CDS and the Liquidity Provision

in the Bond Market

_______________

Massimo MASSA

Lei ZHANG

2012/114/FIN

CDS and the Liquidity Provision in the Bond Market

Massimo Massa*

Lei Zhang**

* Professor of Finance The Rothschild Chaired Professor of Banking Co-Director of the

Hoffmann Research Fund at INSEAD, Boulevard de Constance 77305 Fontainebleau Cedex,

France. Email: Massimo.massa@insead.edu

** Nanyang Business School, Nanyang Technological University, Division of Banking and

Finance, 50 Nanyang Avenue, Singapore 639798. Email: zhangl@ntu.edu.sg

Corresponding author

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Abstract

We study the effect of credit default swap (CDS) on the corporate bond market. We

argue that CDS, by reducing the need of investors to liquidate the bonds in the face of

credit deterioration of the issuer, reduces fire sale risk and provides bond liquidity.

Given that bond investors are segmented by regulation – i.e., insurance companies,

banks, and pension funds are subject to the risk-based capital requirements and in

principle only hold high quality assets, – we expect the liquidity provision role of

CDS to be concentrated among investment grade bonds. We test this hypothesis using

a comprehensive sample of US corporate bonds with CDS contracts information over

the 2001-2009 period. We show that the presence of CDS reduces yield spreads and

increases liquidity for investment grade bonds. This effect is stronger during the

financial crisis period. We provide a proper instrumental variable identification that

pins down the need for CDS contracts by exploiting the level of loan concentration of

the banks which the bond issuer borrows from. We also examine two events that have

been shown to trigger forced sales by bond investors: bond rating downgrades from

investment grade to high yield, and the selling pressure of property insurance

companies following Hurricane Katrina. In both cases, the presence of CDS contracts

lowers the impact of fire sales by reducing the drop in bond liquidity and the rise in

yield spreads. Our results have important normative implications, as they suggest that

– at least for the class of investment grade bonds – CDS may actually help to reduce

risk contagion around financial crises.

Keywords: CDS, bond liquidity, yield spreads, fire sales, crisis

JEL Classification: G10, G15, G21

Electronic copy available at: http://ssrn.com/abstract=2164675

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Introduction

The recent financial crisis has brought to the fore the role of credit derivatives and its implications for

the financial markets. The markets for credit derivatives, in particular, credit default swaps (CDSs)

have been found controversial in the spread of the financial crisis (Allen and Carletti, 2006, Stulz,

2010). Chris Wolf, a hedge fund manager, stated that “CDS has become essentially the dark matter of

the financial universe”.1 George Soros has called for most or all trading in credit default swaps to be

banned or strictly regulated.2 The rapid rise of such a market has made the problem even more acute:

the total notional amount of the CDS market has grown from $6 trillion to $41 trillion from 2004 to

20083.

The “indictment” is based on the way credit insurance would affect the debtor-creditor relation in

the case of distress of the borrower, turning creditors into “empty creditors” (Bolton and Oehmke,

2011).4 Indeed, CDS contracts may affect the incentives of the lenders and increase the risk of

bankruptcy of the borrowing firm. The lenders, if protected in the case of distress, would have lower

incentives to restructure the debt, as they can benefit from their CDS positions (“the empty creditor

problem”). The lower risk in the presence of distress, would also reduce the lenders’ incentives to

effectively monitor the borrowers (Thompson, 2007, Parlour and Winton, 2008).

Regardless of this almost consensual theoretical dark view, the empirical evidence on the impact

of CDS on the underlying firm is mixed at best. On the one hand, the presence of CDS contracts

appears to reduce loan quality, increases bankruptcy risk and borrowing costs, and lowers the

efficiency of the bond market (Ashcraft and Santos, 2007, Purnanandam, 2011, Subrahmanyan et al.,

2011, Kalimipalli and Nayak, 2011). On the other hand, CDS seems to stimulate bank credit supply

and improve the borrowing terms – e.g., maturity and spreads – of the firms for which such an

                                                            1 “The $55 trillion question”, by Nicholas Varchaver, Fortune Magazine, Sep. 30, 2008. 2 “One way to stop bear raids”, Wall Street Journal, Mar. 24, 2009. 3 This figure is from the statistics provided by the Bank of International Settlements. 4 The use of derivatives can in general allow a “decoupling of voting and cash-flow rights in common equity through the judicious use of derivatives to hedge cash-flow risk” (Hu and Black, 2006, 2007, Kahan and Rock, 2007).

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insurance contract exists, enabling them to have higher leverage and borrow at longer maturities

(Hirtle, 2008, Saretto and Tookes, 2011).

In this paper, we focus on one important yet undocumented role of CDS: the reduction in forced

sale (fire sale) risk in the underlying bonds. The recent financial crisis has brought to attention the

transmission mechanism related to the need of financial intermediaries – i.e., mutual funds, hedge

funds, insurance companies etc. – to meet the withdrawals from their investors. This phenomenon has

been alternatively termed as “fire sales” (Coval and Stafford, 2007, Shleifer and Vishny, 2011),

“financial run” (Bernardo and Welch, 2004), or “forced sales due to regulatory pressure” (Ellul et al.,

2011).

The idea is that, if a shock induces some financial intermediaries to liquidate an asset, the

forecast of such a sale will lead the other players in the market to try to preempt it by selling as well.

The ensuing massive sales can drastically negatively affect the asset price and its liquidity. This effect

will be especially relevant in the bond market, given that most corporate bonds are traded over-the-

counter with high search costs, and the liquidity in this market arises from the bond dealers’

committing risk capital to market making. More importantly, regulatory pressures may force some

large institutional bond investors to sell in the presence of a drop in market value or a downgrade in

bond ratings, and thus create significant fire sale or liquidity risk for the bonds.

For example, consider the largest class of investors in the corporate bond market: the insurance

companies 5 . By regulation insurance companies can only hold investment grade bonds, and the

amount of risk-based capital required by the state regulator is based on the credit quality of their asset

holdings (Ellul et al., 2011). A negative shock to the bonds they hold, such as a rating downgrade, will

require them to post additional equity capital, unless they choose to sell the bonds.

However, if such insurance companies were to buy CDS protection, the need to find new capital

would be largely reduced. In other words, CDS contracts allow investors who are required by

regulation to hold high quality bonds to defer the sale in the case the bonds get downgraded or even                                                             5 At the end of the second quarter of 2005, insurance companies hold $574 billion of publicly issued corporate bonds, among which $484 billion are held by life insurance companies and $90 billion are held by property and reinsurance companies.  

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lose the coveted investment grade status. More importantly, this would lower the other players’

preemptive incentives to front-run the insurance companies, and effectively induces a lower need to

rush to sell the bonds in the market.

Moreover, the presence of CDS may induce arbitrageurs to provide liquidity when bonds are

subject to such fire-sale related liquidity shocks. Indeed, if bonds are temporarily underpriced because

of liquidity shocks, the CDS basis arbitrage strategy – i.e., buy bonds and buy CDS protection – would

facilitate liquidity provision in the bond market (Choudhry, 2006). In other words, CDS reduces the

“limit of arbitrage” (Shleifer and Vishny, 1997) in the bond market.

This suggests that bonds issued by firms with CDS contracts would suffer less fire sale risk. The

lower risk of fire sale should increase bond liquidity and reduce bond yield spreads. This intuition

provides a new and novel angle – unexplored in the literature till now – on the role of CDS in the

corporate bond market.

We hypothesize that the presence of CDS contracts lowers the yield spreads of the bonds and

increases their liquidity. Such effect should concentrate among investment grade bonds – the ones that

may experience fire sale risk due to regulatory pressures. High yield bonds, already held by investors

who are not subject to regulatory constraints in terms of the quality of the assets they hold – e.g.,

hedge funds and high yield mutual funds – should be less subject to fire sale risk.

We test this hypothesis using data on a comprehensive sample of US corporate bonds with CDS

contracts information over the 2001 to 2009 period. In the first part of our analysis, we focus on the

overall relationship between CDS contracts and both corporate yield spreads and bond liquidity. We

document a strong negative relationship between yield spreads and the availability of CDS contracts in

the case of investment grade firms. The presence of CDS contracts reduces the bond yield spread by

22 bps. There is instead no relationship for high yield bonds. The link between CDS contracts and

yield spreads is stronger during the financial crisis period (the 2001-2002 dotcom crash, the 2008-

2009 subprime crisis), in line with our hypothesis of a liquidity provision role of CDS.

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Then, we focus on bond liquidity. We follow Bao et al. (2011) and define bond illiquidity as the

implied bid-ask spread based on the auto-covariances of bond price changes. We find that, in the case

of investment grade bonds, the existence of CDS contracts lowers bond illiquidity by 9% compared to

the unconditional mean. In contrast, in the case of high yield bonds, the presence of CDS contracts

actually increases bond illiquidity. These effects are even stronger during the financial crisis. That is,

the presence of CDS contracts increases liquidity for investment grade bonds and reduces it for high

yield bonds.

To provide a causal interpretation of our analysis, we consider an instrumental variable

identification that exogenously pins down the need for CDS contracts in the market. This is based on

the level of loan concentration of the lending banks, from which the bond issuer borrows its bank debt.

The intuition is that banks tend to use credit derivatives to hedge their loan positions. The less

diversified their loan portfolio is, the higher the incentive they have to purchase CDSs for hedging

purposes.

We directly provide evidence of this claim, by explicitly relating the degree of concentration of

the loan portfolios of the banks across different industries and geographical regions to their use of

credit derivatives for hedging purposes. We show a strong positive relationship between the use of

credit derivatives and the degree of concentration of the loan portfolios, both in the case of notional

amount and in the case of notional amount standardized by the assets of the bank.6 The economic

significance is also sizable. One standard deviation more concentrated loan portfolio is related to 59%

higher use of credit derivatives for hedging purposes.

The instrumental variable analysis confirms the previous findings, displaying a strong negative

relationship between the presense of CDS contracts and yield spreads and a positive relationship

between CDS presense and bond liquidity for investment grade bonds. One standard deviation

increase in the instrumented presence of CDS contracts lowers yield spread by 26 bps and reduces

illiquidity by 8%. No effect is there for high yield bonds.                                                             6 There is instead no relationship between loan concentration and the bank’s use of derivatives to hedge interest rate risk and foreign exchange risk. This implies that the degree of loan concentration is directly related to the bank’s hedging of credit risk but not some general hedging purposes.

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Several robustness checks confirm our results. In particular, we exploit a proxy of depth in the

CDS market, as measured by the number of CDS quotes provided by the CDS dealers (Qiu and Yu,

2012). Consistently with the previous results, we find that the CDS depth reduces bond yield spreads

and increases bond liquidity for investment grade bonds. No effect is there for high yield bonds.

In the second part of our analysis, we focus on two events that have been shown in the literature

to trigger forced sales by bond institutional investors: bond rating downgrades from investment grade

to high yield grade, and the selling pressure of property insurance companies following Hurricane

Katrina.

The downgrade from investment grade to high yield triggers forced sales as insurance companies

liquidate their holdings in the downgraded bonds (“fallen angels”) to comply with the NAIC’s

regulatory constraints (Ellul et al., 2011) 7. We argue that the presence of CDS contracts lowers the

impact of the forced selling pressure of insurance companies upon such downgrade. And indeed, we

find that bonds without CDS contracts experience a 150% higher drop in institutional ownership than

the bonds with CDS contracts. This provides a direct evidence in support of our hypothesis, as the

lower drop in institutional ownership can only be attributed to the lower need to sell assets generated

by the presence of CDS. Consistently, the presence of CDS contracts lowers the impact of fallen

angels in terms of both yield spreads and bond liquidity. Bonds without CDS contracts experience a

200% (150%) higher increase in yield spread (bond illiquidity) than bonds with CDS contracts.

Next, we examine an even more exogenous event: the shock to property insurance companies

provoked by Hurricane Katrina. Hurricane Katrina (23-30, August, 2005) is the costliest natural

disaster in the history of the United States with an insured damage of over $40 billion. The selling

pressure of Katrina-exposed property insurance companies, driven by the need to meet redemption

claims, generated a selling of the bonds held by those investors (Massa and Zhang, 2011). These

                                                            7 Ellul et al. (2011) show that the selling pressure of insurance companies generates significant price drops on the bonds downgraded from investment grade to high yield. Indeed, consistent with Ellul et al. (2011), we find a significant 4% decrease in bond institutional ownership around the quarter of such downgrade, which is much higher than that of an average 1% decrease in bond institutional ownership around other rating downgrades not crossing the investment grade/high yield threshold.

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forced sales can only be attributed to supply-side shocks – i.e., Katrina-exposed property insurance

companies – as opposed to firm-specific shocks such as rating downgrades8.

We document that the presence of CDS contracts reduces the impact of forced sales in terms of

both the drop in bond prices as well as the drop in liquidity. Specifically, in the face of the selling

pressure of Katrina-exposed property insurance companies, bonds without CDS contracts experience a

45% higher increase in yield spreads than that of bonds with CDS contracts. In line with the previous

results, the increase on bond illiquidity concentrated among bonds without CDS contracts.

Our study contributes to several strands of literature. First, our paper relates to the emerging

literature on the impact of CDS contracts on the underlying firm. The theoretical literature has mostly

focused on the effects of CDS on renegotiation between debtors and creditors, and the associated costs

and benefits (e.g., Arping, 2004, Hu and Black, 2008a, b, Yavorsky, 2009, Bolton and Oehmke, 2010,

Subrahmanyam et al., 2011, Gormley et al., 2011), as well as the ensuing managerial incentive in risk

taking (Thompson, 2007, Parlour and Winton, 2008)9.

The empirical literature (e.g., Hull et al., 2004, Longstaff et al., 2005, Norden and Wagner, 2008,

Norden and Weber, 2009, Chen et al., 2010, Ismailescu and Phillips, 2011, Kim 2011, Nashikkar et al.,

2011) does not provide unequivocal evidence. On the one hand, CDSs are found to reduce loan quality,

increase bankruptcy risk and increase the probability of credit rating downgrade (Ashcraft and Santos,

2007, Peristiani and Savino, 2011, Purnanandam, 2011, Subrahmanyan et al., 2011). On the other hand,

Bedendo et al. (2011) find that CDS contracts do not significantly increase the probability of

bankruptcy when the firm is already in distress. CDSs are also found to stimulate bank credit supply

and improve borrowing terms, and enable firms to maintain higher leverage and borrow at longer

maturities (Hirtle, 2008, Saretto and Tookes, 2011). We contribute to this literature by providing

                                                            8 In this case, the concern of a potential spurious correlation between the presence of CDS contracts and firm-specific shocks in the proximity of rating downgrades can be ruled out. 9 On the bright side, Duffee and Zhou (2001) argue that CDS allows for the decomposition of credit risk into components that

have different sensitivities to information, thus potentially helping banks overcome a lemon problem when hedging credit risk.  

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evidence of the bright side of CDS contracts on the bond market and showing how this is directly

related to bond liquidity.

Second, we contribute to the literature on fire sales and financial crisis (Shleifer and Vishny,

2011, Bernardo and Welch, 2004, Coval and Stafford, 2007) and on fire sales in the bond market in

particular (Da and Gao, 2009, Ellul et al., 2011). We contribute by focusing on the way CDS contracts

reduces the market impact of such selling pressures on the bond market, improving liquidity and

reducing yield spread.

Third, we contribute to the literature on the impact of bond illiquidity on corporate yield spreads.

Bao et al. (2010) find that in the cross-section, bond illiquidity explains the individual bond yield

spreads with large economic significance. Friewald et al. (2012) and Nielsen et al. (2012) confirm that

illiquidity explains a large part of the variation in yield spreads across bonds after accounting for credit

risk, and the yield spread contribution from bond illiquidity increased dramatically during the period

of the subprime crisis. We contribute to this literature by showing that the presence of CDS contracts

provides liquidity to investment grade bonds by reducing fire sale risk, which directly translates into

lower yield spreads, and consistently, this effect is significantly stronger during the crisis period.

Fourth, we contribute to the literature on the financial effects of large natural disasters (Sprecher

and Pearl, 1983, Dividson et al., 1987, Shelor et al., 1990, Aiuppa et al., 2003, Ewing et al., 2006,

Blau et al., 2008). We show how the existence of CDS contracts may muffle such effects.

Our findings have important normative implications as well. Indeed, they show that the presence

of CDS contracts – at least for the class of investment grade bonds – does in fact reduce the effect of

fire sales and this benefit is especially stronger during the period of financial crisis. This evidence

suggests that, contrary to the general media perception, CDS may actually help to reduce credit risk

transfer and contagion around financial crises (Stulz, 2010).

The remainder of the paper is organized as follows. In Section II, we describe the data and the

construction of the main variables. In Section III, we present evidence on the link between the

presence of CDS contracts and both yield spreads and bond liquidity. In Section IV, we use an

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instrumental variable specification to establish a causal relationship. In Section V, we examine the role

of CDS in two fire-sale related events. A short conclusion follows.

II. Construction of Data and Variables

We use data from multiple sources. The data on monthly bond yield spreads come from Bank of

America-Merrill Lynch Corporate Master Index Compositions. The BofA-Merrill data have been used

in previous studies (Schaefer and Strebulaev, 2008, Acharya et al., 2009). These data cover most rated

US publicly issued corporate bonds (Acharya et al., 2009) and provide bond-level information on the

option-adjusted yield spread, coupon rate, duration, face value, and credit ratings. We require that each

bond must be included in the index for over 24 months.

We obtain information on a number of bond characteristics – such as the offering date, the

maturity date, offering amount, seniority, callability, fungibility and credit enhancement – from the

Mergent Fixed Income Securities Database (FISD)10. This database reports the characteristics of

nearly all US fixed income securities. We merge the BofA-Merrill data with the Mergent FISD using

bond CUSIPs.

We obtain information on the tick-by-tick bond transactions from the Trade Reporting and

Compliance Engine database (TRACE) from 2002 to 2009. TRACE is the Financial Industry

Regulatory Authority (FIRA) over-the-counter (OTC) corporate bond market real-time price

dissemination service. TRACE consolidates transaction data for all eligible corporate bonds -

investment grade, high yield and convertible debt. It provides detailed records on the time of trade

execution, price, yield and some information on trading volume.

We get information on CDS contracts from the Markit CDS database. This provides daily firm-

level data on CDS spreads for the period from 2001 through 2009. The CDS spread is the periodic fee

that the protection buyer pays to the protection seller in a credit default swap contract until the contract

matures or a credit event occurs, in which case the protection buyer delivers defaulted bonds to the

                                                            10 The FISD data used in our analysis are based on the 2009 edition of the FISD database.

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seller in exchange for the face value of the issue in cash (physical settlement) or the protection seller

directly pays the difference between the market value and face value of the issue to the protection

buyer (cash settlement). The “Restructuring Clause” of a CDS contract specifies the credit events that

trigger settlement.

Typically, Markit reports a composite daily CDS spread, which is an average across all the quotes

provided by market makers after a series of data cleaning tests. The Markit database also provides

identifying information on the reference entity (such as firm name and ticker), and the terms of the

CDS contract (maturity, currency denomination and restructuring clauses).11 We focus on the spreads

of all the CDS contracts written on US firms and denominated in US dollars.

Our combined sample includes 158,122 bond-month observations (3,468 firm-year observations)

from January 2001 to December 2009. We provide the descriptive statistics in Table I. For each

variable, we report the data frequency, source, number of observations, mean and stand deviation. The

detailed definitions of each variable can be found in the Appendix. In our sample, 83 percent of the

bonds are investment grade bonds, 64% of the bonds are callable bonds, and 9% of the bonds have

credit enhancement. On average, among the bond issuers, 74% of the bond issuers have CDS contracts

outstanding during the sample period. This fraction is higher among investment grade issuers (78%)

than high yield issuers (61%).

III. CDS, Yield Spreads and Bond Liquidity

A. Yield Spreads

We begin by relating the presence of CDS contracts to corporate yield spreads. We estimate a pooled

specification in which the bond yield spread is regressed on a CDS presence dummy, and a set of

control variables. The dependent variable is the option-adjusted bond yield spread. It is defined as the

number of percentage points that the treasury spot curve must be shifted in order to match discounted

                                                            11 Specifically the maturity of CDS contracts ranges from 6 months up to 30 years, and there are four major restructuring clauses (full restructuring, modified restructuring, modified-modified restructuring and no-restructuring). A detailed discussion of different restructuring clauses can be found in Packer and Zhu (2005). 

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cash flows to the bond’s price. The CDS presence dummy (“CDS presence”) is a dummy variable

equal to 1 if the issuing firm has quoted CDS contracts on its bonds in the previous month and 0

otherwise.

The set of control variables includes major bond characteristics – such as coupon rate, duration,

offering amount, callability, fungibility and credit enhancement – and important firm characteristics –

such as equity volatility, equity beta, market-to-book, book leverage, book size, profitability, cash

holding and dividend payment as well as industry fixed effects at the two-digit SIC level, time fixed

effects at the monthly level and credit rating fixed effects at the issue level. We also consider

specifications with time (monthly) × credit rating fixed effects. All firm-year (month) variables are

taken at the end of the previous year (month). We always cluster the errors at the firm level12.

We report the results in Table II. In Panel A, we report the overall results, while in Panel B, we

consider the results around the period of financial crisis. We start with the overall results. Columns

(1)-(3) are for the subsample of investment grade bonds, while columns (4)-(6) are for the subsample

of high yield bonds. In column (1), we control for bond characteristics including coupon rate, duration,

offering amount, callability, fungibility and credit enhancement, time fixed effects at the monthly level,

and credit rating fixed effects at the issue level. In column (2), we control for time × credit rating fixed

effects. In column (3), we control for firm characteristics including equity volatility, equity beta,

market-to-book, book leverage, book size, profitability, cash holding and dividend payment. Columns

(4)-(6) follow the same specifications as in columns (1)-(3), respectively.

The results display a significantly negative relationship between yield spread and the availability

of CDS contracts in the case of investment grade issuers. The results hold across different

specifications and are economically sizable. The presence of CDS contracts reduces the yield spread

by 22bps, which represents a 14% increase relative to the unconditional mean. However, there is no

relationship in the case of high yield bonds.

                                                            12 All of our results are consistent and statistically more significant if we cluster the errors at the bond level.

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The results on the control variables are largely in line with expectations. For both investment

grade and high yield bonds, the coupon rate is positively related to bond yield spreads, supporting a

tax-based explanation. Consistent with Campbell and Takslar (2003), even after controlling for credit

rating × time fixed effects, equity volatility and firm leverage are strongly positively related to bond

yield spreads13. Equity beta (firm profitability) is positively (negatively) related to bond yield spreads

but only so in a significant way for investment grade bonds. Interestingly, bond duration is positively

related to yield spreads for investment grade bonds while negatively related for high yield bonds14.

In Panel B, we interact the variable of CDS presence with a dummy variable indicating periods of

financial crisis: the dotcom crisis (2001-2002) and the subprime crisis (2008 and the first half of 2009).

Columns (1-(3) are for the subsample of investment grade bonds. Columns (4)-(6) are for the

subsample of high yield bonds. We follow the same specifications as in Panel A. We always include

time (monthly) or credit rating × time fixed effects and therefore the crisis period dummy is omitted

from the regressions.

Also in this case, the results show a significantly negative relationship between the presence of

CDS contracts and yield spreads for investment grade and no relationship for high yield bonds. More

interestingly, we see that the link between CDS presence and yield spreads is significantly stronger

during the crisis period, in line with our hypothesis of a fire-sale risk related role of CDS.

B. Bond Illiquidity

Next, we relate the presence of CDS contracts to bond illiquidity. Following Bao et al., (2011), we

define bond illiquidity as the implied bid-ask spread based on the auto-covariances of bond price

changes: Bond Illiquity 2√γ (0 if γ 0 , where γ Cov ∆p , ∆p , and p is the log price at

time t. The focus variable (“CDS presence”) as well as the control variables are defined as in the

                                                            13 We find that the inclusion of credit rating × time fixed effects renders firm size insignificant in explaining bond yield spreads. 14 This finding can be explained as follows. Bond issuers with longer duration bonds face higher interest rate risk but lower short-term refinancing risk. In the case of high yield bonds, the benefit due to lower refinancing risk may dominate so that bond duration is negatively related to yield spreads. 

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previous specification. All the firm-year (month) variables are taken at the end of the previous year

(month).

We report the results in Table III. In Panel A, we consider the overall results, while in Panel B,

we consider the results around the period of financial crisis. The layout of the columns is the same as

in Table II. We find that, in the case of investment grade rating, bonds of firms with CDS contracts are

more liquid than those of firms with no CDS contracts. This result holds across the different

specifications and is economically significant. The existence of CDS contracts lowers bond illiquidity

by 9% compared to the unconditional mean. In contrast, CDS presence increases illiquidity in the case

of high yield bonds by 8% relative to the unconditional mean.

If we focus on the crisis period, in line with the findings on yield spreads, we see that the impact

of the presence of CDS contracts on bond liquidity is significantly stronger. That is, the presence of

CDS contracts increases liquidity for investment grade bonds especially during the financial crisis

period. This evidence consistently supports the liquidity provision role of CDS on the bond market.

IV. An Instrumental Variable Identification

A. Main Results

The previous results document a significant relationship between the presence of CDS contracts and

bond liquidity and yield spreads, providing strong support for our hypothesis. However, it may not be

enough to establish a causal relationship. Indeed, it may be possible that CDS contracts exist in the

very firms characterized by unobserved liquidity and risk features that also determine yield spreads

and bond liquidity. To address this issue, we provide an instrumental variable specification.

We use as instrument the level of loan concentration of the lending banks which the bond issuer

borrows from. The intuition is that banks use credit derivatives to hedge their loan positions. The less

diversified their overall loan portfolio is, the higher is the incentive they have to purchase CDSs for

hedging purposes. To provide evidence of this claim, we link the degree of concentration of a bank’s

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loan portfolio across different industries and geographical regions to its use of credit derivatives,

foreign exchange derivatives and interest rate derivatives (for hedging purposes).

The analysis is done at the bank level. First, for each bank, we define a measure of loan

concentration based on the bank loan data from LPC Dealscan 15 . We focus on all of the loan

transactions in the US. For each bank-year, we classify its existing loans into different industries (two

digit SIC)-states pairs. We then calculate the herfindal index as the proxy for loan concentration. We

expect that banks whose loans are concentrated in a specific region and industry would face a higher

credit risk and have greater incentives to purchase credit derivatives for protection.

Next, we link (by name matching) LPC Dealscan with the Bank Regulatory database, which

contains balance sheet and off-balance sheet information of US banks 16 , and more importantly,

detailed information on the banks’ use of interest rate, foreign exchange and credit derivatives

specifically for hedging purposes. We collect the notional amounts of such derivatives positions.

Finally, we regress the use of credit derivatives, foreign exchange derivatives and interest rate

derivatives (for hedging purposes) to the degree of concentration of a bank’s loan portfolio across

different industries and geographical regions. We report the results in Table IV, Panel A. The

dependent variable in columns (1)-(2) is the log value of the notional amount of credit derivatives

(RCFDA535). The dependent variable in columns (3)-(4) is the notional amount of credit derivatives

divided by the bank’s total asset (RCFDA535/RCFD2170). The dependent variable in column (5) is

the notional amount of foreign exchange derivatives divided by the bank’s total asset

(RCFD8726/RCFD2170), while the dependent variable in column (6) is the notional amount of

interest rate derivatives divided by the bank’s total asset (RCFD8725/RCFD2170). Bank size is the log

value of total asset (RCFD2170). We provide a detailed description of each data item in the Appendix.

The results show a significantly positive relationship between the use of credit derivatives for

hedging and the degree of concentration of the loan portfolio of the bank, both in case of notional

                                                            15 Dealscan is a comprehensive dataset that contains detailed information relating to the start and expiration dates of loan deals along with the names of the lending banks, loan amounts, and terms and conditions of the loans. 16 We require the bank’s total amount of commercial and industrial loans (RCON1766) to be larger than $100 million. This requirement makes sure that the banks we use are commercial banks actively involved in the corporate loan market.  

14  

amount and in case of notional amount standardized by bank asset. The economic significance is

sizable. One standard deviation more concentrated portfolio is related to 59% (133%) higher use of

credit derivatives in notional amount (notional amount standardized by the bank asset), compared to

the unconditional mean.

The last two columns of Panel A (Table IV), provide some placebo tests. They show that such a

relationship does not exist in the case of interest rate derivatives and foreign exchange derivatives.

This significantly increases our confidence in the results for the credit derivatives, suggesting that the

concentration of the loan portfolio is directly related to credit risk, but not to interest risk and foreign

exchange risk, which are arguably more systemic than credit risk and are less likely to be managed by

loan diversification. These findings make us confident to exploit the concentration of the loan

portfolio of the banks as a proxy for the market demand for credit protection – i.e., as an instrument

for the presence of CDS contracts.

Therefore, we proceed to link the presence of CDS contracts to bond yield spread and bond

illiquidity, by instrumenting the CDS presence with the degree of loan concentration of the lending

banks. We proceed as follows. First, for each bank, we calculate the degree of concentration of its loan

portfolio using the herfindhal as a measure of concentration. Second, we aggregate the degree of loan

concentration at the issuer level, by taking the value (loan amount)-weighted herfindal among all the

banks from which the issuer borrows in the prior 5 years.

Then, we link the degree of weighted average concentration of the lending banks to the existence

of a CDS contract for the specific firm. We estimate a probit regression of the CDS presence dummy

on the loan herfindal and a set of control variables. We report the results in columns (1) and (2) of

Table IV, Panel B. At the bottom of these columns, we also report the F-test to provide the Staiger and

Stock (1997) test of weakness of instrument on the loan herfindal variable.

The results show that the presence of CDS contracts is strongly positively related to the degree of

concentration of the lending banks. This means that, the more concentrated the loan portfolios of the

banks are, the higher the probability that the firm has a CDS on its bonds. Moreover, the F-test

15  

delivers a Staiger and Stock (1997) statistic of weak instruments higher than 5. This comfortably

allows us to trust the strength of our instrument.

Then, we estimate an instrumental variable specification using the degree of loan portfolio

concentration of the lender as instrument17. We report the results in Table IV, Panel B, columns (3)-(6).

In columns (3)-(4), we use as dependent variable the bond yield spread, while in columns (5)-(6), we

use the bond illiquidity. In Columns (3) and (5), we break down the analysis for the subsample of

investment grade bonds, while in columns (4) and (6) we focus on the subsample of high yield bonds.

In all the specifications, we include industry, time and credit rating fixed effects, and cluster the errors

at the issuer level.

The results support the previous ones, displaying a negative relationship between CDS presence

and yield spread and a positive one between CDS presence and bond liquidity for investment grade

bonds. In particular, one standard deviation increase in the instrumented CDS presence dummy lowers

yield spread by 26 bps and reduces illiquidity by 8%. No effect is there for high yield bonds.

As a robustness check, we also consider an alternative instrument based on the average loan

herfindal among issuers with the same industry (two-digit SIC code), region (state level) and basic

rating category (investment grade/high yield). That is, the instrument is not based on the set of banks

that lend to the specific firm, but on all the banks that are lending to similar firms in terms of

geography, rating and industry. This alleviates any residual concern on endogeneity induced by the

fact that the previous instrument was based on the banks that had chosen to lend to the specific firm.

The results are reported in Panel C, Table IV. For brevity, we mute the control variables and only

report the variables of interest. The findings are consistent with the previous ones in Panel B. In fact,

the statistical significance is even stronger. The F-test in the first-stage regression delivers a Staiger

and Stock (1997) statistic of weak instruments over 25. These results display a causal link between

                                                            17 We follow Wooldrige (2001) and use the fitted value from the probit regression (column (2), Panel B, Table IV) as the instrumental variable.  

16  

CDS and both yield spreads and bond liquidity, i.e., the presence of CDS contracts lowers yield

spreads and increases bond liquidity for investment grade bonds.

B. Robustness Checks

We now consider some robustness checks to the previous results in Table IV. We start by considering

an alternative measure of CDS presence in the market. We redefine “CDS presence” as a dummy

variable equal to 1 if the issuing firm has the most liquid CDS contracts in the previous month and 0

otherwise – i.e., CDS contracts with 5-year maturity and “MR” restructuring clause18. This follows the

literature (Zhang, et al., 2008, Cao, et al., 2010, Elkamhi, et al., 2010) that not all the CDS contracts

are equally liquid and the effect of the CDS should be concentrated among the most liquid ones.

We report the results in Table V. Panel A and Panel B follow the same specifications as in Panel B

and Panel C of Table IV, respectively. In the interest of brevity we only report the interested variables.

We find that the results are very much similar to the previous ones.

Next, we focus on the subsample of bonds issued by firms with CDS contracts trading in the

market. In this case, we measure the CDS presence using a proxy of depth in the CDS market. We use

the number of dealers providing CDS quotes as a measure for the depth of CDS contract.19 This

measure has been used by Qiu and Yu (2012) who document that the CDS depth is higher for

investment grade bonds than for the high yield ones, and it is significantly related to the endogenous

liquidity provision by informed financial institutions. We therefore focus on the instrumental variable

specification instead of a simple OLS regression, with the CDS depth instrumented by the loan

concentration of the lending banks. We construct the instrument in the same way as in Table IV.

We report the results in Table VI. In Panel A, columns (1) and (2), we run OLS regressions of the

CDS composite depth on the loan herfindal. We perform the F-test to identify the weakness of the loan

herfindal variable. Then, we use it as the instrument for the CDS composite depth in columns (3)-(6).

                                                            18 Under the “MR” (modified restructuring) clause, the restructuring agreements are counted as a credit event, but the deliverable obligation against the contract has to be limited to those with a maturity of 30 months or less after the termination date of the CDS contract or the reference obligation that is restructured (regardless of maturity). 19 The Markit data only provide information on the number of dealers in the 5-year maturity contracts. Therefore, we define CDS composite depth as the log number of dealers in the CDS contracts with 5-year maturity.  

17  

The dependent variable in columns (3)-(4) is the bond yield spread, while dependent variable the

dependent variable in columns (5)-(6) is the bond illiquidity. Columns (3) and (5) are for the

subsample of investment grade bonds. Columns (4) and (6) are for the subsample of high yield bonds.

In all of the specifications, we control for industry, time and credit rating fixed effects, and cluster the

standard errors at the issuer level. In Panel B, we use as instrument is the average loan herfindal

among issuers in the same industry (two-digit SIC code), region (state level) and basic rating category

(investment grade/high yield). For brevity, we only report the interested variables in the table.

The results show that the loan portfolio concentration also explains the degree of depth of the CDS

market. This evidence clearly shows that the demand for CDS contracts is indeed related to the banks’

need to rely on credit derivatives for hedging purposes. The Staiger and Stock (1997) tests of weak

instrument are comfortably passed. Then, when we focus on the impact of CDS depth on bonds. The

results display a negative relationship between the CDS depth and both bond yield spreads and bond

illiquidity. One standard deviation higher instrumented CDS composite depth is related to a 28 bps

lower yield spread and 6% lower bond illiquidity for investment grade bonds. Consistent with the

previous findings, no effect is there for high yield bonds.

V. Event-based Analysis

We now focus on two events that may affect the behavior of institutional investors holding corporate

bonds: the rating downgrade from investment grade status to high yield, and the selling pressure of

Katrina-exposed property insurance companies following Hurricane Katrina.

A. Falling Angels

We begin by focusing on the bonds experiencing rating changes. We are interested in bonds that are

downgraded from investment grade to high yield (“fallen angels”). Ellul et al. (2011) show that such

downgrade triggers the forced sales of insurance companies, and generates large negative liquidity-

driven effect on bond prices. In line with their findings, we find that in our sample, there is a

18  

significant 4% decrease in bond institutional ownership20 around the quarter of such downgrade (9%

decrease relative to the ownership before the downgrade). In direct contrast, the average drop in bond

institutional ownership around other rating downgrades not crossing the investment grade/high yield

threshold21 is only 1%. Our hypothesis predicts that bond issuers without CDS contracts should

experience a greater drop in institutional bond ownership, a higher increase in bond yield spreads and

bond illiquidity, upon such rating downgrade from investment grade to high yield.

Our test is structured as follows. We regress the changes in bond ownership, the change in bond

yield spreads and the changes in bond illiquidity around the month of rating changes on a “fallen angel”

indicator, its interaction with a “no CDS” indicator and a set of control variables. For a given bond-

month (bond quarter),22 we define the fallen angel indicator as a dummy taking the value of 1 in the

month (quarter) in which the bond is downgraded from investment grade to high yield and zero

otherwise. The “no CDS” indicator equals 1 if the bond issuer has no CDS contracts in the previous

month (quarter) and 0 otherwise. The variable of focus is the interaction term. We expect it to be

negatively related to the changes in bond institutional ownership, while positively related with both

the changes in bond yield spreads and the changes in bond illiquidity.

We report the results in Table VII. In Panel A, we focus on the changes in bond institutional

ownership around rating changes, and in Panel B and Panel C on the changes in bond yield spreads

and the changes in bond illiquidity, respectively. Columns (1)-(3) are based on the full sample of

rating changes including both rating downgrades and rating upgrades. In column (1), we only interact

the fallen angel dummy with the “No CDS” dummy. In column (2), we add the interaction terms of

fallen angel dummy with bond characteristics including bond duration, offering amount and bond age.

In column (3), we add additional interaction terms of fallen angel with risk characteristics such as

                                                            20 We derive the data on quarterly institutional holdings of corporate bonds from Lipper’s eMAXX fixed income database from the first quarter of 2001 to the second quarter of 2008. It contains details of fixed income holdings for U.S. and European insurance companies, U.S., Canadian and European mutual funds, and leading U.S. public pension funds. 21 The threshold rating category that defines investment grade and high yield status is BBB-, i.e., above BBB-, investment grade; below BBB-, high yield. 22 The test on the change in bond institutional ownership is at the bond-quarter level given the quarterly frequency in the institutional holdings data. The tests on the change in bond yield spreads and the change in bond liquidity are at the bond-month level.  

19  

equity volatility and equity beta.23 Columns (4)-(6) follow the same specifications as columns (1)-(3),

except that they are only based on the subsample of rating downgrades. In the interest of brevity, in

columns (3) and (6), we don’t report the results on firm-level controls. We always include time ×

credit rating fixed effects, industry fixed effects at the two-digit SIC level, and cluster the errors at the

issuer level.

The results show a significantly negative coefficient on the interaction term between the “no CDS”

dummy and the “fallen angel” dummy for the changes in bond institutional ownership, while a

significantly positive one for both the changes in yield spreads and the changes in bond illiquidity.

This holds across the different specifications. The effect is not only statistically significant, but also

economically relevant. Bonds without CDS contracts experience a 150% higher drop in institutional

ownership than that of bonds with CDS presence. A similar effect is there in the case of yield spread

and bond illiquidity. Bonds without CDS contracts experience a 200% (150%) higher increase in yield

spread (bond illiquidity) than that of bonds with CDS presence. These findings strongly support our

hypothesis that the presence of CDS contracts reduces the fire sale effect for investment grade bonds

upon rating downgrades.

B. Hurricane Katrina

The second experiment is based on Hurricane Katrina (August 23-30, 2005) and the Katrina-exposed

property-casualty insurance companies. Hurricane Katrina is the costliest natural disaster in the history

of the United States, with a total property damage estimated at $81 billion (2005USD) and almost

$40.6 billion of insured losses (Knabb et al., 2005)24. It represents a large exogenous shock to the

property insurance and reinsurance industry, especially for companies with large insurance exposure to

Katrina25. Given that insurance companies are the largest corporate bond holders, this provides an

                                                            23 The purpose of including these additional interaction terms is to eliminate concerns that the result on the interaction of the fallen angel dummy and the no-CDS contracts dummy may be driven by other bond or firm characteristics. 24 A special report by Towers Perrin Co. (2005 studying the impact of Hurricane Katrina on the insurance industry estimates the range of privately insured loss to be between 40$ and 55$ billion. 25 Here by large exposure, we mean those insurance companies that have large market share of insurance business in the Gulf region (state of Mississippi, Alabama, and Louisiana). For example, State Farm Insurance, which has the largest market share in the Gulf region (26.62%), states the following words on its website: “In a typical year, State Farm receives between

20  

ideal experiment in which the selling pressure of the bonds held by exposed insurance companies is

not related to firm specific characteristics (e.g., credit risk), but is driven by the market concerns on the

forced sales by the affected insurance companies to meet redemption claims.

Previous research in the finance and insurance literature has studied the effect of natural disasters

on the stock prices of insurance companies (e.g., Sprecher and Pearl, 1983, Dividson et al., 1987,

Shelor et al., 1990, Aiuppa et al., 2003, Blau et al., 2008). The empirical evidence suggests that

insurers’ stock prices decline in response to the loss effect of hurricanes and the effect is particularly

strong for insurers with more regional exposure (Lamb, 1995, 1998). Massa and Zhang (2011) show

that the selling pressure of Katrina-exposed insurance companies induced an increase in the short-

selling on the bonds held by those investors, and led to significant price drops up to seven months after

the hurricane.

In this context, we test how the presence of CDS contracts may help to reduce such impact on

bond yield spreads and bond liquidity. We use the pre-Katrina exposed insurance bond ownership to

proxy for the selling pressure of the bonds after the hurricane26.

First, we identify the set of property & casualty insurance and reinsurance companies that are

considered to have high exposure to Hurricane Katrina, using data from the Holborn Corporation

(2005) Hurricane Katrina report 27 . The Holborn Report lists the names of property & casualty

(reinsurance) companies along with their 2004 market shares in the states of Louisiana, Mississippi,

and Alabama, and whether they have rating or outlook changes immediately after the hurricane. We

include the top ten property insurance companies by their market shares (including both personal and

                                                                                                                                                                                          600,000-800,000 catastrophe claims. In 2005, we received that number in a six week period immediately following Katrina. Since then, nearly 100 percent of all claims have been resolved. In total, State Farm has paid more than $3.1 billion in claims as a result of Katrina, which does not include payments to policy holders from the National Flood Insurance Program…”. 26 We focus on the pre-Katrina property insurance ownership for the following reasons. First, it is exogenous with respect to the changes in bond liquidity and yield spreads given the total unexpectedness of insured damages. Second, the economic rationale can be explained with a simple example. Suppose that Start Farm Insurance has 10 billion bond holdings before Katrina, invested in two bonds, 8 billion in bond A and 2 billion in bond B. For each bond, the total issue outstanding is 100 billion. Therefore, before Katrina, State Farm’s ownership in bond A is 8% and ownership in bond B is 2%. After Katrina, State Farm needs to immediately liquidate 5 billion to deal with insurance claims. Ideally, it would want to liquidate its bonds across the boards and keep the portfolio balanced. In this case, it should sell 4 billion in bond A and 1 billion in bond B. As a result, State Farm's ownership in bond A would drop from 8% to 4% and the ownership in bond B would drop from 2% to 1%. Therefore, the forced liquidation will have a much bigger impact on bond A than on bond B because of higher pre-Katrina ownership. In other words, higher exposed property ownership implies higher forced selling pressure after Katrina. 27 The Holborn report is publicly available at the URL: http://www.holborn.com/holborn/-reportsKatrina.html. 

21  

commercial lines) and eight reinsurance companies with negative rating outlook changes. The names

of those insurance companies are provided in the Appendix.28

Then, we define the pre-Katrina exposed insurance bond ownership as the par amounts held by

property and reinsurance companies with high exposure to hurricane Katrina at the end of the second

quarter of 2005 divided by the amount of bond issue outstanding. Non-exposed bond ownership is

defined as the difference between total institutional ownership minus the exposed insurance ownership.

In our sample of corporate bonds, the pre-Katrina exposed property insurance ownership ranges from

0% (1-percentile) to 12% (99-percentile) of bond issue outstanding, with a mean of 1.3% and a

standard deviation of 2.4%.

Next, we regress the changes in bond yield spreads and the changes in bond liquidity around

Katrina, on the pre-Katrina exposed insurance ownership, a “no CDS” dummy as defined before, and

the interaction term between them. Our variable of interest is the interaction term. Our hypothesis

predicts a positive relationship between the interaction term and both the changes in yield spreads as

well as the changes in bond illiquidity around Katrina.

We report the results in Table VIII. In columns (1)-(3), the dependent variable is the change in

bond yield spreads from Aug 23, 2005 to Sep 9, 2005 (the two weeks during which Hurricane Katrina

formed and fully dissipated). In columns (4)-(6), the dependent variable is the difference of bond

illiquidity between Sep 2005 and Aug 2005. In column (1), we only interact the pre-Katrina exposed

insurance ownership with the “no CDS” dummy. In column (2), we add the interaction term of non-

exposed institutional bond ownership with the “no CDS” trading dummy. In column (3), we interact

the exposed insurance ownership with bond characteristics including bond duration, offering amount

and bond age. Columns (4)-(6) follow the same specifications as in columns (1)-(3), respectively.

The results show a significantly positive coefficient on the interaction term between the no-CDS

contracts dummy and the pre-Katrina exposed insurance ownership. This finding holds for both the

                                                            28 We obtain the data on institutional holdings of corporate bonds from Lipper’s eMAXX fixed income database. It is worth mentioning that we exclude those bond issuers that may be directly affected by the hurricane, which include life, property insurance and reinsurance companies, and firms headquartered in the states of Louisiana, Mississippi, and Alabama.  

22  

changes in yield spreads and the changes in bond illiquidity, and it is robust across different

specifications. In the face of the selling pressure of Katrina-exposed property insurance companies,

bonds without CDS contracts experience a 45% higher increase in yield spreads than that of bonds

with CDS presence. Consistently, the impact of pre-Katrina exposed insurance ownership on bond

illiquidity is concentrated among bonds without CDS contracts. Again, this analysis confirms our

hypothesis that CDS contracts reduces the fire sale effect both in terms of bond yield spreads and bond

liquidity.

Conclusion

In this paper, we study the effect of credit default swap (CDS) on the corporate bond market. We

argue that CDS, by reducing the need of investors to immediately sell the bonds in the face of credit

deterioration of the bond issuer, reduces fire sale risk and provides bond liquidity. This effect should

translate into lower bond yield spreads and higher bond liquidity. Given that bond investors are

segmented by regulation, e.g., insurance companies, banks, and pension funds are subject to the risk-

based capital requirements and in principle only hold investment grade bonds, we expect the liquidity

provision role of CDS to be concentrated among investment grade bonds.

We test these hypotheses using a comprehensive sample of US corporate bonds with CDS

contracts information in the 2001-2009 period. We show that the presence of CDS contracts reduces

yield spreads and increases liquidity of investment grade bonds. This effect is stronger during the

financial crisis period. We provide an instrumental variable identification that pins down the need for

CDS contracts by exploiting the level of loan concentration of the banks from which the bond issuer

borrows its bank debt. We find consistent results that the presence of CDS contracts reduces yield

spreads and increases liquidity for investment grade bonds. No such effect is there for high yield

bonds.

We also examine two events that have been shown to trigger forced sales by bond investors:

bond rating downgrades from investment grade to high yield, and the selling pressure of property

23  

insurance companies following Hurricane Katrina. In both events, the presence of CDS contracts

lowers the impact of fire sales by reducing the drop in bond liquidity and lowering the rise in yield

spreads.

Our findings provide a novel view of the role of credit derivatives on the underlying bond market,

not limited to the monitoring or restructuring incentives of the lenders, but related to fire sale risk and

bond liquidity. Our results also have important normative implications, as they suggest that – at least

for the class of investment grade bonds – the presence of CDS contracts may actually help to reduce

risk contagion around financial crises.

24  

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Towers Perrin Corporation, 2005, Hurricane Katrina: Analysis of the Impact on the Insurance Industry.

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Jump Risks of Individual Firms, Review of Financial Studies 22, 5099-5131.

27

Appendix: Variable Definitions

Option-adjusted yield spreads: the number of percentage points that the fair value of the treasury spot curve is shifted to match the present value of the discounted cash flows to the bond’s price. For securities with embedded options, such as callability, a log normal short interest rate model is used to evaluate the present value of the securities’ potential cash flows. In this case, the option-adjusted spread is equal to the number of percentage points that the short interest rate tree must be shifted to match the discounted cash flows to the bond’s price.

Bond illiquidity: for each bond-month, it is defined as the implied bid-ask spread based on the auto-covariances of bond price changes: 2 γ (0 if γ 0), where γ Cov ∆p , ∆p ) and p is the log bond price (“clean price”) at time t. We use the tick-by-tick transaction data from TRACE to calculate the price changes. We require the number of bond transactions to be larger than 10 for each bond-month.

Coupon rate: the interest rate paid on a bond as a percentage of the issuing amount (par value).

Duration: the average maturity of a bond’s cash flows.

Offering amount: the dollar amount of bond issuing outstanding.

Callability: a dummy variable equal to 1 if the bond is callable. A callable bond gives the issuer the right to early redemption at a given price (redemption price) or a given date (call date).

Fungibility: a dummy variable equal to 1 if the bond is fungible. Fungible bonds can be “reopened” in the future by increasing the total amount outstanding of the issue.

Credit enhancement: a dummy variable equal to 1 if the bond has credit enhancements, e.g., guarantees, letters of credit, etc.

Bond age: the number of years since the bond issuing date.

Bond rating fixed effects (issue level): 21 credit rating dummies, each corresponding to the current month composite rating (simple averages of ratings from Moody’s, S&P and Fitch) from AAA to CCC3. The rating correspondences are detailed below.

Numeric Composite Moody's S&P Fitch 1 AAA Aaa AAA AAA 2 AA1 Aa1 AA+ AA+ 3 AA2 Aa2 AA AA 4 AA3 Aa3 AA- AA- 5 A1 A1 A+ A+ 6 A2 A2 A A 7 A3 A3 A- A- 8 BBB1 Baa1 BBB+ BBB+ 9 BBB2 Baa2 BBB BBB 10 BBB3 Baa3 BBB- BBB- 11 BB1 Ba1 BB+ BB+ 12 BB2 Ba2 BB BB 13 BB3 Ba3 BB- BB- 14 B1 B1 B+ B+ 15 B2 B2 B B 16 B3 B3 B- B- 17 CCC1 Caa1 CCC+ CCC+ 18 CCC2 Caa2 CCC CCC 19 CCC3 Caa3 CCC- CCC- 20 CC Ca CC CC 21 C C C C

Investment grade: a dummy variable indicating that the bond credit rating is above or equal to BBB3.

High yield: a dummy variable indicating that the bond credit rating is below BBB3.

CDS presence: a dummy variable equal to 1 if the issuing firm has quoted CDS contracts on its bonds in the previous month and 0 otherwise.

28

Equity volatility: for each stock-month, it is the standard deviation of daily stock returns in the month.

Equity beta: for each stock-month (i,t), we estimate the factor loadings by running the following regression:

, , , 1 , 1 , , ,( ) ,i s f s i t i t m s f s i sr r a r r where we use the previous 180 days as the

estimation period, and we require a minimum of 90 observations for each regression. The dependent variable is the daily return of firm i at day s less the risk-free rate sfr , . The independent variable is the

excess return of market portfolio over the risk-free rate ( sfsm rr ,, ).

Market value of assets: stock price (data199) * shares outstanding (data25) + short term debt (data34) + long term debt (data9) + preferred stock liquidation value (data10) – deferred taxes and investment tax credits (data35).

Market-to-Book Ratio: market value of assets/book assets (data6)

Total debt: long term debt (data9) + short term debt (data34)

Book leverage: total debt/book assets (data6)

Firm size: log (book assets) (data6)

Profitability: income before extraordinary items (data20)/book assets (data6)

Cash holding: cash and short-term investments (data1/data6)

Dividend payer: a dummy variable equal to 1 if the firm pays cash dividends in the year

Industry fixed effects: the two-digit SIC industry dummies

Banks’ use of derivatives for hedging: the Bank Regulatory database contains off-balance sheet data on the banks’ use of derivatives for hedging purposes from the CALL reports. Operationally, we use the following data items: credit derivatives (RCFDA535, notional amount of credit derivatives on which the reporting bank is the beneficiary); FX derivatives (RCFD8726, notional amount of foreign exchange derivative contracts marked to market, with purposes not trading); Interest rate derivatives (RCFD8725, notional amount of interest rate derivative contracts marked to market, with purposes not trading.). Bank size is defined as the log value of total assets (RCFD2170). We require the bank’s total amount of commercial and industrial loans (RCON1766) to be larger than $100 million.

Exposed insurance companies to Hurricane Katrina: we follow the methodology of Massa and Zhang (2011) to identify Katrina-exposed property insurance and reinsurance companies. The set of property & casualty insurance and reinsurance companies that are considered to have high exposure to Hurricane Katrina is identified using data from the Holborn Corporation (2005) Hurricane Katrina report, publicly available at the URL: http://www.holborn.com/holborn/-reportsKatrina.html. The Holborn Report lists the names of property & casualty (re)insurance companies along with their 2004 market shares in the states of Louisiana, Mississippi, and Alabama, and whether they have rating or outlook changes immediately after the hurricane. We include the top ten property insurance companies by their market shares (including both personal and commercial lines) and eight reinsurance companies with negative rating (outlook) changes that can be identified in Lipper/EMAXX as managing firms. These firms are: State Farm Insurance Company, Allstate Insurance Co Group, Progressive Casualty Group, Alfa Insurance, Mississippi Farm Bureau Casualty Insurance, United Services Automobile Association, Nationwide Assurance, American Modern Home Insurance, American International Insurance, St. Paul Travelers Companies, Ace American Reinsurance, Alea North America Insurance, Endurance Reinsurance Corp of America, Odyssey America Reinsurance, Olympus Insurance, Partner Reinsurance United States, Transatlantic Reinsurance United States.

29

Table I Summary Statistics

In this table, we present summary statistics of the major variables used in later analysis. Our data come from

multiple sources. The data on bond yields spread, duration and issue-level credit ratings come from Bank of

America-Merrill Lynch Corporate Master Index Compositions. Additional bond characteristics including coupon

rate, bond offering amount, callability, fungibility, credit enhancement come from Mergent FISD. We obtain

information on bond transactions from the Trade Reporting and Compliance Engine database (TRACE). We get

the information on CDS contracts from the Markit CDS database. Firm-level stock return and accounting

information come from CRSP and Compustat. We include equity volatility, equity beta, market-to-book, book

leverage, book size, profitability, cash holding and dividend payment. Our combined sample include 158122

bond-month observations (3468 firm-year observations) from January 2001 to December 2009. For each

variable, we report the data frequency, source, number of observations, mean and stand deviation. The detailed

definitions of each variable can be found in the Appendix.

Frequency Source N Mean Std. Dev. Bond Characteristics Option-adjusted spread (%) Month BofA-Merrill Lynch 158122 2.12 2.65 Duration Month BofA-Merrill Lynch 158122 5.81 3.39 Investment grade Month BofA-Merrill Lynch 158122 0.83 0.38 Log(offering amount) Month Mergent FISD 158122 5.94 0.68 Coupon rate Month Mergent FISD 158122 6.55 1.31 Callability Month Mergent FISD 158122 0.64 0.48 Fungibility Month Mergent FISD 158122 0.44 0.50 Credit enhancement Month Mergent FISD 158122 0.09 0.29 Bond age Month Mergent FISD 158122 4.54 3.44 Bond illiquidity Month TRACE 73380 1.71 1.38 Firm Characteristics CDS presence Month Markit 38384 0.74 0.44 Equity volatility Month CRSP 38384 0.02 0.02 Equity beta Month CRSP 38384 0.96 0.49 Book size Year Compustat 3468 9.18 1.30 Market-to-book Year Compustat 3468 1.21 0.89 Book leverage Year Compustat 3468 0.31 0.16 Profitability Year Compustat 3468 0.13 0.08 Cash holding Year Compustat 3468 0.07 0.09 Dividend payer Year Compustat 3468 0.79 0.41

30

Table II CDS Presence and Bond Yield Spread

In this table, we link the presence of CDS contracts to corporate yield spreads. The dependent variable is the

option-adjusted (OA) yield spread, defined as the number of percentage points that the treasury spot curve must

be shifted in order to match discounted cash flows to the bond’s price. Our interested variable is “CDS presence”,

a dummy variable equal to 1 if the issuing firm has quoted CDS contracts on its bonds in the previous month and

0 otherwise.

In Panel A, columns (1)-(3) are for the subsample of investment grade bonds, while columns (4)-(6) are for

the subsample of high yield bonds. In column (1), we control for bond characteristics including coupon rate,

duration, offering amount, callability, fungibility and credit enhancement, time fixed effects at the monthly level,

and credit rating fixed effects at the issue level. In column (2), we control for time × credit rating fixed effects. In

column (3), we control for firm characteristics including equity volatility, equity beta, market-to-book, book

leverage, book size, profitability, cash holding and dividend payment. Columns (4)-(6) follow the same

specifications as in columns (1)-(3), respectively. The detailed definitions of each variable can be found in the

Appendix. All firm-year (month) variables are taken at the end of the previous year (month). We control for

industry fixed effects at the two-digit SIC level, and always cluster the standard errors at the firm level.

In Panel B, we interact the variable of CDS presence with a dummy variable indicating the financial crisis

period. The crisis period includes year 2001, 2002, 2008 and the first half of 2009. Columns (1)-(3) are for the

subsample of investment grade bonds. Columns (4)-(6) are for the subsample of high yield bonds. We follow the

same specifications as in Panel A. We always include time (monthly) fixed effects therefore the crisis period

dummy is dropped out of the regression. ***, ** and * represent significance levels at 1%, 5% and 10%

respectively using heteroscedasticity robust standard errors with t-statistics given in parentheses.

31

Table II (Cont’d)

Panel A: Main Regression

Dep: Bond Yield Spread Investment Grade High Yield (1) (2) (3) (4) (5) (6) CDS presence -0.161*** -0.141*** -0.215*** 0.258 0.448** 0.089 (-3.40) (-3.28) (-5.43) (1.01) (2.25) (0.52) Controls Coupon rate 0.060*** 0.068*** 0.057*** 0.243*** 0.196*** 0.177*** (5.00) (6.26) (6.02) (2.65) (2.68) (2.70) Duration 0.022*** 0.024*** 0.026*** -0.163*** -0.117*** -0.106*** (7.56) (8.33) (10.69) (-4.23) (-3.99) (-4.42) Log(offering amount) -0.002 -0.013 -0.018 -0.017 -0.015 -0.064 (-0.05) (-0.40) (-1.02) (-0.10) (-0.13) (-0.55) Callability -0.037 -0.012 0.016 -0.090 -0.074 -0.024 (-1.07) (-0.38) (0.60) (-0.47) (-0.50) (-0.19) Fungibility -0.057** -0.052** -0.036** 0.325 0.303 0.220 (-2.21) (-2.30) (-2.05) (1.37) (1.60) (1.34) Credit enhancement -0.061 -0.040 -0.040 -0.158 0.010 0.077

(-0.93) (-0.65) (-0.75) (-0.52) (0.04) (0.38) Bond age -0.007 -0.007 -0.001 -0.023 0.002 -0.000

(-1.37) (-1.28) (-0.16) (-0.75) (0.08) (-0.01) Equity volatility 34.744*** 65.207***

(11.99) (10.07) Equity beta 0.172*** 0.334 (3.96) (1.18) Book size -0.035 -0.068 (-1.35) (-0.97) Market-to-book -0.058** -0.593*** (-2.06) (-3.29) Book leverage 0.565*** 2.470*** (3.03) (3.21) Profitability -0.922** -1.454 (-2.24) (-1.63) Cash holding -0.311 0.512 (-1.50) (0.51) Dividend payer 0.160** -0.055 (2.45) (-0.23) Industry (two-digit SIC) FE Y Y Y Y Y Y Time (monthly) FE Y - - Y - - Credit rating (issue level) FE Y - - Y - - Time × Credit rating FE - Y Y - Y Y Clustering Issuer Issuer Issuer Issuer Issuer Issuer Number of Obs. 131,202 131,202 131,202 26,920 26,920 26,920 R-squared 0.614 0.670 0.722 0.628 0.736 0.772

Panel B: Interaction with Financial Crisis Period

Dep: Bond Yield Spread Investment Grade High Yield (1) (2) (3) (4) (5) (6) CDS presence -0.044 -0.065* -0.117*** 0.134 0.214 0.023 (-0.83) (-1.65) (-3.18) (0.52) (1.07) (0.13) CDS presence * Crisis period -0.223** -0.147** -0.188*** 0.282 0.553 0.161 (-2.52) (-2.12) (-3.19) (0.63) (1.36) (0.51) Same Specifications as Panel A Y Y Y Y Y YNumber of Obs. 131,202 131,202 131,202 26,920 26,920 26,920 R-squared 0.614 0.670 0.722 0.629 0.737 0.772

32

Table III CDS Presence and Bond Illiquidity

In this table, we link the presence of CDS contracts to bond illiquidity. The dependent variable is the implied

bid-ask spread based on the auto-covariances of bond price changes: 2√γ (0 if γ 0 ), where

γ Cov ∆p , ∆p ) and p is the log price at time t. Our interested variable is “CDS presence”, a dummy

variable equal to 1 if the issuing firm has quoted CDS contracts on its bonds in the previous month and 0

otherwise.

In Panel A, columns (1)-(3) are for the subsample of investment grade bonds, while columns (4)-(6) are for

the subsample of high yield bonds. In column (1), we control for bond characteristics including coupon rate,

duration, offering amount, callability, fungibility and credit enhancement, time fixed effects at the monthly level,

and credit rating fixed effects at the issue level. In column (2), we control for time × credit rating fixed effects. In

column (3), we control for firm characteristics including equity volatility, equity beta, market-to-book, book

leverage, book size, profitability, cash holding and dividend payment. Columns (4)-(6) follow the same

specifications as in columns (1)-(3), respectively. The detailed definitions of each variable can be found in the

Appendix. All firm-year (month) variables are taken at the end of the previous year (month). We control for

industry fixed effects at the two-digit SIC level, and always cluster the standard errors at the firm level.

In Panel B, we interact the variable of CDS presence with a dummy variable indicating the market crisis

period. The crisis period covers year 2001, 2002, 2008 and the first half of 2009. Columns (1)-(3) are for the

subsample of investment grade bonds. Columns (4)-(6) are for the subsample of high yield bonds. We follow the

same specifications as in Panel A. We always include time (monthly) fixed effects therefore the crisis period

dummy is dropped out of the regression. ***, ** and * represent significance levels at 1%, 5% and 10%

respectively using heteroscedasticity robust standard errors with t-statistics given in parentheses.

33

Table III (Cont’d)

Panel A: Main Regression

Dep: Bond Illiquidity Investment Grade High Yield (1) (2) (3) (4) (5) (6) CDS presence -0.156*** -0.129*** -0.144*** 0.327*** 0.279*** 0.184* (-3.02) (-2.65) (-3.00) (2.86) (2.66) (1.76) Controls Coupon rate -0.044*** -0.040*** -0.041*** -0.096** -0.084** -0.080** (-4.22) (-3.71) (-4.05) (-2.36) (-2.49) (-2.12) Duration 0.144*** 0.146*** 0.146*** 0.128*** 0.134*** 0.135*** (26.70) (26.63) (26.25) (8.25) (7.62) (7.53) Log(offering amount) -0.173*** -0.175*** -0.182*** -0.255*** -0.207*** -0.280*** (-6.24) (-6.91) (-8.65) (-3.29) (-2.79) (-3.39) Callability 0.068** 0.079** 0.092*** 0.088 0.072 0.122 (2.13) (2.55) (2.78) (0.90) (0.77) (1.25) Fungibility -0.007 -0.010 -0.000 0.108 0.109 0.124* (-0.28) (-0.41) (-0.01) (1.24) (1.50) (1.67) Credit enhancement -0.058 -0.068 -0.070 0.025 -0.009 0.018

(-0.92) (-1.23) (-1.36) (0.25) (-0.10) (0.17) Bond age 0.061*** 0.061*** 0.062*** 0.081*** 0.080*** 0.076***

(13.18) (12.81) (13.22) (4.24) (5.29) (4.77) Equity volatility 13.654*** 14.463***

(8.31) (5.18) Equity beta -0.039 0.041 (-1.11) (0.40) Book size 0.001 0.074 (0.03) (1.36) Market-to-book -0.080** -0.068 (-2.17) (-0.40) Book leverage 0.392*** 0.536 (2.79) (1.22) Profitability 0.236 -0.346 (0.59) (-0.70) Cash holding 0.225 0.326 (1.03) (0.42) Dividend payer 0.102 0.075 (1.33) (0.85) Industry (two-digit SIC) FE Y Y Y Y Y Y Time (monthly) FE Y - - Y - - Credit rating (issue level) FE Y - - Y - - Time × Credit rating FE - Y Y - Y Y Clustering Issuer Issuer Issuer Issuer Issuer Issuer Number of Obs. 64,024 64,024 64,024 9,356 9,356 9,356 R-squared 0.390 0.418 0.426 0.412 0.510 0.519

Panel B: Interaction with Financial Crisis Period

Dep: Bond Illiquidity Investment Grade High Yield (1) (2) (3) (4) (5) (6) CDS presence -0.048 -0.037 -0.053 0.122 0.169 0.104 (-0.89) (-0.86) (-1.34) (0.95) (1.48) (0.87) CDS presence * Crisis period -0.394** -0.340*** -0.336*** 0.607** 0.336* 0.251 (-2.49) (-3.27) (-3.74) (2.39) (1.75) (1.24) Same Specifications as Panel A Y Y Y Y Y YNumber of Obs. 64,024 64,024 64,024 9,356 9,356 9,356 R-squared 0.392 0.419 0.427 0.415 0.511 0.519

34

Table IV Instrumental Regression

In this table, we perform an instrumental regression analysis to examine the impact of CDS presence on bond

yield spread and bond illiquidity. We intend to use the level of loan concentration of the lending banks, from

which the bond issuer borrows its bank debt, to identify the demand for CDS contracts, i.e., as an instrument for

the presence of CDS contracts.

In Panel A, to justify the loan concentration-based instrument, we examine the concentration of a bank’s

loan portfolio across different industries and geographical regions, and relate it to the bank’s use of credit

derivatives, foreign exchange derivatives and interest rate derivatives for hedging purposes. The analysis is done

at the bank level. First, we construct banks’ loan concentration based on the data from LPC Dealscan. We only

focus on bank borrowers in the US. For each bank-year, we classify its existing loans into different industry (two

digit SIC)―state pairs. We then calculate the herfindal across those pairs as the concentration of the bank’s loan

portfolio. Next, we perform name matching by bank names to link LPC Dealscan with the Bank Regulatory

database, which contains the off-balance sheet data on banks’ use of derivatives for hedging purposes. We

require the bank’s total amount of commercial and industrial loans (RCON1766) to be larger than $100 million.

The dep. var. in columns (1)-(2) is the log value of the notional amount of credit derivatives (RCFDA535). The

dep. var. in columns (3)-(4) is the notional amount of credit derivatives divided by the bank’s total asset

(RCFDA535/RCFD2170). The dep. var. in column (5) is the notional amount of foreign exchange derivatives

divided by the bank’s total asset (RCFD8726/RCFD2170), while the dep. var. in column (6) is the notional

amount of interest rate derivatives divided by the bank’s total asset (RCFD8725/ RCFD2170). Bank size is the

log value of total asset (RCFD2170). Detailed descriptions of each data item are given in the appendix.

In Panel B, we link the presence of CDS contracts to bond yield spread and bond illiquidity, with CDS

presence instrumented by the loan concentration of the lending banks. We proceed as follows. First, at the issuer

level, we calculate the loan herfindal as the value (loan amount)-weighted bank herfindal among all the banks

from which the issuer borrows in the last 5 years. In columns (1) and (2), we run a probit regression of the CDS

presence dummy on loan herfindal. We perform the F-test to identify the weakness of the loan herfindal variable.

Then, we calculate the fitted value from column (2) and use it as the instrument for CDS presence in columns

(3)-(6). The dep. var. in columns (3)-(4) is the bond yield spread, while the dep. var. in columns (5)-(6) is the

bond illiquidity. Columns (3) and (5) are for the subsample of investment grade bonds, while columns (4) and (6)

are for the subsample of high yield bonds. In all the specifications, we include industry, time and credit rating

fixed effects, and cluster the standard errors at the issuer level.

Panel C follows the same specifications as in Panel B, except that the instrument is the average loan

herfindal among issuers with same industry (two digit SIC), region (state) and basic rating category (investment

grade/high yield). ***, ** and * represent significance levels at 1%, 5% and 10% respectively using

heteroscedasticity robust standard errors with t-statistics given in parentheses.

Panel A: Banks’ Loan Concentration and the Use of Derivatives for Hedging

Dep. var.: Credit Derivatives (log of notional amount)

Credit Derivatives (notional amount/assets)

FX (notional

amount/assets)

Interest Rate (notional

amount/assets) (1) (2) (3) (4) (5) (6) Loan herfindal 3.318*** 3.318*** 0.051*** 0.051*** 0.000 0.005 (8.97) (4.70) (4.39) (3.06) (0.20) (0.08) Bank size 2.695*** 2.695*** 0.029*** 0.029*** 0.001*** 0.082*** (22.26) (9.27) (4.98) (3.13) (2.99) (3.28) Year FE Y Y Y Y Y Y Clustering - Bank - Bank Bank Bank Number of obs. 922 922 922 922 922 922 R-squared 0.559 0.559 0.208 0.208 0.025 0.109

35

Table IV (Cont’d)

Panel B: Instrumental Regression (Loan Herfindal)

Dep.: CDS Presence Dep.: Bond Yield Spread Dep.: Bond Illiquidity First Stage

(Probit Regression) Investment

Grade High Yield

Investment Grade

High Yield

(1) (2) (3) (4) (5) (6) CDS presence (instrumented by fitted value from first stage)

-1.437*** 1.736 -0.698*** 1.057

(-3.81) (1.14) (-2.71) (1.35) Controls Coupon rate -0.110*** -0.041 0.043*** 0.301** -0.047*** -0.098*** (-3.29) (-1.23) (3.49) (2.59) (-4.06) (-2.68) Duration 0.008 -0.001 0.024*** -0.149*** 0.135*** 0.118*** (0.96) (-0.12) (7.34) (-3.78) (21.70) (6.76) Log(offering amount) 0.383*** 0.034 -0.037* 0.082 -0.162*** -0.188*** (4.09) (0.39) (-1.81) (0.57) (-7.84) (-3.01) Callability -0.286*** -0.125 -0.058* -0.233 0.072** -0.050 (-3.26) (-1.43) (-1.71) (-1.13) (1.99) (-0.44) Fungibility 0.047 0.033 -0.025 0.598*** -0.005 0.100 (0.55) (0.36) (-0.99) (2.71) (-0.19) (0.92) Credit enhancement -0.897*** -0.938*** -0.239** -0.218 -0.143* 0.065

(-4.68) (-4.52) (-2.12) (-0.68) (-1.84) (0.24) Bond age 0.039** -0.006 -0.004 0.048 0.059*** 0.074***

(2.33) (-0.40) (-0.95) (1.65) (11.93) (4.76) Equity volatility 2.174 37.610*** 67.597*** 10.145*** 10.585***

(0.77) (9.84) (8.03) (6.45) (4.65) Equity beta 0.275** 0.174*** 0.398 -0.062 -0.037

(2.49) (3.29) (1.31) (-1.64) (-0.41) Book size 0.435*** 0.005 -0.107 -0.006 0.023 (5.72) (0.15) (-0.59) (-0.23) (0.34) Market-to-book 0.019 -0.048 -0.443** -0.053 0.010 (0.21) (-1.32) (-2.07) (-1.35) (0.05) Book leverage 1.624*** 0.730*** 0.504 0.311* -0.406 (2.67) (3.67) (0.55) (1.79) (-0.86) Profitability -1.424* -1.646*** 0.056 -0.379 0.452 (-1.76) (-3.33) (0.04) (-0.94) (0.96) Cash holding 0.858 -0.147 1.481 -0.073 0.196 (0.96) (-0.45) (0.80) (-0.27) (0.23) Dividend payer 0.477** 0.177 -0.304 0.026 -0.122 (2.55) (1.57) (-0.82) (0.43) (-0.93) Loan herfindal 2.028*** 2.074***

(4.81) (4.08) Industry, Time, Credit rating FE Y Y Y Y Y Y Clustering Issuer Issuer Issuer Issuer Issuer Issuer Number of Obs. 136,386 136,386 114,712 21,674 56,391 7,460 F-test 23.13 16.65

Panel C: Instrumental Regression (Average Loan Herfindal)

Dep. Dep.: CDS Presence Dep.: Bond Yield Spread Dep.: Bond Illiquidity First Stage

(Probit Regression) Investment

Grade High Yield

Investment Grade

High Yield

(1) (2) (3) (4) (5) (6) CDS presence (instrumented by fitted value from first stage)

-1.682*** 1.707 -0.862*** 1.014

(-3.80) (1.13) (-3.76) (1.31) Average loan herfindal 2.567*** 2.659***

(4.87) (5.01) Same Specifications as Panel B Y Y Y Y Y Y Number of Obs. 136,386 136,386 114,712 21,674 56,391 7,460 F-test 23.74 25.11

36

Table V Robustness Check I: Instrumental Regression Using 5-year CDS Contracts

In this table we perform a robustness check to the IV results in Table IV. In particular, we redefine “CDS

presence” as a dummy variable equal to 1 if the issuing firm has the most liquid CDS contracts trading in the

previous month and 0 otherwise, i.e., CDS contracts with 5-year maturity and “MR” restructuring clause. Panel

A and Panel B follow the same specifications as in Panel B and Panel C of Table IV, respectively. For brevity we

only report the interested variables.

Panel A: Instrumental Regression (Loan Herfindal)

Dep.: CDS Presence Dep.: Bond Yield Spread Dep.: Bond Illiquidity First Stage

(Probit Regression) Investment

Grade High Yield

Investment Grade

High Yield

(1) (2) (3) (4) (5) (6) CDS presence (instrumented by fitted value from first stage)

-1.462*** 1.154 -0.676*** 1.118

(-3.73) (0.77) (-2.74) (1.29) Loan herfindal 1.797*** 1.882***

(4.69) (4.03) Same Specifications as in Table IV, Panel A

Y Y Y Y Y Y

F-test 21.99 16.20

Panel B: Instrumental Regression (Average Loan Herfindal)

Dep. Dep.: CDS Presence Dep.: Bond Yield Spread Dep.: Bond Illiquidity First Stage

(Probit Regression) Investment

Grade High Yield

Investment Grade

High Yield

(1) (2) (3) (4) (5) (6) CDS presence (instrumented by fitted value from first stage)

-1.678*** 1.145 -0.838*** 1.073

(-3.82) (0.77) (-3.89) (1.26) Average loan herfindal 2.278*** 2.423***

(4.65) (4.89) Same Specifications as in Table IV, Panel B

Y Y Y Y Y Y

F-test 21.66 23.88

37

Table VI Robustness Check II: Instrumental Regression Using CDS Composite Depth

In this table, we perform another robustness check, by focusing the subsample of bonds issued by firms with CDS contracts trading in the market. Specifically, we link the depth of CDS contracts to bond yield spreads and bond illiquidity. Following Qiu and Yu (2012), we use the number of dealers providing CDS quotes as a proxy for the depth of CDS contract. The Markit data only provide information on the number of dealers in the 5-year maturity contracts. Therefore we define CDS composite depth as the log number of dealers in the CDS contracts with 5-year maturity. Qiu and Yu (2012) show that CDS depth is significantly related to the endogenous liquidity provision by informed financial institutions. We therefore rely on the instrumental regression instead of a simple OLS regression, with CDS composite depth instrumented by the loan concentration of the lending banks. We construct the instrument based on the bank herfindal in the same way as described in Table IV.

In Panel A, in columns (1) and (2), we run OLS regressions of the CDS composite depth on the loan herfindal. We perform the F-test to identify the weakness of the loan herfindal variable. Then, we use it as the instrument for the CDS composite depth in columns (3)-(6). The dep. var. in columns (3)-(4) is the bond yield spread, while the dep. var. in columns (5)-(6) is the bond illiquidity. Columns (3) and (5) are for the subsample of investment grade bonds. Columns (4) and (6) are for the subsample of high yield bonds. In all of the specifications, we control for industry, time and credit rating fixed effects, and cluster the standard errors at the issuer level. Panel B follows the same specifications as in Panel A, except that the instrument is the average loan herfindal among issuers in the same industry (two digit SIC), region (state) and basic rating category (investment grade/high yield). ***, ** and * represent significance levels at 1%, 5% and 10% respectively using heteroscedasticity robust standard errors with t-statistics given in parentheses.

Panel A: Instrumental Regression (Loan Herfindal)

Dep.: CDS Composite Depth

Dep.: Bond Yield Spread Dep.: Bond Illiquidity

First Stage (OLS Regression)

Investment Grade

High Yield

Investment Grade

High Yield

(1) (2) (3) (4) (5) (6) CDS composite depth (instrumented by loan herfindal)

-0.670*** -2.177 -0.203** -0.174

(-3.50) (-0.53) (-2.44) (-0.15) Loan herfindal 0.438*** 0.427***

(4.69) (3.35) Other controls Y Y Y Y Y Y Industry, Time, Credit rating FE Y Y Y Y Y Y Clustering Issuer Issuer Issuer Issuer Issuer Issuer Number of Obs. 117,389 117,389 100,531 16,858 51,527 6,491 F-test 22.00 11.19

Panel B: Instrumental Regression (Average Loan Herfindal)

Dep.: CDS Composite Depth

Dep.: Bond Yield Spread Dep.: Bond Illiquidity

First Stage (OLS Regression)

Investment Grade

High Yield

Investment Grade

High Yield

(1) (2) (3) (4) (5) (6) CDS composite depth (instrumented by average loan herfindal)

-0.805*** -0.849 -0.249*** 0.320

(-3.12) (-0.33) (-3.22) (0.44) Average loan herfindal 0.490*** 0.505***

(4.87) (3.56) Same Specifications as Panel B Y Y Y Y Y Y Number of Obs. 117,389 117,389 100,531 16,858 51,527 6,491 F-test 23.76 12.69

38

Table VII CDS Presence and the Impact of Fallen Angels

In this table, we focus on a particular sample of bond-months (bond-quarters) during which the bond experiences rating changes. We examine how the presence of CDS contracts may alter the impact of “fallen angels” on bond institutional ownership, yield spreads and bond illiquidity. For a bond-month (bond-quarter), it is defined as a fallen angel if the bond is downgraded from investment grade to high yield. Our interested variable is the interaction term between the fallen angel dummy and the “No CDS” dummy (1-“CDS presence” dummy).

Panel A: Change in Institutional Bond Ownership

In Panel A, we focus on the changes in bond institutional ownership around the quarter of rating changes. The data on quarterly institutional bond holdings are from Lipper’s eMAXX fixed income database. Bond institutional ownership is calcualted as the total institutional holdings divided by the bond issue outstanding. The dependent variable is the change in bond institutional ownership relative to the previous quarter. Columns (1)-(3) are based on the full sample of rating changes including both rating downgrades and rating upgrades. In column (1), we only interact the fallen angel dummy with the “No CDS presence” dummy. In column (2), we add the interaction terms of fallen angel with bond characteristics including bond duration, offering amount and bond age. In column (3), we further interact the fallen angel with risk characteristics such as stock volatility and beta. Columns (4)-(6) follow the same specifications as in columns (1)-(3) but based on the subsample of rating downgrades. For brevity, in columns (3) and (6), we don’t report the firm-level controls, which include equity volatility, equity beta, market-to-book, book leverage, book size, profitability, cash holding and dividend payment. We always include time (quarterly) × credit rating fixed effects, industry fixed effects at the two-digit SIC level, and cluster the standard errors at the issuer level.

Dep: Change in bond ownership around rating changes

Full Sample on Rating Change Sub-sample on Rating Downgrade

(1) (2) (3) (4) (5) (6) Fallen angel -0.040*** -0.028 -0.015 -0.034** -0.053 -0.022 (-4.03) (-0.48) (-0.25) (-2.36) (-0.92) (-0.40) Fallen angel * No CDS -0.060** -0.060** -0.060** -0.079*** -0.077*** -0.074*** (-2.27) (-2.21) (-2.24) (-2.93) (-2.84) (-2.97) Controls No CDS -0.009 -0.009 -0.011* 0.002 0.002 -0.001 (-1.57) (-1.56) (-1.80) (0.27) (0.27) (-0.08) Coupon rate 0.001 0.001 0.001 0.001 0.001 0.001 (0.54) (0.58) (0.47) (0.33) (0.34) (0.34) Duration 0.001** 0.001** 0.001** 0.001* 0.001* 0.001* (2.42) (2.42) (2.42) (1.67) (1.74) (1.80) Log(offering amount) 0.004 0.004 0.005 0.007 0.007 0.006 (1.10) (1.13) (1.28) (1.57) (1.47) (1.39) Callability 0.003 0.003 0.002 0.005 0.005 0.005 (0.71) (0.70) (0.50) (1.02) (1.01) (0.95) Fungibility 0.003 0.003 0.003 0.002 0.002 0.001 (0.66) (0.65) (0.53) (0.42) (0.41) (0.24) Credit enhancement 0.004 0.004 0.005 -0.013 -0.014 -0.010

(0.64) (0.62) (0.72) (-1.34) (-1.35) (-1.00) Bond age -0.003*** -0.003*** -0.003*** -0.002* -0.002* -0.002*

(-3.10) (-3.09) (-2.97) (-1.85) (-1.87) (-1.76) Fallen angel * Duration -0.000 -0.000 -0.001 -0.001

(-0.23) (-0.12) (-0.27) (-0.25) Fallen angel*Log(offering amt) -0.002 -0.006 0.003 -0.001

(-0.23) (-0.61) (0.32) (-0.13) Fallen angel * Bond age 0.001 0.000 0.001 -0.000 (0.46) (0.10) (0.41) (-0.17) Fallen angel * Equity volatility -0.370 -0.514* (-1.47) (-1.67) Fallen angel * Equity beta 0.023** 0.029**

(2.03) (2.29) Firm-level controls - - Y - - YIndustry, Time × Credit rating FE Y Y Y Y Y Y Clustering Issuer Issuer Issuer Issuer Issuer Issuer Number of Obs. 3,340 3,340 3,340 2,234 2,234 2,234 R-squared 0.247 0.247 0.253 0.325 0.325 0.336

39

Table VII (Cont’d)

Panel B: Change in Bond Yield Spread

In Panel B, we focus on the change in bond yield spread around the month of rating changes. The dependent variable is

the change in bond yield spread relative to the previous month. Columns (1)-(3) are based on the full sample of rating changes including both rating downgrades and rating upgrades. In column (1), we only interact the fallen angel

dummy with the “No CDS” dummy. In column (2), we add the interaction terms of fallen angel with bond characteristics including bond duration, offering amount and bond age. In column (3), we further add the interaction

terms of fallen angel with risk characteristics such as equity volatility and equity beta. Columns (4)-(6) follow the same specifications as columns (1)-(3), except that they are based on the subsample of rating downgrades. For brevity,

in columns (3) and (6), we don’t report the results on firm-level controls, which include equity volatility, equity beta, market-to-book, book leverage, book size, profitability, cash holding and dividend payment. We always include time

(monthly) × credit rating fixed effects, industry fixed effects at the two-digit SIC level, and cluster the standard errors at the firm level.

Dep: Change in yield spread around rating changes

Full Sample on Rating Change Sub-sample on Rating Downgrade

(1) (2) (3) (4) (5) (6) Fallen angel 0.497** -0.595 -1.587 0.842 -0.232 -1.584 (2.13) (-0.64) (-1.46) (1.28) (-0.20) (-1.22) Fallen angel * No CDS 1.618*** 1.679*** 1.943*** 1.848** 1.884*** 2.385*** (2.65) (2.74) (3.47) (2.58) (2.63) (3.61) Controls No CDS -0.042 -0.045 -0.008 0.006 -0.000 0.053 (-0.51) (-0.54) (-0.09) (0.05) (-0.00) (0.37) Coupon rate 0.015 0.013 0.017 0.034 0.030 0.035 (1.00) (0.86) (1.07) (1.58) (1.38) (1.60) Duration -0.006 -0.009** -0.010** -0.014** -0.019*** -0.020*** (-1.52) (-2.07) (-2.16) (-2.18) (-2.72) (-2.82) Log(offering amount) 0.027 0.019 0.008 0.033 0.024 0.017 (1.13) (0.75) (0.29) (0.97) (0.62) (0.44) Callability -0.062 -0.063 -0.064 -0.098 -0.099 -0.106 (-1.32) (-1.34) (-1.36) (-1.43) (-1.45) (-1.57) Fungibility -0.045 -0.046 -0.054 -0.091 -0.093* -0.104* (-1.27) (-1.30) (-1.55) (-1.63) (-1.66) (-1.90) Credit enhancement 0.095* 0.096* 0.115** 0.193** 0.190** 0.213**

(1.86) (1.88) (2.11) (2.09) (2.04) (2.16) Bond age -0.009 -0.008 -0.011 -0.017* -0.015 -0.020*

(-1.30) (-1.13) (-1.59) (-1.86) (-1.50) (-1.97) Fallen angel * Duration 0.057** 0.062*** 0.070*** 0.073***

(2.40) (2.59) (2.68) (2.93) Fallen angel*Log(offering amt) 0.145 0.181 0.138 0.176

(0.92) (1.12) (0.73) (0.94) Fallen angel * Bond age -0.012 -0.004 -0.023 -0.012 (-0.52) (-0.22) (-0.99) (-0.55) Fallen angel * Equity volatility 0.205 -8.313 (0.02) (-0.79) Fallen angel * Equity beta 0.604** 1.049***

(1.98) (2.59) Firm-level controls - - Y - - YIndustry, Time × Credit rating FE Y Y Y Y Y Y Clustering Issuer Issuer Issuer Issuer Issuer Issuer Number of Obs. 4,972 4,972 4,972 3,443 3,443 3,443 R-squared 0.720 0.721 0.726 0.736 0.737 0.746

40

Table VII (Cont’d)

Panel C: Change in Bond Illiquidity

In Panel C, we focus on the change in bond illiquidity around the month of rating changes. The dependent variable is

the change in bond illiquidity relative to the previous month. Columns (1)-(3) are based on the full sample of rating changes including both rating downgrades and rating upgrades. In column (1), we only interact the fallen angel

dummy with the “No CDS” dummy. In column (2), we add the interaction terms of fallen angel with bond characteristics including bond duration, offering amount and bond age. In column (3), we further add the interaction

terms of fallen angel with risk characteristics such as equity volatility and equity beta. Columns (4)-(6) follow the same specifications as columns (1)-(3), except that they are based on the subsample of rating downgrades. For brevity, in columns (3) and (6), we don’t report the results on firm-level controls, which include equity volatility, equity beta,

market-to-book, book leverage, book size, profitability, cash holding and dividend payment. We always include time (monthly) × credit rating fixed effects, industry fixed effects at the two-digit SIC level, and cluster the standard errors

at the firm level.

Dep:Change in bond illiquidity around rating changes

Full Sample on Rating Change Sub-sample on Rating Downgrade

(1) (2) (3) (4) (5) (6) Fallen angel 0.616 0.434 1.550 2.628** 0.964 2.858 (1.42) (0.33) (0.90) (2.41) (0.50) (1.35) Fallen angel * No CDS 3.381*** 3.662*** 3.566*** 3.955*** 4.475*** 6.323*** (4.15) (4.23) (3.85) (4.00) (4.34) (4.88) Controls No CDS -0.148 -0.144 -0.052 0.486 0.507 0.195 (-0.83) (-0.80) (-0.28) (1.00) (1.06) (0.41) Coupon rate -0.029 -0.028 -0.032 -0.037 -0.035 -0.026 (-0.92) (-0.86) (-1.05) (-0.83) (-0.77) (-0.56) Duration 0.014 0.014 0.016 0.028* 0.029* 0.031** (1.29) (1.28) (1.44) (1.94) (1.95) (2.06) Log(offering amount) -0.072 -0.075 -0.094 -0.130 -0.155 -0.167* (-1.11) (-1.11) (-1.40) (-1.43) (-1.65) (-1.72) Callability 0.070 0.070 0.073 0.026 0.024 0.022 (0.83) (0.83) (0.88) (0.23) (0.21) (0.19) Fungibility -0.078 -0.071 -0.071 -0.020 -0.009 -0.008 (-1.04) (-0.95) (-0.95) (-0.19) (-0.09) (-0.08) Credit enhancement 0.015 0.009 -0.035 -0.089 -0.106 -0.165

(0.09) (0.05) (-0.20) (-0.28) (-0.33) (-0.49) Bond age 0.000 -0.003 -0.002 0.009 0.004 -0.001

(0.01) (-0.16) (-0.16) (0.40) (0.17) (-0.03) Fallen angel * Duration -0.023 -0.050 -0.029 -0.047

(-0.59) (-1.27) (-0.74) (-1.20) Fallen angel*Log(offering amt) 0.016 0.001 0.254 0.112

(0.08) (0.00) (1.03) (0.46) Fallen angel * Bond age 0.053 0.035 0.075 0.054 (1.07) (0.87) (1.50) (1.24) Fallen angel * Equity volatility -82.553*** -67.602*** (-3.39) (-2.64) Fallen angel * Equity beta 0.890 -0.022

(1.47) (-0.03) Firm-level controls - - Y - - YIndustry FE Y Y Y Y Y YTime × Credit rating FE Y Y Y Y Y Y Clustering Issuer Issuer Issuer Issuer Issuer Issuer Number of Obs. 2,143 2,143 2,143 1,422 1,422 1,422 R-squared 0.360 0.361 0.369 0.421 0.423 0.436

41

Table VIII CDS Presence and the Impact of Exposed Insurance Ownership around Hurricane Katrina

In this table, we examine how the presence of CDS contracts may alter the impact of Hurricane Katrina on bond

yield spreads and bond illiquidity, through the channel of exposed property insurance and reinsurance companies

and their bond holdings. Our interested variable is the interaction term between the pre-Katrina exposed

insurance bond ownership and the “No CDS” dummy (1-“CDS presence” dummy). The data on institutional

holdings of corporate bonds are from Lipper’s eMAXX fixed income database. We exclude those bond issuers

that may be directly affected by the hurricane, which include life and property (re) insurance companies, and

firms headquartered in the states of Louisiana, Mississippi, and Alabama.

First, we identify the set of property & casualty insurance and reinsurance companies that are considered to

have high exposure to Hurricane Katrina, using data from the Holborn Corporation (2005) Hurricane Katrina

report, publicly available at the URL: http://www.holborn.com/holborn/-reportsKatrina.html. The Holborn

Report lists the names of property & casualty (re)insurance companies along with their 2004 market shares in the

states of Louisiana, Mississippi, and Alabama, and whether they have rating or outlook changes immediately

after the hurricane. We include the top ten property insurance companies by their market shares (including both

personal and commercial lines) and eight reinsurance companies with negative rating (outlook) changes that can

be identified in Lipper/EMAXX as managing firms. The names of those insurance companies are provided in the

appendix.

Then, we define pre-Katrina exposed insurance bond ownership as the par amounts held by property and

reinsurance companies with high exposure to hurricane Katrina at the end of the second quarter of 2005 divided

by the amount of bond outstanding. Non-exposed bond ownership is defined as the difference between total

institutional ownership minus the exposed insurance ownership.

In columns (1)-(3), the dependent variable is the change in bond yield spread from Aug. 23, 2005 to Sep 9,

2005 (the two weeks during which Hurricane Katrina formed and fully dissipated) . In columns (4)-(6), the

dependent variable is the difference of bond illiquidity between Sep 2005 and Aug 2005. In column (1), we only

interact the pre-Katrina exposed insurance ownership with the “No CDS” dummy. In column (2), we add the

interaction term of non-exposed institutional ownership with the “No CDS” dummy. In column (3), we interact

exposed insurance ownership with bond characteristics including bond duration, offering amount and bond age.

Columns (4)-(6) follow the same specifications as in columns (1)-(3), respectively. For brevity, in columns (3)

and (6), we don’t report the results on firm-level controls including equity volatility, equity beta, market-to-book,

book leverage, book size, profitability, cash holding and dividend payment. We always control for credit rating

fixed effects (issue level), industry fixed effects at the two-digit SIC level, and we cluster the standard errors at

the firm level. ***, ** and * represent significance levels at 1%, 5% and 10% respectively using

heteroscedasticity robust standard errors with t-statistics given in parentheses.

42

Table VIII (Cont’d)

Dep: Change in yield spread (Aug 23, 2005—Sep 9, 2005)

Dep: Change in bond illiquidity (Aug, 2005—Sep, 2005)

(1) (2) (3) (4) (5) (6) Pre-Katrina exposed insurance ownership

0.194* 0.196* 2.921** -0.581 -0.574 -1.478

(1.83) (1.85) (2.24) (-0.51) (-0.50) (-0.11) Pre-Katrina exposed insurance ownership * No CDS

1.522** 1.460** 1.337** 11.566*** 11.336** 10.340**

(2.56) (2.47) (2.40) (2.96) (2.57) (2.31) Controls No CDS -0.028 -0.034 -0.009 -0.042 -0.066 -0.042 (-1.50) (-1.25) (-0.30) (-0.28) (-0.38) (-0.25) Coupon rate -0.005 -0.005 -0.001 -0.011 -0.011 -0.008 (-1.05) (-1.04) (-0.31) (-0.43) (-0.42) (-0.31) Duration 0.006*** 0.006*** 0.005*** -0.001 -0.001 0.000 (4.21) (4.21) (4.04) (-0.05) (-0.06) (0.00) Log(offering amount) 0.038*** 0.038*** 0.020*** -0.025 -0.025 -0.038 (2.90) (2.91) (2.62) (-0.54) (-0.53) (-0.67) Callability 0.001 0.001 0.011 0.115* 0.115* 0.128** (0.14) (0.16) (1.34) (1.94) (1.95) (2.00) Fungibility 0.005 0.005 0.005 0.007 0.008 0.007 (0.66) (0.66) (0.61) (0.14) (0.16) (0.14) Credit enhancement -0.005 -0.005 -0.005 -0.078 -0.076 -0.057

(-0.40) (-0.38) (-0.41) (-0.81) (-0.78) (-0.59) Bond age 0.004* 0.004* 0.002 0.008 0.008 0.002

(1.90) (1.91) (0.87) (0.56) (0.57) (0.10) Non-exposed ownership -0.039** -0.040** -0.009 -0.031 -0.035 -0.003 (-2.09) (-2.11) (-0.70) (-0.22) (-0.24) (-0.02) Non-exposed ownership * No CDS

0.017 0.013 0.082 0.100

(0.39) (0.29) (0.19) (0.24) Pre-Katrina exposed insurance ownership * Duration

-0.010 -0.183

(-0.30) (-0.33) Pre-Katrina exposed insurance ownership * Log (offering amt)

-0.404* -0.008

(-1.83) (-0.00) Pre-Katrina exposed insurance ownership * Bond age

-0.056 0.363

(-1.50) (0.90)

Firm-level controls - - Y - - YIndustry FE Y Y Y Y Y YCredit rating FE Y Y Y Y Y Y Clustering Issuer Issuer Issuer Issuer Issuer Issuer Number of Obs. 1,830 1,830 1,830 1,098 1,098 1,098 R-squared 0.322 0.322 0.352 0.076 0.076 0.080