An Empirical study of US Corporate Credit Spreads Joy Pathak December 15, 2011AbstractThis 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-

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 10year 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

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 highrated 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.

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:

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 Std. Minimu Maximu N Statist ic CDS Spreads Statistic Statistic Statistic Statistic Statistic m m Sum Mean Deviatio n Skewness Kurtosis

Statist Std. Statist Std. ic Error .093 ic .219 Error .186

688 263.329 1052.590 309900.1 450.4363 206.8232 1.224 7 7 767 03 966

Date

688 05-Jan- 13-Sep- * 2009 2011 **:**:**

10-May- 6851:58: -.008 2010 19

.093 -1.215

.186

USGG10YR Index USGG2YR Index Slope

688

1.9183

3.9859 2187.813 3.179962 .4439838 -.565 9

.093 -.378

.186

688

.1688

1.3980 504.5768 .733397 .2482896 -.257

.093 -.593

.186

688

1.4917

2.9124 1683.237 2.446566 .3209773 -.775 1

.093 -.350

.186

Dollar Spot Index Ted Spread

688 72.9330 89.1050 54727.74 79.54614 3.886716 40 0 3

.473

.093 -.678

.186

688 10.5700 133.5100 23641.37 34.36245 27.41243 1.853 00 6 90

.093 2.187

.186

VIX Index

688 14.6200 56.6500 17527.70 25.47630 8.747419 1.147 00 8 9

.093

.542

.186

SPX Index

688 676.530 1363.610 762568.6 1108.384 160.4047 -.473 0 0 600 680 026

.093 -.489

.186

SPX Return

687 -6.6634

7.0758 30.2513 .044034 1.403752 -.163 5

.093 3.684

.186

RTY Index

688 343.260 865.2900 446843.0 649.4812 125.0072 -.196 0 800 21 224

.093 -.774

.186

RTY Return

687 -8.9095

8.4002 43.9583 .063986 1.907586 -.045 6

.093 2.534

.186

BBOX Index Implied Vol

688 688

83.98

122.45 69025.65 100.3280 8.23730

.534

.093 -.522 .093 1.366

.186 .186

.0000 96.0973 28784.94 41.83858 15.12971 1.331 32 0 51 .629

Implied Correlation HY Implied Correlation IG Valid N (listwise)

600 25.4052 65.7603 26679.86 44.46644 5.169471 93 9 5

.100

.660

.199

600 32.4068 65.5762 26380.61 43.96768 7.278448 30 599 8 2

.545

.100 -.737

.199

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

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.

ACKNOWLEDGEMENTSWe 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 RateDescriptive Statistics Mean CDS Spreads USGG10YR Index Slope BBOX Index 450.436303 3.179962 2.446566 100.3280 Std. Deviation 206.8232966 .4439838 .3209773 8.23730 N 688 688 688 688

Correlations USGG10YR CDS Spreads Pearson Correlation CDS Spreads USGG10YR Index Slope BBOX Index Sig. (1-tailed) CDS Spreads USGG10YR Index Slope BBOX Index N CDS Spreads USGG10YR Index Slope BBOX Index 1.000 -.289 -.675 .171 . .000 .000 .000 688 688 688 688 Index -.289 1.000 .837 .589 .000 . .000 .000 688 688 688 688 Slope -.675 .837 1.000 .322 .000 .000 . .000 688 688 688 688 BBOX Index .171 .589 .322 1.000 .000 .000 .000 . 688 688 688 688

Coefficients

a

Standardized Unstandardized Coefficients Model 1 (Constant) USGG10YR Index Slope BBOX Index a. b. B 1051.436 343.602 -867.995 4.286 Std. Error 59.107 21.650 25.570 .675 .738 -1.347 .171 Coefficients Beta t 17.789 15.871 -33.946 6.350 Sig. .000 .000 .000 .000

Dependent Variable: CDS Spreads

VolatilityDescriptive Statistics Mean CDS Spreads VIX Index Implied Vol 450.436303 25.476308 41.838580 Std. Deviation 206.8232966 8.7474199 15.1297151 N 688 688 688

Correlations CDS Spreads Pearson Correlation CDS Spreads VIX Index Implied Vol Sig. (1-tailed) CDS Spreads VIX Index Implied Vol N CDS Spreads VIX Index Implied Vol 1.000 .840 .927 . .000 .000 688 688 688 VIX Index .840 1.000 .905 .000 . .000 688 688 688 Implied Vol .927 .905 1.000 .000 .000 . 688 688 688

Coefficients

a

Standardized Model Unstandardized Coefficients Coefficients t Sig.

B 1 (Constant) VIX Index Implied Vol -80.306 .103 12.623

Std. Error 9.150 .795 .460

Beta -8.777 .004 .923 .129 27.451 .000 .897 .000

a. Dependent Variable: CDS Spreads

Macro-EconomicDescriptive Statistics Mean CDS Spreads Dollar Spot Index Ted Spread SPX Index RTY Index 450.436303 79.546140 34.362456 1108.384680 649.481221 Std. Deviation 206.8232966 3.8867163 27.4124390 160.4047026 125.0072224 N 688 688 688 688 688

Correlations CDS Spreads Pearson Correlation CDS Spreads Dollar Spot Index Ted Spread SPX Index RTY Index Sig. (1-tailed) CDS Spreads Dollar Spot Index Ted Spread SPX Index RTY Index N CDS Spreads Dollar Spot Index Ted Spread SPX Index 1.000 .457 .881 -.832 -.798 . .000 .000 .000 .000 688 688 688 688 Dollar Spot Index .457 1.000 .623 -.672 -.617 .000 . .000 .000 .000 688 688 688 688 Ted Spread .881 .623 1.000 -.751 -.696 .000 .000 . .000 .000 688 688 688 688 SPX Index -.832 -.672 -.751 1.000 .990 .000 .000 .000 . .000 688 688 688 688 RTY Index -.798 -.617 -.696 .990 1.000 .000 .000 .000 .000 . 688 688 688 688

Correlations CDS Spreads Pearson Correlation CDS Spreads Dollar Spot Index Ted Spread SPX Index RTY Index Sig. (1-tailed) CDS Spreads Dollar Spot Index Ted Spread SPX Index RTY Index N CDS Spreads Dollar Spot Index Ted Spread SPX Index RTY Index 1.000 .457 .881 -.832 -.798 . .000 .000 .000 .000 688 688 688 688 688 Dollar Spot Index .457 1.000 .623 -.672 -.617 .000 . .000 .000 .000 688 688 688 688 688 Ted Spread .881 .623 1.000 -.751 -.696 .000 .000 . .000 .000 688 688 688 688 688 SPX Index -.832 -.672 -.751 1.000 .990 .000 .000 .000 . .000 688 688 688 688 688 RTY Index -.798 -.617 -.696 .990 1.000 .000 .000 .000 .000 . 688 688 688 688 688

Coefficients

a

Standardized Unstandardized Coefficients Model 1 (Constant) Dollar Spot Index Ted Spread SPX Index RTY Index a. Dependent Variable: CDS Spreads B 3166.051 -20.881 4.531 -1.914 1.403 Std. Error 119.869 .941 .154 .152 .174 -.392 .601 -1.484 .848 Coefficients Beta t 26.413 -22.191 29.474 -12.618 8.071 Sig. .000 .000 .000 .000 .000

Macro-economic Implied CorrelationsDescriptive Statistics

Mean CDS Spreads Implied Correlation HY Implied Correlation IG 450.905113 44.466449 43.967688

Std. Deviation 215.9712259 5.1694715 7.2784482

N 600 600 600

Correlations Implied CDS Spreads Pearson Correlation CDS Spreads Implied Correlation HY Implied Correlation IG Sig. (1-tailed) CDS Spreads Implied Correlation HY Implied Correlation IG N CDS Spreads Implied Correlation HY Implied Correlation IG 1.000 .430 .779 . .000 .000 600 600 600 Correlation HY .430 1.000 .382 .000 . .000 600 600 600 Implied Correlation IG .779 .382 1.000 .000 .000 . 600 600 600

Coefficients

a

Standardized Unstandardized Coefficients Model 1 (Constant) Implied Correlation HY Implied Correlation IG a. Dependent Variable: CDS Spreads B -776.024 6.488 21.344 Std. Error 49.446 1.130 .803 .155 .719 Coefficients Beta t -15.694 5.741 26.589 Sig. .000 .000 .000