Explaining Changes in Corporate Credit Spreads

Explaining Changes in Corporate Credit Spreads
Panagiotis Panas

MSc Thesis Presentation
▪ Introduction
▪ Literature Review
▪ Methodology & Data
▪ Results & Analysis
▪ Conclusion
▪ References - Bibliography
31/08/2016Cameron Hume Limited 2

Introduction

Preliminary Remarks
▪ Bond Markets have become one of the most important sources of capital for
corporate firms
▪ $600 billion in 2007
▪ $1.2 & $1.8 trillion in 2011 & 2012 respectively
▪ Decline in bank lending
▪ Interest tax shield benefit
▪ Motivation to create firm value
▪ Extensive Issuance
▪ Increased probability of financial distress
▪ Costs due to conflicts between the shareholder of a firm and its creditors

Preliminary Remarks
▪ More risk averse investors are attracted from corporate bonds
▪ Less volatile than equity instruments
▪ Scheduled pattern of cash flows
▪ Seniority over corporate assets in the case of a default event
▪ Compensation for investing in riskier corporate bonds, including the default risk
of the issuing firm
▪ Premium charged is expressed through the credit spread
▪ Difference between the corporate bond yield and the risk-free benchmark yield

Preliminary Remarks
▪ Default risk has been investigated through credit risk models
▪ Accounting ratio models
▪ Beaver (1966), Altman (1968), Deakin (1972) & Ohlson (1980)
▪ Structural models
▪ Black & Scholes (1973) and Merton (1974)
▪ Reduced form models
▪ Jarrow & Turnbull (1995) and Duffie & Singleton (1999)
▪ Default is viewed as a shareholder’s option which is triggered when the value of
corporate assets falls below the default threshold

Scope
▪ Explain the variation in credit spread changes using a sample of US corporate
straight bonds
▪ Examined period Feb 2010 – Dec 2014
▪ Mortgage crisis in 2008
▪ Recession in the US economy over the period 2007-2009
▪ Opportunity for relative comparisons
▪ Inspiration:
▪ The Determinants of Credit Spread Changes. The Journal of Finance, 56(6), pp.
2177-2207, Collin-Dufresne, et al (2001)

Scope
▪ Pooled OLS Regression method
▪ Dependent variable
▪ Changes in credit spreads
▪ Explanatory variables
▪ Structural models of default
▪ Non-default component (Liquidity & Market Sentiment)
▪ Macroeconomic factors (GDP, Inflation & Unemployment)

Literature Review

Yield & Credit Spread Definitions
▪ 𝑃 = 𝐶
1−1/(1+𝑦) 𝑛
𝑦
+
𝑀
(1+𝑦) 𝑛
▪ Yield (y) is the interest rate which equalizes the present value of the cash
flows with the cost of the investment
▪ Earned return if the bond is held until its maturity date
▪ Convex relationship between the price and the required yield for an option
free bond
▪ Price appreciation is greater when the corresponding yield decreases
compared to the capital loss when the required yield increases by the same
amount of basis points

Yield & Credit Spread Definitions
▪ 𝐶𝑟𝑒𝑑𝑖𝑡 𝑆𝑝𝑟𝑒𝑎𝑑 𝑡 = 𝐶𝑜𝑟𝑝𝑜𝑟𝑎𝑡𝑒 𝐵𝑜𝑛𝑑 𝑌𝑖𝑒𝑙𝑑 𝑡 − 𝐵𝑒𝑛𝑐ℎ𝑚𝑎𝑟𝑘 𝑌𝑖𝑒𝑙𝑑 𝑡
▪ The difference between the yield on a corporate bond and the yield on a
benchmark security (risk free asset, i.e. US treasury bond) with a comparable
maturity
▪ Indicator of credit risk
▪ Reflects the compensation for bearing risks associated with holding a non-
benchmark security

Accounting Ratio Based Models
▪ Beaver (1966 & 1968)
▪ Not all ratios predict equally well the inability of a firm to meet its financial
obligations and hence they should be used with discretion
▪ Altman (1968)
▪ Extremely accurate accounting ratio model in predicting bankruptcy default in
consistency with the more recent credit rating systems
▪ Ohlson (1980)
▪ Bankruptcy probability can be assessed using four financial measures including
profitability, leverage, firm size and liquidity

Accounting Ratio Based Models
▪ Although accounting ratio models are widely used by practitioners they suffer
some drawbacks
▪ Lack in theory of what drives the default process
▪ Assume linearity while the world is inherently nonlinear
▪ Do not address off-balance sheet items (not practical for complex organizations)
▪ Not really useful when credit quality declines fast
▪ Accounting data appear at discrete time intervals

Reduced-Form Models
▪ Default is treated exogenously (driven by outside factors)
▪ Alleviates the credit risk modelling as it focuses on the default event itself and ignores
the causes of default
▪ Rely on the theoretical framework of the Poisson stochastic or pure jump process
▪ Jarrow & Turnbull (1995) and Duffie & Singleton (1999)
▪ Two of the most famous reduced-form models

Structural Models
▪ Black & Scholes (1973) and Merton (1974)
▪ BSM Model
▪ Debt and equity are treated as contingent claims on a firm’s asset value
▪ Equity can be formulated as a European call option on corporate assets with a strike
price equal to the face value of the firm’s debt
▪ 𝐸 = max(𝐴 𝑇 − 𝐷, 0)
▪ Bondholders have a long position on a firm’s underlying assets but they have
sold a call option to shareholders
▪ 𝐵 𝑇 = 𝐴 𝑇 − max(𝐴 𝑇 − 𝐷, 0)
▪ Shareholders receive a payoff (𝐸) only if the market value of the firm’s assets
(𝐴 𝑇) is higher than the contractual payments to debtholders (𝐷)

Structural Models
▪ Although BSM-model gives an intuitive framework about the credit risk of a firm, it
relies on some unrealistic assumptions
▪ No interest rate risk
▪ One type of debt in a firm’s capital structure
▪ Occurrence of the default only at maturity
▪ Black & Cox (1976)
▪ Default can occur at any time prior to maturity when the value of the firm assets falls below the limit of
the default threshold
▪ Geske (1979)
▪ Loosens the assumption of one type of debt by providing a compound option model
▪ Longstaff & Schwartz (1995)
▪ Incorporate the interest rate risk which is a great concern for investors

Graphical Intuition
▪ In order to determine the default
probability and understand a firm’s
distance to default
▪ Look at firm’s leverage which defines the
default point
▪ Identify other factors which affect a firm’s
value process

Determinants of Credit Spreads & Hypotheses
▪ H1: A spot rate increase results to a decrease in corporate credit spreads
▪ Duffee (1998)
▪ An inverse relationship between credit spreads and spot rates is confirmed
▪ Interest rate risk is priced in the valuation of corporate bonds
▪ Longstaff & Schwartz (1995)
▪ The risk free rate has an effect on the expected future value of a firm’s asset which in
turn affects its bonds’ credit spreads
▪ The principle behind this negative relation is the fact that an increase in spot rate leads
to a higher reinvestment rate and consequently to a higher expected firm value
▪ Lower default probability as the distance to default (DTD) increases

▪ H1: A spot rate increase results to a decrease in corporate credit spreads
▪ Kim, et al (1993)
▪ The level of interest rate risk is fairly independent of the default risk and thus credit
spreads are not affected
▪ Davies (2008)
▪ The changes in credit spreads follow the same direction as the changes in risk-free rate

▪ H2: A positive steepness of the yield curve leads to lower credit spreads
▪ Landschoot (2008)
▪ A positive steep slope in the yield curve implies an increase in future spot rates and
hence a decrease in credit spreads and hence a decrease in credit spreads
▪ Estrella & Hardouvelis (1991)
▪ A positive slope implies an improvement in economic activity
▪ The yield curve is used as a predictor for the future economic activity
▪ Credit spreads are expected to narrow in periods of economic expansion and widen
during periods of recession

▪ H3: An increase in firm leverage causes corporate credit spreads to widen
▪ Collin-Dufresne, et al (2001) and Tang & Yan (2010)
▪ The default threshold is determined by the capital structure of the firm
▪ The default point raises by increasing the degree of leverage in the capital structure
▪ The distance to default (DTD) becomes narrower
▪ Credit spreads are expected to widen with leverage
▪ Investors require a higher premium to compensate the increased default risk of the firm
▪ Leland (1994), Anderson & Sundaresan (2000) and Molina (2005)
▪ Leverage and asset volatility contribute significantly in corporate spreads variation

▪ H4: An improvement in business climate leads to an increase in recovery rates
and decreases corporate credit spreads
▪ Collin-Dufresne, et al (2001)
▪ In an event of bankruptcy only a fraction of the invested amount is paid back to investors
▪ The recovery rate is on the top of the default probability
▪ The overall business climate is assumed to be related with the expected recovery rates
▪ It makes sense to argue that in periods of recession the expected recovery rates
decrease

▪ H5: An increase in the volatility of a firm’s value leads to a credit spread increase
▪ A more volatile asset value has a higher probability to fall below the default point
▪ An accurate measure of asset volatility does not exist
▪ Campbell & Taksler (2003)
▪ An increase in idiosyncratic volatility in the stock market occurred in the same time
period with the increase in corporate credit spreads
▪ A strong relationship between corporate bond yields and equity volatility is confirmed,
supporting the evidence of its significance to the cost of borrowing for firms

The Non-Default Component
▪ Credit spreads are not represented only by the default risk of the firm
▪ Collin-Dufresne, et al (2001), Elton, et al (2001), Campbell & Taksler (2003)
▪ Fail to estimate the dynamics and the level of credit spreads using variables inspired by
the structural models of default
▪ Default risk variables have a limited explanatory power as only a 20 to 25% of the
variation in the credit spread changes is explained
▪ Credit spreads are driven by a single common systematic factor

▪ A wide spectrum of non-default factors has been examined in the existing
literature
▪ Tang & Yan (2010)
▪ Liquidity effects and taxes to be the most agreed upon factors of the non-default
component
▪ Elton, et al (2001)
▪ Taxes account for even 36% of bond spreads, they should not be considered as a
determinant of credit spread variation since tax rates do not change frequently

▪ H6: A more illiquid bond has a wider credit spread
▪ One of the main assumptions of the structural BSM-model is that continuous
trading takes place, implying no liquidity risk
▪ Due to the fact that corporate bonds do not trade on formal exchanges but on
over-the-counter (OTC) markets, this assumption does not hold
▪ Amihud & Mendelson (1986)
▪ Liquidity risk plays a significant role in asset pricing where rational investors require a
higher yield for investing in more illiquid securities
▪ Liquidity risk has a great impact on corporate credit spreads

▪ H7: An increase in market sentiment leads to narrower credit spreads
▪ The “flight to quality” phenomenon is usually observed in periods of high uncertainty
about the future economic activity as investors become more risk averse
▪ Investors require a higher compensation during economic downturns for investing in
risky corporate debt which results to more discounted bond prices and higher yields
▪ Tang & Yan (2010)
▪ The aggregate level of corporate credit spreads is significantly affected by the market
sentiment
▪ Credit spreads widen when investors become more risk averse

Macroeconomic Factors
▪ Credit spreads are driven by a single common systematic factor
▪ Macroeconomic factors are considered as a systematic risk
▪ It is reasonable to suppose a relationship between corporate credit spreads and different
macroeconomic variables
▪ Boardman & McNealy (1981)
▪ Direct influence of the general economic environment on the spread of the default risk
▪ Hackbarth, et al (2006)
▪ Credit spreads are higher during recession times
▪ The operating cash flow of a firm is dependent on the current economic environment

▪ Inflation risk
▪ Erodes the purchasing power of the future cash flows (coupon and principal payments)
▪ Interest rate that the issuer promises to pay is fixed for the life of the issue
▪ Davies (2008)
▪ Corporate bonds perform poorly during periods of high inflation and thus investors should be aware of
inflation risk, especially those who invest in high grade bonds
▪ Wu & Zhang (2008)
▪ Positive relationship between inflation shocks and credit spreads for all maturity and credit rating
categories
▪ Kang & Pflueger (2015)
▪ Inflation risk is priced in corporate credit spreads
▪ An increase in the inflation-stock correlation by one standard deviation is associated with a 14 basis
points rise in bond yield spreads

▪ GDP Growth
▪ Indicates economic well-being and this is generally a positive signal for investors
▪ Wu & Zhang (2008)
▪ Positive output shocks which can be measured in terms of GDP growth reduce the default
risk and the bond yield spreads, especially for short-term and low grade debts
▪ Tang & Yan (2010)
▪ Credit default swap spreads narrow with an increase in GDP growth rate
▪ Credit spreads widen with an increased volatility in the GDP growth
▪ A negative relationship between credit spreads and GDP growth rate is expected

▪ The existing literature regarding the relationship between the unemployment rate
and the credit spread is restricted
▪ Krueger & Kenneth (2003)
▪ Markets react in employment news
▪ The benchmark 30-year treasury interest rate would increase by 6 basis points due to an
unexpected rise in employment
▪ The 3-month treasury bill would increase by 8 basis points
▪ The effects of changes in unemployment rate on long term interest rates are statistically
insignificant
▪ Boyd, et al (2005)
▪ A rise in unemployment strengthen the market expectations for a stagnant growth rate
▪ A decrease in unemployment would limit the spread due to an increase in the benchmark
yield as well as due to market expectations for a higher growth rate

▪ H8: Macroeconomic (or general) factors explain the variation in corporate credit
spreads better than the firm & bond specific variables
▪ Instead of stating different hypotheses for the effect of the macroeconomic variables, it is
more reasonable to test if these factors could explain credit spreads better than firm &
bond specific variables
▪ A comparison between the variables which represent the overall economic environment
and the ones that are firm related is being made

Methodology & Data

Methodology & Data
▪ The aim of this work is to investigate what factors explain the variation in the US
corporate credit spreads over the period Feb 2010- Dec 2014
▪ The dataset for this empirical analysis has two dimensions; a time-series dimension
as each corporate bond has monthly observations and a cross-sectional one as
several bonds are included for each month
▪ The hypotheses are tested using as a basic method the pooled OLS regression
▪ The basic regression model employed
▪ 𝛥𝐶𝑆𝑡
𝑖
= 𝛼 + 𝛽1
𝑖
𝛥𝑟𝑡
10
+ 𝛽2
𝑖
𝛥𝑟𝑡
10 2
+ 𝛽3
𝑖
𝛥𝑠𝑙𝑜𝑝𝑒𝑡 + 𝛽4
𝑖
𝛥𝑙𝑒𝑣 𝑡
𝑖
+ 𝛽5
𝑖
𝛥𝑖𝑑𝑣𝑜𝑙 𝑡
𝑖
+ 𝛽6
𝑖
𝛥𝑖𝑙𝑙𝑖𝑞𝑡
𝑖
+ 𝛽7
𝑖
𝑆&𝑃500 𝑡 + 𝛽8
𝑖
𝛥𝑉𝐼𝑋𝑡 + 𝛽9
𝑖
𝛥𝐶𝐶𝐼𝑡

Methodology & Data
▪ Explanatory variables with their corresponding predicted signs

Data Sources & Filtering
▪ The main source for the US corporate bond, equity and accounting data is
Datastream which is one of the largest global financial numerical databases
▪ The data selection and filtering are in line with Avramov, et al (2007) and
Elton, et al (2001)
▪ Each bond should satisfy a set of criteria in order to remain in the final
sample
▪ Reasons for exclusion
▪ No corresponding equity data in Datastream
▪ Issuing firms trade outside the US market
▪ Issuer is a financial or utility firm
▪ Less than one year to mature
▪ Less than 30 successive monthly observations, bonds issued after 2011

Corporate Credit Spread
▪ The dependent variable of the model is the so-called credit spread
▪ A time series of monthly data on credit spreads for each bond is downloaded for
the Datastream database.
▪ A manual calculation of the credit spreads is avoided since they are automatically
calculated by Datastream as the difference between the yield on the corporate
bond and the equivalent US treasury security (in basis points)
▪ Interest compounding frequency and maturity to be taken into account
▪ A linear interpolation is used so as to obtain the full yield curve
▪ When the maturity of a corporate bond is longer than the longest benchmark then
the yield is compared with the longest benchmark and it is not extrapolated.
▪ Same procedure is followed when a bond has a shorter maturity than the shortest
benchmark security

Explanatory Variables
▪ All interest rate variables are provided from Datastream
▪ The 10-year benchmark treasury yield (𝑟𝑡
10
) can be used as the spot rate level
because it coincides with the mean time to maturity for the corporate bonds
included in the examined sample
▪ Chen, et al (2011) and Collin-Dufresne, et al (2001)
▪ A squared term of the spot rate should be included as an independent variable so as to
account for any nonlinear relationship due to convexity
▪ The slope of the term structure of interest rates is calculated as the difference
between the 10-year and the 2-year treasury yields (𝑠𝑙𝑜𝑝𝑒𝑡 = 𝑟𝑡
10
− 𝑟𝑡
2
)
▪ Duffeen (1998)
▪ The 3-month treasury bill rate is used as the spot rate while the slope variable is constructed
as the difference between the 10-year and the 3-month treasury benchmark

▪ The leverage ratio is used as a proxy for the default threshold
▪ 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑡
𝑖
=
𝐵𝑜𝑜𝑘 𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝐷𝑒𝑏𝑡
𝐵𝑜𝑜𝑘 𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝐷𝑒𝑏𝑡+𝑀𝑎𝑟𝑘𝑒𝑡 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝐸𝑞𝑢𝑖𝑡𝑦
▪ Due to the fact that accounting data are provided quarterly, linear interpolation
is used as an estimation for monthly observations
▪ This could bias results but it is assumed that debt level is stable over time and
hence the effect is negligible
▪ Data can be retrieved from CRSP, Compustat & Datastream

▪ Collin-Dufresne, et al (2001) and Chen, et al (2011)
▪ The VIX index can be used as a proxy for the market wide volatility.
▪ Daily data are provided by the Chicago Board Options Exchange (CBOE) through
WRDS and monthly observations are calculated by averaging the daily values within
each month
▪ The VIX index allows for a quite precise view of investors’ expectations on future
market volatility (S&P500)
▪ A high value of VIX indicates large fluctuations in share prices and uncertainty

▪ Idiosyncratic Equity volatility is used as a proxy for the corporate assets
volatility
▪ 𝐼𝑑𝑖𝑜𝑠𝑦𝑛𝑐𝑟𝑎𝑡𝑖𝑐 𝑉𝑜𝑙𝑎𝑡𝑖𝑙𝑖𝑡𝑦 𝑡
𝑖
= σ 𝑡
𝑖
𝑟𝑞
2
− σ 𝑡 𝑟 𝑀
2
▪ Itis calculated as the sum of squared daily equity returns (𝑟𝑞
2
) while market wide
volatility is calculated as the sum of squared returns on S&P500 index (𝑟 𝑀
2
)
▪ Daily stock and market prices can be downloaded either from CRSP (WRDS) or
Datastream
▪ It has been shown that a short term volatility measure has a higher explanatory
power on credit spread dynamics

▪ Due to data availability and easiness, daily bid and offer prices, provided by
Datastream, are used as a proxy for liquidity
▪ 𝐵𝑖𝑑 − 𝑜𝑓𝑓𝑒𝑟 𝑠𝑝𝑟𝑒𝑎𝑑 𝑡
𝑖
=
𝑀𝑜𝑛𝑡ℎ𝑙𝑦 𝑂𝑓𝑓𝑒𝑟 𝑃𝑟𝑖𝑐𝑒−𝑀𝑜𝑛𝑡ℎ𝑙𝑦 𝐵𝑖𝑑 𝑃𝑟𝑖𝑐𝑒
𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑜𝑓 𝑀𝑜𝑛𝑡ℎ𝑙𝑦 𝐵𝑖𝑑+𝑂𝑓𝑓𝑒𝑟 𝑃𝑟𝑖𝑐𝑒
▪ The presence of negative bid-offer spread is due to possible data errors and
hence observations are dropped out
▪ Extreme values of bid-ask spread are also excluded

▪ Tang & Yan (2010)
▪ The Consumer Confidence Index (CCI) can be used as a proxy for the market
sentiment.
▪ CCI is an economic indicator for the degree of optimism regarding the health of the
US economy which is based on consumers’ spending and savings
▪ The consumer confidence index is measured monthly through household surveys of
consumers’ perceptions about the current as well as the future state of the economy
▪ CCI time series data are available through Datastream

▪ Monthly data for macroeconomic explanatory variables are provided by the
Federal Reserve Bank of St. Louis as well as from Datastream
▪ These include the inflation measure CPI which is given monthly, the quarterly
GDP growth rate (%) which can be converted to monthly observations using
cubic or linear interpolation in line as well as the unemployment rate
▪ The inflation rate is calculated as follows below, where 𝑡 is the time in months
▪ 𝐼𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛 𝑡 = log
𝐶𝑃𝐼𝑡
𝐶𝑃𝐼𝑡−1
▪ Due to the fact that interpolation can cause severe statistical problems, it may
be preferable to use the industrial production index as a proxy for GDP which is
measured monthly and it is provided by Datastream

▪ Avramov, et al (2007) and Collin-Dufresne, et al (2001)
▪ Recovery rate is related with the overall business climate
▪ The returns on the S&P500 index can be used as a proxy by using monthly value-
weighted returns

Regression Modelling Requirements
▪ In order to obtain unbiased as well as efficient results it is crucial to ensure that the
six OLS Time-Series assumptions

▪ The strict exogeneity assumption which rarely holds in finance can be relaxed if
the size of the sample is big enough and the time-series are stationary and
weakly dependent
▪ Stationarity is fulfilled since the first difference is constructed which transforms
non-stationary variables to stationary ones. It can be formally confirmed using
the Dickey-Fuller test
▪ Weak dependence is also crucial as it replaces the random sampling
assumption. A time-series variable is said to be weakly dependent if the
𝑐𝑜𝑟𝑟(𝑋𝑡, 𝑋𝑡+ℎ) approaches zero sufficiently fast with an increase in ℎ
▪ The Law of Large Numbers (LLN) as well as the Central Limit Theorem (CLT)
can be applied since there are at least 30 observations in all N regressions

▪ Assumption TS.1 does not hold if a regression model is misspecified
▪ This happens when higher order terms or lags have been excluded from the
regression model while they have explanatory power
▪ Misspecification is checked by using Ramsey’s RESET test
▪ Perfect collinearity among the explanatory variables is ruled out according to
assumption TS.2
▪ In order to check if this assumption is violated one can look at the correlation
matrix obtained by STATA

▪ The most important assumption when performing a time-series regression is TS.3
▪ Regressors are required to be exogenous, meaning that independent variables and
error term are contemporaneously uncorrelated
▪ A failure of TS.3 means that at least one regressor in the model is endogenous and
some unobserved factors affect both dependent and independent variables in the
same period
▪ Due to the fact that one cannot control for everything this assumption will be always
violated
▪ Since the model relies on a well-defined theory there is not a strong evidence of a
severe violation of assumption TS.3

▪ Assumption TS.4 requires homoscedasticity of the error term and this is controlled
using the White’s test
▪ If residuals are heteroskedastic then the Driscoll and Kraay robust standard errors
▪ The no serial correlation assumption (TS.5) can be tested by using either the Breush-
Godfrey’s test which controls for first order autocorrelation or Cumby-Huizinga’s test
which can be performed for up to the fourth lag
▪ One lag variable could be included in the model in order to correct for autocorrelation in
some of the error terms
▪ Finally, it should be mentioned that the use of winsorization in order to account for the
presence of outliers is avoided in this study although it is suggested by many academic
researchers
▪ Instead, it is decided to drop extreme values from the final sample using mainly the
initial summary statistics of the credit spread, the bid-ask spread as well as the firm
leverage variable

Results & Analysis

Sample Profile
▪ The final sample consists of 239 straight bonds issued by 129 non-financial firms
▪ Bonds are categorised according to the latest credit rating provided by Standard &
Poor’s and the average time to maturity
▪ The sample consists mainly of medium and long term investment grade bonds
▪ 70% of the bonds have a credit rating of 𝐵𝐵𝐵− and above

Descriptive Statistics – Explanatory Variables
▪ The 10-year spot rate for the period 2010-
2014 has a mean value of the 2.49% which is
considered very low compared to the value of
6.7% which the average of the last 40 years
▪ The mean change in the benchmark rate has
a negative value of 0.023 showing that the
spot rate decreases during the examined
period
▪ On the other hand, the slope variable is
positive and ranges between 1.28 to 2.83
with an average value of 2.07 indicating that
market participants expect an increase in the
future expected rates

CCI vs VIX
▪ The average value of the market wide volatility for the
examined period is considered relatively low as it is
equal to 18.37
▪ VIX ranges between 11.54 and 36.53 showing a
market with a small amount of volatility, especially after
July 2010
▪ Generally, VIX values less than 20 indicate non-volatile
times in equity markets while values higher than 30
correspond to stressful periods
▪ Times with higher volatility are associated with an
inclined market sentiment confirming the negative
correlation of 0.15 between CCI and VIX
▪ The CCI variable has an average of 67.96 with an
increasing trend after November 2011 and a value of
93.1 in the last month of the examined time period

▪ Inflation rate ranges between 0.018% to
0.286% with an average value 0.142 which is
below the target of 2% which is usually
adopted from the majority of central banks
▪ The gross domestic product (GDP) variable
has a positive trend
▪ The mean value of unemployment is equal to
7.96%.
▪ The average value of the industrial
production rate is 0.242 ranging between -
0.705% and 1.505%
▪ The S&P500 average monthly return is equal
to 1.06%

▪ The average value of leverage is 0.34
▪ Consistent with the studies of Tang & Yan
(2010) and Chen, et al (2011)
▪ The negative mean value of the change
indicates a decrease in the degree of the
leverage for the examined sample of
issuing firms

Idiosyncratic Volatility vs Credit Spread
▪ The idiosyncratic equity volatility has an
average value of 0.0046
▪ The changes of the same variable have a
positive mean value of 0.0001 indicating a
small increase in the firm specific volatility
▪ This can be attributed to the high
amounts of volatility in the last term of
2011 where the highest levels of credit
spreads are also observed which is
consistent with the work of Campbell &
Taksler (2003)
▪ After 2011, the average idiosyncratic
volatility starts to become more stable as
fluctuations diminish with no impact on
the magnitude of credit spreads

Liquidity
▪ The proportional bid-ask spread which is
used so as to measure the illiquidity of a
bond has an average value of 0.56 or 56
basis points
▪ The negative mean value of the changes in
illiquidity indicates that market liquidity has
been improved during the examined period
except the second semester of 2013
▪ The latter could be attributed to the
announcement of the former chairman of the
FED for a plan regarding the reduction in
bond purchases from $85 to $65 billion
which was abandoned in September 2013.
▪ For comparison purposes, Chen, et al
(2007) find a higher average bid-ask spread
of 84.6 basis points over the time period
1995 – 2003

Descriptive Statistics – Dependent Variable
▪ The average credit spread for the whole sample is equal to 207.2 basis points
▪ Avramov, et al (2007)
▪ 246 basis points over the period 1990 – 2003
▪ On average, credit spreads have decreased in the examined time period

Descriptive Statistics – Dependent Variable
▪ The mean credit spread decreases when moving from small firms (group 1) to the big
ones (group 4)
▪ This is not attributed only to the size of the firms but also to the credit rating which
also differs by group
▪ Firms with small market capitalization have bonds with a lower average credit rating
(𝐵𝐵𝐵−
) compared to bonds issued by larger corporates which are rated with an
average of 𝐴−
▪ Therefore, it is hard to tell that differences in credit spreads are explained due to
differences in firm’s market capitalization as compensation for credit risk is present

Average Credit Spreads
▪ The same trend is observed for all
subsamples of bonds with group 1
to face higher fluctuations
▪ The latter is confirmed by the value
of the standard deviation which is
the highest one

Correlation Matrix
▪ This table presents the correlation matrix
including both dependent and explanatory
variables for a sample of 239 US corporate
bonds that trade actively over the period
2010-2014
▪ The 10-Year spot rate (𝛥𝑟𝑡
10
) and the slope
are highly correlated which could cause
multicollinearity problems
▪ Market wide volatility is weakly correlated
with firm idiosyncratic equity volatility

Pooled OLS Regression
▪ Four models are employed overall
▪ Model 1, which is the basic model, includes the
explanatory variables inspired by the default risk
framework as well as the “non-default component”
which is defined by the so-called proportional bid-ask
spread (illiquidity factor) and a proxy for the market
sentiment
▪ Model 2 employs only the firm & bond specific factors
while model 3 consists only of macroeconomic
variables
▪ Model 4 takes into account all the variables being
referred previously

▪ Model 1 shows that all default risk variables as well
as the illiquidity factor and the market sentiment
proxy are statistically significant at 99% level except
the idiosyncratic equity volatility variable
▪ This model explains about 21% of the credit spread
variation while all predicted signs are confirmed
except the wide market volatility (𝛥𝑉𝐼𝑋) which is
found to be negative
▪ Similarly in model 2, the idiosyncratic equity volatility
is the only variable which is not statistically
significant with 14% percent of the variation in credit
spreads to be explained

▪ Despite the fact that most of the macroeconomic
factors in model 3 are statistically significant except
the 10-year spot rate and the unemployment rate,
only a small portion of the variation in credit spreads
is explained by this model
▪ The latter is confirmed in model 4 where all variables
are included since the percentage of the credit
spread variation is the same as the one explained by
model 1
▪ Nevertheless, the expected signs in model 3 are
those predicted with inflation risk to be priced in
corporate bonds as a widening in the credit spread
occurs with an increase in the inflation rate
▪ An increase in the GDP growth as proxied by the
industrial production index narrows credit spreads

Market Capitalization Groups
▪ In general lines, the percentage of the explained
portion in the variation of credit spreads increases
as one moves from small market CAP firms to
larger ones
▪ Overall, these results are not surprising since firms
with larger Market CAP issue bonds with a higher
credit quality
▪ The explained variation increases from 18% to 27%

Credit Rating Groups
▪ The minimum 𝑅2 is observed when only
investment grade bonds are included in the
regression analysis and it is equal to 11%
▪ The maximum 𝑅2 is equal to 32.1% and it is
observed for the group of the speculative
grade bonds

Firm Leverage Groups
▪ The explanatory power of the model is greater
for highly leveraged firms as it increases from
15.5% to 28%
▪ In conclusion, these results are consistent with
the existing academic literature where it is
stated that variables inspired from the
structural models framework have greater
explanatory power for bonds which are more
likely to default, including the firm default risk

Interest Rate Variables
▪ The 10-year spot rate 𝛥𝑟10 has the expected negative sign in all regression
analyses
▪ It is highly significant except for firms with a small market capitalization and
speculative grade bonds
▪ Consistent with the empirical findings
▪ Longstaff & Schwartz (1995), Duffee (1998) and Collin-Dufresne, et al (2001)
▪ It is found that an increase in the risk-free rate narrows the credit spread for all bonds
▪ The sensitivity of the interest rate increases monotonically across both credit rating and firm
leverage groups
▪ This is not the case in this study especially for the former while some consistency is observed for
leverage groups but still there is not any visible pattern

▪ Larger firms are affected more by changes in spot rate which is attributed to the
correlation between the changes in interest rates and a firm’s asset value
▪ (Longstaff & Schwartz, 1995)
▪ However, this does not hold for the group with the largest companies since the
magnitude of the interest rate coefficient decreases
▪ A reasonable explanation for this trend could be the asset diversification of the
largest firms which probably makes them less dependent on the changes of
interest rates
▪ Economic significance of the interest rates for the whole sample of bonds
▪ An increase of 1% in the spot rate would lead to narrower corporate credit spreads by 48.5 basis
points or 0.485% on average

▪ The slope of the yield curve is highly statistically significant with the predicted
negative sign to be confirmed for the whole sample as well as for the examined
subsamples of bonds
▪ The convexity variable is not always statistically significant and mixed signs are
observed
▪ A slope steepening would decrease credit spreads as this is a sign of higher future
spot rates which would affect positively the firm value process.
▪ An increase of 1% in the slope variable would lead to narrower credit spreads by
19.26 basis points on average

Firm Leverage
▪ The default threshold is determined by the leverage ratio which has a positive and statistically
significant influence on corporate credit spread as it was expected according to the structural
models framework
▪ An increase by 1 unit in changes of leverage would increase credit spread by 302 basis points or
3.02% on average for the whole sample
▪ The strong relationship between changes in leverage and credit spread dynamics implies that the
cost of borrowing for a firm is significantly affected by the firm’s capital structure decisions
▪ It is also observed that the sensitivity to leverage increases monotonically across market
capitalization and credit rating groups from larger to smaller firms and from bonds with low credit
risk to bonds with high credit risk respectively
▪ For small firms, an increase of 1 unit in leverage would increase credit spread by 4.67% on
average while the corresponding increase for a large firm would be only 0.89%
▪ A possible explanation for this large difference can be attributed to the so-called “to big to fail”. In
other words, investors do not perceive a change in the leverage of big firms as risky as a change
in the leverage of a small firm and hence they do not require a higher compensation

Volatility
▪ The coefficient of idiosyncratic volatility is positive implying that asset volatility
increases the default risk of the firm and consequently widens credit spread
▪ The estimate is not statistically significant for the whole sample while the sign changes
when the sample is divided according to firm market capitalisation
▪ The most reasonable result is obtained for the largest firms of the sample where
idiosyncratic volatility is positive and statistically significant while the reasons behind
these inconsistent results remain unclear
▪ Opposite sign is obtained for the market wide volatility where the coefficient is
statistically significant at 99% level
▪ One could suppose that a strong correlation between wide market volatility and
idiosyncratic equity volatility can cause multicollinearity problems
▪ However, these variables are not highly correlated (𝑐𝑜𝑟𝑟 𝛥𝑉𝐼𝑋, 𝛥𝑖𝑑𝑣𝑜𝑙 = 0.1) while
idiosyncratic equity volatility is not correlated to any of the explanatory variables

Volatility
▪ The fact that idiosyncratic volatility does not provide consistent results it might
mean that it is a poor proxy for a firm’s asset volatility but this view is not
supported in the existing literature as a strong relationship between credit
spreads and equity volatility is confirmed
▪ Campbell & Taksler (2003), Avramov, et al (2007) and Chen, et al (2011)
▪ Although the VIX index has an opposite sign it seems from the magnitude of the
coefficient that larger firms are less affected by the market wide volatility
▪ This may be due to a long history of stability of the firms included in group 4
even in periods of economic turndowns

Liquidity
▪ Consistent result with the theory and in line with the studies of Perraudin &
Taylor (2003), Longstaff, et al (2005) and Chen, et al (2007)
▪ The illiquidity factor is positive and statistically significant
▪ Credit spreads tend to widen with larger values of the proportional bid-ask
spread indicating that investors receive a compensation for holding more illiquid
assets
▪ The coefficient of the illiquidity variable implies an increase of 20.23 basis
points in credit spread for each percentage increase in the bid-ask spread
▪ Moreover, it is proved that illiquidity is priced more in speculative grade bonds
while bonds issued by large companies seem to be more liquid

Market Sentiment
▪ The monthly returns on S&P500 index are used as a proxy for the expected
recovery rate
▪ The coefficient on S&P500 returns is statistically significant and negative in
consistency with the theory
▪ Credit spreads tend to be narrower in periods of higher stock market returns
▪ However, it is hard to tell if this due to higher expected recovery rates since an
improved business climate is also related to a lower default probability
▪ An increase of 1% in S&P500 returns would narrow credit spreads by 1.8 basis
points on average
▪ Last but not least, a higher increase in the expected recovery rate is observed
for highly leveraged firms as well as for speculative grade bonds as the
magnitude of the coefficient becomes larger

Consumer Confidence Index
▪ Finally, as hypothesized, the consumer confidence index has a negative and
statistically significant coefficient
▪ Indicates that an improvement in market sentiment leads to narrower credit
spreads
▪ The intuition behind the negative relationship between credit spreads and
market sentiment is that investors tend to be less risk averse in good periods
and require less compensation for bearing risk which in turn is translated to
tighter spreads
▪ (Tang & Yan, 2010)

Fixed Effect Regression Analysis
▪ A fixed-effect regression is performed since the
Hausman specification test indicated a preference
over the random-effect regression
▪ The fixed-effect model allows for arbitrary correlation
between unobserved effects and explanatory
variables and produces consistent estimates
▪ Fixed-effect models have been also employed in the
literature by Chen, et al (2007) and Demirovic, et al
(2015) among others

Fixed Effect Regression Analysis
▪ Similar results are obtained from the fixed-effect
regression with the explanatory power of the models to
be the same compared to the pooled OLS regression
analysis, except model 2 where the adjusted 𝑅2
is
slightly lower
▪ The magnitude of the coefficients varies across the
variables of the models
▪ Leverage, spot rate and illiquidity factor to be the most
statistically and economically significant variables in
both types of regression
▪ In general lines, the coefficients of the fixed-effects
regressions are of slightly lower economic significance

Specific vs Common Factors (i)
▪ Two regression models are employed overall
▪ The first model regress the bond & firm specific
variables on the changes in credit spreads
including the firm leverage, the idiosyncratic equity
volatility as well as the bond illiquidity factor
▪ The second model includes six general variables
▪ Common factors seem to outperform the firm &
bond specific ones for the whole sample.
▪ The first model explains the 13.7% of the credit
spread variation while the second one with the
common factors explain the 15%

▪ The explanatory power of the first model reduces
as one moves from small to big firms
▪ There is not a clear pattern for the second model
▪ The changes in leverage and the illiquidity factor
are the most statistically significant variables
▪ The economic significance of the leverage and the
illiquidity factor falls as one moves from firms with
low market capitalization to firms with a high
market value

▪ Larger firms are affected more by changes in spot
rate which is attributed to the correlation between
the changes in interest rates and a firm’s asset
value (Longstaff & Schwartz, 1995)
▪ However, this does not hold for the group with the
largest companies since the magnitude of the
interest rate coefficient decreases
▪ A reasonable explanation for this trend could be
the asset diversification of the largest firms which
probably makes them less dependent on the
changes of interest rates

Specific vs Common Factors (ii)
▪ Firm & bond specific factors can explain only a
small portion in credit spreads variation for
investment grade bonds, ranging from 3.9% to 7.5%
▪ The explanatory power of model 1 jumps to 23.4%
when speculative grade bonds are examined which
is the only case where specific factors outperform
the common ones

Specific vs Common Factors (iii)
▪ Both models explain the same amount of the
variation in credit spreads (≈19%) for highly
leveraged firms
▪ Common factors outperform in all other
subsamples of bonds

Conclusion

Conclusion
▪ The main aim of this study is to examine which factors determine the changes in
corporate credit spreads
▪ The empirical analysis is based on a sample of monthly data on 239 straight
bonds issued by non-financial firms
▪ The examined corporate bonds trade actively in the US market over the period
Feb 2010 – Dec 2014
▪ This study differs from the existing literature regarding the examined time period
and the general economic environment as only a few studies have been
elaborated after the subprime mortgage crisis in 2008 and the recession in the
US economy during the period 2007-2009

Conclusion
▪ The credit spread determinants are inspired from the framework of the structural
models of default as well as from the non-default component which includes bond
illiquidity and market sentiment
▪ Macroeconomic factors are included in the initial regression analysis so as to
examine if they explain any portion of the credit spread variation as previous
studies like Collin-Dufresne, et al (2001) support that a large part of unexplained
variation is due to a single systematic factor

Conclusion
▪ The first outcomes of this study indicate that larger firms enjoy lower and more
stable credit spreads which is not only attributed to the higher market value but also
to the fact that these firms issue bonds with a higher credit quality
▪ Overall, it is observed that credit spreads have been reduced, especially after 2012
▪ The liquidity of the market has been improved in total as indicated from the illiquidity
measure with an exception of an illiquidity spike in 2013
▪ It is shown that the idiosyncratic equity volatility which is used as a proxy for firms’
asset volatility is positively correlated with the changes in credit spreads with the
maximum value of the latter to coincide with the highest value of firms’ asset
volatility
▪ It is confirmed that an improved market results to a less volatile market

Conclusion
▪ The basic model employed in this work explains the 20.7% of the variation in credit
spreads for the whole sample which is close to the 25% found in the work of Collin-
Dufresne, et al (2001)
▪ One possible explanation for weaker results of this study might be the composition
of the examined sample which consists mainly of investment grade bonds
▪ Macroeconomic factors like inflation, unemployment and GDP growth which is
proxied by the industrial production index have very limited explanatory power
which is in contrast with the some of the existing studies (Avramov, et al., 2007)
▪ The latter could be due to the different periods considered as GDP growth and
inflation rate could be significant determinants of credit spreads during periods of
economic booms or in recession times which is not the case for this study

Conclusion
▪ In general lines, most of the findings are in line with the theory as well as with the
academic literature
▪ An increase in the spot rate, in the slope of the yield curve, in S&P500 returns as
well as in market sentiment leads to narrower credit spreads
▪ Conversely, an increase in leverage and in illiquidity measure tend to widen credit
spreads
▪ In contrast with the theory, wide market volatility has an opposite sign compared to
hypothesized one with the reasons for these results to remain unclear
▪ All variables are statistically significant at 99% except the idiosyncratic equity
volatility while leverage, spot rate and illiquidity are the most significant variables
economically

Conclusion
▪ The explanatory power of the regression model seems to be higher for smaller firms
reaching up to 27.1%
▪ On the other hand, the explanatory power increases monotonically when one
moves from bonds with low credit risk to bonds with high credit risk (up to 32.1%) as
well as from firms with low leverage to highly leveraged firms (up to 28.1%)
▪ The magnitude and the statistical significance of the coefficients change across
different groups of bonds with the largest difference to be observed for the changes
in leverage and the changes in the illiquidity factor, especially for the group
including bonds with high credit risk
▪ Besides the pooled OLS regression, a fixed-effect regression is performed giving
similar results in terms of the explanatory power of the models but with slightly
lower coefficient magnitudes

Conclusion
▪ Common factors explain more of the variation compared to firm & bond specific
ones but both have limited explanatory power, 15% and 13.7%, respectively
▪ Credit spreads are affected more by common factors when the issuer of the bond is
a big firm while specific factors outperform in explaining credit spread variation for
smaller companies
▪ The latter means that bonds issued from big issuing firms are affected more from
changes in the general economic conditions as investors might not perceive
changes in firm specific factors as risky as with same changes in small firms
▪ The explanatory powers of the two models are equivalent for speculative bonds as
well as for bonds issued by highly leveraged firms. In all other cases common
factors outperform, consistently with the existing literature

Limitations & Future Work
▪ Data access and availability were the most significant limitations during the
elaboration of this study
▪ The fact that there is not a common and unique identifier that connects equity and
accounting data with bond data leads not only to a time-consuming process of data
collection
▪ Each firm’s id has to be looked up manually in Datastream
▪ Risk of mismatching firm specific data with bond data
▪ Mistakes in the matching procedure can lead to erroneous final results.

▪ Although grouping bonds according to market capitalization, leverage and
credit rating sheds light on various aspects of the analysis, the relatively small
size of the examined sample does not permit to take into account market
capitalization, credit rating and maturity simultaneously
▪ Homogenous groups of bonds in terms of credit rating and time to maturity
with different market capitalization would be more informative and would
provide us with more valuable insights regarding the impact of the firm size
▪ This study examines a sample of 239 bonds out of at least 6,000 US
corporate bonds
▪ An increase in data would also enrich the representativeness of the sample

▪ Liquidity plays an important role as a part of the credit spread magnitude
▪ It would be of particular interest to examine the non-default component solely since
the academic literature is not so extensive on this area
▪ The availability of historical credit ratings would be valuable
▪ Credit rating can be used as a proxy for the default risk giving the chance to focus
on non-default factors

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