an empirical study of us corporate credit spreads, pathak 2011
DESCRIPTION
This paper presents an analysis of the factors that affect US corporate credit spreads. Using datafrom Bloomberg we investigate the various determinants that cause changes in credit spreads ofUS corporate firms. As previous research has shown, the variables that should be based ontheory determine credit spread changes have limited explanatory power. Our study breaks apart arange of variables into three different sections and analyzes them individual in the groups andtogether using multiple regressions. We investigate the spot rate, interest rate volatility and slopefor the interest rate effects and find strong relationships between spot rate and slope with creditspreads. For the effects of volatility and market uncertainty we find strong relationships betweencredit spreads and market volatility proxied by VIX and firm volatility proxied by an average ofCall and Put implied volatility. TED spreads, SPX and RTY returns show strong relationshipsbetween macro-economic variables and credit spreads. Implied default correlations in theInvestment Grade and High Yield market also show a strong positive relationship with creditspreads. Our research investigates certain macro-economic variables that have not beenresearched before and re-establishes previous findings for other variables post-2007 crisis.TRANSCRIPT
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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.
-
Electronic copy available at: http://ssrn.com/abstract=1989012
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.
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Electronic copy available at: http://ssrn.com/abstract=1989012
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.
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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
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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.
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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.
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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
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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
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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
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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
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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.
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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
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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
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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.
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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.
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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
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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
Descriptive Statistics
Mean Std. Deviation N
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
a. Dependent Variable: CDS Spreads
Macro-Economic
Descriptive Statistics
Mean Std. Deviation N
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
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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
Unstandardized Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
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
a. Dependent Variable: CDS Spreads
Macro-economic Implied Correlations
Descriptive Statistics
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Mean Std. Deviation N
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
Unstandardized Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
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
a. Dependent Variable: CDS Spreads