Asset reliability and security prices: evidence from credit markets

Asset reliability and security prices: evidencefrom credit markets

Navneet Arora • Scott Richardson • Irem Tuna

Published online: 2 October 2013

� Springer Science+Business Media New York 2013

Abstract We assess the relation between asset reliability and security prices.

Concerns about asset reliability are increasing with the move to fair value

accounting in general purpose financial reports. We provide pertinent evidence from

credit markets. A key benefit of using credit market data to explore the capital

market implications of asset reliability is the theoretical basis of Duffie and Lando

(Econometrica 69(3):633–664, 2001). They show that asset reliability (measure-

ment) concerns should be concentrated in short-term credit spreads. Thus a focus on

credit term structure can facilitate a cleaner identification of the impact of asset

reliability on security prices. We find that asset reliability issues, attributable to

SFAS 157 disclosures of Level 2 and, especially, Level 3 financial assets for a set of

US financial institutions over the period of August 2007 to March 2009, are a

significant determinant of short-term credit spreads and the shape of the general

credit term structure. Our findings are robust to a variety of control variables and

research design choices.

Keywords Credit markets � Asset reliability � Credit term structure � Value

relevance

JEL Classification G12 � G14 � M41

N. Arora

Citadel LLC, Chicago, IL, USA

S. Richardson (&) � I. Tuna

London Business School, London, UK

e-mail: [email protected]

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Rev Account Stud (2014) 19:363–395

DOI 10.1007/s11142-013-9254-7

1 Introduction

This paper explores the relation between accounting reliability and the term

structure of credit spreads. Reliability is one of the primary desirable characteristics

of information presented in general purpose financial reports. SFAC 2 defines

reliability as ‘‘the quality of information that assures that information is reasonably

free from error and bias and faithfully represents what it purports to represent.’’

Watts (2003) and others have noted concerns about the costs likely to arise from

sacrificing the reliability of financial information contained in general purpose

financial reports.

We explore the relation between accounting reliability and credit markets for

several reasons. First, credit markets offer a richer environment due to the existence

of multiple instruments for a given company. This term structure of traded

instruments allows for sharper tests to focus on a specific instrument, or set of

instruments, where the effect of reliability is expected to be greatest. Second, using

multiple instruments for a given firm reduces the influence of potentially correlated

omitted variables (e.g., economic rents or expectations of earnings growth) that

affect security prices as they are likely to be common across these instruments.

Therefore associations between asset reliability and credit term structure can

abstract away from these correlated omitted variables, providing a cleaner

identification of the impact of asset reliability on security prices. Third, a seminal

paper by Duffie and Lando (2001) presents a theoretical model that implies a

testable hypothesis linking asset reliability with credit term structure.

Duffie and Lando (2001) present a model where a firm is owned by equity

holders who are fully informed about its assets. The equity holders will optimally

liquidate the firm if its asset value falls below a predetermined default threshold.

The creditors receive only partial information through periodic accounting

statements. The degree to which these statements can offer information on the

intrinsic firm value is described as the ‘‘accounting precision’’ and is negatively

correlated with the level of accounting noise, a. For example, when a = 0, creditors

have complete information. As a gets higher, the information of creditors gets less

reliable (i.e., noisier). Duffie and Lando (2001) show that, for a [ 0, there is a

nonzero default intensity (i.e., the arrival of default can be a complete surprise to the

creditors). This is because they cannot observe the asset value with precision, and

therefore, at any given moment, the asset value can fall below the default barrier.

Note that the precision of accounting information described by Duffie and Lando

(2001) is separate from the underlying asset volatility that is the basis for most

structural models of credit spreads. The precision, or lack thereof, of accounting

information is an additional source of credit risk, which is most relevant for short-

term credit spreads. Over the longer term, the uncertainty around the evolution of

asset value will dominate the lack of precision in information available to creditors.

Thus the effect of noise in accounting information will decrease with horizon of

debt. For two firms with different precision of accounting information, but

otherwise identical, the credit spread term structures will be farther apart at the short

end than at the long end. The firm with noisier accounting information will have

proportionately higher default risk in the short run, as perceived by its creditors.

364 N. Arora et al.

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This will lead to a less steep or flatter term structure compared to a firm with more

precise information. At the limit in the model of DL, a = 0, when information on

asset values is very precise, the spread for debt with zero maturity will be zero and

will rise rapidly with maturity. In practice, however, credit term-structures exhibit

significant short-term spreads (e.g., Helwege and Turner 1999), indicating the

plausibility of a role of asset unreliability at shorter maturities.

Our proxy for reliability comes from the assumption that asset measurement error

is expected to be greatest for components of the balance sheet that have the greatest

amount of discretion embedded in them. Examples include estimated allowance for

bad debts in net receivables, estimates for obsolescence in net inventory balances,

and model assumptions embedded in the fair values of Level 3 financial assets. An

extensive literature, starting with Sloan (1996), has documented lower earnings

persistence for these accrual components of earnings. The reliability issues

associated with certain balance sheet accounts matters greatly to investors

forecasting the firm’s future cash flows and their associated risks. Indeed, the

evidence that the stock market does not appear to price the lower persistence of the

least reliable components of financial statements suggests a significant misallocation

of capital market resources due to the provision of low reliability information.

In this paper, we test whether asset reliability has an impact on short-term credit

spreads and the underlying credit term structure. We focus on financial institutions

as our measure of asset reliability, relying on disaggregated disclosures of financial

assets, which make up a large portion of the total asset values of financial

institutions. Our empirical analysis starts towards the end of 2007, as that is when

the new disclosures took effect, and it also coincides with the start of the recent

financial crisis. Our empirical analysis stops in March 2009, as the terms and

conditions of CDS contracts changed then, hampering comparisons of spreads

across periods. While our focus on financial institutions during the financial crisis

may limit the generalizability of our results, we note that, for our sample of financial

institutions, we can measure asset reliability for a significant portion of total assets.

We find strong evidence that asset reliability is associated with short-term credit

spreads, consistent with the theory of Duffie and Lando (2001). Specifically, we find

that credit term structure is flatter at the short end for firms with greater asset

reliability issues, as captured by larger magnitudes of Level 2 and, especially, Level

3 assets. This effect is robust to controlling for measures of liquidity, jump risk,

default barrier uncertainty, and general credit risk.

A criticism with our selected proxy for asset reliability might be that it is a more

general reflection of asset composition and hence credit risk. Although our asset

reliability proxy is weakly correlated with standard measures of credit risk (e.g.,

parametric and nonparametric correlations between our measure of asset reliability

and five-year CDS spread levels are negative and between -0.23 and -0.04), we

still control for these measures of credit risk and document incremental significance

for our measures of asset reliability. Note, too, that our proxy for asset reliability is

the ratio of Level 2 and Level 3 financial assets to total assets. For the average

(median) financial institution in our sample, 32 (35) percent of total assets are of this

type. Clearly, there is a portion of total assets missing from our analysis, but for

Asset reliability and security prices 365

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financial institutions we can capture a meaningful portion of uncertainty in asset

values.

In terms of economic significance, our measures of asset reliability can explain

between 4 and 5 percent of the cross-sectional variation in credit term structure on a

standalone basis. Even after controlling for a variety of other determinants of credit

term structure, including a comprehensive measure of the shape of medium-term

credit spreads, we still find a robust relation between asset reliability and short-term

credit spreads. Overall, our findings show that asset reliability is an economically

and statistically important determinant of security prices. Documenting this relation

in credit markets enables us to make stronger inferences on the importance of asset

reliability. We can confirm the theoretical predictions from Duffie and Lando

(2001), i.e., the effects of asset reliability should be greatest for short-term credit

spreads.

The rest of the paper is organized as follows. Section 2 reviews the literature and

develops our hypothesis. Section 3 describes the data we use. Section 4 discusses

the empirical results. Section 5 concludes.

2 Literature review and hypothesis development

Our objective is to expand our understanding of the role that asset reliability (or

uncertainty with respect to the measurement of asset values) has on security prices.

In this section, we introduce credit term structure as a theoretically motivated

variable to identify and quantify the impact of asset reliability on security prices.

2.1 Literature review

A seminal paper by Merton (1974) laid the groundwork for structural models that

would later be used as the benchmark to describe credit spread levels and term

structure. Relative to this benchmark, however, actual short-term credit spreads tend

to be significantly higher (e.g., Huang and Huang 2002; Eom et al. 2004).

Prior literature provides two main explanations for the existence of significant

short-term credit spreads. These are (1) liquidity and (2) uncertainty leading to a

‘‘jump risk.’’ The first explanation is that short-term spreads are premiums for

illiquidity, i.e., the inability to exit large positions quickly without impacting the

price in an adverse and significant way. For example, Chacko (2005) and Downing

et al. (2005) find that liquidity risk is a priced factor in corporate bond returns.

The second explanation is that short-term spreads are premiums for genuine

jump-related credit-risk. Researchers have used two approaches in their study of this

explanation. The first approach proposes reduced form modelling as an alternative

to structural modelling to explain credit term structure (e.g., Duffie and Singleton

1999). Reduced form modelling assumes default as an inaccessible event, i.e., its

arrival is a complete surprise, which allows for the existence of default intensity that

could lead to nonzero spreads even at very short horizons.

The second approach is revised structural modelling. Prior research that uses

revised structural modelling derives jump-related credit risk from three sources. The

366 N. Arora et al.

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first is true jump risk in the asset process. Zhou (2001), for example, shows

theoretically that different credit term structure shapes can be obtained when a jump

risk is introduced in the asset process. The second source is default barrier

uncertainty. Assuming uncertainty in the default boundary due to incomplete

information from the investors’ perspective makes the arrival of default a surprise

even at very short horizons (e.g., Finger et al. 2002; Giesecke 2006).

The third source is what we term ‘‘asset reliability.’’ This relates to uncertainty of

the reported asset values used by creditors in setting prices in credit markets. As

described in the introduction, Duffie and Lando (2001) develop a model in which

the presence of incomplete information to credit investors can cause default

intensity in the very near term, thus causing an additional component for short-term

spreads. Under this approach, there is a true asset value, but credit investors do not

get to observe this because the firm issues infrequent financial statements, and

investors are uncertain about whether the reported asset values reflect that value. If

the firm were to issue frequent, timely, and reliable asset values, then the Duffie and

Lando (2001) model would imply no short term credit spreads. Thus we expect the

role of asset reliability to be important in credit markets as it adds to uncertainty on

the evolution of asset values. More importantly, we have strong theoretical reasons

for believing that this effect should be concentrated in short-term, as opposed to

longer-term, credit spreads. This is because the asset evolution risk (i.e., asset

volatility) grows with duration while asset reliability is independent of duration.

Therefore, at longer durations, asset volatility starts dominating asset reliability risk.

At least four theoretical constructs are relevant for explaining the existence of

and cross-sectional variation in short-term credit spreads: (1) liquidity, (2) asset

uncertainty or jump risk, (3) default barrier uncertainty, and (4) asset reliability.

While we focus on identifying the role of the fourth component, we must ensure that

this effect is incremental to the other three components.

Our paper is closely related to Yu (2005), who finds that credit spreads are

smaller for firms with superior accounting quality, especially at shorter horizons.

However, there are several key differences between Yu (2005) and our work. First,

Yu (2005) uses the Association for Investment Management and Research (AIMR)

disclosure rankings as a proxy for incomplete information on asset values. AIMR

scores have been used elsewhere as measures of disclosure quality more generally,

so its link to asset reliability per se is unclear. By using recent fair value disclosures

for Level 2 and Level 3 financial assets, we focus our analysis on an arguably

superior measure of asset reliability. Second, to capture the effect of disclosure

quality on credit term structure, Yu (2005) uses an interaction between AIMR scores

and bond maturity. However, the remaining variables are not interacted with bond

maturity. His regression specification therefore assumes that the sole source of term-

structure variation is disclosure quality. This ignores the well known empirical fact

that riskier firms have a flatter (sometimes inverted) credit term structure. Our

research design allows us to control for various determinants of credit term structure

and isolate the relative importance of asset reliability. Third, Yu (2005) uses cash

bond spreads as the proxy for credit spreads, while we use credit default swap

spreads. As elaborated in the next section, spreads from credit default swap

contracts are a much cleaner measure of the credit risk, because we can remove the


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effects of tax, liquidity, optionality, and duration that are reflected in cash bond

spreads (e.g., Elton et al. 2001). In summary, no prior research has yet identified a

link between asset reliability and credit term structure.

There are related papers examining the association between measures of asset

reliability and relevance and equity prices or equity returns. Two recent examples

are Kolev (2011) and Song et al. (2010). Both papers find that the association

between equity prices (or returns) and fair value estimates of financial assets is

decreasing in the relative precision with which these estimates are computed.

Inferences from these studies are, however, limited as the determinants of equity

prices are difficult to completely control for. A key determinant of equity value, for

example, is expectation of longer-term earnings growth, which is a function of

accounting conservatism and expected economic rents. These are very difficult to

measure reliably, and hence inferences from equity data are clouded by the

influence of measurement error (see, e.g., Lambert 1996 and Holthausen and Watts

2001). A benefit of examining credit market data is that correlated omitted variables

such as economic rents and expected earnings growth can be mitigated in at least

two ways. First, the inability of a researcher to measure these growth options is less

of a concern when examining credit market data as creditors are less concerned with

upside. Second, there is typically a set of credit instruments traded for a given firm

at a point in time, and thus the impact of correlated omitted variables (e.g., inability

to measure economic rents) become less significant. Of course, this assumes that

any correlated omitted variable does not have a term structure component.

Given our focus on financial institutions, it is also important to recognize a

related literature in financial economics that examines capital market consequences

of the asset opacity of financial institutions—and banks, in particular. Calomiris and

Wilson (2004), for example, note that banks endogenously adjust (1) the risk profile

of their assets and (2) leverage, in response to changes in the macroeconomic

environment, so as to help mitigate time variation in deposit risk. A consequence of

these choices is that for a given level of targeted deposit risk, banks match their

capital levels for a given level of asset risk such that in the cross section banks with

riskier asset profiles may optimally choose to have a higher capital cushion. Given

our focus on financial institutions, the asset risk profile of the banks we examine (via

the Level 1/2/3 disclosures of financial assets) may be related to the overall riskiness

of a particular bank and its capital choices. Thus it is important to control for bank

level measures of asset risk in our empirical analysis.

2.2 Hypothesis and research design

We focus on asset reliability as an explanation for short-term credit spreads. Our

objective is to examine whether asset reliability matters in setting security prices. As

described above, we are focusing on credit market data because there is theoretical

support for asset reliability to affect credit spreads, in particular, short-term credit

spreads. The theory of Duffie and Lando (2001) suggests that proxies of asset

reliability should be positively correlated with short-term credit spreads and also

with the relative flatness of the front end of the credit term structure. Therefore our

testable hypothesis, in alternative form, is the following:

368 N. Arora et al.

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H1: Ceteris paribus, short-horizon credit risk as a fraction of overall credit risk is

higher for firms with lower asset reliability.

We test this hypothesis using the following regression model (firm- and time-

subscripts implicit) and predict bAR to be positive:

CDS1Y

CDS5Y¼ aþ bCRCredit Riskþ bLRLiquidity Riskþ bSLOPE

CDS5Y

CDS10Y

þ bAJR Asset Jump Risk

þ bDBU Default Barrier Uncertaintyþ bARAsset Reliabilityþ e

ð1Þ

CDS1Y/CDS5Y is the ratio of the spread on a one-year CDS contract to the spread

on a five-year CDS contract. It measures the relative steepness of the front end of a

given firm’s yield curve. By focusing on the slope of term structure as a dependent

variable, we can control for all the variables that may impact credit risk level (and

therefore slope) in one parsimonious specification. Our main focus is to test whether

our proxy for asset reliability can explain cross-sectional variation in short-term

credit spreads relative to longer-term credit spreads. Each firm will have certain

amount of default risk in the short run simply because of its overall higher credit

risk. Even though liquidity is a key determinant of short-term spreads, Covitz and

Downing (2007) show that credit risk is also an important consideration for short-

term credit spreads. Indeed, it is well known that riskier firms have flatter credit

term structures (e.g., Jarrow et al. 1997). The general relation between credit term

structure and credit risk is shown clearly in Fig. 1. We sort each firm with liquid

CDS contracts (a sample of 431 firms) as at March 2009 into five equal-sized groups

based on CDS5Y. We then calculate the average CDS spread for the various terms

that are available to us (6 months, 1, 3, 5, 7, 10, 15 and 30 years). We plot the

average CDS spreads for the lowest, middle, and highest spread quintiles. Figure 1

shows that riskier firms have flatter, and even inverted, credit term structures.

As stated above, we use the slope of the credit term structure as our dependent

variable. An alternative approach would be to use credit spread levels, as in Yu

(2005). The regression specification in Yu (2005) has a set of interaction variables

between AIMR scores and several maturity variables so as to capture the influence

of disclosure quality on the credit term structure as opposed to simply the credit

spread level. However, the approach in Yu (2005) lacks a complete set of

interactions. Specifically, variables like leverage and volatility that reflect credit risk

are not interacted with the maturity variables. Therefore a clean inference cannot be

made about the role of AIMR scores on credit spread term structure. Rather than

report a regression with a large set of interaction variables, we prefer the more

parsimonious approach of using CDS1Y/CDS5Y as the dependent variable. In Sect.

4.2.9, we discuss robustness tests where we use CDS1Y as our dependent variable,

and in Sect. 4.2.4 we note the fragility of the results in Yu (2005) when allowing

credit risk to influence credit term structure directly.

Our proxies for credit risk are CDS5Y and MLEV. CDS5Y is the CDS spread for

a five-year contract. Corporate credit default swaps are most liquid at the five-year

point (see, e.g., Longstaff et al. 2005). This is widely recognized as one of the most

accurate measures of credit risk for an issuer. MLEV is market leverage, which is


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measured as the ratio of the sum of short-term and long-term debt to market

capitalization. For both measures of credit risk, we expect a positive association

with the dependent variable CDS1Y/CDS5Y, reflecting the increasing impact that

credit risk has on short-term credit spreads as shown in Fig. 1.

Our proxies for liquidity risk are MCAP and DEPTH. MCAP is the log of market

capitalization, and DEPTH is the number of dealers providing quotes for the five-

year CDS contract as reported by MarkIt Partners. We expect issuers with greater

liquidity (i.e., larger firms and those issuers with more dealers) to have lower short-

term credit spreads and steeper credit term structures. Arguably, these proxies are

measured with error, as we do not have complete access to credit market data. CDS

contracts are typically traded over the counter (OTC), and it is therefore challenging

to obtain a clear measure of liquidity. In particular, our measure of DEPTH could be

viewed as a filter rather than a cross-sectional measure of liquidity (i.e., once there

are certain number of dealers making a market, that issuer is liquid, and any

additional depth does not reflect additional liquidity).

Our proxy for default barrier uncertainty is REFINANCE. This is measured as

the log ratio of short-term debt to long-term debt. Assuming that the firm wishes to

retain its capital structure, this variable measures its need to access debt markets in

the near term. We expect firms with greater short-term debt to need capital market

access to roll over or refinance that debt. We expect these firms to have higher short-

term credit spreads and flatter credit term structure, especially during the recent

financial crisis, as that was a relatively challenging market environment in which to

access credit.

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0 5 10 15 20 25 30

Tenor

CD

S S

pre

ad

LOW MIDDLE HIGH

Fig. 1 Credit term structure across spread quintiles. Each firm is assigned to five buckets based onCDS5Y as at March 2009. The average values of the respective points on the credit curve are plotted inthe figure

370 N. Arora et al.

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Our measure of asset reliability is L23/TA. This is computed as the ratio of

financial assets marked to market as either Level 2 or Level 3 assets under FAS157

divided by total assets. This variable, therefore, captures the extent of total assets

where there are concerns about reliable measurement. We combine both Level 2 and

Level 3 assets in our asset reliability proxy, as the balance sheet value of both is

based on models rather than prices observed in active markets. Specifically, Level 2

assets have values are based on quoted prices in inactive markets or based on

models with directly or indirectly observable inputs. Level 3 assets have values

based on prices or valuation techniques that require inputs that are both

unobservable and significant to the overall fair value measurement. These inputs

reflect management’s views about the assumptions a market participant would use

in pricing the asset. Needless to say, there are greater asset reliability issues

associated with Level 3 as compared to Level 2 assets, and we exploit this in Sect.

4.2.1.

In addition to examining the relation between CDS1Y/CDS5Y and L23/TA, we

also decompose L23/TA into two components: L23/FA and FA/TA. The first

variable, L23/FA, captures the fraction of financial assets that are Level 2 or Level

3. The second variable, FA/TA, captures the fraction of total assets that are financial

assets. Strictly speaking, this decomposition needs to be in log space, so we perform

our analysis on raw and logged values. The aim of this decomposition is to focus the

analysis onto L23/FA, as this is the purest measure of asset reliability we can

identify, and then control for financial asset intensity, FA/TA.

Our proxy for jump risk is implied asset volatility. For this measure, we require

data from the equity option markets. Specifically, we extract implied volatility from

liquid out-of-the-money put option contracts at the start of each month. We then de-

lever this implied equity volatility to compute asset volatility (i.e., implied volatility

divided by market leverage). We expect firms with greater jump risk to have higher

short-term credit spreads and flatter credit term structures. There is likely error in

our measure of jump risk, as we use options with a 90-day maturity and associate

the extracted measure of jump risk with credit derivatives with a longer maturity.

We also make assumptions about dividend policy and risk-free rates to reverse

engineer the implied volatility. Given this measurement error issue and the data

requirement reducing our sample size, we defer the analysis with jump risk to the

sensitivity analysis in Sect. 4.2.2.

Finally, we also control for CDS5Y/CDS10Y, which is the ratio of the spread on

a five-year CDS contract to the spread on a 10-year CDS contract. This ratio

measures the relative steepness of the middle portion of a given firm’s yield curve.

This variable allows us to better focus on the front end of the term structure to be

consistent with theory. Note that, while our specification captures the effect of a

wider set of variables (e.g., credit risk, jump risk, default barrier uncertainty, and

liquidity), we may have omitted a variable that impacts the credit term structure for

a firm. Our reason for including CDS5Y/CDS10Y is to capture, in reduced form, the

impact of all other firm attributes on the general shape of the credit term-structure.

This is a conservative research design choice, which could hinder the detection of

cross-sectional differences in credit term structure at the front end.


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3 Sample selection and data

Our sample period is from August, 2007 to March, 2009. The main reason for this is

the availability of Level 1/2/3 financial asset disclosures and regulatory changes in

the CDS market. Before 2007, firms were not required to make detailed disclosures

relating to their financial assets. Our sample therefore contains some early adopters

of SFAS 157. We have retained the full sample of early adopters and mandatory

adopters to span the entirety of the financial crisis period. Results are similar if we

instead start the analysis in January 2008, the period after which SFAS157

disclosures were mandatory. From April 2009 onward, regulators have made

significant progress toward standardization of CDS contracts, which has led to the

elimination of most popular documentation terms (conditions to be recognized as

defaults). This makes temporal analysis of CDS spreads problematic for the period

after March 2009.

A CDS contract is an insurance contract against a debt default by a specific

borrower. As an example, imagine that counterparty A buys credit protection on

IBM from counterparty B by paying a fixed spread of, say, 100 basis points per year

for a term of 5 years. If IBM does not default during this period, then B does not

make any payments to A. If IBM does default, however, B pays A the difference

between the par value of the bond and the post-default value (typically determined

by a simple auction mechanism) of a specific IBM bond. In essence, the protection

buyer (counterparty A) can sell the bond back at par to the protection seller

(counterparty B) in the event of a default. Thus the CDS contract insures

counterparty A against the loss of value associated with default by IBM. For more

details, see Longstaff et al. (2005).

We use CDS spreads rather than bond spreads for multiple reasons. First, as

mentioned earlier, CDS spreads are not as contaminated by liquidity and tax

concerns. Funding positions through CDS contracts are considerably less capital

intensive than funding a bond position. For example, if an investor wants $1 million

worth of long exposure to Ford, he can (1) purchase $1 million worth of physical

Ford bonds or (2) sell CDS protection through a $1 million notional contract. The

first choice requires a physical outlay of $1 million in cash, while the second

requires a much smaller capital outlay (typically less than 5 percent of the notional

amount). This difference in capital intensity can give rise to large basis differences

(i.e., the difference between bond and CDS spreads for the same issuer) in times of

market stress such as was seen in 2008 and 2009. We do not want basis differences

to contaminate our results, so we focus exclusively on CDS spreads.

Second, the CDS contract is written on standardized horizons, the most common

being 5 years, followed by 1, 3, 7 and 10 years. The contract is also standardized in

its terms and conditions. It comes, for example, with varying definitions of

documentation clauses that stipulate the different events that will be recognized as

defaults triggering the default payment by the counterparty. In contrast, bonds often

have embedded optionality and significantly different maturities, all of which

complicate cross-sectional comparisons compared to spreads extracted from the

CDS market.

372 N. Arora et al.

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Third, this data comes with the number of counterparty quotes available, a

potential measure of liquidity. The CDS data is provided by MarkIt, a benchmark

provider of credit market pricing data. MarkIt receives contributed CDS data from

market makers from their official books and records. This data then undergoes

rigorous cleaning, whereby MarkIt tests for stale data, outliers, and inconsistencies.

If a contribution fails any one of these tests, MarkIt discards it.

Our data for leverage, market capitalization, refinancing needs, and Level 1, 2, 3,

and total assets come from Compustat. All variables are measured at the start of

each month over the period August 2007 through to March 2009. All data are

obtained from quarterly financial statements. To ensure that the data was available

to the market, we wait two full months after the end of the respective fiscal period

before using the data. We carry forward financial statement data across months until

the next set of financial statements is released to the market. We require at least

three dealer quotes for a firm-month observation to be included in our analysis. We

remove firm-month observations where a given explanatory variable is in the

extreme percentiles. We only use CDS contracts with a modified restructuring (MR)

clause and those denominated in US dollars.

Finally, we focus our empirical analysis on US financial institutions. Our sample

firms are listed in ‘‘Appendix 2’’ and span four primary financial industries: (1)

banks, (2) diversified financials, (3) insurance, and (4) real estate. As Fig. 2 shows,

the ratio of Level 2 and Level 3 financial assets to total assets is far higher for

financial institutions. Given that our measure of asset reliability is based on Level 2

and Level 3 financial assets, we focus on this sector as we believe we have greater

construct validity for this set of firms. In Sect. 4.2.3, we describe our empirical

analysis using Level 2 and Level 3 assets as a measure of asset reliability for the full

set of firms with available credit market data.

Note a potential selection issue with our measure of asset reliability. Under

SFAS 159, firms can choose to use fair values for eligible financial assets and

liabilities when they were not previously required to record these financial assets

and liabilities at their fair values. Thus firm characteristics, such as general credit

risk, could drive cross sectional variation in our measures of asset reliability. This

would only be a problem for our empirical analysis to the extent that a firm elects

to use fair value as a basis for recurring measurement of its financial assets. The

option to use fair values on financial liabilities does not affect our measures

directly. To help quantify the extent of this problem, we have looked at Call

Reports filed after the effective date of SFAS 157 and SFAS159 to identify

financial institutions electing the fair value option. We can unambiguously

identify election of the fair value option under SFAS159 for 14 financial

institutions. In unreported analysis, we exclude these firms from our analysis. The

sample drops from 1,310 firm months to 1,121 firm months. We continue to find

very similar results. In particular, the economic and statistical significance of our

key variables (L23/FA, L2/FA, and L3/FA) remains unchanged. However, we

concede that our inability to construct a robust selection model for the fair value

option might limit our inferences.


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4 Results

4.1 Main analyses

We present descriptive statistics for our sample in Table 1. Over our entire sample

period, the mean (median) CDS spread decreases (increase) with the maturity of the

swap contract. The average (median) spread is 3.61 percent (0.94 percent) for one-

year maturity swaps, whereas it is 3.25 percent (1.16 percent), 3.08 percent (1.35

percent), and 2.76 percent (1.42 percent) for three-year, five-year, and 10-year

maturities, respectively. This shows that the average firm over the sample period

(financial crisis) has a downward sloping term structure. This is expected as the

yield curve is flatter, or inverted, when default risk is higher (see, e.g., Duffie and

Singleton 1999). It is also interesting to note the differences in credit term structure

implied from mean and median values. The difference is due to the primary relation

between credit risk and credit term structure discussed earlier. Firms that are riskier

(as evidenced by higher spread levels) will have a flatter credit term structure, and

the averages reported in Table 1 reflect this.

Our CDS1Y/CDS5Y and CDS5Y/CDS10Y variables capture the relative

steepness of the front end and middle section of a given firm’s yield curve,

respectively. Based on the whole sample period, the comparison of CDS1Y/CDS5Y

and CDS5Y/CDS10Y shows that the yield curve is steeper at the front portion

relative to the medium one, as demonstrated by one-year spreads being 79 percent

of five-year spreads, while 5-year spreads are 99 percent of 10-year spreads.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

EGY MAT IND CDI CST HC FIN IT TEL UTI

Sector

Lev

el 2

/3 a

sset

s re

lati

ve t

o t

ota

l ass

ets

Mean Q3 Median Q1

Fig. 2 Distribution of Level 2 and Level 3 financial assets as a fraction of total assets across sectors.Sector membership is based on the GICS schema. EGY, MAT, IND, CDI, CST, HC, IT, TEL, UTIL are theenergy, materials, industrials, consumer discretionary, consumer staples, health care, informationtechnology, telecommunication service, and utilities sectors, respectively

374 N. Arora et al.

123

Ta

ble

1D

escr

ipti

ve

stat

isti

cs

Var

iab

leM

ean

Std

.d

ev.

P1

P1

0P

25

P5

0P

75

P9

0P

99

CD

S1

Y0

.036

10

.069

00

.000

60

.00

14

0.0

03

00

.009

40

.037

40

.096

50

.33

84

CD

S3

Y0

.032

50

.053

50

.001

50

.00

28

0.0

05

00

.011

60

.036

40

.085

50

.25

46

CD

S5

Y0

.030

80

.046

10

.002

10

.00

41

0.0

07

00

.013

50

.034

70

.076

80

.21

56

CD

S1

0Y

0.0

27

60

.037

30

.003

50

.00

52

0.0

08

10

.014

20

.030

70

.066

90

.17

23

CD

S1

Y/C

DS

5Y

0.7

90

.44

0.2

00

.28

0.4

40

.70

1.1

21

.43

1.9

9

CD

S5

Y/C

DS

10

Y0

.99

0.2

00

.55

0.7

30

.84

0.9

81

.14

1.2

51

.45

To

tal

Ass

ets

20

7,1

45

36

7,7

47

1,9

68

4,7

03

9,0

89

26

,75

01

80

,31

47

89

,45

41

,71

5,7

46

MC

AP

8.9

91

.46

5.1

87

.27

8.0

49

.01

9.9

91

0.9

21

1.9

8

ML

EV

3.9

46

.89

0.0

30

.17

0.2

90

.86

2.8

81

4.9

72

5

DE

PT

H9

.26

5.3

13

35

81

21

72

5

RE

FIN

AN

CE

0.2

80

.40

00

0.0

20

.10

0.3

30

.97

1.5

9

L2

3/T

A0

.32

0.2

80

00

.00

0.3

50

.59

0.6

70

.85

L2

3/F

A0

.70

0.3

50

00

.59

0.8

60

.97

1.0

01

FA

/TA

0.3

80

.32

00

.00

0.0

30

.39

0.7

00

.79

0.9

1

See

‘‘A

pp

endix

1’’

for

var

iab

led

efinit

ion

s.T

her

ear

e1

,31

0fi

rm-m

on

tho

bse

rvat

ion

so

ver

the

per

iod

Au

gu

st2

00

7th

roug

hM

arch

20

09


123

Over the entire sample period, the average financial institution has market

leverage, MLEV, of 3.94; log market capitalization, MCAP, of 8.99; DEPTH of

9.26; and Refinance of 0.28. Our sample covers large and liquid firms.

We proxy for asset reliability based on the extent to which a company’s total

assets consist of Level 2 or Level 3 assets. Over the entire sample period, Level 2

and Level 3 assets represent 32 (70) percent of total (financial) assets. As discussed

in Sect. 2, we have chosen to focus on the reliability of financial assets as this is an

unambiguous identification of assets whose value is more uncertain to investors. We

are, of course, missing direct measures of uncertainty in the measurement of other

assets on the balance sheet (e.g., loan receivables, other held to maturity assets, and

property, plant and equipment). We are therefore at risk of incorrectly attributing

relations between credit term structure and our measure of asset reliability. In

particular, L23/TA could indicate the general asset composition of the firm, and

hence it could be a potential measure of credit risk. It is important to adequately

control for standard measures of credit risk in our multiple regression analysis and

also choose a sample of firms where we believe our proxy for asset reliability is less

subject to this criticism (e.g., financial institutions).

We document monthly averages of pairwise correlations in Table 2. We italicize

correlations that are insignificant. CDS1Y/CDS5Y and CDS5Y are positively

correlated (0.65 Pearson, 0.61 Spearman), consistent with the pattern in Fig. 1,

indicating that the credit term structure is flatter when default risk is higher. Larger

firms have steeper yield curves, as evidenced by a negative correlation between

CDS1Y/CDS5Y and MCAP (-0.18 Pearson, -0.08 Spearman). REFINANCE and

CDS1Y/CDS5Y are positively correlated (0.15 Pearson, 0.17 Spearman), indicating

that firms with a need to refinance their debt are perceived to be riskier in the short

term. These firms are likely to need to repay or refinance their debt in a market

characterized by little liquidity during our time period, thereby increasing

uncertainty about the default barrier, leading to higher short-term credit spreads

and flatter credit term structure. As hypothesized, L23/TA is positively correlated

with CDS1Y/CDS5Y (0.14 Pearson, 0.10 Spearman). This initial evidence is

consistent with a role for asset reliability in explaining cross-sectional variation in

security prices.

Turning to our multiple regression analysis, Table 3 shows the results of our

primary regression of CDS1Y/CDS5Y on measures of credit risk, liquidity risk, and

asset reliability. Regression coefficients are from pooled regressions with test

statistics based on clustered standard errors (by firm and month). Model I in Table 3

shows that CDS1Y/CDS5Y is positively associated with MLEV and CDS5Y,

indicating that higher credit risk results in a flatter yield curve. This model explains

42.1 percent of the variation in CDS1Y/CDS5Y. In model II, although MCAP is

only marginally significant, our proxies of credit risk, MLEV and CDS5Y, are

positively associated with the dependent variable, indicating again that firms with

higher credit risk have flatter credit term structures. Surprisingly, the coefficient on

DEPTH is strongly positive. This suggests that financial institutions with more

dealers making markets have higher levels of short-term credit risk, which is not

consistent with the traditional liquidity explanation for short-term credit spreads.

We conjecture that dealers chose to make markets in select financial institutions

376 N. Arora et al.

123

Ta

ble

2C

orr

elat

ion

s

CD

S1

Y/C

DS

5Y

CD

S5

YM

LE

VM

CA

PD

EP

TH

CD

S5

Y/C

DS

10

YR

EF

INA

NC

EL

23

/TA

L2

3/F

AF

A/T

A

CD

S1

Y/C

DS

5Y

0.6

51

0.4

90

-0

.18

10

.456

0.7

34

0.1

50

0.1

40

0.1

46

0.1

24

CD

S5

Y0

.613

0.3

25

-0

.45

30

.187

0.6

48

-0

.13

5-

0.0

37

-0

.050

-0

.069

ML

EV

0.5

45

0.5

47

-0

.08

40

.310

0.3

63

0.4

37

0.1

55

0.2

45

0.1

11

MC

AP

-0

.083

-0

.421

-0

.04

60

.333

-0

.144

0.3

48

0.0

24

0.1

39

0.0

70

DE

PT

H0

.413

0.1

67

0.2

87

0.3

58

0.4

47

0.2

41

0.2

82

0.2

68

0.2

92

CD

S5

Y/C

DS

10

Y0

.697

0.7

40

0.6

17

-0

.09

30

.439

0.0

69

0.0

35

-0

.004

0.0

12

RE

FIN

AN

CE

0.1

65

-0

.042

0.4

41

0.4

63

0.2

79

0.1

57

0.2

20

0.2

43

0.2

13

L2

3/T

A0

.098

-0

.226

-0

.03

60

.10

90

.313

-0

.017

0.1

60

0.5

41

0.9

70

L2

3/F

A0

.070

-0

.148

0.1

16

0.1

08

0.1

48

-0

.020

0.2

45

0.3

93

0.4

66

FA

/TA

0.0

83

-0

.246

-0

.07

60

.14

40

.320

-0

.033

0.1

21

0.9

53

0.2

19

See

‘‘A

pp

endix

1’’

for

var

iable

defi

nit

ions.

Pea

rson

(Spea

rman

)co

rrel

atio

ns

are

above

(bel

ow

)th

edia

gonal

.T

her

ear

e1,3

10

firm

-month

obse

rvat

ions

over

the

per

iod

August

2007

thro

ugh

Mar

ch2009.

Pai

rwis

eco

rrel

atio

nco

effi

cien

tsar

eco

mpute

dea

chm

onth

,an

dw

ere

port

aver

ages

acro

ssth

e20

month

sin

the

table

.T

oco

mp

ute

stat

isti

cal

signifi

cance

of

the

pai

rwis

eco

rrel

atio

ns,

we

use

the

Fam

aan

dM

acbet

hap

pro

ach.

Ita

lici

zed

coef

fici

ents

are

insi

gn

ifica

nt

atth

e5

per

cen

tle

vel


123

during the financial crisis and that these financial institutions were often very risky

entities (e.g., Morgan Stanley, Lehman, and Bear Stearns).

As before, MLEV and CDS5Y are positively, and MCAP is negatively associated

with CDS1Y/CDS5Y in Model III. We again see the positive association for

DEPTH. As expected, CDS5Y/CDS10Y is strongly positively associated with

CDS1Y/CDS5Y, indicating that firms with flatter medium credit term structures are

more likely to have a flatter short-term credit term structure. Finally, our proxy for

default barrier uncertainty, REFINANCE, is not significant in the multiple

regression analysis.

We introduce our main variable of interest in Models IV-VI, which document

that financial institutions with more Level 2 and Level 3 assets have higher ratios

of one-year CDS spreads to five-year CDS spreads. This result, as expected, is

stronger for the L23/FA variable. As discussed in Sect. 2, L23/FA is the purest

measure of asset reliability for our sample of firms. While these measures of asset

reliability can explain 1 to 2 percent of the cross-sectional variation in credit term-

structure, it is important to note that Model VI in Table 3 only finds marginal

support for the L23/FA variable after controlling for financial asset intensity, FA/

TA. We note that the additive decomposition of L23/TA into its components, L23/

FA and FA/TA, should be in log space. These regressions are reported in Table 4,

and again we find strong evidence of a positive association between L23/FA and

CDS1Y/CDS5Y but only marginal significance after controlling for FA/TA. As

discussed earlier, we our priors are for a stronger relation between asset

(un)reliability and credit spreads for Level 3 as opposed to Level 2 financial

assets. We test this in Sect. 4.2.1 below.

In Models VII-IX, we assess the ability of asset reliability to explain credit term

structure after controlling for credit risk, liquidity, default barrier uncertainty, and

general term-structure as captured in CDS5Y/CDS10Y. We continue to find (1) the

expected positive relation between MLEV and CDS5Y and CDS1Y/CDS5Y, (2) the

expected strong positive relation between CDS1Y/CDS5Y and CDS5Y/CDS10Y,

and (3) the surprising positive relation between DEPTH and CDS1Y/CDS5Y. More

importantly, we find a strong positive relation between L23/FA and CDS1Y/

CDS5Y, and this is robust to controlling for financial asset intensity, FA/TA, in

Model IX. The results in Table 4 show that these relations are robust to examining

the decomposition of L23/TA in log form.

Overall, our results in Tables 3 and 4 are consistent with our hypothesis. Asset

reliability is an important determinant of security prices and is associated with

flatter credit term structure in short-term credit spreads, as suggested by the

theoretical valuation model of Duffie and Lando (2001).

4.2 Additional analyses

In this section, we discuss the robustness of our primary regression analyses along

several dimensions.

378 N. Arora et al.

123

Ta

ble

3P

rim

ary

reg

ress

ion

anal

ysi

s

CD

S1Y

CD

S5Y

i;t¼

aþ

b CD

S5Y

CD

S5Y

i;tþ

bM

LE

VM

LE

Vi;

tþ

b MC

APM

CA

Pi;

tþ

bD

epth

Dep

thi;

tþb

CD

S5Y

CD

S1

0Y

CD

S5Y

CD

S1

0Y

i;tþ

b RE

FIN

AN

CE

RE

FIN

AN

CE

i;tþ

bL

23

TA

L2

3T

Ai;

tþ

e i;t

Mo

del

III

III

IVV

VI

VII

VII

IIX

a0

.58

05

0.7

79

9-

0.3

77

30

.74

31

0.6

74

80

.672

0-

0.4

22

8-

0.4

65

9-

0.4

74

8

10

.48

3.5

9-

2.0

91

0.7

21

0.0

11

0.1

7-

2.1

9-

2.3

6-

2.3

7

b CD

S5Y

4.3

39

24

.084

61

.69

65

1.7

23

71

.748

11

.74

86

3.5

53

.22

2.2

12

.25

2.3

12

.31

b ML

EV

0.0

20

20

.014

50

.00

96

0.0

09

60

.008

20

.00

83

3.6

92

.82

2.3

22

.22

2.0

12

.00

b MC

AP

-0

.044

7-

0.0

26

9-

0.0

24

0-

0.0

27

1-

0.0

26

3

-1

.97

-1

.80

-1

.56

-1

.80

-1

.72

b DE

PT

H0

.025

10

.00

79

0.0

06

70

.006

20

.00

59

5.4

63

.01

2.3

72

.40

2.1

2

b CD

S5Y

_C

DS10Y

1.2

49

91

.260

41

.275

41

.27

79

9.1

09

.07

9.3

39

.27

b RE

FIN

AN

CE

0.0

58

50

.048

50

.048

70

.04

57

1.0

70

.85

0.8

60

.80

b L23_T

A0

.15

84

0.0

72

6

1.2

11

.25

b L23_F

A0

.17

12

0.1

57

00

.126

30

.11

75

2.4

51

.53

3.4

52

.67

b FA

_T

A0

.033

10

.02

30

0.2

30

.41

R2

0.4

20

50

.491

70

.69

66

0.0

10

20

.01

93

0.0

19

70

.698

40

.705

70

.70

59

See

‘‘A

pp

end

ix1’’

for

var

iable

defi

nit

ions.

Reg

ress

ion

coef

fici

ents

are

from

poole

dre

gre

ssio

ns

of

1,3

10

firm

-month

obse

rvat

ions

over

the

per

iod

August

2007

thro

ugh

Mar

ch2

00

9.

Tes

tst

atis

tics

are

bas

edo

ncl

ust

ered

stan

dar

der

rors

(by

firm

and

mo

nth

)an

dar

ein

ital

ics


123

Ta

ble

4S

upple

men

tal

regre

ssio

nan

alysi

s

CD

S1Y

CD

S5Y

i;t¼

aþ

bC

DS

5Y

CD

S5Y

i;tþ

bM

LE

VM

LE

Vi;

tþ

bM

CA

PM

CA

Pi;

tþ

bD

epth

Dep

thi;

tþ

bC

DS5Y

CD

S10Y

CD

S5Y

CD

S10Y

i;tþ

bR

EF

INA

NC

ER

EF

INA

NC

Ei;

tþ

blo

gðL

23

TAÞ

log

L23

TA

i;t

�� þ

e i;t

Model

III

III

IVV

VI

VII

VII

IIX

a0.5

805

0.7

799

-0.3

773

0.8

260

0.8

191

0.8

057

-0.3

359

-0.3

504

-0.3

389

10.4

83.5

9-

2.0

911.7

613.0

611.5

1-

1.8

0-

1.9

4-

1.8

4

bC

DS

5Y

4.3

392

4.0

846

1.6

965

1.7

258

1.6

721

1.6

960

3.5

53.2

22.2

12.3

02.2

22.2

7

bM

LE

V0.0

202

0.0

145

0.0

096

0.0

093

0.0

084

0.0

088

3.6

92.8

22.3

22.1

42.0

92.0

9

bM

CA

P-

0.0

447

-0.0

269

-0.0

267

-0.0

288

-0.0

283

-1.9

7-

1.8

0-

1.7

8-

1.9

7-

1.9

3

bD

EP

TH

0.0

251

0.0

079

0.0

048

0.0

055

0.0

048

5.4

63.0

11.8

42.1

31.8

6

bC

DS

5Y

_C

DS

10

Y1.2

499

1.2

999

1.3

026

1.3

104

9.1

09.5

69.8

69.8

6

bR

EF

INA

NC

E0.0

585

0.0

291

0.0

426

0.0

331

1.0

70.5

10.7

80.5

9

blo

g(L

23

_T

A)

0.0

128

0.0

219

1.1

14.3

7

blo

g(L

23

_F

A)

0.0

205

0.0

277

0.0

239

0.0

160

2.5

11.5

45.3

02.1

7

blo

g(F

A_

TA

)-

0.0

103

0.0

122

-0.4

61.4

4

R2

0.4

205

0.4

917

0.6

966

0.0

054

0.0

121

0.0

136

0.7

096

0.7

108

0.7

127

See

‘‘A

ppen

dix

1’’

for

var

iable

defi

nit

ions.

Reg

ress

ion

coef

fici

ents

are

from

poole

dre

gre

ssio

ns

of

1,3

10

firm

-month

obse

rvat

ions

over

the

per

iod

August

2007

thro

ugh

Mar

ch2009.

Tes

tst

atis

tics

are

bas

edon

clust

ered

stan

dar

der

rors

(by

firm

and

month

)an

dar

ein

ital

ics

380 N. Arora et al.

123

4.2.1 Separating Level 2 and Level 3 financial assets

Our primary regression analysis treats Level 2 and Level 3 assets as having

equivalent asset reliability issues. It is reasonable to assert that there will be lower

asset reliability for Level 3 financial assets, as these are financial assets whose value

is determined largely by discretionary management inputs. In this subsection, we

discuss the results from separating these two types of uncertain financial assets.

At the outset, note the relatively small magnitude of Level 3 financial assets. For

our sample of financial institutions, Table 1 shows that 32 percent of total assets

consisted of Level 2 and Level 3 financial assets. However, Level 2 assets comprise

the vast majority. Specifically, the average firm has 29 (3) percent of its total assets

in Level 2 (3) financial assets. Despite the small magnitude of total assets reflected

in Level 3 financial assets, this is the category of financial assets with the greatest

amount of management discretion and lowest reliability. For highly levered entities

such as the financial institutions in our primary sample, even small changes in the

point estimates of asset value can have a very large impact on credit spreads. Hence

our presumption of a stronger association between CDS1Y/CDS5Y and Level 3

financial assets relative to Level 2 financial assets.

Table 5 reports the regression analysis splitting L23/TA and L23/FA into their

component variables: L2/TA, L3/TA, L2/FA, and L3/FA, respectively. We only

report the log specification for the sake of brevity. We find strong evidence that L3/

TA and L3/FA are positively associated with CDS1Y/CDS5Y, whereas L2/TA and

L2/FA are not. Furthermore, the decomposed asset reliability variables can explain

considerably more variation in credit term structure. Models IV-VI in Table 4 have

explanatory power of between 1 and 2 percent, compared to 4 to 6 percent or more

for Models IV–VI in Table 5. We view this as strong evidence in support of H1.

To help visualize the significance of our results, in Fig. 3, we plot the average

credit term structure for a subset of financial institutions with LOW and HIGH

levels of asset reliability. For the 13 financial institutions in our sample with no

Level 2 or Level 3 financial assets, we classify them as having HIGH asset

reliability. For the 14 banks with more than 21 % of their financial assets in the form

of Level 3, we classify them as having LOW asset reliability. For these sub-groups

of financial institutions, we take the average spread across all CDS contracts traded

during the August 2007 through March 2009 period. Figure 3 shows that the group

of banks having LOW asset reliability has an inverted credit term structure relative

to the group of banks with HIGH asset reliability. Furthermore, the difference in

CDS spreads for the longer term contracts (i.e., 5, 7 and 10 years) are muted,

suggesting that this difference in short-term credit spreads is attributable to our

treatment variable, asset reliability, and not simply to underlying credit risk.

4.2.2 Controlling for jump risk

To address the possibility that our asset reliability measures could be capturing jump

risk, we directly control for jump risk. We use implied volatility data from the

equity option markets for a subset of our sample where we can extract out-of-the-

money put option implied volatility as our proxy for jump risk. We de-lever this


123

Ta

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a0

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b CD

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84

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4

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53

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b ML

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50

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b MC

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b RE

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0.0

58

50

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00

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1.0

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60

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b log(L

2_T

A)

-0

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00

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8

-1

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0.4

9

b log(L

3_T

A)

0.0

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50

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b log(L

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0.0

01

40

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84

0.0

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80

.002

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0.1

11

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0.8

20

.26

382 N. Arora et al.

123

Ta

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‘‘A

pp

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for

var

iab

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10

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erio

dA

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ust

20

07

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ug

h

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ch2

00

9.

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tst

atis

tics

are

bas

edo

ncl

ust

ered

stan

dar

der

rors

(by

firm

and

mo

nth

)an

dar

ein

ital

ics


123

equity implied volatility (i.e., equity implied volatility divided by market leverage),

and we include the resulting measure of asset volatility as an additional explanatory

variable. We can only obtain reliable options market data for a subset of our sample

(847 firm-month observations). Clearly our measure of jump risk is not perfect. For

example, we use options with a 90-day maturity and associate the extracted measure

of jump risk for credit derivatives that have a much longer maturity. Likewise,

reverse-engineering the implied volatility parameter from observed option prices

requires us to make assumptions about dividend payout and risk-free rates. Our

results in this subsection should be interpreted subject to these sources of

measurement error.

In Table 6, we report the regression results after including asset volatility. As

expected, we find a very strong relation between asset volatility and CDS1Y/

CDS5Y.

Model II in Table 6 shows that 46.4 percent of the cross-sectional variation in

CDS1Y/CDS5Y can be explained by asset volatility alone. Even after controlling

for jump risk, we still find that L3/TA and L3/FA are positively associated with

CDS1Y/CDS5Y, whereas L2/TA and L2/FA are insignificant, consistent with our

priors of asset reliability issues being concentrated in Level 3, as opposed to Level

2, financial assets.

4.2.3 All firms

Our primary sample includes only financial institutions. The reason for this choice is

that our proxy for asset reliability, L23/TA, reflects more of the total asset value for

this sector relative to other sectors. However, as Fig. 2 shows, Level 2 and Level 3

financial assets are held by firms across many sectors, although they represent a

smaller fraction of total assets.

With that limitation in mind, Table 7 reports our regression specification

estimated on the full sample of 7,947 firm-months for the August 2007 to March

2009 period. For sake of brevity, we only report the log specification. (Results

are similar for the raw specification.) We report the results for the combined

L23/TA specification rather than the split specification because nonfinancial

institutions rarely hold Level 3 financial assets. For the full sample of firms, we

find the same general results as for financial institutions: (1) measures of credit

risk, CDS5Y and MLEV, are positively associated with CDS1Y/CDS5Y; (2)

CDS5Y/CDS10Y is strongly positively related to CDS1Y/CDS5Y; (3) REFI

NANCE is now positively associated with CDS1Y/CDS5Y as expected; and (4)

DEPTH is not reliably associated with CDS1Y/CDS5Y. In Models IV–IX, we

find very strong evidence that L23/TA and L23/FA are positively associated with

CDS1Y/CDS5Y. We interpret this as general support for asset reliability

affecting security prices consistent with the theoretical valuation model of Duffie

and Lando (2001). But we have chosen to focus our primary empirical analysis

on financial institutions where we believe we have greater construct validity for

our proxy of asset reliability.

384 N. Arora et al.

123

4.2.4 Alternative measures of accounting uncertainty

To address the possibility that our asset reliability proxies may be correlated with

more general concerns of financial reporting transparency, we also control for the

annual report readability measure from Li (2006). A benefit of this additional

measure is that it helps to differentiate our analysis from that of Yu (2005).As

discussed above, Yu (2005) uses AIMR disclosure scores as his proxy for the asset

reliability construct in Duffie and Lando (2001). Unfortunately, AIMR scores were

produced only up to 1996, so we cannot use the AIMR measures with our sample. In

unreported tests, we have confirmed with Yu’s original data that the significance of

his findings between AIMR scores and short-term credit spreads are sensitive to the

inclusion of interactions of standard credit risk measures (such as leverage and

volatility) and his measure of disclosure quality.

An alternative is to use general measures of financial report readability. Our

priors are for no association between this transparency measure and credit term

structure. The theory developed by Duffie and Lando (2001) speaks to asset

reliability, whereas the FOG index in Li (2006) and the AIMR scores in Yu (2005)

are more general measures of transparency and information uncertainty.

We can measure the FOG score for a subset of our firms. The sample size for this

analysis is 1,115 firm-months (down from 1,310 firm-months). For this subsample,

we find that L23/TA measure is positively correlated with Li’s reporting

transparency measure (0.16 Pearson, 0.09 Spearman). When we include the

reporting transparency variable by itself in the regression, it is not significantly

associated with CDS1Y/CDS5Y. Furthermore, when included in addition to our

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0 1 2 3 4 5 6 7 8 9 10

Tenor

CD

S5Y

HIGH Asset Reliability (L23/FA=0) LOW Asset Reliability (L3/FA > 0.21)

Fig. 3 Credit term structure for financial institutions with LOW (HIGH) asset reliability. Financialinstitutions are split into two groups based on the composition of their respective financial assets.Financial institutions with no Level 2 or Level 3 financial assets are classified as HIGH asset reliability(13 firms). Financial institutions with Level 3 financial assets representing more than 20 percent offinancial assets (e.g., L3/FA [ 0.20) are classified as LOW asset reliability (14 firms)


123

asset reliability variable and control variables, the financial reporting transparency

measure remains insignificant, whereas our asset reliability variables retain their

significance.

Table 6 Supplemental regression analysis

CDS1YCDS5Yi;t

¼ aþ bCDS5Y CDS5Yi;t þ bMLEV MLEVi;t þ bMCAPMCAPi;t þ bDepthDepthi;t þ bCDS5Y CDS10Y

CDS5YCDS10Yi;t

þ bREFINANCEREFINANCEi;t þ blogðL2 TAÞ log L2TAi;t

� �þ blogðL3 TAÞ log L3

TAi;t

� �

þbrASSETrASSETi;t þ ei;t

Model

I II III IV V

a -0.2262 0.2946 -0.0267 0.0032 0.0018

-1.25 5.19 -0.16 0.02 0.01

bCDS5Y 1.4017 1.8695 1.9573 2.0167

2.22 2.99 3.23 3.25

bMLEV 0.0081 0.0074 0.0057 0.0062

1.64 1.47 1.20 1.21

bMCAP -0.0297 -0.0357 -0.0410 -0.0409

-1.90 -2.38 -2.60 -2.61

bDEPTH 0.0122 0.0099 0.0099 0.0095

5.25 3.59 3.65 3.51

bCDS5Y_CDS10Y 0.9253 0.9585 0.9818 0.9890

7.19 7.28 7.63 7.70

bREFINANCE 0.0672 0.0512 0.0585 0.0559

1.24 0.91 1.11 1.02

blog(L2_TA) 0.0005

0.06

blog(L3_TA) 0.0255

1.94

blog(L2_FA) 0.0043 0.0021

0.66 0.28

blog(L3_FA) 0.0269 0.0250

3.32 2.73

blog(FA_TA) 0.0050

0.60

brASSET 0.2968 0.8535 0.2245 0.2035 0.1936

3.53 10.10 2.64 2.60 2.40

R2 0.7231 0.4637 0.7297 0.7353 0.7356

Reduced sample with available equity option market data and unlevered implied volatility

See ‘‘Appendix 1’’ for variable definitions. Regression coefficients are from pooled regressions of 847

firm-month observations over the period August 2007 through March 2009. Test statistics are based on

clustered standard errors (by firm and month) and are in italics. The smaller sample size in this table is due

to the additional requirement of liquid equity options

386 N. Arora et al.

123

4.2.5 Excluding DEPTH

The primary regression results described in Sect. 4.1 showed strong evidence of an

unexpected positive association between credit term structure and DEPTH (our

measure of liquidity). Comparing the results for the full sample of firms reported in

Table 7, we find that this positive association is concentrated in the sample of

financial firms. We conjectured earlier that this positive relation is likely to be

endogenous as dealers are inclined to make markets for issues that are likely to

generate trading activity. During the financial crisis, there was considerable activity

for large financial institutions such as Morgan Stanley, Bear Stearns, Barclays, RBS,

and Lehman, all of whom faced considerable short-term credit risk leading to high

short term credit spreads.

In unreported analysis, we have estimated the regressions reported in Tables 3

and 4 without the DEPTH variable. Our inferences are unchanged from excluding

this variable. Specifically, Models VII–IX continue to have an explanatory power

between 70 and 71 percent, and our asset reliability variables continue to exhibit

similar levels of significance. The coefficient on L23/FA in Model IX from Table 3

(4) is 0.12 (0.02) with a robust t-statistic of 2.67 (2.17), whereas the coefficient on

L23/FA in Model IX for the re-estimated version of Table 3 (4) excluding DEPTH

is 0.12 (0.02), with a robust t-statistic of 2.63 (2.16).

4.2.6 Switching the dependent variable to CDS3Y/CDS5Y

All of our empirical analysis has focused on explaining cross-sectional variation in

CDS1Y/CDS5Y as the theoretical valuation model of Duffie and Lando (2001)

notes that the role for asset reliability to explain credit spreads is concentrated in

short-term spreads. We have replicated our analysis using CDS3Y/CDS5Y as the

dependent variable. We confirm that measures of asset reliability explain less of the

variation in CDS3Y/CDS5Y relative to CDS1Y/CDS5Y. Specifically, Models IV-

VI in Table 5 show that between 4 and 6 percent of the cross-sectional variation in

CDS1Y/CDS5Y could be explained by L2/FA, L3/FA, and FA/TA. In an equivalent

regression with CDS3Y/CDS5Y as the dependent variable, L2/FA and L3/FA can

explain only between 2 and 3 percent of the cross-sectional variation. Furthermore,

while model IX in Table 5 reports a robust test statistic of 3.45 for the L3/FA

variable, an equivalent regression with CDS3Y/CDS5Y as the dependent variable

produces a marginally significant coefficient on L3/FA (robust test statistic of 1.74).

We interpret this evidence supporting the role of asset reliability to explain short-

term credit spreads, consistent with H1.

4.2.7 Firm fixed effects

In unreported tests, we conduct our regression analyses including firm fixed effects

with standard errors clustered by firm. The results from this analysis suggest that the

relation between our measures of asset reliability and credit term structure are

robust to controlling firm specific credit term structures over our period.

Specifically, estimates of Model IX from Table 4 (Table 5) continue to show a


123

robust relation between L23/FA (L3/FA) and CDS1Y/CD5Y, with a robust test

statistic of 2.37 (3.44). These test statistics are similar in magnitude to the 2.17 and

3.45 reported in Tables 4 and 5, respectively. The remaining determinants of credit

term structure retain their significance in these fixed effect regressions. (For

example, the front end of credit curves is flatter for riskier firms as measured by

CDS5Y and MLEV.)

4.2.8 Robust regression

In further unreported analyses, we have re-estimated all of our regressions using

quantile regressions. Our dependent variable, CDS1Y/CDS5Y, has a skewed

distribution, and a Kolmogorov–Smirnov test rejects normality at conventional

levels. To help address this issue and minimize the influence of extreme values of

our dependent variable, we have used median regressions with standard errors

clustered by firm and month. The results from these robust regressions are very

similar to those reported in Tables 3, 4, and 5. Specifically, re-estimating regression

Model V (IX) from Table 5 using quantile regression, we find robust test statistics

of 1.92 (2.39) on our independent variable of interest, L3/FA. The other

determinants of credit term structure (i.e., CDS5Y, MLEV, MCAP, DEPTH,

CDS5Y/CDS10Y, REFINANCE) continue to exhibit similar significance and

directional associations with CDS1Y/CDS5Y.

4.2.9 Credit spread level regressions

Our reported regression analysis focuses on explaining cross-sectional variation in

CDS1Y/CDS5Y. We have deliberately selected this dependent variable as we

believe it best fits the predictions of Duffie and Lando (2001). Their structural

model introduces asset reliability as an additional determinant of short-term credit

spreads relative to longer-term spreads. That said, we have also run our regression

analysis using CDS1Y as our dependent variable. Specifically, we regress CDS1Y

on CDS5Y, CDS10Y, and the same set of determinants as used in Tables 4 and 5.

We continue to find a robust positive association between L3/FA and CDS1Y

(robust test statistic of 2.52) with this alternative spread level specification.

4.2.10 Expected Default Frequency (EDF)

Our reported regression analysis has used CDS5Y and MLEV as the primary

measures for credit risk. A potential concern with this specification is the inclusion

of CDS5Y in both the dependent and independent variables with the possibility of a

mechanical relation. First, we note that, if there were a mechanical relation, this

would lead to a negative association (not the positive one we find). Second, we have

re-estimated all of our regression analysis using the Expected Default Frequency

(EDF) measure from Moody’s/KMV as an independent variable instead of CDS5Y.

EDF measures physical default probability, which is a direct measure of credit risk.

Using EDF in lieu of CDS5Y we find very similar results to those reported in

Tables 3, 4, and 5. As an example, the regression coefficient for L3/FA in Model IX

388 N. Arora et al.

123

Ta

ble

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1

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b RE

FIN

AN

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0.0

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30

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80

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b log(L

23_T

A)

0.0

14

30

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01

3.0

33

.62

b log(L

23_F

A)

0.0

08

70

.007

70

.008

20

.007

2

3.6

93

.09

4.5

73

.91


123

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ble

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nti

nu

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gðL

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del

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III

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0.0

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5

Fu

llsa

mp

leo

ffi

rms

See

‘‘A

pp

end

ix1

’’fo

rvar

iable

defi

nit

ions.

Reg

ress

ion

coef

fici

ents

are

from

poole

dre

gre

ssio

ns

of

7,9

47

firm

-month

obse

rvat

ions

over

the

per

iod

August

2007

thro

ugh

Mar

ch2009.

Tes

tst

atis

tics

are

bas

edon

clust

ered

stan

dar

der

rors

(by

firm

and

month

)an

dar

ein

ital

ics.

Eac

hre

gre

ssio

nin

cludes

ase

tof

sect

or

fixed

effe

cts

bas

edo

nth

e

2-d

igit

GIC

Ssc

hem

a.(C

oef

fici

ents

no

tre

po

rted

for

sak

eo

fb

rev

ity

.)

390 N. Arora et al.

123

of Table 5 becomes 0.03, with a robust test statistic of 3.06. This compares to the

reported regression coefficient of 0.03 and associated test statistic of 3.45 in

Table 5. In summary, our results are very robust to the use of EDF in lieu of CDS5Y

as a measure of credit risk. We prefer to use CDS5Y as our primary measure of

credit risk as it reflects the totality of credit market information, and EDF is known

to be less useful in predicting default for financial institutions (see, e.g., Crosbie and

Bohn 2003).

4.2.11 Netting of derivative positions in financial asset disclosures

A few of our sample firms report Level 1/2/3 financial asset disclosures including

derivative positions for which they do not have true economic exposures. Most of

these firms report the total net effect of these derivative positions, but they do not do

so in a manner that allows us to accurately distribute that net amount across Level

1/2/3 financial assets. In our sample, only 15 out of 87 firms report any level of

netting due to derivative positions. Of these 15, only nine report an economically

material amount (i.e., greater than 5 percent of total assets). These firms are JP

Morgan, Bank of America, Merrill Lynch, Wells Fargo, Goldman Sachs, Morgan

Stanley, AIG, Fannie Mae, and Federal Home Loans. For all sample firms, we

manually collected the footnote disclosures pertaining to Level 1/2/3 financial

assets, as Compustat does not capture netting information.

These firms are the larger and more systemically important institutions in our

sample. They engage in considerable use of derivatives that are subsequently netted

out in fair value disclosures. It is therefore important to be sure that the effects we

document are attributable to the reliability of financial assets and not simply the

systemic importance of certain institutions during the crisis period. When we

exclude these nine firms from our analysis, our results are unchanged. In addition,

our primary analyses, which focus on the L23/FA measure, are not distorted by the

grossing up of derivative positions, as the numerator and denominator are both

affected by the inclusion of these derivative positions.

5 Conclusion

In this paper, we document an economically significant association between asset

reliability and credit spreads. Using a primary sample of 1,310 firm-month

observations for liquid US financial institutions, our results suggest that the credit

term structure is flatter at the short end for firms with lower asset reliability as

captured by larger magnitudes of Level 2 and, especially, Level 3 financial assets

relative to total (or total financial) assets. Our findings are robust to a variety of

control variables and sensitivity analyses and are consistent with theoretical

valuation models.

We introduce credit term structure as a candidate measure for the identification of

settings where asset reliability concerns are expected to be greatest. Future research

could extend our analysis to examine alternative proxies of asset reliability. While

we chose to focus on the uncertainty associated with the valuation of financial assets


123

for a set of financial institutions, there are clearly other assets on the balance sheet

(e.g., receivables; inventory; property, plant and equipment; deferred tax assets; and

intangibles) where concerns exist about reliability. Previous research has examined

the implications of asset reliability for equity returns (e.g., Richardson et al. 2005).

These alternative measures of asset reliability could also be examined to assess,

more generally, the impact of asset reliability on the term structure of credit spreads.

Our empirical analysis offers a new setting for academics and non-academics

interested in understanding how asset reliability influences security prices. As

secondary market trading has expanded greatly in credit markets, there is now a rich

panel of data to be used to enhance understanding of accounting attributes such as

relevance and reliability to a broader set of capital market participants. Such

research should be relevant to academics and non-academics interested in

understanding determinants of security valuation and the consequences of standard

setting choices impacting attributes such as the trade-off between relevance and

reliability.

Acknowledgments We thank seminar participants at Bocconi University, London Business School,

London School of Economics, University of Sydney, and the MEAFA Conference. We also thank Darrell

Duffie, Paul Dunmore, John Hand, Anya Kleymenova, S. P. Kothari, Francis Longstaff, Doron Nissim

(editor), Stephen Penman, Stephen Schaefer, Lakshmanan Shivakumar, Kari Sigurdsson, Regina

Wittenberg-Moerman, Fan Yu, Bin Zeng, and two anonymous referees for their useful comments. We are

grateful to Feng Li for making available his measures of financial reporting transparency. We also thank

Jing Zhang at Moody’s Analytics for providing us with EDF data. An earlier version of this paper was

titled ‘‘Asset measurement uncertainty and credit term structure.’’ The views expressed here are those of

the authors and do not reflect the views of Citadel LLC, its affiliates, or employees.

Appendix 1: Variable definitions

CDS1Y/CDS5Y: The ratio of the spread on a 1-year CDS contract to the spread on a

5-year CDS contract. It measures the relative steepness of the front end of a given

firm’s credit term structure.

CDS5Y/CDS10Y: The ratio of the spread on a five-year CDS contract to the

spread on a 10-year CDS contract. It measures the relative steepness of the middle

portion of a given firm’s credit term structure.

MCAP: The log of market capitalization.

MLEV: Market leverage, measured as the ratio of the sum of short-term and long-

term debt to market capitalization.

DEPTH: The number of dealers providing quotes for the 5-year CDS contract as

reported in the MarkIt database.

REFINANCE: The log ratio of short-term debt to long-term debt. It is a measure

of companies’ need to access debt markets in the near term.

L23/TA: The ratio of financial assets marked to market as either Level 2 or Level

3 assets under FAS157 relative to total assets.

L2(3)/TA: The ratio of financial assets marked to market as Level 2 (3) assets

under FAS157 relative to total assets.

L23/FA: The ratio of financial assets marked to market as either Level 2 or Level

3 assets under FAS157 relative to total financial assets.

392 N. Arora et al.

123

L2(3)/FA: The ratio of financial assets marked to market as Level 2 (3) assets

under FAS157 relative to total financial assets.

CDSJY: The quoted spread for a given firm’s credit default swap contract for J

year maturity, where J = 1, 3, 5 or 10.

rAssets: Our proxy for jump risk. It is the option-implied volatility of a liquid out-

of-the-money put option with maturity of 90 days.

All variables are measured at the start of each month over the period August 2007

through March 2009. We stop our analysis at March 2009 due to the changes in the

CDS market at that time.

We require at least three dealer quotes for a firm-month observation to be

retained. We remove firm-month records where a given variable is in the extreme

percentiles (i.e., delete top and bottom 1 percent).

We use US dollar-denominated CDS contracts with a modified restructuring

(MR) clause.

Appendix 2: Sample firms

Banks

COUNTRYWIDE FINANCIAL CORP POPULAR INC

FANNIE MAE RADIAN GROUP INC

FEDERAL AGRICULTURE MT REGIONS FINANCIAL CORP

FEDERAL HOME LOAN MORT SUNTRUST BANKS INC

KEYCORP U S BANCORP

MGIC INVESTMENT CORP/W WACHOVIA CORP

NATIONAL CITY CORP WELLS FARGO & CO

PMI GROUP INC WMI HOLDINGS CORP

Diversified financials

AFFILIATED MANAGERS GR GOLDMAN SACHS GROUP IN

AMERICAN EXPRESS CO JANUS CAPITAL GROUP IN

AMERIPRISE FINANCIAL I JPMORGAN CHASE & CO

BANK OF AMERICA CORP LEGG MASON INC

BEAR STEARNS COMPANIES INC. LEHMAN BROTHERS HOLDINGS INC

CAPITAL ONE FINANCIAL MERRILL LYNCH & CO INC

CIT GROUP INC MORGAN STANLEY

DISCOVER FINANCIAL SVC SCHWAB (CHARLES) CORP

E TRADE FINANCIAL CORP SLM CORP

FRANKLIN RESOURCES INC STATE STREET CORP

Insurance

AFLAC INC LINCOLN NATIONAL CORP

ALLSTATE CORP LOEWS CORP

AMBAC FINANCIAL GROUP MARKEL CORP

AMERICAN FINANCIAL GROUP INC. MARSH & MCLENNAN COS

AMERICAN INTERNATIONAL GROUP MBIA INC

AON PLC METLIFE INC


123

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Documents

Asset reliability and security prices: evidence from credit markets