain only a small portion of changes in yield spreads and that the unexplainedportion is driven mainly by factors that are independent of both credit-risk and standard liquidity measures.

1

pricing. These studies suggest that liquidity clientele is an important aspect when it comes

to understanding the overall liquidity effect on asset pricing.2 However, despite such promi-

nent theoretical predictions, so far there is little direct empirical evidence on the existence

of liquidity clientele and its impact on asset pricing.

We empirically analyze the presence of liquidity clientele and its effect in the corporate

bond market using data on investment portfolios of insurance companies, that are by far the

largest type of investors of corporate bonds. Specifically, according to the Federal Reserve

Flow of Funds Accounts data, insurance firms collectively hold about one third of all cor-

porate bonds issued in the U.S. market. Our data include detailed mandatory disclosure of

insurance companies’ corporate bond investments, such as the information on each individ-

ual insurer’s bond holdings and security transactions (that allows us to construct quarterly

holdings of individual insurers), for a sample of 5,662 bonds held by 3,226 insurers over the

period 2003–2009.

Using the data, we quantify insurers’ liquidity preferences and develop measures of the liq-

uidity clientele profile for each corporate bond based on the principle of revealed preference.

Specifically, we quantify an insurer’s illiquidity/liquidity preference (ILP) as the weighted

average illiquidity of the corporate bonds it holds, with a high ILP indicating a high pref-

erence for illiquidity.3 Then, for each corporate bond, we quantify its illiquidity/liquidity

clientele (ILC) by the weighted average ILP of its holders, with a high ILC indicating its

holders having a high penchant for illiquidity or a low preference for liquidity. Using these

measures, we shed light on several fundamental questions regarding the liquidity clientele

effect in the corporate bond market.

2Recently Beber, Driessen, and Tuijp (2012) provide a model of the liquidity risk clientele effect. Theyshow that liquidity risk premium is quite different in the presence of heterogeneous investment horizons thanin the case of homogeneous investment horizons.

3We employ various bond-level liquidity measures proposed in the literature that can be constructed usingthe Trade Reporting and Compliance Engine (TRACE) data. They include the Amihud (2002) illiquidityratio, Roll’s (1984) effective spread, the latent dependent variable estimate of Lesmond, Ogden, and Trzcinka(1999), the imputed round-trip cost of Feldhutter (2012), and a combined measure λ proposed by Dick-Nielsenet al. (2012), which averages over standardized liquidity measures of the Amihud ratio, the implied round-trip cost, as well as their corresponding daily standard deviations. Our results are robust to the variationsof the bond liquidity measures.

2

We begin with an investigation of the liquidity preference of insurers. We find a large

dispersion of the illiquidity preference (ILP) measures across insurers. Such cross-sectional

difference of liquidity preference is also very persistent, in that insurers with high illiquidity

preference (ILP) continue to have high ILP at least three years after the initial ranking.

Further, insurers’ liquidity preference can be linked to various firm characteristics indicative

of their investment horizons. For example, insurers with higher ILPs tend to have much

longer holding periods of their bond portfolios, be older and larger, and are more likely to

be life insurers (whose claim payment horizons are typically longer than that of property

casualty insurers). These patterns are consistent with the notion of “liquidity clientele”

conjectured by Amihud and Mendelson (1986) and Constaninides (1986).

We then examine the effect of liquidity clientele on corporate bond yield spreads. Based

on both double-sorted portfolios and panel regressions, we find that insurers’ liquidity prefer-

ence interacts with bond liquidity to affect the yield spreads. More illiquid bonds are found

to command higher yield spreads; and more importantly, we find that liquidity clientele at-

tenuates the liquidity premium effect. For example, with the Amihud measure of bond-level

liquidity, among corporate bonds in the lowest illiquidity clientele (ILC) quintile (bonds held

by insurers with the highest preference for liquidity), the average yield spread difference be-

tween the highest and lowest illiquidity quintiles is 1.46%. By contrast, among bonds in the

highest ILC quintile (bonds held by insurers with the strongest penchant for illiquidity), the

average yield spread difference between the highest and lowest illiquidity quintiles is only

0.94%. In other words, going from the lowest to the highest ILC quintile, the liquidity pre-

mium in the yield spread is reduced by more than a third. We obtain qualitatively similar

results with other bond liquidity measures. The result remains robust after controlling for

bond credit rating, maturity, and other bond characteristics.

Finally, we investigate the liquidity clientele effect in different bond subsamples and

across different time periods. We find that the attenuating role of liquidity clientele on the

liquidity premium is visible among bonds with investment-grade bonds, but appears weak

among speculative bonds. Further, our evidence suggests that the clientele effect exists

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strongly both before and during the recent financial crisis. We conjecture that the weak

liquidity clientele effect among speculative bonds may be due to two reasons. First, as

existing studies (e.g, Huang and Huang, 2012) argue, credit risk is much more important

than liquidity as a determinant of yield spread for these bonds. Second, the insurance

industry is heavily regulated. Specifically due to risk-based capital regulations, insurers

are not aggressive investors in the speculative bond market; i.e., they are not the marginal

investors of these bonds (e.g., Campbell and Taksler, 2003; Ellul, Jotikasthira, and Lundblad,

2011).4

The rest of the paper is organized as follows. Section 2 introduces the measures for

insurer-level illiquidity preference and bond-level liquidity clientele. Section 3 discusses data

and our measures of bond liquidity, insurers’ liquidity preference, and the liquidity clientele

of corporate bonds. Section 4 presents the empirical results. Section 5 concludes.

2 Implications of Liquidity and Liquidity Clientele Ef-

fects

In this section we first discuss the main implications of the Amihud and Mendelson (1986)

model that serve as the basis of our hypothesis on the liquidity clientele effect. We then

introduce empirical measures of illiquidity preference and illiquidity clientele that can be

used to test the hypothesis.

2.1 The Effects of Liquidity and Liquidity Clientele

We illustrate the idea of liquidity premium and liquidity clientele effect using the Amihud

and Mendelson (1986) model. In this model, the bid-ask spread is the source of a security’s

illiquidity. An investor’s liquidity preference is driven by her investment horizon. Both

the bid-ask spread and the investment horizon are exogenously specified. An investor’s net

4In our sample, insurers hold 13% of non-investment grade bonds outstanding; by contrast, insurers hold37% of investment-grade bonds outstanding.

4

expected return per period on a security is the gross expected return per period (i.e., expected

return before trading cost) minus the amortized trading cost (i.e., bid-ask spread divided by

the number of holding periods). The net expected return of a security is determined by the

risk of cash flows, which is also exogenously specified.

Suppose first that investors have the same horizon. In equilibrium, among securities with

the same risk hence the same net expected return, those with higher trading cost must have

higher gross expected return. This is known to be the unconditional liquidity premium effect.

Consider next the case where investors’ horizons are heterogeneous. Suppose two securi-

ties have different trading costs. For simplicity, the trading cost for the liquid asset is set to

be zero. If the market is in equilibrium, the illiquid security must offer a greater gross ex-

pected return to make the two assets have the same net return. Further, the gross expected

returns of the two securities are such that investors with a longer horizon prefer the illiquid

security and investors with a shorter horizon prefer the liquid security. Intuitively, this is

because investors with a longer horizon have a lower amortized trading cost; thus they have

a competitive advantage (i.e., requiring a lower gross return) for holding the illiquid security,

relative to investors with a shorter horizon. Put differently, if short-horizon investors hold

the illiquid asset, it would offer a lower net return than the net return from the liquid asset

providing the gross return of illiquid security offered to long-horizon investors. The tendency

for investors with less demand for liquidity (i.e., long-horizon investors) to hold more illiquid

securities is known as the liquidity clientele phenomenon.

Liquidity clientele has an asset pricing implication for the liquidity premium. In the

model, because long-horizon investors can more effectively amortize the trading cost, they

require less compensation in the gross expected return for per unit of trading cost. This

results in the liquidity clientele effect—namely, the liquidity premium is lower for securities

held by long-horizon investors. Amihud and Mendelson (1986) take this liquidity clientele

effect further to derive a concave relation between the trading cost and the liquidity premium:

since illiquid securities tend to be owned by investors with a long horizon, after integrating

out the effect of the security ownership (i.e., the liquidity clientele), the liquidity premium

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associated with per unit of trading cost decreases with trading cost.

Amihud and Mendelson (1986) empirically document a concave relation between the

stock returns and the quoted bid-ask spreads. As mentioned above, this concave relation is

consistent with the liquidity clientele effect without explicitly conditioning on the liquidity

clientele. Nonetheless, more direct evidence on the liquidity clientele effect, i.e., a lower

liquidity premium among securities held by investors with a stronger illiquidity preference,

remains lacking. To a large extent, this is because typically we do not know identities of

security holders, especially their liquidity preferences. As such, data on insurance companies’

corporate bond holdings offer a unique opportunity to directly test the liquidity clientele

effect.

2.2 Empirical Implications

We now apply the main idea of Amihud and Mendelson (1986) to corporate bonds whose

yield spreads are considered to include a liquidity component. Let S and ILQ denote the

yield spread and the illiquidity of a bond, respectively. It then follows from the unconditional

liquidity premium effect that∂S

∂ILQ> 0. (1)

The association between yield spreads and illiquidity may be considered as the liquidity

premium, π. The liquidity clientele effect suggests that the liquidity effect decreases securities

held by investors with a stronger illiquidity preference, measured by ILC (illiquidity clientele).

∂π

∂ILC=

∂2S

∂ILQ ∂ILC< 0, (2)

The construction of ILC involves two steps. In the first step, we introduce a measure of

an insurer’s illiquidity preference (ILP) based on liquidity levels of corporate bonds that the

insurer holds. Let ILQj,t, j = 1, . . . , N , denote the time-t value of an illiquidity measure for

bond j. Insurer i ’s illiquidity preference is defined to be the weighted-average illiquidity of

all corporate bonds that the insurer holds as follows:

ILPi,t =

Ni∑j=1

wi,j,tILQj,t =

∑Ni

j=1 Vi,j,tILQj,t∑Ni

j=1 Vi,j,t(3)

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where wi,j,t is the weight of bond j in insurer i ’s corporate bond portfolio, Vi,j,t is the dollar

value of holding by the insurer on bond j, and Ni is the number of corporate bonds held by

the insurer. Thus, by definition, insurers holding more illiquid bonds have greater ILPs, i.e.,

exhibiting a preference for illiquidity.

In the second step, given the illiquidity preference ILPi,t of insurers, we quantify the

illiquidity clientele (ILC) for each bond as the weighted-average of illiquidity preferences

across insurers holding the bond:

ILCj,t =M∑i=1

ηi,j,tILQj,t =

∑Mi=1 Vi,j,tILPi,t∑M

i=1 Vi,j,t(4)

where ηi,j,t is the percentage of bond outstanding held by insurer i, and M is the total number

of insurers. It follows that corporate bonds held more by insurers with high ILPs (i.e., those

with a strong illiquidity preference) have greater ILCs.

3 Data

3.1 Sample

Our base sample begins with those corporate bonds included in the Mergent Fixed In-

vestment Securities Database (FISD) database over the period from 2003 through 2009;

specifically, we consider the following eight types of bonds: i) US corporate Debentures

(CDEB); ii) US Corporate MTN (CMTN); iii) US corporate MTN (CMTZ); iv) US cor-

porate passthrough trust (CPAS); v) US Corporate PIK bond (CPIK); vi) US corporate

zero (CZ); vii) US corporate insured debenture (UCID); viii) and US corporate bank note

(USBN). The total number of corporate bonds covered in the initial sample is 25,857. We

subsequently restrict the sample to all fixed-rate U.S. corporate bonds and exclude bonds

with special features such as options (e.g., call, put, sinking fund, convertible, exchangeable),

asset-backed, credit enhancement, floating rate bonds, foreign currency, preferred security,

and odd frequency of coupon payments. This filter drives the sample size down to 12,572

bonds. We then exclude bonds with missing data on bond characteristics such as issue date,

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maturity date, issue price, issuance size, coupon rate, and credit rating. This leaves 9,246

bonds left in the sample. Additionally, we exclude bonds that have less than six months to

maturity. This results in 8,273 unique corporate bonds between January 2003 and December

2009.

Next, we obtain trading information of each bond, e.g., the date and time of execution,

the price, and the yield, from TRACE. We include only bond transaction data with regular

sale condition, non-commission, non-special price and non-when-issued basis in the sample.

To avoid potentially erroneous observations in the TRACE data, we use the following filters

proposed by Dick-Nielsen (2009): (a) We delete any duplicates identified by the message se-

quence number; (b) we skip any trade reported as a reversal as well as the original trade; and

(c) we exclude two types of same-day corrections as follows: if the correction is cancelation,

both reports are deleted, and if it is a correction only the original is deleted. Additionally,

we exclude trades under $100,000 following Bessembinder et al. (2009). Then we apply a

median filter and a reversal filter (Edwards, Harris, and Piowowar 2007) in order to delete

any prices with data errors: The former eliminates any trade where the price deviates from

the daily median or from a nine-trading-day median centered at the trading day by more

than 10%; The latter filter eliminates any trade with an absolute price change deviating from

the lead, lag, and average lead/lag price change by at least 10%.5

Lastly, we merge the above sample of corporate bonds with their trading information

with the National Association of Insurance Commissioners (NAIC) Schedule D quarterly

holding data (detailed in Appendix A) and the database covering financial statement data.

We require bonds held at least by one insurer in any quarter during the sample period. The

final sample consists of 5,662 bonds. Insurers hold roughly 70% of the cleaned corporate

bonds covered in the FISD databases.

5There are three Phases of the TRACE database as described in several studies such as Lin, Wang, andWu (2011). In Phase I over July 2002–February 2003, the TRACE database includes US investment-gradecorporate bonds with issue size greater than $1 billion. In Phase II (March 2003–September 2004), TRACEexpands its coverage to include bonds with ratings of A and above and with issue size greater than $100million and Baa bonds with issue size less than $1 billion. In Phase III starting from October 2004, TRACEcovers all publicly traded corporate bonds. As a robustness check, we also consider the samples that startfrom July 2002 (phase I) and from October 2004 (phase III), and obtain similar results in both cases.

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3.2 Insurers’ Quarterly Holdings of Corporate Bonds

To implement our liquidity clientele measure as defined in Eq. (4), we need data on each

insurer’s quarterly holdings of corporate bonds. Schedule D filings available in the NAIC

database include both year-end holdings and intra-year transaction data (date, price, and

quantity) on corporate, municipal, government bonds, stocks, and real estate etc. held by

insurance companies.6 We describe briefly how to extract quarterly holdings of corporate

bonds from these filings here, and go into details in Appendix A.

Consider an insurer i and its holdings of bond j. To obtain the dollar value of the holdings

at the end of quarter t in a given year, we first aggregate the par values of all the buying

and selling transactions from the beginning of the year to the end of quarter t so we can

compute the insurer’s net trading of bond j up to that quarter end. Then we add the par

value of net trading to insurer i ’s holdings of bond j at the end of the prior year.

Panel A of Table 1 reports sample statistics for corporate bonds held by insurers in our

sample. While the number of unique bonds (column 2) ranges from 2,408 in 2003 to 4,801 in

2005, the number of unique bonds held by insurers (column 3) varies between 1,391 in 2009

and 3,081 in 2005. Overall, 68% (5,662 out of 8,273) of sample bonds are held by insurers.

Columns 4 through 8 illustrate bond holdings by five S&P rating groups, respectively: AAA-

AA (including AA+, AA, AA-), A (including A+, A, A-), BBB (including BBB+, BBB,

BBB-), and Speculative bonds (Spec, including all ratings below BBB-). (If a bond’s S&P

rating is missing, we use its rating from Moody’s or Fitch; we exclude bonds without a

rating.) As expected, the majority of corporate bonds held by insurers are rated between

A+ and BBB-. Columns 9 through 12 report respectively the number of bonds held by four

maturity bins: shorter than 2 years, from 2 to 5 years, from 5 to 10 years, and exceeding 10

years. It appears that insurers’ bond holdings are concentrated in bonds with short (time-to)

maturities (shorter than 2 years). It is known that insurance companies often hold a bond

until it matures.

6This data has also been used by Chakravarty and Sarkar (1999), Hong and Warga (2000), Collin-Dufresneet al. (2001), Schultz (2001), and Campbell and Taksler (2003).

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Panel B of Table 1 reports the information on insurance companies’ aggregate quarterly

holdings of individual bonds, more precisely, the aggregated par value held by insurers in each

quarter scaled by the amount outstanding of the issue at the end of the quarter. Specifically

the panel illustrates the distribution of the percentage holding of corporate bonds by insurers,

including the 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles, as well as the mean

and standard deviations of the variable, both by year and the average over the entire sample

period. Note that insurers on average own 34.49% of outstanding amounts with the range

from 27.59% in 2009 to 36.86% in 2004 and 2006.7 The median percentage holding is 30.75%,

close to the mean holdings of 34.49%, and the standard deviation of the percentage holdings

is around 23.31%. Overall, the aggregate corporate bond holdings of insurers are relatively

stable over time.

3.3 Yield Spreads and Bond Liquidity

In this subsection we construct quarterly yield spreads and illiquidity levels for each sample

bond, two important sets of data to be used in our empirical analysis.

For a given bond, we obtain its yields in quarter ends for all trades on the last day with

trading records in the quarter during which the bond is traded. However, some bonds are

traded very infrequently. If a bond does not trade during the last month of a particular

quarter, we use its average yield over the quarter. The yield spread is computed as the

difference between the corporate bond yield and the fitted yield on an otherwise similar

Treasury bond. Following Duffee (1998) and Collin-Dufresne, Goldstein, and Martin (2001),

we use a linear interpolation scheme for the quarter-end Treasury yield rates reported by the

Federal Reserve Board of Governors (Fed) for maturities of 1, 2, 3, 5, 7, 10, 20, and 30 years

7According to the Flow of Funds accounts published by the Federal Reserve, insurance companies holdabout one-third of outstanding corporate bonds (e.g., Campbell and Taksler, 2003; Hotchkiss, Warga, andJostova, 2002). Hong and Warga (2000) report that insurance companies account for roughly 25% of themarket for non-investment grade debt, while their share of trading in the investment-grade debt market isaround 40%. Schultz (2001) estimates that life insurance companies by themselves hold about 40% of allcorporate bonds. Further, Ellul, Jotikasthira, and Lundblad (2011) find that on average, insurance companieshold about 34 percent of investment-grade bonds and only 8 percent of non-investment grade bonds. Thus,the NAIC database should be adequately representative of corporate bond transactions.

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to approximate the entire yield curve. We obtain Treasury bond yields data from Economic

Research, Federal Reserve Bank of St. Louis.8

To estimate illiquidity levels of individual corporate bonds, we consider five commonly-

used corporate bond liquidity measures. They include the Amihud (2002) illiquidity measure

(Amihud) that estimates the price impact of a trade per unit traded; the Roll (1984) measure

(Roll) for percentage bid-ask spreads based on the negative covariance between consecutive

returns; the LOT (1999) measure for round-trip transaction costs; the imputed roundtrip cost

(IRC) used in Dick-Nielsen et al. (2012) as a measure of transaction cost; and the λ measure

proposed by Dick-Nielsen et al. (2012) that loads evenly on the Amihud, IRC, Amihud

risk (the standard deviations of the daily Amihud measure), and IRC risk measures (the

standard deviations of imputed roundtrip costs measured over one quarter). See appendix

B for details of these liquidity measures. We obtain time series of illiquidity levels for each

corporate bond, using each of the five liquidity measures. To alleviate the effect of outliers,

we winsorize all variables at 1% and 99% before including them in our tests.

Figure 1 plots the average liquidity measures of all sample bonds from the first quarter in

2003 to the fourth quarter in 2009, separately for investment-grade and speculative bonds.

The pattern for the alternative liquidity measures is similar. Illiquidity was high over the

financial crisis from the mid of 2007 to the end of 2009 and peaked in the Q3 or Q4 in 2008.

For instance, the Amihud illiquidity ratio is 2.43% for investment-grade bonds and 2.80%

for speculative bonds in the fourth quarter of 2008.

In Figure 2, we depict the piecewise relation between illiquidity and yield spreads based

on alternative measures for illiquidity. This is to capture the potential nonlinear relation

between bond illiquidity and yield spreads. To do this, we breakdown the full spectrum of

a liquidity measure into 5 groups with similar distances. For instance, as the 5th and 95th

percentiles for the Amihud measure are 0.05% and 4.03%, we therefore choose 1.00%, 2.00%,

3.00%, 4.00%, and 5.00% (maximum) as the five knots to breakdown Amihud ratios. The

piecewise regression between one of the liquidity measures and yield spreads is performed

8For further information regarding treasury constant maturity data, please refer tohttp://www.treasury.gov/resource-center/data-chart-center/interest-rates/Pages/yieldmethod.aspx.

11

in each quarter. We then average all the time-series parameters (i.e., the intercept and the

coefficients) and calculate the fitted yield spreads. The plot of the fitted yield spreads with

respect to corresponding liquidity value gives rise to the piecewise relation. As revealed in

the figure, all the graphes are strikingly similar: yield spreads of corporate bonds are an

increasing and concave function of bond illiquidity. It should be noted that this pattern re-

sembles Amihud and Mendelson (1986) where they examine the relation between the market

observed excess returns of common stocks and the associated trading costs (proxied by the

bid-ask spreads) and find the excess returns are concave in stock trading costs.

4 Empirical Results

In this section we present results from our empirical analysis. Section 4.1 investigates liquid-

ity preference of insurers and the persistence of such preference. In Section 4.2, we explore

the determinants of insurers’ liquidity preference. Section 4.3 tests the liquidity clientele

effect in corporate bond yield spreads.

4.1 Liquidity, Liquidity Preference, and Liquidity Clientele

In this subsection, we first construct illiquidity preference (ILP) for individual insurers and

illiquidity clientele (ILC) for individual bonds. We then provide empirical evidence on the

presence of liquidity preference of insurers and the persistence of such a preference.

Given a particular measure of corporate bond illiquidity considered earlier, we estimate

ILP and ILC using Eqs. (3) and (4), respectively. As a result, we obtain five different sets

of estimates of the three key liquidity-related measures including corporate illiquidity, ILP

and ILC. We then examine these estimates both cross-sectionally and time series wise.

Table 2 reports the cross sectional variation of four main variables used in our empirical

analysis—namely, the corporate bond yield spread and the three aforementioned liquidity-

related variables. Specifically, the table reports the 5th, 10th, 25th, 50th, 75th, 90th and 95th

percentiles, as well as the mean, standard deviation, skewness, and excess kurtosis of each

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variable. This provides an aggregate picture of the substantial cross sectional variation of

each variable. For time-varying explanatory variables, the statistics reported are computed

as the time series averages for each individual bond. Note that the distribution of bond yield

spreads is largely symmetric, as indicated by a mean of 203 basis points (bp), a median of

138 bp, and a standard deviation of 220 bp. This is comparable to corporate yield spreads

reported in other studies such as Chen, Liao, and Tsai (2011) and Friewald, Jankowitsch,

and Subrahmanyam (2012).

Results on the bond illiquidity, reported in Panel A of the table, show that the Amihud

measure, Amihud, has a mean of 1.06%, a median of 0.77%, and a standard deviation of 1.53.

It suggests that a trade of $1,000,000 in a bond, on average, moves the price by 1.06%. The

variation in liquidity across bonds is remarkably high and ranges between 0.05% and 4.03%

for the 5th and the 95th percentiles. This is consistent with the findings in Dick-Nielsen,

Feldhutter, and Lando (2012) and Friewald, Jankowitsch, and Subrahmanyam (2012). The

Roll measure has a mean of 1.86 and a standard deviation of 2.07 with high variation across

bonds as well. The distribution of LOT is skewed. The mean is 17.65%, but the median

is 4.47%. The median roundtrip cost in percentage of the price is 0.22% according to the

IRC measure. The cost is less than 0.04% for the 5% most liquid bonds, suggesting that

transaction costs are modest for a large part of the corporate bond market. For λ, we observe

a mean of -0.15 and a median of -0.28. The reported distributions are consistent with those

reported in prior studies.

Panel B reports the distribution for insurer-level ILP based on alternative liquidity mea-

sures (ILPAmihud, ILPRoll, ILPLOT , ILPIRC , and ILPλ). The mean and median of ILP based

on the Amihud ratio are 1.07% and 0.92%, respectively. The variation of the variable is

remarkably large with the range between 0.42% in the 5th percentile and 2.22% in the 95th

percentile. We find similar patterns in the other liquidity preference measures. The results

suggest that insurers have a substantial different preference on the liquidity of corporate

bonds.

Finally, in Panel C, we report the cross sectional distribution of bond ILCs. The mean

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and median of ILC based on the Amihud ratio are 0.93% and 0.79%. Under the Amihud

ratio, the 25th-percentile value (i.e., first quartile) of bond-level ILC is 0.58% while the

75th-percentile value (i.e., 3rd quartile) is 1.09%. The inter-quartile ILC spread of 0.51% is

also lower than that of the inter-quartile ILQ spread of 0.82%. The heterogeneity of the ILC

measures based on the LOT is again large with the range between 4.84% in the 5th percentile

and 24.56% in the 95th percentile. The result indicates a wide dispersion of liquidity clientele

among bonds. In addition, the liquidity clientele has the largest standard deviation using the

Roll measure while the standard deviation of liquidity clientele IRC measure is the lowest,

which is consistent with the pattern of bond liquidity. The pattern revealed in the table

is consistent with what is observed from untabulated results on correlations between the

various liquidity proxies and liquidity clientele measures.9

Figure 3 plots the persistence of insurers’ liquidity preference on corporate bonds. At

the end of each year (i.e, in the fourth quarter), we sort insurers into quintiles based on

insurers’ liquidity preference measures. From the lowest quintile to the highest, each quintile

is assigned a ranking from one (Q1) to five (Q5). We then trace the level of liquidity

preference of each quintile for the next three years (Y+1, Y+2, and Y+3). As illustrated

in Figure 2, insurers in the highest ILP quintile continue to have the highest ILP level at

least three years after initial ranking, which indicates insurers’ preferences on the liquidity

of bonds are highly persistent.

Figure 4 plots the persistence of liquidity clientele measures. We note that insurers in

the highest ILC quintile continue to have the highest ILC level at least three years after

initial ranking. Bonds with high level of liquidity clientele also stay in the group of the

highest liquidity clientele. This graph clearly shows how liquidity clientele measures persist

9First, there is positive correlation among yield spread and illiquidity proxies as well as liquidity clientelemeasures. For example, the correlation is 0.32 between the Roll and Amihud measures and is 0.23 betweenthe Roll’s measure and IRC. This is consistent with the findings in Dick-Nielsen, Feldhutter, and Lando(2012). Second, all the liquidity measures are positively correlated. The λ measure is highly correlated withthe Amihud ratio (0.72) and with IRC (0.86) while the correlation between the λ measure and LOT/Rollis much lower. This suggests the different measures capture different aspects of bond liquidity. Third, allthe liquidity clientele measures are positively correlated and there are high correlations for λ-based clientelemeasure and the Amihud ratio/IRC than for λ-based clientele measure and LOT/the Roll’s measure.

14

over time, suggesting insurers have a stable fractional holding of bonds as insurers’ liquidity

preference is stable over time (shown in Figure 4).

4.2 Determinants of Insurer Liquidity Preference

In this subsection, we link the insurers liquidity preference to firm characteristics that po-

tentially affect insurers’ tendency in holding illiquid assets. The variables considered include

firm size measured by total assets (TA), firm age (Age), reinsurance ratio (REINS), re-

turn on assets (ROA), average investment horizon (Horizon, measured as the average length

of bond holding for an insurer),10 the percentage of life insurers in an ILP decile (Life),

and the percentage of life insurance business relative to accidental and healthcare business

(PctLife).11 Age here is the number of years since firms are incorporated. Reinsurance ratio

is calculated as the ratio of the insurance premiums that an insurer cedes to other insurers

through the reinsurance arrangement relative to the sum of insurance premiums it collects

and the insurance premium the insurer accepts from other insurers through the reinsurance

arrangement. ROA is net income of an insurer scaled by its total assets. Data for these

insurer characteristics come from the NAIC Infopro database. We set equal weights on each

insurer attribute when computing the average for decile groups.

The above characteristics essentially reflect two attributes of insurers: the ability to

invest in illiquid assets and the investment horizon of an insurer. Firm size, age, the ratio of

reinsurance use, and firm ROA belong to the former category while bond holding horizon,

the LIFE indicator, and the percentage of business in life insurance business are in the latter

category. We expect to see an insurer has a greater ILP either because it has the flexibility

in investing illiquid assets or it has longer investment horizon.

To test this conjecture, we apply sorted portfolios to test the relationship between insurer

liquidity preference and various insurers’ characteristics. To be specific, at the end of each

10The estimation involves two steps. In the first step, we obtain an insurer’s continuous holding horizonfrom a new bond is purchased to the period the bond is completely sold. in the second step, we calculatethe average holding horizon of an insurer in a particular quarter using the market value of the bond held bythe insurer as the portfolio weight.

11Healthcare and accidental insurance typically have much shorter claim durations than does life insurance.

15

quarter during our sample period, we sort insurers into equal-weighted decile portfolio based

on one of the ILP measures (1 being the lowest and 10 being the highest decile rank for

ILP) and calculate the cross-sectional average characteristics. The findings are presented

in Table 3. The table shows that insurers with great liquidity preference tend to be older

and larger firms. Under the Amihud-based ILP measure, The average firm size (age) for the

bottom group is $0.53 billion (39.50 years) and that of the top group is $0.84 billion (49.61

years). High ILP firms use less reinsurance than low ILP firms. It is possible that the use of

reinsurance reflects an insurer’s ability in internally absorbing risks. Insurers having better

ability in digesting risks internally use less reinsurance than those weak in risk taking. An

interesting, while puzzling, finding is that higher ILP firms are typically poorer in operating

performance (lower ROA). This cannot be explained by the investment flexibility/risk taking

ability story, while a likely explanation is the gambling incentive of insurers: when insurers

perform poorly in their main business, they may invest more illiquid assets to increase their

investment performance.

Further, insurers in high ILP decile ranks hold corporate bonds for longer horizon, and

are more likely to be a life insurer than insurers with low liquidity preference. When the

bond-level liquidity measure is the Amihud ratio, the fraction of life insurers is roughly 20%

for the bottom ILP decile while the fraction is 46% for the top ILP decile. The average

horizon holding a bond increases from 4.21 years to 6.73 years. There is also evidence that

among life insurers high ILP firms are engaged in more of life insurance (typically having

longer claim payment horizons than health and accidental insurance that life insurers are

also engaged in).

4.3 Evidence on the Liquidity Clientele Effect on Yield Spreads

4.3.1 Two-way Sorted Portfolios

In this subsection, we examine the liquidity clientele effect in the pricing of liquidity across

bonds, based on two-way sorted portfolios. Specifically, in the beginning of each quarter, we

sort bonds first by one of the five ILCs into quintile groups. Then within each ILC quintile

16

(reported in rows in Table 4), we sort bonds into quintiles by the corresponding ILQ measure

(reported in columns in the table). This results in 25 equal-sized portfolios. In terms of ILQ,

Q1 is the least illiquid (or most liquid) group and Q5 is the least liquid group. In terms of

ILC, Q1 represents a group of bonds held by insurers with the lowest liquidity preference

(i.e., insurers with highest liquidity demand) and Q5 represent a group of bonds held by

insurers with the highest ILP.

Table 4 reports both the raw yield spreads (in the five columns on the left) and the

characteristic-adjusted yield spreads (discussed below, in the five columns on the right) for

each of the 25 portfolios. To obtain the average yield spread, we compute the cross sectional

average yield spread for each of the 25 groups first, then take time-series averages. We can

see that in the lowest ILC quintile (i.e., Q1), the average bond yield spreads monotonically

increase in the illiquidity quintile ranks, regardless of the illiquidity measure used. For

instance, measuring bond illiquidity with the Amihud ratio (Panel A), we find that, for the

lowest ILC (Q1) group, the average yield spread for the most liquid bond group is 0.85%,

while that of the most illiquid bonds is 2.31%. The difference in the yield spreads between

these two groups is 1.46%, with a t-statistic of 7.78. The differences in yield spreads between

most liquid and most illiquid quintile groups remain significantly positive for all the ILC

ranks. In the highest ILC (Q5) group, the average yield spread of the Q1 ILQ bonds is

2.03% while that of Q5 ILQ bonds is 2.97%, leading to a difference in yield spread between

Q5 and Q1 ILQ bonds of 0.94%. Altogether, the difference of yield spread differences across

Q5 and Q1 ILC quintiles is -0.52%, which is significant at the 1% significance level. The

similar pattern holds for alternative liquidity measures. An increase in ILC reduces yield

spreads of corporate bonds, consistent with our hypothesis on the clientele effect.

We can also explore variations over time in the average clientele effect of yield spreads—

namely, the average difference in yield spread differences between the top and bottom ILC

quintiles. Figure 5 plots the average clientele effect from the first quarter in 2003 to the

fourth quarter in 2009. The figure illustrated in Panel A is based on yield spreads. Using

λ as the liquidity measure, the clientele effect is shown to be negative in almost every

17

quarter throughout the entire sample period. The visual inspection of the figure leads to a

concern that the effect appears to be much stronger during the financial crisis. Yield spreads

of corporate bonds are much higher in the crisis period than in the non-crisis period. We

therefore further assess the liquidity clientele effect by plotting the the difference in difference

of the relative yield spreads, that is to adjust the difference in the yield spreads of the most

illiquid and liquid quintile groups with the average yield spread across all bonds in a quarter.

We find the difference between the clientele effect between the crisis and non-crisis periods

is no longer that large.

One concern about assessing the effect of liquidity clientele based on yield spreads is

that yield spreads could be affected by various factors unrelated to liquidity and investors’

liquidity preference. To address this concern, we introduce the characteristics-adjusted yield

spread, which is calculated as the raw yield spread minus the average yield spread of bonds

within the same rating categories and the same bond maturity ranges.

Spreadaj,t = Spreadj,t −1

N

N∑k=1

Spreadk,t, (5)

where k is an index for matched bonds based on rating categories and maturity bins and N

is the total number of matched bonds. The matching is on the five rating categories and

four maturity bins described in Section 3.2.

We report the results of the characteristic-adjusted yield spreads in columns 7 through

11 in Table 4. The difference in the average characteristic-adjusted spread for Q1 ILC bonds

is 0.93% (t=3.90) considering Amihud measure while that of Q5 ILC is 0.60% (t=4.16).

The difference of yield spread difference across Q5 and Q1 ILC groups is -0.33%, with a

t-statistic of -2.21. An interesting observation is that, despite that the pattern here is

consistent with that of raw yield spreads, the magnitude of the characteristic adjustment

yield spreads is much smaller. This indicates that bond maturity and ratings explain away a

significant portion of cross sectional variations of yield spread. We observe the same pattern

in the liquidity clientele effect on characteristics adjusted yield spreads when applying other

liquidity measures.

18

Collectively, the results from the sorted portfolio analysis confirm that liquidity clientele

plays a significant role in determining liquidity premiums.

4.3.2 Panel Regressions

To directly test the liquidity clientele, we estimate the following panel regression for the

bond yield spreads that takes into account the fixed year and issuer effects:

Spreadi,t = α0 + α1ILQi,t + α2T2i,t + α3T3i,t + α4ILQi,t ∗ T2i,t (6)

+α5ILQi,t ∗ T3i,t + α6Controli,t + εi,t,

where Spreadi,t is the yield spread of bond i in quarter t ; ILQ is the measure of bond liquidity

(including Amihud, Roll, LOT, IRC, and λ). Since these measures are normally used as the

illiquidity in the literature, the high values mean the low liquidity (or high illiquidity). To

test the difference-in-difference effect, we sort all bonds into terciles on their liquidity clientele

measures (ILCAmihud, ILCRoll, ILCLOT , ILCIRC , and ILCλ) each quarter. T1 (T2, T3) is the

indicator variable that equals 1 if the bonds are in the first (second, third) group and zero

otherwise. Our main interests are the interactions between ILQ and T2 as well as those

between ILQ and T3. They reflect the effect of liquidity clientele on the impact of liquidity

on asset valuation. We expect a negative sign on the coefficient of the interaction, that is,

liquidity clientele attenuates the impact of liquidity on yield spreads.

We include various control variables in the regressions: i) the time since issuance in years

(bond age), ii) the bond’s time to maturity in years, iii) the logarithm of bond issue size (i.e.,

the bond’s face value issued in millions of dollars), and iv) coupon payments. To control for

credit risk, we follow Dick-Nielsen et al. (2012) to add the following accounting variables:

the ratio of operating income to sales, ratio of long term debt to assets, leverage ratio, equity

volatility, and pretax interest coverage dummies to the regression. The accounting data are

obtained as of the end of the previous calendar year from the COMPUSTAT. The market

value of equity is obtained as of 1 day prior to the bond transaction date. These are similar

to the measures used in Blume et al. (1998), Collin-Dufresne et al. (2001), and Campbell

19

and Taksler (2003). We also include dummies for bond rating from Standard & Poor’s.12 We

compute two-dimensional cluster robust standard errors (Petersen, 2009), which corrects for

the fixed time effect, fixed issuer effect, and heteroskedasticity in the residuals. The results

are presented in Table 5.

The table presents results based on two specifications. In Regressions 1-5 (the baseline

results), we use a specification without control variables. Not surprisingly, we note that

the coefficient estimates on ILQ are positive and significant at a 1% level across all illiquid-

ity measures. This implies that, consistent with the prior evidence in the literature, high

illiquidity demands high premium. For example, a one percentage increase in the Amihud

measure would lead to 0.29% increase in yield spread. The coefficient estimates on T2 and

T3 are significantly positive in all specifications. Since these coefficient estimates reflect the

effect of liquidity clientele on yield spreads in different subsamples, this result is consistent

with the notion that insurance companies (at least in our sample) trade for liquidity. More

importantly, the coefficient estimates on the interactions between ILQ and T2 as well as be-

tween ILQ and T3 are significantly negative. This finding is consistent with our expectation

that the liquidity clientele effect weakens the liquidity premium.

Columns 6 through 10 in Table 5 report results from the same regression tests that,

however, control for bond characteristics and credit risk. The coefficient estimates on ILQ

are significantly positive and the coefficient estimates on the interactions between ILQ and

T2 as well as ILQ and T3 are significantly negative across all illiquidity measures except one,

for which the parameter estimate on the interaction between ILQ and T2 is insignificantly

positive. This finding is robust across all liquidity measures.

We now turn to a discussion of the results for regressions with control variables largely

used in the literature. The coefficients of bond maturity are significantly positive, consistent

with the evidence documented in the literature that long term bonds have higher yield

spreads. Coefficient estimates on coupon payments are also significantly positive, suggesting

that high coupon bonds (normally high-yield bonds) have high yield spreads. As might be

12We assign integer numbers to ratings (i.e., AAA=26, AA+=25, and so on).

20

expected, the coefficient on long term debt to assets is negative. In terms of adjusted R2,

we find a relative improvement when considering the control variables.

Overall, the panel data regressions show a significant decrease in yield spread with liq-

uidity clientele effect. This is consistent with our expectation that the liquidity premium on

these securities may be attenuated when illiquid securities predominantly attract investors

with low liquidity preference.

4.3.3 Subsample Analysis

In this subsection, we address the following question: which types of corporate bonds and

under what market conditions does liquidity clientele matter most? We investigate the

clientele effect in various subsamples, including: i) investment grade and speculative-grade

bonds, ii) bonds of different maturities, and iii) sub-sample periods before and in the financial

crisis. In regressions that follow, we use the characteristic adjusted yield spreads as the

dependent variable. The setup is identical to Table 5. The results are reported in Table 6.

To preserve space, the coefficient estimates for control variables are not tabulated.

In Panel A of Table 6, we examine the liquidity clientele effect separately for investment

and non-investment grade bonds. The coefficients on ILQ*T2 and ILQ*T3 are both signifi-

cantly negative, regardless of illiquidity measures used. For instance, based on the Amihud

ratio, the coefficient on ILQ*T2 is -0.13 (t-stat = -1.88) and the coefficient on ILQ*T3 is

-0.30 (t-stat = -2.05). For speculative bonds, the coefficients on ILQ*T2 and ILQ*T3 are no

longer significantly negatively. When measuring ILQ with the Amihud raito, the coefficient

on ILQ*T2 is 0.01 (t-stat = 0.59) and the coefficient on ILQ*T3 is -0.03 (t-stat = -1.37).

The analysis suggests the liquidity clientele effect holds strongly among investment grade

bonds. However, it does not hold for speculative grade bonds.

Panel B of Table 6 illustrates the liquidity clientele effects across different maturity

groups. Short-term bonds are those whose maturities are less than 5 years. Long-term

bonds are those whose maturities are 5 years and longer.13 The result suggests that the

13Not tabulated, short-term bonds account for roughly 40% of corporate bonds held by insurers.

21

clientele effect holds most strongly among long-term bonds, while there are some significant

results for short-term bonds. Use the analysis measuring ILQ with the Amihud ratio as

an example. Among short-term bonds, the coefficient on ILQ*T2 is -0.01 (t-stat = -0.40)

and the coefficient on ILQ*T3 is 0.01 (t-stat = 0.64). However for long-term bonds, the

coefficients on ILQ*T2 and ILQ*T3 are -0.16 (t-stat = -3.71) and -0.22 (t = -3.35).

Panel C of Table 6 investigates the clientele effects in different sample periods – before

and during the financial crisis. We breakdown our sample to pre-crisis (2003:Q1 - 2007:Q1)

and in the crisis (2007:Q2 - 2009:Q4). Our particular interest is on the effect in the financial

crisis period. On the one hand, credit risk becomes a more important issue for investors to

consider in the crisis. Consequently, investors may focus less on the liquidity and the liquidity

clientele effect becomes less stronger. On other hand, liquidity premiums are higher in the

crisis period in general. The preference of holding illiquid bonds could be even stronger

in the crisis period. We find that the clientele effect holds in both periods, which is more

consistent with the latter case.

We subsequently examine why the clientele effects is insignificant among speculative

grade bonds and relatively short-term bonds (with maturities less than 5 years). Our specific

hypothesis is that in these subsamples insurers are not the major stakeholders of the bonds

in these subsamples, therefore they are no longer the marginal traders of bonds in these

subsamples. To test this, we breakdown bonds into two sets of subsamples: i) based on

bond ratings (i.e., investment-graded bonds and speculative bonds) and ii) based on bond

maturities (short-term bonds whose maturities no longer than 5 years) and long-term bonds

(maturity exceeding 5 years). We look at the percentage holding of insurers for bonds.

Shown in Table 7, insurers hold a smaller fraction of speculative bonds than investment-

graded bonds (reported in section (i)). Their holding of short-term bonds is also relative

small (in section (ii)). The result is consistent with the conjecture that insurers may not be

the marginal traders of short-term bonds and speculative grade bonds as they are not the

major stakeholders of these bonds.

Besides the analysis for the entire corporate bond sample, in Table 7 we also compare

22

insurers’ fractional holding of corporate bonds in the bottom (T1) and top (T3) tercile groups

sorted by illiquidity clientele measures (ILCs). We find that insurers hold a smaller fraction

of bonds in the bottom tercile than bonds in the top tercile. The insurers’ holding percentage

is merely 9% of T1 bonds and while it is 16% of T3 bonds when the Amihud measure is used

to measure illiquidity. The unbalance in the holding of two tercile groups makes detecting a

significant clientele effect even more difficult.

Finally, reported in Table 8, we investigate the liquidity clientele effect for a subset of

bonds with at least 20% of par value held by insurers. Consistent with the full-sample

analysis, we perform panel regressions of yield spread on alternative liquidity measures, the

dummies for the tercile groups based on bond ILCs, and the interaction between illiquidity

and tercile dummies. We also control for factors that affect yield spread and report the

results. The reported results suggest that the liquidity clientele effect is robust in the subset

of corporate bonds.

5 Conclusions

Although the notion of “liquidity clientele” that some investors may command less compen-

sation for holding illiquid securities is well known in the finance literature, it has never been

formally tested to our best knowledge. In this study, we take advantage of data on corporate

bond holdings of an important group of corporate bond investors—insurance firms—and

introduce a novel measure of liquidity clientele on individual bonds based on bond holdings

of insurers and the corporate bond liquidity. We present evidence that across insurers there

may exist substantial heterogeneity in their liquidity preference. Such liquidity preference

persists over time and is found to be correlated with the size of firm, firm age, investment

horizon of insurers. Insurers with lower liquidity preference are more likely to be life insurers

which have longer claim payment horizons and less demanding in liquidity. These findings

support the presence of liquidity clientele conjectured by Amihud and Mendelson (1986) and

Constantinides (1986).

23

In the further analysis on the liquidity clientele effect, we document that insurers’ liquidity

preference interacts with bond liquidity to affect corporate bond credit spreads. Liquidity

clientele attenuates the liquidity premiums. The pattern is found to hold for investment-

grade bonds and for relatively long-maturity bonds. The finding that credit risk matters

much more than the liquidity risk among the holding of speculative bonds is also consistent

with the stylized facts documented in the literature (e.g., Huang and Huang, 2012). As such,

our findings indicate that the liquidity clientele matters most exactly where liquidity matters

most for yield spreads.

24

Appendix A: Quarterly Insurers’ Bond Holdings

We use the Schedule D of the National Association of Insurance Commissioners (NAIC)

database to construct quarterly insurers’ bond holdings. Schedule D includes a detailed

holding and transaction data of all financial assets of insurance companies, including corpo-

rate, municipal, government bonds, stocks, real estate, among others. Our quarterly holding

is based on 1) long-term bonds owned on December 31 of current year, 2) long-term bonds

acquired in the current year, 3) long-term bonds sold, redeemed or otherwise disposed in the

current year, and 4) long-term bonds acquired during the year and fully disposed of during

current year.14

To obtain the holding of the first quarter in a given year, we first compute the trading

(buy minus sell) of a bond by an insurer in the first quarter and then sum up the first-quarter

trading of the bond and holdings at the end of the previous year. The second quarter holding

is evaluated as the sum of the trading in the second quarter and the holdings of the first

quarter. We compute the holdings in the third quarter and fourth quarter (year-end) in the

same way. To ensure the accuracy of quarterly holding, we calculate a discrepancy ratio,

measured as the absolute value of the difference between the year-end holdings reported in

Schedule D in year t and the fourth-quarter holdings in year t scaled by the average of these

two holdings. We consider the observations of a bond in a year if its discrepancy ratio in

the fourth quarter of the year exceeds 0.1 as an outlier and remove the observations from

the sample. If an insurer has more than 10% of its holding at the year end being eliminated

from the sample, we remove the whole portfolio of the insurer in the year.

Before any cleanse, there are 1,945,926 insurer-bond-year observations corresponding to

3,226 unique life and nonlife insurers in the quarterly corporate bond holding dataset over

14Note that bonds included in Schedule D of the NAIC database are broken down into night categories: i)U.S. government bonds, ii) all other government bonds, iii) States, territories and possessions, iv) politicalsubdivisions of States, territories and possessions, v) special revenue and special assessment obligations andall non guaranteed obligations of agencies and authorities of governments and their political subdivisions, vi)public utilities, vii) industrial and miscellaneous, viii) credit tenant loans, and ix) parents, subsidiaries andaffiliates bonds. Within each category, bonds are further separated into issuer obligations and mortgage orasset backed securities. We consider issuer obligations of public utilities, industrial and miscellaneous bonds,and parents, subsidiaries and affiliates bonds as corporate bonds.

25

the sample period from 2003 to 2009. After imposing the constraint on the discrepancy

ratio, there are 1,945,926 insurer-bond-year observations in the quarterly holding dataset

corresponding to 3,226 unique insurers.

Appendix B: Liquidity measures

In this appendix we describe in detail the procedure that we calculate liquidity measures.

We winsorize the top and bottom 1 percentile observations for each of the following liquidity

measures.

B.1. Amihud measure

This is a well-known measure originally used for the equity market by Amihud (2002). It

relates the price impact of trades to the trading volume. A larger Amihud measure indicates

that trading a bond causes its price to move more in response to a given volume of trading,

in turn, reflecting higher illiquidity. We calculate daily Amihud measure as the daily average

of absolute returns divided by the trade size (in million $) of consecutive transactions.

Amihudi,t =1

Ni,t

Ni,t∑j=1

∣∣∣Pi,j−Pi,j−1

Pi,j−1

∣∣∣Qi,j

, (7)

where Ni,t is the number of returns of bond i on day t. At least two transactions are required

on a given day to calculate the measure, and we define a quarterly Amihud measure by taking

the median of daily measures within the quarter.

B.2. Roll measure

Roll (1984) and Bao, Pan, and Wang (2011) show that under certain assumptions, adjacent

price movements can be interpreted as a bid-ask bounce. The daily Roll measure equals two

times the square root of minus the covariance between consecutive returns. A larger Roll

measure (low covariance) implies higher round-trip costs, in turn, reflecting higher illiquidity.

Rolli,t = 2√−cov(Ri,tRi,t−1), (8)

26

We consider days with at least one transaction using a rolling window of 21 trading days. We

define a quarterly Roll measure by taking the median of daily measures within the quarter.

B.3. LOT measure

Named after Lesmond, Ogden, and Trzcinka (1999) Lesmond, Ogden and Trzcinka (1999),

LOT measures illiquidity in the sense that bond price would capture new information when

investors trade. More illiquid bonds would have few trades, thus more zero returns. It is

estimated as the following:

LOTi,t = α2,j − α1,j (9)

where α1,j and α2,j are buy-side and sell-side costs estimated from the log likelihood function

below:

LnL =∑

1

Ln1

(2πσ2j )

1/2−∑

1

1

2σ2j

(Rj + α1,j − βj1Durationj,t ∗ ∆Rft)

−βj2Durationj,t ∗ ∆S&PIndex)2 +∑

Ln1

(2πσ2j )

1/2

−∑

2

1

2σ2j

(Rj + α2,j − βj1Durationj,t ∗ ∆Rft

−βj2Durationj,t ∗ ∆S&PIndex)2

+∑

0

Ln(Φ2,j − Φ1,j)

This measure represents the round-trip transaction costs. A larger LOT measure indicates

higher round-trip costs, in turn, reflecting higher illiquidity.

B.4. Imputed roundtrip cost (IRC)

It is based on the dispersion of traded prices around the market-wide consensus valuation

(e.g., Dick-Nielsen, Feldhutter, and Lando, 2012). A larger dispersion indicates that it is

unlikely that the bond can be bought close to its fair value and thus represents higher trading

costs and higher illiquidity. The daily IRC measure is the average of roundtrip costs on that

27

day for different trade size.

IRCi,t =Pmaxi,t − Pmin

i,t

Pmini,t

(10)

we estimate quarterly roundtrip costs by averaging over daily estimates. For each bond, we

generate bond clientele measures based on the above liquidity measures and the insurers’

liquidity preference described in equation (1).

B.5. The measure λ

Following Dick-Nielsen, Feldhutter, and Lando (2012), we construct a measure λ. It is an

equally weighted sum of four variables normalized to a common scale: Amihud measure, IRC,

and the variability of each of these two measures. To be specific, for each bond and quarter,

we first calculate the four liquidity measures: Amihud, IRC, and the standard deviations

of the daily Amihud measure and IRC measure over a quarter. Then we normalize each

measure and sum up as our measure λ.

λi,t =4∑j=1

Lji,t − µj

σj(11)

where Lji,t is one of four liquidity measures. µj and σj are the mean and standard deviation

of each measure across bonds and quarters.

28

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30

Table 1: Sample Statistics for Corporate Bonds Held by Insurers

This table reports the numbers of unique corporate bonds in the sample and in different categories and thedistribution of the percentage holding of corporate bonds by insurance firms. Panel A shows the numberof unique sample bonds, bonds held by insurers, and bonds held by insurers in different rating categories(keep the last rating of each bond) in each year and throughout the entire sample period. At the end ofeach year (i.e., fourth quarter), we count the number of unique bonds in the sample, bonds held by insurers,and bonds held by insurers in five bond rating categories and in four bond maturity bins. The five ratingbins include i) AAA-rating; ii) AA-rating (including AA+, AA, AA-); iii) A-rating (including A+, A, A-);iv) BBB-rated (including BBB+, BBB, BBB-); and v) speculative bonds (Spec, including all ratings belowBBB-). The four maturity bins include i) <2 years; ii) 2-5 years; iii) 5-10 years; and iv) > 10 years. The lastrow of the panel reports the numbers of unique bonds in different categories throughout the entire sampleperiod. Panel B reports the distribution for the percentage of corporate bonds held by insurers, includingthe 1st, 5th, 25th, 50th, 75th, 95th, and 99th percentiles, as well as the mean and standard deviation of themeasure. The percentage of corporate bonds held by insurers is computed as the aggregated par value ofthe bond held by all the insurance companies in our sample scaled by the par value outstanding of the bondat the end of the quarter. The last row of the panel reports the time-series averages of the statistics fromthe cross sectional distributions over the sample years. The sample period is from 2003:Q1 to 2009:Q4.

Panel A: Number of unique bonds

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Bonds held by insurers

All by bond rating by bond maturity

Year bonds Total AAA AA A BBB Spec <2y 2-5y 5-10y >10y

2003 2,408 1,843 82 350 1,141 229 41 422 683 451 2872004 3,845 2,829 82 360 1,216 960 211 807 984 627 4112005 4,801 3,081 83 354 1,174 1,044 426 891 1,067 617 5062006 4,608 2,755 79 339 1,060 913 364 870 897 505 4832007 4,585 2,460 81 321 926 833 299 745 730 514 4712008 4,573 2,056 53 133 759 822 289 584 587 477 4082009 4,399 1,391 11 119 451 550 260 414 305 336 336All 8,273 5,662 155 679 2,335 1,835 658 1,586 1,644 1,283 1,149

Panel B: Distribution of the percentage holding of corporate bonds by insurers (%)

Year P1 P5 P25 Mean Median Std Dev P75 P95 P99

2003 1.12 4.67 15.98 32.90 29.45 20.96 47.58 72.23 84.232004 0.67 4.20 17.15 36.86 33.88 23.59 53.93 78.93 93.732005 0.40 3.26 15.97 36.31 32.99 24.23 53.85 80.04 94.132006 0.15 3.10 16.13 36.86 34.00 24.52 54.53 82.01 94.822007 0.33 3.34 14.06 32.67 29.29 22.17 48.30 73.50 88.702008 0.22 2.72 14.47 33.62 29.67 23.23 49.47 77.31 91.672009 0.10 1.05 10.05 27.59 23.27 21.29 41.35 67.50 86.74All 0.30 3.07 15.06 34.49 30.75 23.31 51.04 77.49 91.90

31

Table 2: Cross-sectional Distributions of Liquidity, Liquidity Preference, andLiquidity Clientele Measures

This table reports the cross-sectional distribution of yield spreads, bond liquidity measures, insurer liquid-ity preference, and bond clientele measures. Panel A reports the distribution of yield spreads (basis point),computed as the difference between the yield of the corporate bonds and a U.S. Treasury yield with the samematurity. Panel B reports the distribution of bond liquidity measures (ILQ) including Amihud measure (in%), Roll measure, LOT (in %), imputed roundtrip cost (IRC, in %), and the measure λ. Panel C reportsinsurers’ liquidity preference measures (ILP) and Panel D report bond liquidity clientele measures (ILC).The distributional attributes include the 5th, 10th, 25th, 50th, 75th, 90th and 95th percentiles, as well asthe mean, standard deviation “Stdev”, skewness “Skew”, and excess kurtosis “Kurto” of each variable. Weobtain each statistic in the fourth quarter of each year first, then compute the average over time. The sampleperiod is from 2003:Q1 to 2009:Q4.

N P5 P10 P25 Mean Median P75 P90 P95 Stdev Skew Kurto

Spread (%) 5,662 0.26 0.45 0.77 2.03 1.38 2.45 4.20 5.87 2.20 3.58 20.92

Panel A: Liquidity measures (ILQ)

Amihud (%) 5,130 0.05 0.08 0.41 1.06 0.77 1.53 2.62 4.03 1.23 4.95 45.30Roll 5,191 0.30 0.39 0.65 1.86 1.19 2.22 4.02 5.45 2.07 3.52 19.69LOT (%) 5,303 0.15 0.30 0.98 17.65 4.47 18.76 44.77 75.21 37.30 5.70 48.45IRC (%) 5,082 0.04 0.08 0.13 0.28 0.22 0.36 0.56 0.73 0.24 3.17 27.29λ 4,621 -0.65 -0.59 -0.46 -0.15 -0.28 0.00 0.41 0.76 0.49 3.04 22.55

Panel B: Insurer liquidity preference measures (ILP)

LPAmihud(%) 3,222 0.42 0.53 0.74 1.07 0.92 1.19 1.71 2.22 0.65 3.23 17.21LPRoll 3,226 0.53 0.62 0.78 1.18 1.02 1.41 1.93 2.36 0.61 2.02 7.13LPLOT (%) 3,226 0.41 0.77 1.84 6.85 4.02 9.02 15.91 21.04 9.11 6.94 111.10LPIRC(%) 3,226 0.16 0.19 0.23 0.30 0.27 0.33 0.44 0.53 0.13 2.96 16.91LPλ 3,226 -0.34 -0.27 -0.17 -0.02 -0.08 0.06 0.29 0.51 0.29 2.75 14.50

Panel C: Bond liquidity clientele measures (ILC)

LCAmihud(%) 5,662 0.43 0.48 0.58 0.93 0.79 1.09 1.59 1.99 0.51 2.02 5.86LCRoll 5,662 0.81 0.91 1.13 1.53 1.41 1.76 2.23 2.66 0.67 3.21 27.99LCLOT (%) 5,662 4.84 6.00 7.85 12.34 10.70 15.17 20.29 24.56 7.48 5.24 81.25LCIRC(%) 5,662 0.18 0.19 0.21 0.30 0.27 0.34 0.46 0.55 0.12 1.65 3.44LCλ 5,662 -0.33 -0.30 -0.25 -0.06 -0.13 0.02 0.29 0.52 0.26 1.65 3.18

32

Table 3: Liquidity Preference and Insurer Characteristics

This table reports firm characteristics of insurers portfolios formed on liquidity preference (ILP), which isestimated using weighted average bond liquidity with the weight of bonds shares held by insurers. From 2003to 2009, in each quarter we form 10 deciles based on liquidity preference (ILP) and then calculate insurercharacteristics in each decile. ILP is calculated as the value-weighted average of bond liquidity measures.The weight is the percentage shares of bonds held by an insurer. Bond liquidity measures include Amihudmeasure, Roll measure, LOT, IRC, and the measure λ. The definition of liquidity measures is provided inAppendix B. Insurer characteristics include total assets (TA), firm age (Age), reinsurance ratio (REINS),return on assets (ROA), average holding horizon (Horizon), the fraction of life insurers in a decile (Life), andthe percentage of insurance premiums generated by life policies (PctLife). To obtain the average holdinghorizon for each insurer, we first take the difference between the last and first dates for consecutive holding ofa bond by an insurer. Then we compute the average holding horizon in each quarter weighted by the holdingamount. We set equal weights on each insurer attribute when computing the average for decile groups. Thedifference between the top and bottom decile group and associated t-statistics are reported in the last tworows of the table. We compute the average of each measure in the fourth quarter of each year first, thencompute the average over time. The sample period is from 2003:Q1 to 2009:Q4.

TA Age REINS ROA Horizon Life PctLife($billion) (Year) (%) (%) (Year) (%) (%)

Panel A: ILPAmihud

D1 (Bottom) 0.53 39.50 39.01 2.92 4.21 19.80 65.16D2 0.57 40.16 35.45 2.63 5.12 21.89 62.75D3 1.05 40.65 33.60 2.51 5.49 24.99 69.56D4 1.42 43.91 30.40 2.64 5.80 29.49 68.80D5 1.97 45.76 30.40 2.15 5.93 31.10 67.81D6 2.67 49.41 30.07 2.26 6.20 36.25 68.73D7 2.67 51.41 29.82 2.33 6.21 38.75 68.25D8 2.48 54.52 27.14 2.11 6.31 41.95 73.40D9 1.75 53.99 27.10 2.06 6.30 44.86 70.74D10 (Top) 0.84 50.61 25.60 1.80 6.73 46.15 72.18D10-D1 0.31*** 11.56*** -13.41*** -1.12*** 2.52*** 26.35*** 7.02***(t-stat) (2.61) (9.58) (-8.98) (-4.99) (12.42) (10.34) (3.42)

Panel B: ILPRoll

D1 (Bottom) 0.33 37.97 36.26 2.67 3.84 15.56 58.59D2 0.29 40.11 33.51 2.56 4.29 15.82 58.31D3 0.51 40.43 32.51 2.77 4.70 16.84 52.02D4 0.68 44.26 32.48 2.72 5.01 20.63 60.54D5 0.92 44.53 33.07 2.94 5.39 22.01 60.57D6 1.35 46.31 31.18 2.56 5.94 29.55 66.27D7 1.67 48.70 29.83 2.13 6.26 37.18 69.75D8 3.07 51.87 28.55 1.84 6.84 39.20 76.50D9 4.04 58.35 27.52 1.67 7.22 38.70 77.13D10 (Top) 3.09 55.80 23.52 1.55 7.26 40.00 72.77D10-D1 2.76*** 17.83*** -12.74*** -1.13*** 3.42*** 24.44*** 14.18***(t-stat) (12.25) (12.42) (-17.62) (-4.57) (20.49) (18.29) (7.62)

33

TA Age REINS ROA Horizon Life PctLife($billion) (Year) (%) (%) (Year) (%) (%)

Panel C: ILPLOT

D1 (Bottom) 0.31 36.90 35.07 2.73 3.51 18.10 58.27D2 0.37 39.43 34.24 2.55 4.09 14.85 62.40D3 0.50 40.94 33.74 2.60 4.54 18.58 57.79D4 0.64 43.39 31.95 2.80 4.91 19.45 58.09D5 0.78 44.14 31.36 2.61 5.37 23.26 59.82D6 0.98 45.38 32.55 2.44 5.64 29.48 64.14D7 1.72 49.95 30.38 2.27 6.21 37.58 69.59D8 3.12 53.89 28.27 1.98 6.93 40.46 75.45D9 4.30 58.31 25.94 1.61 7.39 42.27 77.54D10 (Top) 3.20 55.69 24.98 1.77 7.49 41.36 72.72D10-D1 2.89*** 18.79*** -10.09*** -0.96*** 3.98*** 23.26*** 14.45***(t-stat) (9.37) (15.98) (-5.99) (-4.35) (19.70) (11.61) (7.82)

Panel D: ILPIRC

D1 (Bottom) 0.39 40.25 37.09 2.76 4.23 18.12 60.13D2 0.54 40.49 33.64 2.72 5.01 18.14 63.59D3 0.79 41.49 33.57 2.56 5.49 22.99 65.91D4 1.13 43.77 32.27 2.41 5.83 26.78 67.28D5 2.03 45.13 30.61 2.50 6.11 31.90 71.57D6 2.96 51.31 27.47 2.37 6.40 37.06 72.34D7 2.79 50.53 29.12 2.24 6.37 39.91 71.95D8 2.76 54.38 30.34 2.02 6.33 42.55 71.91D9 1.77 52.61 27.71 1.84 6.41 41.11 69.13D10 (Top) 0.81 48.68 26.57 1.95 6.55 46.73 70.79D10-D1 0.42*** 8.43*** -10.52*** -0.80*** 2.32*** 28.61*** 10.66***(t-stat) (6.32) (8.35) (-6.34) (-3.40) (9.36) (10.25) (2.68)

Panel E: ILPλ

D1 (Bottom) 0.48 40.49 37.85 2.79 4.10 18.27 64.03D2 0.71 40.18 34.26 2.75 4.50 19.48 61.93D3 0.87 42.62 32.63 2.52 5.19 24.75 69.69D4 1.74 45.52 30.56 2.39 5.61 30.81 73.81D5 2.44 46.99 29.39 2.46 6.17 34.29 69.47D6 2.85 49.30 28.74 2.46 6.30 36.24 69.46D7 2.61 50.56 30.97 2.15 6.34 37.72 73.02D8 2.20 51.70 28.47 1.99 6.42 42.70 67.85D9 1.49 53.07 28.32 1.986 6.44 46.87 68.02D10 (Top) 0.64 48.79 26.77 1.91 6.51 44.39 71.77D10-D1 0.16 8.30*** -11.08*** -0.88*** 2.41*** 26.12*** 7.74***(t-stat) (1.41) (9.64) (-8.39) (-3.13) (10.06) (17.22) (2.82)

34

Table 4: Yield Spreads across Illiquidity and Illiquidity Clientele Measures

This table reports bond yield spreads across different groups of illiquidity and illiquidity clientele measures.In each quarter we first bonds into 5 groups based on ILC measures, then within each ILC quintile, we sortbonds into quintile groups based on the ILQ measures. We calculate the yield spread in each of the 25pairs. The raw yield spreads and the characteristics-adjusted yield spreads are reported. We report the yieldspreads for alternative liquidity measures from Panel A through E. We compute the average yield spreadsin each quarter first, then compute the average over time. The sample period is from 2003:Q1 to 2009:Q4.

Raw yield spreads Characteristics-adjusted yield spreads

Q1 Q2 Q3 Q4 Q5 Q5-Q1 Q1 Q2 Q3 Q4 Q5 Q5-Q1

ILQ ILC ILC

Panel A: AmihudQ1 (low) 0.85 1.35 1.67 1.69 2.03 -0.96 -0.60 -0.32 -0.24 0.01Q2 1.76 1.80 1.81 1.84 2.18 -0.18 -0.22 -0.24 -0.11 0.18Q3 1.89 1.91 2.04 1.93 2.22 -0.11 -0.22 -0.11 -0.08 0.22Q4 1.96 2.11 2.04 2.12 2.37 -0.03 -0.10 -0.07 0.05 0.33Q5 (high) 2.31 2.49 2.56 2.71 2.97 -0.03 0.08 0.21 0.41 0.62Q5-Q1 1.46*** 1.14*** 0.88*** 1.02*** 0.94*** -0.52*** 0.93*** 0.67*** 0.53*** 0.65*** 0.60*** -0.33**(t-stat) (7.78) (7.02) (6.50) (6.57) (5.40) (-2.94) (3.90) (3.10) (3.52) (4.21) (4.16) (-2.21)

Panel B: RollQ1 0.91 1.05 1.39 1.80 2.41 -0.68 -0.57 -0.33 -0.04 0.24Q2 1.20 1.53 1.84 2.10 2.47 -0.48 -0.31 -0.12 0.02 0.29Q3 1.61 1.93 2.22 2.46 2.61 -0.33 -0.15 0.01 0.20 0.41Q4 1.88 2.23 2.40 2.45 2.67 -0.21 -0.05 0.13 0.18 0.51Q5 2.39 2.39 2.47 2.55 2.76 -0.08 -0.01 0.12 0.25 0.47Q5-Q1 1.47*** 1.34*** 1.08*** 0.75*** 0.34*** -1.13*** 0.60*** 0.56*** 0.45*** 0.30** 0.23* -0.37**(t-stat) (5.18) (6.79) (7.90) (5.68) (3.66) (-3.38) (3.43) (3.79) (3.68) (2.57) (1.76) (-3.45)

Panel C: LOTQ1 1.26 1.20 1.34 1.78 2.57 -0.68 -0.47 -0.38 -0.18 0.34Q2 1.50 1.66 1.71 2.03 2.43 -0.36 -0.29 -0.20 -0.03 0.24Q3 1.79 1.87 2.19 2.37 2.51 -0.30 -0.22 0.01 0.21 0.41Q4 1.89 1.96 2.25 2.39 2.66 -0.28 -0.14 0.03 0.24 0.51Q5 2.24 2.36 2.61 2.62 2.78 -0.12 0.01 0.24 0.22 0.49Q5-Q1 0.98*** 1.15*** 1.27*** 0.84*** 0.21 -0.77*** 0.56** 0.49*** 0.62*** 0.40*** 0.15 -0.41***(t-stat) (5.52) (5.94) (5.76) (7.49) (1.57) (-3.46) (2.69) (3.35) (3.62) (3.24) (1.53) (-3.18)

Panel D: IRCQ1 1.10 1.11 1.21 1.44 2.00 -0.55 -0.57 -0.50 -0.34 0.20Q2 1.51 1.51 1.61 1.92 2.59 -0.28 -0.33 -0.25 -0.05 0.45Q3 1.73 1.88 2.01 2.31 2.78 -0.20 -0.17 -0.11 0.13 0.47Q4 1.99 2.06 2.34 2.48 2.89 -0.06 -0.12 0.05 0.09 0.55Q5 2.18 2.38 2.66 2.80 2.95 -0.01 -0.01 0.16 0.39 0.60Q5-Q1 1.08*** 1.27*** 1.45*** 1.35*** 0.95*** -0.13 0.54*** 0.57*** 0.66*** 0.72*** 0.40*** -0.14(t-stat) (5.41) (6.66) (6.93) (7.14) (5.58) (-1.38) (2.94) (3.50) (3.98) (3.91) (2.70) (-1.55)

Panel E: λQ1 1.06 1.24 1.26 1.46 2.03 -0.49 -0.30 -0.19 -0.13 0.15Q2 1.54 1.55 1.67 1.83 2.22 -0.32 -0.36 -0.29 -0.09 0.17Q3 1.82 1.83 1.95 2.15 2.82 -0.22 -0.23 -0.12 0.02 0.30Q4 1.96 2.04 2.21 2.35 2.82 -0.18 -0.17 0.01 0.11 0.33Q5 2.22 2.36 2.29 2.55 2.85 0.08 0.01 0.14 0.35 0.49Q5-Q1 1.16*** 1.12*** 1.03*** 1.09*** 0.82*** -0.34** 0.57*** 0.31*** 0.33*** 0.48*** 0.34*** -0.23**(t-stat) (5.84) (6.55) (7.87) (7.16) (4.98) (-2.56) (3.30) (3.48) (3.97) (4.42) (3.21) (-2.34)

35

Table 5: Panel Regressions of Yield Spreads

This table reports the results of panel regressions with the issuer- and year-fixed effects. The dependentvariable is yield spread. Each quarter, we sort bonds into terciles based on liquidity clientele measures anddefine two indicators, T2 (median ILC group) and T3 (high ILC group), for bonds in tercile groups 2 and3. Independent variables include alternative ILQ measures (that is, Amihud, Roll, LOT, IRC, and λ), T2,T3, and the interactions between ILQ and T2 as well as ILQ and T3. The column heading specifies ILQmeasures used in the regressions. The control variables include the year since issued, time to maturity,issue size, coupon payments, the ratio of operating income to sales, ratio of long term debt to assets (LTD),leverage ratio, equity volatility (EqVol), and pretax interest coverage dummies. The regressions additionallyinclude dummy variables for all the ratings, the fixed issuer effect, and the fixed time effect. The regressioncoefficients and adjusted R-squares are presented. The t-statistics are reported in the parentheses and arecomputed using two-dimensional cluster robust standard errors.

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)Amihud Roll LOT IRC λ Amihud Roll LOT IRC λ

Intercept 1.53*** 0.79*** 1.59*** 1.20*** 1.87*** 12.36*** 0.16 -0.22 12.91*** 1.44**(28.07) (13.99) (32.26) (21.46) (28.91) (16.07) (0.29) (-0.40) (16.92) (2.35)

ILQ 0.29*** 0.34*** 0.01*** 2.06*** 1.00*** 0.10*** 0.21*** 0.02*** 0.88*** 0.35***(24.61) (33.70) (30.15) (33.09) (28.74) (7.09) (14.49) (23.37) (11.41) (8.42)

T2 0.17*** 0.27*** 0.11*** 0.22*** 0.13*** 0.12*** 0.23*** 0.15*** 0.20*** 0.22***(8.20) (12.48) (5.52) (8.84) (5.48) (4.75) (8.54) (6.22) (6.57) (8.38)

T3 0.38*** 0.42*** 0.27*** 0.45*** 0.06** 0.15*** 0.31*** 0.25*** 0.29*** 0.15***(16.93) (17.16) (12.65) (16.84) (2.45) (5.53) (9.63) (8.73) (8.35) (4.85)

ILQ*T2 -0.17*** -0.18*** -0.11*** -0.45*** -0.09** -0.08*** -0.10*** -0.08*** -0.17* -0.08(-11.19) (-16.07) (-2.99) (-5.93) (-2.13) (-4.43) (-6.22) (-8.14) (-1.82) (-1.63)

ILQ*T3 -0.31*** -0.28*** -0.21*** -1.34*** -0.41*** -0.06*** -0.17*** -0.22*** -0.56*** -0.19**(-17.52) (-26.89) (-19.71) (-20.14) (-11.08) (-3.98) (-11.50) (-17.89) (-6.89) (-2.39)

Age 0.13 0.30 -0.15 0.36 -0.67(0.31) (0.76) (-0.40) (0.87) (-1.42)

Maturity 1.05*** 0.71*** 0.69*** 0.56*** 0.22(7.77) (5.08) (5.45) (3.80) (1.32)

Issue size -0.41 2.43 3.03 -1.09 -1.43(-0.18) (1.09) (1.41) (-0.47) (-0.56)

Coupon 0.04*** 0.01 0.03** 0.02** 0.05***(3.35) (1.35) (2.66) (2.26) (4.15)

Income to sales 0.07 0.10 0.11* 0.08 -0.01(1.16) (1.68) (1.87) (1.28) (-0.05)

LTD -0.02*** -0.02*** -0.02*** -0.02*** -0.02***(-8.84) (-9.00) (-8.37) (-8.06) (-5.68)

Leverage 0.05*** 0.05*** 0.05*** 0.06*** 0.04***(19.85) (20.13) (20.97) (18.91) (15.94)

EqVol 0.59*** 0.60*** 0.61*** 0.59*** 0.55***(55.55) (57.88) (59.98) (54.73) (48.55)

Pretax1 -0.19*** -0.16** -0.17** -0.21*** -0.20***(-2.85) (-2.47) (-2.69) (-3.13) (-2.82)

Pretax2 -0.19*** -0.18*** -0.19*** -0.21*** -0.20***(-3.27) (-3.15) (-3.37) (-3.41) (-3.15)

Pretax3 -0.16*** -0.14*** -0.14*** -0.16*** -0.17***(-3.13) (-2.83) (-3.00) (-3.16) (-3.22)

Rating dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes YesFixed effect - issuer Yes Yes Yes Yes Yes Yes Yes Yes Yes YesFixed effect - quarter Yes Yes Yes Yes Yes Yes Yes Yes Yes YesAdj.R2 0.37 0.36 0.38 0.40 0.41 0.48 0.46 0.45 0.49 0.50

36

Table 6: Liquidity Clientele Effects in Subsamples

This table reports the results of panel regressions of yield spreads for three sets of subsamples: i) investment-grade and specula-tive bonds (reported in Panel A), ii) short- and long-term bonds (Panel B), and iii) Before and during the financial crisis (PanelC). In each quarter and within each subgroup, we sort bonds into terciles based on liquidity clientele measures and define twoindicators, T2 (equal to 1 for bonds in the median ILC group and 0 otherwise) and T3 (equal to 1 for bonds in the high ILCgroup and 0 otherwise). Independent variables include alternative ILQ measures (that is, Amihud, Roll, LOT, IRC, and λ), T2,T3, and the interactions between ILQ and T2 as well as ILQ and T3. The control variables include the year since issued, time tomaturity, issue size, coupon payments, the ratio of operating income to sales, ratio of long term debt to assets (LTD), leverageratio, equity volatility, and pretax interest coverage dummies. The fixed year and issuer effects are considered. The columnheading specifies ILQ measures used in the regressions. The coefficients for the control variables are suppressed. The t-statisticsare reported in the parentheses and are computed using two-way clustered robust standard errors. Ratings of corporate bondsabove BBB- are investment grade bonds; otherwise, they are speculative bonds. Short-term bonds are those whose maturitiesare shorter than 5 years. Long-term bonds are those whose maturities are above 5 years. The period before the financial cri-sis is (2003:Q1 - 2007:Q1). The period in the financial crisis is 2007:Q2 - 2009:Q4. The sample period is from 2003:Q1 to 2009:Q4.

Panel A: Investment Grade Bonds vs. Speculative Bonds

Investment-grade bonds Speculative-grade bondsAmihud Roll LOT IRC λ Amihud Roll LOT IRC λ

ILQ 0.06*** 0.05*** 0.01*** 0.33*** 0.20*** 0.15*** 0.29*** 0.02*** 1.56*** 1.15***(3.95) (3.99) (4.49) (4.35) (5.62) (4.42) (7.53) (12.13) (8.01) (9.69)

T2 0.05 0.32** 0.08 0.16 -0.11 0.10*** 0.11*** 0.10*** 0.12*** 0.29***(0.48) (2.43) (0.76) (1.14) (-1.05) (5.40) (5.00) (5.17) (5.00) (13.07)

T3 0.13 0.45*** 0.25** 0.38*** 0.01 0.23*** 0.21*** 0.19*** 0.30*** 0.26***(1.16) (3.62) (2.36) (2.76) (0.08) (10.12) (8.06) (8.05) (10.45) (9.91)

ILQ*T2 -0.13* -0.19*** -0.10*** -0.48** -0.54*** 0.01 0.06*** 0.01*** 0.41*** 0.22***(-1.88) (-3.29) (-4.61) (-2.45) (-3.41) (0.59) (4.46) (4.95) (4.84) (5.32)

ILQ*T3 -0.30** -0.29*** -0.24*** -0.98*** -0.90*** -0.03 -0.01 -0.01 -0.16* -0.05(-2.05) (-6.35) (-8.33) (-4.10) (-5.83) (-1.37) (-1.00) (-0.60) (-2.02) (-1.23)

Panel B: Short-term Bonds vs. Long-term Bonds

Short-term bonds Long-term bondsAmihud Roll LOT IRC λ Amihud Roll LOT IRC λ

ILQ 0.06*** 0.12*** 0.01*** 0.47*** 0.15*** 0.14*** 0.04* 0.04 0.62** 0.49***(3.04) (6.55) (13.18) (4.58) (3.02) (3.90) (1.71) (1.12) (2.52) (4.24)

T2 0.02 -0.01 -0.01 -0.05 0.11*** 0.17 0.05 0.03 0.76*** 0.40**(0.61) (-0.21) (-0.05) (-1.42) (3.36) (1.69) (0.23) (0.21) (3.18) (2.50)

T3 -0.02 -0.03 0.01 -0.11* 0.01 0.18* 0.09 0.21 0.62*** 0.29*(-0.50) (-0.66) (0.30) (-2.01) (0.17) (1.88) (0.50) (1.54) (2.93) (1.93)

ILQ*T2 -0.01 0.03* 0.01** 0.37*** 0.25*** -0.16*** -0.09* -0.11*** -0.35* -0.14*(-0.40) (1.90) (2.15) (3.03) (4.05) (-3.71) (-1.97) (-2.91) (-1.96) (-1.91)

ILQ*T3 0.01 -0.06*** -0.05** 0.20 0.33*** -0.22*** -0.16*** -0.21** -0.98*** -0.42***(0.64) (-3.32) (-2.50) (1.54) (5.20) (-3.35) (-3.77) (-2.37) (-2.94) (-3.58)

Panel C: Before vs. During the Crisis

Before the crisis In the crisisILQ 0.06*** 0.05*** 0.01** 0.30*** 0.12*** 0.07*** 0.24*** 0.02*** 1.06*** 0.43***

(4.29) (5.34) (2.06) (5.22) (4.06) (3.01) (10.18) (15.50) (8.42) (6.42)T2 0.04*** 0.07*** 0.03** 0.10*** 0.15*** 0.18*** 0.33*** 0.21*** 0.30*** 0.25***

(3.05) (5.24) (2.63) (6.93) (8.30) (3.45) (5.73) (4.25) (4.48) (4.80)T3 0.05*** 0.12*** 0.05*** 0.18*** 0.15*** 0.26*** 0.45*** 0.36*** 0.52*** 0.26***

(3.16) (7.30) (3.66) (10.38) (8.00) (4.65) (6.55) (6.01) (6.99) (4.14)ILQ*T2 -0.02* -0.04*** -0.03* -0.24** -0.13* -0.05* -0.09*** -0.10*** -0.22 -0.13*

(-2.03) (-3.44) (-2.02) (-2.56) (-1.78) (-1.85) (-3.43) (-5.01) (-1.46) (-1.99)ILQ*T3 -0.04*** -0.05*** -0.38*** -0.16*** -0.43*** -0.12*** -0.18*** -0.31*** -0.69*** -0.23***

(-2.90) (-4.88) (-2.81) (-3.73) (-2.91) (-3.93) (-7.36) (-11.07) (-5.26) (-2.83)

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Table 7: Percentage Holding of Insurers across Subsamples

The table reports the percentage holdings by sample insurers for all bonds and for bonds in the bottom andtop tercile groups sorted by the illiquidity clientele (ILC) measure. For each of the five illiquidity measures(Amihud, Roll, LOT, IRC, λ), we first divide bonds into investment-grade (IG) and speculative/high-yield(HY) bonds, or short-term (<5 years) and long-term bonds (> 10 years). Within each subsample, we sortbonds into terciles based on ILCs. T1 is for the low ILC tercile group and T3 is for the high tercile group.We calculate the average percentage holdings by aggregate insurers (%) for a group in each quarter and thencompute the time-series averages. The sample period is from 2003 to 2009.

Amihud Roll LOT IRC λ

All T1 T2 T3 T1 T2 T3 T1 T2 T3 T1 T2 T3 T1 T2 T3

(i) By bond rating groups

IG 36.52 25.36 38.79 45.40 30.28 38.74 40.53 26.03 38.69 44.82 29.20 38.65 41.70 31.09 38.23 40.23

HY 13.23 9.10 14.52 15.98 12.43 14.02 13.20 9.48 14.38 15.71 13.21 13.81 12.70 13.80 13.87 12.05

(ii) By bond maturity groups

Short 27.10 19.39 30.75 37.14 24.06 32.30 30.93 20.04 32.05 35.19 23.75 32.86 30.68 26.75 31.72 28.84

Long 39.36 31.04 44.72 42.26 37.50 47.22 33.30 32.29 43.34 42.40 38.50 47.30 32.23 39.40 47.72 30.91

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Table 8: Liquidity Clientele Effect - Bonds with over 20% Insurance Holding

This table reports the results of panel regressions for corporate bonds held at least 20% by insurers withthe issuer- and year-fixed effects. The dependent variable is yield spread. Each quarter, we sort bonds intoterciles based on liquidity clientele measures and define two indicators, T2 (median ILC group) and T3 (highILC group), for bonds in tercile groups 2 and 3. Independent variables include alternative ILQ measures(that is, Amihud, Roll, LOT, IRC, and λ), T2, T3, and the interactions between ILQ and T2 as well asILQ and T3. The column heading specifies ILQ measures used in the regressions. The control variablesinclude the year since issued, time to maturity, issue size, coupon payments, the ratio of operating income tosales, ratio of long term debt to assets (LTD), leverage ratio, equity volatility (EqVol), and pretax interestcoverage dummies. The regressions additionally include dummy variables for all the ratings, the fixed issuereffect, and the fixed time effect. The regression coefficients for the key variables and adjusted R-squares arereported while skipping the coefficients on control variables. The t-statistics are reported in the parenthesesand are computed using two-dimensional cluster robust standard errors.

(1) (2) (3) (4) (5)Amihud Roll LOT IRC λ

Intercept 10.19*** 12.67*** 9.52*** 20.02*** 10.87**(6.69) (14.12) (9.59) (22.04) (7.07)

ILQ 0.17*** 0.25*** 0.12*** 1.26*** 0.45***(9.95) (15.83) (24.05) (13.40) (10.49)

T2 0.11*** 0.20*** 0.10*** 0.28*** 0.13***(3.58) (9.42) (7.71) (7.37) (4.06)

T3 0.28*** 0.29*** 0.19*** 0.48*** 0.23***(7.81) (10.38) (5.18) (10.50) (5.86)

ILQ*T2 -0.17*** -0.09*** -0.06*** -0.20*** -0.15***(-7.68) (-12.85) (-9.97) (-6.20) (-4.86)

ILQ*T3 -0.39*** -0.19*** -0.22*** -0.85*** -0.27***(-6.10) (-14.42) (-19.19) (-8.48) (-4.54)

Rating dummies Yes Yes Yes Yes YesFixed effect - issuer Yes Yes Yes Yes YesFixed effect - quarter Yes Yes Yes Yes YesAdj.R2 0.62 0.58 0.58 0.62 0.62N 16,977 19,896 20,399 16,564 14,903

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Figure 1: Liquidity Measures across Investment Grade and Speculative Bondsover Time

This figure reports the average of distinct liquidity measures for investment-grade bonds and spec-ulative bonds over the sample period: 2003:Q1 to 2009:Q4.

2004Q1 2006Q3 2009Q1012

Amihud

2004Q1 2006Q3 2009Q10

5

Roll

2004Q1 2006Q3 2009Q10

50

LOT

2004Q1 2006Q3 2009Q10

0.5

1IRC

2004Q1 2006Q3 2009Q1-101

Investment-grade bondsSpeculative bonds

40

Figure 2: Yield Spread and Liquidity Measures

The figure shows the relation between yield spread and alternative liquidity measures using piece-wise regressions. We breakdown the full spectrum of a liquidity measure into 5 groups with similardistances. The piecewise regression between one of the liquidity measures and yield spreads isperformed in each quarter. We average all the time-series parameters (i.e., the intercept and thecoefficients) and calculate the fitted yield spreads. The fitted yield spreads (in percentage) withrespect to the corresponding liquidity value are plotted to depict the piecewise relation.

0.01 0.03 0.05

2

4Amihud

1 3 5

2

4Roll

0.1 0.4 0.7

2

4LOT

0.001 0.006 0.011

2

4IRC

-0.8 0 0.8

2

4

Spread

41

Figure 3: Persistence of Insurers’ Liquidity Preference

At the end of each year, we sort insurers into quintiles based on insurers’ liquidity preferencemeasures adjusted for the cross-sectional mean. From the lowest quintile to the highest, eachquintile is assigned a ranking from one (Q1) to five (Q5). We then trace the level of liquiditypreference of each quintile for the next three years (Y+1, Y+2, and Y+3). The sample period isfrom 2003:Q1 to 2009:Q4.

Y+1 Y+2 Y+3-0.2%

0 0.2% 0.4%

ILPAmihud

Y+1 Y+2 Y+3

-0.5

0

0.5

1

ILPRoll

Y+1 Y+2 Y+3-10%

0

10%

ILPLOT

Y+1 Y+2 Y+3-0.1%

0

0.1%

0.2% ILPIRC

Y+1 Y+2 Y+3-0.2

0

0.2

0.4ILP

Q1Q2Q3Q4Q5

42

Figure 4: Persistence of Liquidity Clientele

At the end of each year, we sort bonds into quintiles based on liquidity clientele measures adjustedfor cross-sectional mean. From the lowest quintile to the highest, each quintile is assigned a rankingfrom one (Q1) to five (Q5). We then trace the level of liquidity clientele of each quintile for thenext three years (Y+1, Y+2, and Y+3). The sample period is from 2003:Q1 to 2009:Q4.

Y+1 Y+2 Y+3-0.2%

0

0.2%

0.4% ILCAmihud

Y+1 Y+2 Y+3-0.5

0

0.5

ILCRoll

Y+1 Y+2 Y+3

-5%

0

5%

10%ILCLOT

Y+1 Y+2 Y+3-0.1%

0

0.1% ILCIRC

Y+1 Y+2 Y+3-0.1

0

0.1

ILC

Q1Q2Q3Q4Q5

43

Figure 5: Difference-in-Difference in Yield Spreads

In each quarter, we sequentially sort bonds into 5x5 groups based on λ and the associated illiquidityclientele measure and then plot the differences in differences of yield spreads reported in Panel A. Tocompute the difference in difference, we first calculate the difference in yield spreads between bondswith the highest and the lowest illiquidity for the highest and lowest liquidity clientele groups. Thenwe compute the difference between the difference of the yield spread differences for the highest andlowest liquidity clientele quintile groups. In Panel B, we plot the differences in difference of yieldspreads adjusted for the average yield spread. Each quarter, we first calculate the difference in yieldspreads between bonds with the highest and lowest illiquidity for the highest and lowest liquidityclientele groups scaled by the average yield spread across all bonds in that quarter and then wecompute the difference between the the difference of the yield spread differences for the highest andlowest liquidity clientele quintile groups. The sample period is from 2003:Q1 to 2009:Q4.

2004Q1 2005Q2 2006Q3 2007Q4 2009Q1

-4

-2

0

Panel A: Liquidity Clientele Effect Based on Yield Spreads

2004Q1 2005Q2 2006Q3 2007Q4 2009Q1-1

-0.5

0

0.5Panel B: Liquidity Clientele Effect Based on Relative Yield Spreads

44