An Empirical Study of US Corporate Credit Spreads, Pathak 2011

Electronic copy available at: http://ssrn.com/abstract=1989012

An Empirical study of US Corporate Credit Spreads

Joy Pathak

December 15, 2011

Abstract

This paper presents an analysis of the factors that affect US corporate credit spreads. Using data

from Bloomberg we investigate the various determinants that cause changes in credit spreads of

US corporate firms. As previous research has shown, the variables that should be based on

theory determine credit spread changes have limited explanatory power. Our study breaks apart a

range of variables into three different sections and analyzes them individual in the groups and

together using multiple regressions. We investigate the spot rate, interest rate volatility and slope

for the interest rate effects and find strong relationships between spot rate and slope with credit

spreads. For the effects of volatility and market uncertainty we find strong relationships between

credit spreads and market volatility proxied by VIX and firm volatility proxied by an average of

Call and Put implied volatility. TED spreads, SPX and RTY returns show strong relationships

between macro-economic variables and credit spreads. Implied default correlations in the

Investment Grade and High Yield market also show a strong positive relationship with credit

spreads. Our research investigates certain macro-economic variables that have not been

researched before and re-establishes previous findings for other variables post-2007 crisis.


1 Introduction

Corporate credit risk and the premium of the spread for that risk has become one of the most

important topics in finance ever since the credit crisis of 07/08. The growth of the credit

derivatives market illustrates the attempt of the financial market to measure and possibly control

that risk. This paper presents an analysis of what factors affect credit spreads and what truly are

the components of CDS prices.

There are three main activities that a central bank is interested in doing; monetary policy,

financial stability of the markets, and asset management. When it comes to monetary stability,

credit spreads are studied due to their role in the overall transmission system of the financial

markets. In order to understand the functioning of monetary policy measures, monetary

authorities analyse the interdependence between corporate bonds, government bonds and money

markets. Thus, they can obtain an insight into how the impulses of monetary policy action are

transmitted across financial markets and on towards the real economy. Furthermore, there is

evidence that corporate bonds possess leading indicator properties for the economic climate in

aggregate. So, it can be said that the information content of credit spreads makes them useful as

indicators for monetary policy. Since the crisis in August 1998, central banks have been

increasing their monitoring of potential sources of instability in financial markets. In this context,

the systemic risk in the banking sector is regularly observed. This key risk category is heavily

influenced by the development of aggregate credit risk among banks and financial institutions.

Despite the increasing importance of financial markets, credit risk is still the major component of

most banks activities. Here, corporate bond markets are an important data source, because data

on bank loans are difficult to collect.


Studies on corporate credit spreads by Gruber et al (2001) and Collin-Dufresne et al (2000) said

that a significant part of the movements in credit spreads of corporate bonds are explained by

much more than the expected default risk of the corporation as had been previously suggested.

Historically, in the United States, corporate bond markets have been much less liquid than both

government bonds and stocks. Corporate bonds are also taxed differently than government bonds

since they are taxed at the state level. Furthermore, Longstaff (1999) has argued that corporate

bond markets are illiquid and are thought to be incomplete. Thus, it seems likely that the credit

spread between corporate and government bonds may be only partly attributed to default risk. So

the residual difference between the observed credit spread and this measured default spread may

also be attributed to other factors such as taxes, liquidity, and market risks.

Collin-Dufresne et al (2000) regressed changes in the US corporate credit spreads on a range of

variables like leverage, economic environment indicators and volatilities. They found that a large

part of the dynamics of corporate credit spreads could still not be explained by these variables.

Gruber et al (2001) found that expected default risk only explains about 25% of the observed

credit spreads. Their research concluded that the risk in corporate bonds moved more with

changes in tax effects and a risk premium. They suggested that the risk in corporate bonds are

mostly systematic in nature and cannot be diversified away.

Ming (1998) performs an empirical analysis of emerging market bond spread determination. He

finds explanatory variables for the cross-country differences in bond spreads. He analyzes 4

groups of variables: Liquidity and solvency variables, macroeconomic fundamentals, external

shocks and dummy variables. He finds that the first two groups of factors influence emerging

market bond spreads. Liquidity and solvency variables such as debt-to-GDP ratio, debt-service-

amer.demirovicHighlight Their research concluded that the risk in corporate bonds moved more with changes in tax effects and a risk premium.

ratio, net foreign assets and international reserves-to-GDP ratio are found to be significant and of

the expected sign. These variables capture the countrys ability to repay the debt.

Macroeconomic fundamentals such as the domestic inflation rate and terms of trade capture the

quality of the countrys economic policy which determines its future ability to service its debt.

This paper is organized as follows: Section 2 describes The Variables/Data and outlines the

hypothesis; Section 3 goes through the Results and Section 4 Concludes.

2 Variables and Hypothesis

Credit Spreads The financial term, credit spread is the yield spread, or difference

in yield between different securities, due to different credit quality. The credit spread reflects the

additional net yield an investor can earn from a security with more credit risk relative to one with

less credit risk. The credit spread of a particular security is often quoted in relation to the yield

on a credit risk-free benchmark security or reference rate. The benchmark is usually US

treasuries and the and the securities used for the study are US corporate bonds. The data is

gathered from Bloomberg.

Interest Rates:

Spot Interest Rate ( Longstaff and Schwartz (1995), state that the static effect of a higher

spot rate is to increase the risk neutral drift of the firm value process. A higher drift reduces the

probability of default, and in turn, reduces the credit spreads. A negative relationship is expected

between change in credit spread and interest rate. The spot rate is proxied using the 10 year US

treasury spot rate. This result compliments what is seen in the capital markets. During good

economic conditions investors are willing to take on more risk and sell their treasury bonds and

buy risky assets. This sell-off in the treasury market causes yields to rise. This risk on

environment wherein investor buy into corporate bonds leads to a decrease in the credit spreads

of the firms.

Changes in the slope of the Yield curve ( - The two most important factors driving the

term structure of interest rates are the level and slope of the term structure. If an increase in the

slope of the Treasury curve increases the expected future short rate, then by the same argument

as above, it should also lead to a decrease in credit spreads.

From a different perspective, a decrease in yield curve slope may imply a weakening economy. It

is reasonable to believe that the expected recovery rate might decrease in times of recession.8

Once again; theory predicts that an increase in the Treasury yield curve slope will create a

decrease in credit spreads. We define the slope of the yield curve as the difference between 10-

year and 2-year Benchmark Treasury yields.

Volatility of Interest Rates ( ) Apart from changes in the level of the risk-free interest rate,

we also include its volatility. From a theoretical perspective this factor is motivated by Longstaff

and Schwartz (1995), who introduced stochastic interest rates to Mertons basic setup.

Furthermore, Collin-Dufresne et al (2001) report that squared changes of the yields of 10-year

government bonds add significant explanatory power to their models of credit spread changes in

the US market. The influence of volatility can be interpreted as a quantification of convexity, ie

the curvature in the interdependence between bond yields and bond prices. Concerning the sign

of the respective coefficient, it is not a priori clear if it should be positive or negative, ie if the

amer.demirovicHighlight Collin-Dufresne et al (2001) report that squared changes of the yields of 10-year government bonds add significant explanatory power to their models of credit spread changes in the US market.

credit spread falls or rises as the yield volatility increases. Collin-Dufresne et al (2001) report

with regard to the squared yield of the 10-year government bonds negative coefficients for high-

rated corporate bonds with short maturities and positive coefficients for low-rated short term and

all long-term bonds. This result is consistent with respect to the structural model of default risk

with stochastic interest rates by Longstaff and Schwartz, where the impact of a change in the

yield volatility on the credit spread can be positive or negative. We use the Barclays Swaption

volatility index to proxy interest rate volatility.

Linear Regression 1:

Volatility

Option Volatility ( - Another factor that affects the credit spread according to the

structural approach is the volatility of the firm value. The price of an option increases with the

volatility of the underlying, because increasing volatility makes it more likely that the put option

will be exercised. In the present context a higher volatility implies that large changes of the

leverage become more likely. Hence the probability that the leverage ratio approaches unity, or

that the firm value falls below the face value of the debt and the firm defaults, increases. Again,

the analysis is not done on the basis of the leverage ratio, but we use the volatility of an

appropriate equity index, where we expect that a rise leads to an increase of the credit spread.

This prediction is intuitive: Increased volatility increases the probability of default. We use an

average of Put and Call option volatility to proxy firm level volatility.

amer.demirovicHighlight We use an average of Put and Call option volatility to proxy firm level volatility.

Market Volatility ( In addition to the firm level volatility the same effect can be

expected of market volatility. An increase in the overall market volatility should lead to higher

credit spreads. We use the VIX as a proxy for market volatility.

Linear Regression 2:

Macro-economic

Part A:

Business Climate The general business climate can have a significant effect on individual

firms. Obviously in a good economy with high GDP and no recession companies will flourish

with default probabilities coming down.

The expected recovery rate in turn should be a function of the overall business climate. Even if

the probability of default remains constant for a firm, changes in credit spreads can occur due to

changes in the expected recovery rate. To proxy business climate we look at the US Dollar index

( , S&P ( and Russell 2000 ( returns. We hypothesize that with higher

returns and a higher value of the US dollar the corporate credit spreads of US firms should

tighten to reflect strong overall performance and balance sheets.

Ted Spreads )- The TED spread is an indicator of perceived credit risk in the general

economy.[1]

This is because T-bills are considered risk-free while LIBOR reflects the credit risk

of lending to commercial banks. When the TED spread increases, that is a sign that lenders

believe the risk of default on interbank loans (also known as counterparty risk) is increasing.

Interbank lenders therefore demand a higher rate of interest, or accept lower returns on safe

investments such as T-bills. When the risk of bank defaults is considered to be decreasing, the

TED spread decreases.

Linear regression 3:

Part B:

Implied Default Correlation - The tendency for firms' defaults to cluster is a widely accepted

phenomenon in corporate bond and credit derivatives markets. The general observation is that

regardless of the state of the economy there is some average number of firms that default each

period, and intermittently there are sharp increases in the number of defaults. These spikes, or

default clusters, are not persistent and the number of defaults readily reverts to the pre-cluster

average. Modelling this phenomenon plays a prominent role in bond risk management and in the

valuation of credit derivatives, such as collateralized debt obligations (CDOs), and it is this

phenomenon that is typically modelled by a default correlation parameter. We show that

corporate bond credit spreads are increasing in default correlation, as implied from the CDO

market. We gather data from the Morgan Stanley internal database on implied default

correlations in the high yield and investment graduate tranche markets.

Linear regression 4:

amer.demirovicHighlightWe show that corporate bond credit spreads are increasing in default correlation

3 Empirical Testing and Data

All the data was collected from Bloomberg. The data set is from 2009 to present with daily frequency

for all variables. SAS and SPSS were used to conduct all the statistical analysis. The descriptive

statistics can be seen in Table 1 for all the data used. The primary and secondary variables are shown.

Only US corporates were chosen.

Table 1: Descriptive Statistics

N

Minimu

m

Maximu

m Sum Mean

Std.

Deviatio

n Skewness Kurtosis

Statist

ic Statistic Statistic Statistic Statistic Statistic

Statist

ic

Std.

Error

Statist

ic

Std.

Error

CDS Spreads 688 263.329

7

1052.590

7

309900.1

767

450.4363

03

206.8232

966

1.224 .093 .219 .186

Date 688 05-Jan-

2009

13-Sep-

2011

*

**:**:**

10-May-

2010

6851:58:

19

-.008 .093 -1.215 .186

USGG10YR

Index

688 1.9183 3.9859 2187.813

9

3.179962 .4439838 -.565 .093 -.378 .186

USGG2YR

Index

688 .1688 1.3980 504.5768 .733397 .2482896 -.257 .093 -.593 .186

Slope 688 1.4917 2.9124 1683.237

1

2.446566 .3209773 -.775 .093 -.350 .186

Dollar Spot

Index

688 72.9330 89.1050 54727.74

40

79.54614

0

3.886716

3

.473 .093 -.678 .186

Ted Spread 688 10.5700 133.5100 23641.37

00

34.36245

6

27.41243

90

1.853 .093 2.187 .186

VIX Index 688 14.6200 56.6500 17527.70

00

25.47630

8

8.747419

9

1.147 .093 .542 .186

SPX Index 688 676.530

0

1363.610

0

762568.6

600

1108.384

680

160.4047

026

-.473 .093 -.489 .186

SPX Return 687 -6.6634 7.0758 30.2513 .044034 1.403752

5

-.163 .093 3.684 .186

RTY Index 688 343.260

0

865.2900 446843.0

800

649.4812

21

125.0072

224

-.196 .093 -.774 .186

RTY Return 687 -8.9095 8.4002 43.9583 .063986 1.907586

6

-.045 .093 2.534 .186

BBOX Index 688 83.98 122.45 69025.65 100.3280 8.23730 .534 .093 -.522 .186

Implied Vol 688 .0000 96.0973 28784.94

32

41.83858

0

15.12971

51

1.331 .093 1.366 .186

Implied

Correlation

HY

600 25.4052 65.7603 26679.86

93

44.46644

9

5.169471

5

.629 .100 .660 .199

Implied

Correlation IG

600 32.4068 65.5762 26380.61

30

43.96768

8

7.278448

2

.545 .100 -.737 .199

Valid N

(listwise)

599

5 Results

Interest Rates:

Consistent with the empirical findings of Longstaff and Schwartz ~1995 and Duffee ~1998!, we

find that an increase in the risk-free rate lowers the credit spread for all bonds. A negative

correlation with a coefficient of -0.289 is observed between the 10 year spot rate and credit

spreads.

The slope of the term structure displays a strong negative relationship of -0.675 as hypothesized.

An increase in the slope creates a decrease in credit spreads.

The interest rate volatility as proxied by a swaption volatility index does not show a significant

correlation. This is consistent with the study of Longstaff and Shawrtz. They were not able to see

a significant relationship and hypothesized as us that the relationship can be positive or negative.

Volatility:

Implied volatility showed a strong positive (0.840) relationship with credit spreads. As the

implied volatility of a firm increases the option price increases which would suggest the market

is pricing in higher uncertainty associated with the firm. This would be directly related to the

credit spreads as higher uncertainty would lead to higher credit spreads.

The relationship of market volatility and firm level volatility should generally be similar. This

relationship is further confirmed with the strong positive relationship of 0.927 correlation seen

between market volatility and credit spreads.

Macro-economic

Part A:

US Dollar index showed a positive relationship between credit spreads and the macro-economy.

This rejected our hypothesis of a negative relationship in which a well performing economy

should lead to a higher dollar and a lower credit spread for US firms. A reason behind this could

be that although corporations were performing well and reporting record breaking earnings while

the economy was still recovering from the recession leading to speculative bets on the dollar

pressuring it downwards. This lead to tightening in credit spreads while the dollar weakened.

Federal policies and lowering of interest rate might have led to a lower dollar value while at the

same time corporations strengthened by building up their balance sheets leading to lower credit

spreads.

TED spread is mentioned previously is an indicator of perceived credit risk in the general

economy. Out of all the variables chosen TED spread has the most direct relation to credit

spreads. This was further proven by the strong correlation shown at 0.881. As credit risk in the

economy increases credit spreads of the firms increase.

The last two variables tested in part A were the SPX And RTY index returns. SPX and RTY

index returns show a negative correlation of -0.832 and -0.798 respectively. This further proves

that with a healthy economy and strong macro-economic fundamentals that lead to higher returns

in the capital markets should lead to a tightening of credit spreads.

Part B:

The implied correlation in the defaults in the HY and IG trance markets show a correlation of

0.430 and 0.779 respectively. This is in line with our hypothesis as we expected an increase in

default correlation to be directly proportional to a widening of credit spreads. The HY

relationship does not show as strong of a relationship as IG because of potential volatility in the

HY market.

6 Conclusion

We investigate changes in US corporate credit spreads. As mentioned corporate credit risk has

become quite a hot topic since the crisis of 2007. The growth of the credit default swap market

has grown significantly. This paper goes into a deep investigation of how credit spreads are

amer.demirovicHighlight This is in line with our hypothesis as we expected an increase in default correlation to be directly proportional to a widening of credit spreads

affected by a range of variables. As previous research has shown, the variables that should be

based on theory determine credit spread changes have limited explanatory power. Our study

breaks apart a range of variables into three different sections and analyzes them individual in the

groups and together using multiple regressions. We investigate the spot rate, interest rate

volatility and slope for the interest rate effects and find strong relationships between spot rate

and slope with credit spreads. For the effects of volatility and market uncertainty we find strong

relationships between credit spreads and market volatility proxied by VIX and firm volatility

proxied by an average of Call and Put implied volatility. TED spreads, SPX and RTY returns

show strong relationships between macro-economic variables and credit spreads. Implied default

correlations in the IG and HY market also show a strong positive relationship with credit

spreads. Our research investigates certain macro-economic variables that have not been

researched before and re-establishes previous findings for other variables post-2007 crisis.

We believe that it would be very useful to understand in a deeper fashion how volatility affects

credit spreads. For further research we would like to understand how the individual firm option

volatility skew affects the firms credit spreads. We also plan to investigate how credit spreads of

different ratings react to the variables in this study. We believe that our study should lay the path

to further research in this field as this paper is on the few papers that has studied credit spreads

post 2007 crisis.

ACKNOWLEDGEMENTS

We are very grateful to Dr. Jim Gatheral and Dr. Simina Farcasiu. We would also like to thank

Ken Abbott and Dr. Andrew Lesniewski for their valuable suggestions.

REFERENCES

Longstaff, Francis A., and Eduardo Schwartz, 1995, A simple approach to valuing risky fixed

and f loating rate debt, Journal of Finance 50, 789821.

Collin-Dufresne, P., and R. Goldstein. Do Credit Spreads Reflect Stationary Leverage Ratios? Journal of Finance, 56 (2001), pp. 1929-1957.

Duffee, Gregory R., 1998, The relation between treasury yields and corporate bond yield

spreads, Journal of Finance 53, 22252241.

Merton, R. C., 1972, Theory of Rational Option Pricing, Bell Journal of Economics and

Management Science, 4, Spring, pp. 141-183.

Elton, E., and Gruber, M., Agrawal, D., Mann, C., 2000, Explaining the Rate Spread on

Corporate

Bonds, NYU Working Paper, September, 1999, forthcoming, Journal of Finance.

APPENDIX A

Interest Rate

Descriptive Statistics

Mean Std. Deviation N

CDS Spreads 450.436303 206.8232966 688

USGG10YR Index 3.179962 .4439838 688

Slope 2.446566 .3209773 688

BBOX Index 100.3280 8.23730 688

Correlations

CDS Spreads

USGG10YR

Index Slope BBOX Index

Pearson Correlation CDS Spreads 1.000 -.289 -.675 .171

USGG10YR Index -.289 1.000 .837 .589

Slope -.675 .837 1.000 .322

BBOX Index .171 .589 .322 1.000

Sig. (1-tailed) CDS Spreads . .000 .000 .000

USGG10YR Index .000 . .000 .000

Slope .000 .000 . .000

BBOX Index .000 .000 .000 .

N CDS Spreads 688 688 688 688

USGG10YR Index 688 688 688 688

Slope 688 688 688 688

BBOX Index 688 688 688 688

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) 1051.436 59.107 17.789 .000

USGG10YR Index 343.602 21.650 .738 15.871 .000

Slope -867.995 25.570 -1.347 -33.946 .000

BBOX Index 4.286 .675 .171 6.350 .000

a. Dependent Variable: CDS Spreads

b.

Volatility



CDS Spreads 450.436303 206.8232966 688

VIX Index 25.476308 8.7474199 688

Implied Vol 41.838580 15.1297151 688

Correlations

CDS Spreads VIX Index Implied Vol

Pearson Correlation CDS Spreads 1.000 .840 .927

VIX Index .840 1.000 .905

Implied Vol .927 .905 1.000

Sig. (1-tailed) CDS Spreads . .000 .000

VIX Index .000 . .000

Implied Vol .000 .000 .

N CDS Spreads 688 688 688

VIX Index 688 688 688

Implied Vol 688 688 688

Coefficientsa

Model Unstandardized Coefficients

Standardized

Coefficients t Sig.

B Std. Error Beta

1 (Constant) -80.306 9.150 -8.777 .000

VIX Index .103 .795 .004 .129 .897

Implied Vol 12.623 .460 .923 27.451 .000


Macro-Economic



CDS Spreads 450.436303 206.8232966 688

Dollar Spot Index 79.546140 3.8867163 688

Ted Spread 34.362456 27.4124390 688

SPX Index 1108.384680 160.4047026 688

RTY Index 649.481221 125.0072224 688

Correlations

CDS Spreads Dollar Spot Index Ted Spread SPX Index RTY Index

Pearson Correlation CDS Spreads 1.000 .457 .881 -.832 -.798

Dollar Spot Index .457 1.000 .623 -.672 -.617

Ted Spread .881 .623 1.000 -.751 -.696

SPX Index -.832 -.672 -.751 1.000 .990

RTY Index -.798 -.617 -.696 .990 1.000

Sig. (1-tailed) CDS Spreads . .000 .000 .000 .000

Dollar Spot Index .000 . .000 .000 .000

Ted Spread .000 .000 . .000 .000

SPX Index .000 .000 .000 . .000

RTY Index .000 .000 .000 .000 .

N CDS Spreads 688 688 688 688 688

Dollar Spot Index 688 688 688 688 688

Ted Spread 688 688 688 688 688

SPX Index 688 688 688 688 688

Correlations

CDS Spreads Dollar Spot Index Ted Spread SPX Index RTY Index

Pearson Correlation CDS Spreads 1.000 .457 .881 -.832 -.798

Dollar Spot Index .457 1.000 .623 -.672 -.617

Ted Spread .881 .623 1.000 -.751 -.696

SPX Index -.832 -.672 -.751 1.000 .990

RTY Index -.798 -.617 -.696 .990 1.000

Sig. (1-tailed) CDS Spreads . .000 .000 .000 .000

Dollar Spot Index .000 . .000 .000 .000

Ted Spread .000 .000 . .000 .000

SPX Index .000 .000 .000 . .000

RTY Index .000 .000 .000 .000 .

N CDS Spreads 688 688 688 688 688

Dollar Spot Index 688 688 688 688 688

Ted Spread 688 688 688 688 688

SPX Index 688 688 688 688 688

RTY Index 688 688 688 688 688

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 3166.051 119.869 26.413 .000

Dollar Spot Index -20.881 .941 -.392 -22.191 .000

Ted Spread 4.531 .154 .601 29.474 .000

SPX Index -1.914 .152 -1.484 -12.618 .000

RTY Index 1.403 .174 .848 8.071 .000


Macro-economic Implied Correlations



CDS Spreads 450.905113 215.9712259 600

Implied Correlation HY 44.466449 5.1694715 600

Implied Correlation IG 43.967688 7.2784482 600

Correlations

CDS Spreads

Implied

Correlation HY

Implied

Correlation IG

Pearson Correlation CDS Spreads 1.000 .430 .779

Implied Correlation HY .430 1.000 .382

Implied Correlation IG .779 .382 1.000

Sig. (1-tailed) CDS Spreads . .000 .000

Implied Correlation HY .000 . .000

Implied Correlation IG .000 .000 .

N CDS Spreads 600 600 600

Implied Correlation HY 600 600 600

Implied Correlation IG 600 600 600

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) -776.024 49.446 -15.694 .000

Implied Correlation HY 6.488 1.130 .155 5.741 .000

Implied Correlation IG 21.344 .803 .719 26.589 .000


An Empirical Study of US Corporate Credit Spreads, Pathak 2011

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