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

IntroductionExplaining Changes in Corporate Credit Spreads

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 ReviewExplaining Changes in Corporate Credit Spreads

Yield & Credit Spread Definitions

▪ 𝑃 = 𝐶1−1/(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 𝑆𝑝𝑟𝑒𝑎𝑑𝑡 = 𝐶𝑜𝑟𝑝𝑜𝑟𝑎𝑡𝑒 𝐵𝑜𝑛𝑑 𝑌𝑖𝑒𝑙𝑑𝑡 − 𝐵𝑒𝑛𝑐ℎ𝑚𝑎𝑟𝑘 𝑌𝑖𝑒𝑙𝑑𝑡

▪ 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(𝐴𝑇 − 𝐷, 0)

▪ Bondholders have a long position on a firm’s underlying assets but they have sold a call option to shareholders

▪ 𝐵𝑇 = 𝐴𝑇 −max(𝐴𝑇 − 𝐷, 0)

▪ Shareholders receive a payoff (𝐸) only if the market value of the firm’s assets (𝐴𝑇) 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 & DataExplaining Changes in Corporate Credit Spreads

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

▪ 𝛥𝐶𝑆𝑡𝑖 = 𝛼 + 𝛽1

𝑖𝛥𝑟𝑡10 + 𝛽2

𝑖 𝛥𝑟𝑡10 2 + 𝛽3

𝑖 𝛥𝑠𝑙𝑜𝑝𝑒𝑡 + 𝛽4𝑖𝛥𝑙𝑒𝑣𝑡

𝑖 + 𝛽5𝑖𝛥𝑖𝑑𝑣𝑜𝑙𝑡

𝑖

+ 𝛽6𝑖𝛥𝑖𝑙𝑙𝑖𝑞𝑡

𝑖 + 𝛽7𝑖 𝑆&𝑃500𝑡 + 𝛽8

𝑖 𝛥𝑉𝐼𝑋𝑡 + 𝛽9𝑖𝛥𝐶𝐶𝐼𝑡


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 (𝑟𝑡10) 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 − 𝑟𝑡2 )

▪ 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

▪ 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑡𝑖 =

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 𝑡𝑖 = σ𝑡

𝑖 𝑟𝑞2 − σ𝑡 𝑟𝑀

2

▪ Itis calculated as the sum of squared daily equity returns (𝑟𝑞2) while market wide volatility is calculated as the sum of squared returns on S&P500 index (𝑟𝑀2)

▪ 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 𝑠𝑝𝑟𝑒𝑎𝑑𝑡𝑖 =

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

▪ 𝐼𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛𝑡 = log𝐶𝑃𝐼𝑡

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 & AnalysisExplaining Changes in Corporate Credit Spreads

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 (𝛥𝑟𝑡10) 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


ConclusionExplaining Changes in Corporate Credit Spreads

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


ReferencesExplaining Changes in Corporate Credit Spreads

References

1. Altman, E. I., 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), pp. 589-609.

2. Amihud, Y. & Mendelson, H., 1986. Asset pricing and the bid-ask spread. Journal of Financial Economics, 17(2), pp. 223-249.

3. Anderson, R. & Sundaresan, S., 2000. A comparative study of structural models of corporate bond yields: An exploratory investigation. Journal of Banking and Finance,

24(1), pp. 255-269.

4. Auinger, F., 2015. The Causal Relationship between the S&P 500 and the VIX Index: Critical Analysis of Financial Market Volatility and Its Predictability. 1 ed.

Wiesbaden: Springer Fachmedien Wiesbaden.

5. Avramov, D., Jostova, G. & Philipov, A., 2007. Understanding Changes in Corporate Credit Spreads. Financial Analysts Journal, 63(2), pp. 90-105.

6. Beaver, W. H., 1966. Financial Ratios As Predictors of Failure. Journal of Accounting Research, 4(1), pp. 71-111.

7. Beaver, W. H., 1968. Alternative Accounting Measures as Predictors of Failure. The Accounting Review, 43(1), pp. 113-122.

8. Bekaert, G., Campbell , H. R. & Lundblad, C., 2007. Liquidity and Expected Returns: Lessons from Emerging Markets. The Review of Financial Studies, 20(6), pp.

1783-1831.

9. Bhamra, H. S., Fisher, A. J. & Kuehn, L., 2011. Monetary policy and corporate default. Journal of Monetary Economics, , Vol.(5), pp. [Peer Reviewed Journal], 58(5),

pp. 480-494.

10. Black, F. & Cox, J. C., 1976. Valuing corporate securities: some effects of bond indenture provisions. The Journal of Finance, 31(2), pp. .351-367.


References11. Black, F. & Scholes, M., 1973. The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81(3), pp. 637-654.

12. Boardman , C. M. & Mcenally, R. W., 1981. Factors Affecting Seasoned Corporate Bond Prices. Journal of Financial and Quantitative Analysis, 16(2), pp. 207-226.

13. Boyd , J., Hu, J. & Jagannathan, R., 2005. The Stock Market's Reaction to Unemployment News: Why Bad News Is Usually Good for Stocks. The Journal of Finance, 60(2), pp. 649-

672.

14. Breusch, T. S., 1978. Testing for Autocorrelation in Dynamic Linear Models. Australian Economic Papers, 17(31), pp. 334-355.

15. Campbell, J. Y. & Taksler, G. B., 2003. Equity volatility and corporate bond yields. Journal of Finance, 58(6), pp. 2321-2350.

16. Chen, H., 2010. Macroeconomic Conditions and the Puzzles of Credit Spreads and Capital Structure. The Journal of Finance, 65(6), pp. 2171-2212.

17. Chen, L., Lesmond, D. A. & Wei, J., 2007. Corporate Yield Spreads and Bond Liquidity. The Journal of Finance, 62(1), pp. 119-149.

18. Chen, T.-K., Liao, H.-H. & Tsai, P.-L., 2011. Internal liquidity risk in corporate bond yield spreads. Journal of Banking and Finance, 35(4), pp. 978-987.

19. Collin-Dufresne, P., Goldstein, R. & Spencer, M., 2001. The Determinants of Credit Spread Changes. The Journal of Finance, 56(6), pp. 2177-2207.

20. Covitz, D. & Downing, C., 2007. Liquidity or Credit Risk? The Determinants of Very Short‐Term Corporate Yield Spreads. The Journal of Finance, 62(5), pp. 2303-2328.

21. Crabbe, L. E. & Turner, C. M., 1995. Does the Liquidity of a Debt Issue Increase with Its Size? Evidence from the Corporate Bond and Medium‐Term Note Markets. The Journal of

Finance, 50(5), pp. 1719-1734.

22. Cumby , R. E. & Huizinga, J., 1992. Testing the Autocorrelation Structure of Disturbances in Ordinary Least Squares and Instrumental Variables Regressions. Econometrica: Journal of

Econometric Society, 60(1), pp. 185-195.

23. DataStream, 2016. DataStream Database - Spread Over Benchmark Curve - datatype (SP). [Online] Available at: http://product.datastream.com [Accessed 16 June 2016].

24. Davies, A., 2008. Credit spread determinants: An 85 year perspective. Journal of Financial Markets, 11(2), pp. 180-197.


References25. Deakin, E. B., 1972. A Discriminant Analysis of Predictors of Business Failure. Journal of Accounting Research, 10(1), pp. 167-179.

26. Demirovic, A., Tucker, J. & Guermat, C., 2015. Accounting data and the credit spread: An empirical investigation. Research in International Business and Finance, Volume 34, pp. 233-

250.

27. Dickey, D. & Fuller, W., 1979. Distribution of the Estimators for Autoregressive Time Series With a Unit Root. Journal of the American Statistical Association, 74(365), p. 427.

28. Driscoll , J. C. & Kraay, A. C., 1998. Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data. Review of Economics and Statistics, 80(4), p. 549–560.

29. Duffee, G. R., 1998. The Relation Between Treasury Yields and Corporate Bond Yield Spreads. The Journal of Finance, 53(6), pp. 2225-2241.

30. Duffie, D. & Singleton, K. J., 1999. Modelling the term structure of defaultable bonds. Review of Financial Studies, 12(4), pp. 687-720.

31. Elton, E. J., Gruber, M. J., Agrawal, D. & Mann, C., 2001. Explaining the Rate Spread on Corporate Bonds. The Journal of Finance, 56(1), pp. 247-277.

32. Ericsson, J. & Renault, O., 2006. Liquidity and Credit Risk. The Journal of Finance, , Vol.61(5), pp. [Peer Reviewed Journal], 61(5), pp. 2219-2250.

33. Estrella, A. & Hardouvelis, G. A., 1991. The Term Structure as a Predictor of Real Economic Activity. The Journal of Finance, 46(2), pp. 555-576.

34. Estrella, A. & Mishkin, F., 1996. The Yield Curve as a Predictor of U.S. Recessions. Current Issues in Economics and Finance, 2(7), pp. 1-6.

35. Fabozzi, F. J., 2013. Bond Market, Analysis, and Strategies. 8th ed. Essex: Pearson.

36. Ferson , W. E. & Harvey, C. R., 1991. The Variation of Economic Risk Premiums. Journal of Political Economy, , Vol.(2), p.[Peer Reviewed Journal], 99(2), pp. 385-415.

37. FRED, 2016. Federal Reserve Bank of St. Louis. [Online] Available at: https://stlouisfed.org/

[Accessed 10 June 2016].

38. Gallagher, R., 2016. Quantitative Credit Risk Models, Fixed Income CMSE11212, Edinburgh: University of Edinburgh Business School.


References39. Geske, R., 1979. The valuation of compound options. Journal of Financial Economics, 7(1), pp. 63-81.

40. Geske, R. & Johnson, H. E., 1984. The Valuation of Corporate Liabilities as Compound Options: A Correction. Journal of Financial and Quantitative Analysis, 19(2), pp. 231-232.

41. Grandes, M. & Marcel , P., 2003. The Determinants of Corporate Bond Yield Spreads in South Africa: Firm-Specific or Driven by Sovereign Risk?, s.l.: OECD.

42. Hackbarth, D., Miao, J. & Morellec, E., 2006. Capital structure, credit risk, and macroeconomic conditions. Journal of Financial Economics, 82(3), pp. 519-550.

43. Hoechle, D., 2007. Robust standard errors for panel regressions with cross-sectional dependence. The Stata Journal , 7(3), p. 281–312.

44. Huang, J. Z. & Huang, M., 2012. How Much of the Corporate-Treasury Yield Spread Is Due to Credit Risk?. Review of Asset Pricing Studies, 2(2), pp. 153-202.

45. ICMA, 2013. Economic Importance of the Corporate Bond Markets, Zurich, Switzerland: International Capital Market Association.

46. Jarrow , R., Lando, D. & Turnbull, S., 1997. A Markov model for the term structure of credit risk spreads. Review of Financial Studies, 10(2), pp. 481-523.

47. Jarrow, R. A. & Turnbull, S. M., 1995. Pricing Derivatives on Financial Securities Subject to Credit Risk. The Journal of Finance, 50(1), pp. 53-85.

48. Kang, J. & Pflueger, C., 2015. Inflation Risk in Corporate Bonds. The Journal of Finance, 70(1), pp. 115-162.

49. Kim, J., Ramaswamy, K. & Sundaresan, S., 1993. Does default risk in coupons affect the valuation of corporate bonds?: a contingent claims model. (Financial Distress Special Issue).

Financial Management, 22(3), pp. 117-131.

50. Krueger, A. B. & Kenneth, F. N., 2003. Do Markets Respond More to More Reliable Labor Market Data? A Test of Market Rationality. Journal of the European Economic Association,

1(4), pp. 931-957.

51. Landschoot , A. V., 2008. Determinants of yield spread dynamics: Euro versus US dollar corporate bonds. Journal of Banking and Finance, 32(12), pp. 2597-2605.

52. Landschoot, A. V., 2008. Determinants of yield spread dynamics: Euro versus US dollar corporate bonds. Journal of Banking and Finance, 32(12), pp. 2597-2605.


References53. Leland, H. E., 1994. Corporate Debt Value, Bond Covenants, and Optimal Capital Structure. The Journal of Finance, 49(4), pp. 1213-1252.

54. Leland, H. E. & Toft, . K. B., 1996. Optimal Capital Structure, Endogenous Bankruptcy, and the Term Structure of Credit Spreads. The Journal of Finance, 51(3), pp. 987-1019.

55. Lesmond , D., Ogden, J. & Trzcinka, C., 1999. A new estimate of transaction costs. Review of Financial Studies, 12(5), pp. 1113-1141.

56. Lesmond, D. A., Schill , M. J. & Zhou, C., 2004. The illusory nature of momentum profits. Journal of Financial Economics, 71(2), pp. 349-380.

57. Liu , S., Shi, J., Wang , J. & Wu, C., 2009. The determinants of corporate bond yields. Quarterly Review of Economics and Finance, 49(1), pp. 85-109.

58. Lo, A., Mamaysky, H. & Wang, J., 2004. Asset Prices and Trading Volume under Fixed Transactions Costs. Journal of Political Economy, , Vol.112(5), p.0 [Peer Reviewed Journal],

112(5), pp. 1054-1090.

59. Longstaff , F. A. & Schwartz, E. S., 1995. A Simple Approach to Valuing Risky Fixed and Floating Rate Debt. The Journal of Finance, 50(3), pp. 789-819.

60. Longstaff, F. A., Mithal, S. & Neis, E., 2005. Corporate Yield Spreads: Default Risk or Liquidity? New Evidence from the Credit Default Swap Market. The Journal of Finance, 60(5), pp.

2213-2253.

61. Malkiel, B., Campbell , J. & Lettau, M., 2001. Have Individual Stocks Become More Volatile? An Empirical Exploration of Idiosyncratic Risk. Journal of Finance, 56(1), pp. 1-43.

62. Merton, R. C., 1974. On the pricing of corporate debt: The risk structure of interest rates. Journal of Finance, 29(2), pp. 449-470.

63. Merton, R. C., 1980. On estimating the expected return on the market: An exploratory investigation. Journal of Financial Economics, 8(4), pp. 323-361.

64. Molina, C. A., 2005. Are Firms Underleveraged? An Examination of the Effect of Leverage on Default Probabilities. The Journal of Finance, 60(3), pp. 1427-1459.

65. Ohlson, J. A., 1980. Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1), pp. 109-131.


References66. Palepu, . K. G., Healy , P. M. & Peek, E. B., 2010. Business analysis and valuation IFRS. 2nd ed. Hampshire: Cengage Learning EMEA.

67. Perraudin, W. & Taylor, A., 2003. Liquidity and Bond Market Spreads, s.l.: EFA 2003 Annual Conference Paper.

68. Peter , C. & Jeff , B., 2013. MODELING DEFAULT RISK, s.l.: Moody’s KMV Company .

69. Petersen, M. A., 2009. Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches. The Review of Financial Studies, 22(1), pp. 435-480.

70. Ramsey, J. B., 1969. Tests for Specification Errors in Classical Linear Least-Squares Regression Analysis. Journal of the Royal Statistical Society, 31(2), pp. 350-371.

71. Sayogo, I. H., 2015. Determinants of the US Corporate Bond Yield Spread Before and After Subprime Mortgage Crisis, Edinburgh, UK: University of Edinburgh Business School.

72. Tang, D. & Yan, H., 2010. Market conditions, default risk and credit spreads. Journal Of Banking And Finance, 34(4), pp. 724-734.

73. The Conference Board, 2011. Consumer Confidence Survey® Technical Note, s.l.: The Conference Board - Trusted Insights for Business Worldwide.

74. Warga, A., 1992. Bond Returns, Liquidity, and Missing Data. Journal of Financial and Quantitative Analysis, 27(4), pp. 605-617.

75. White, H., 1980. Heteroscedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroscedasticity. Econometrica, 48(4), p. 817–838.

76. Wooldridge, J. M., 2012. Introduction to econometrics. Europe, Middle East and Africa ed. Andover: Cengage Learning.

77. Wu , L. & Zhang, X. F., 2008. A No-Arbitrage Analysis of Macroeconomic Determinants of the Credit Spread Term Structure. Management Science, 54(6 ), pp. 1160-1175.

78. Yu, F., 2005. Accounting transparency and the term structure of credit spreads. Journal of Financial Economics, 75(1), pp. 53-84.

79. Zhang, B., Hao, Z. & Zhu, H., 2009. Explaining Credit Default Swap Spreads with the Equity Volatility and Jump R


Explaining Changes in Corporate Credit Spreads

Data & Analytics

Transcript of Explaining Changes in Corporate Credit Spreads