Liquidity and Credit Risk

Ericsson, Jan; Renault, Olivier

doi:10.2139/ssrn.424002

Cited by 84 publications

(42 citation statements)

References 29 publications

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“…Vayanos (2004) argues that investors attach a higher value to liquidity when markets are volatile. Ericsson and Renault (2006) motivate and document a positive correlation between credit risk and liquidity premiums for corporate bonds. DickNielsen et al (2012) and Friewald et al (2012) show that -consistent with flight-toquality behavior -liquidity premiums increase more for low-rated than for high-rated corporate bonds during the recent financial crisis.…”

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confidence: 89%

“…In the empirical literature (e.g., Ericsson andRenault, 2006 or Dick-Nielsen et al, 2012), credit risk and liquidity premiums are usually assumed to be positively correlated, which corresponds to positively correlated intensities in our model (as in Schönbucher, 2002). Economically, this positive correlation reflects the pricing effect of the well-known flight-to-quality behavior of investors: bonds become less liquid when their credit quality deteriorates (e.g., Dick-Nielsen et al, 2012;Friewald et al, 2012;and Acharya et al, 2013) as investors shift their portfolios towards risk-free bonds or cash.…”

Section: Risk Factor Correlation and Investor Sentimentmentioning

confidence: 99%

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Investor sentiment, flight-to-quality, and corporate bond comovement

Bethke

Gehde-Trapp

Kempf

2017

Journal of Banking & Finance

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may AbstractWe examine the dynamics of bond correlation using a broad sample of US corporate bonds, and document that bond correlation varies heavily over time. We attribute this variation in bond correlation to variation in risk factor correlation reflecting time-varying flight-to-quality behavior of investors. We show that risk factor correlation increases when investor sentiment decreases, i.e., corporate bond investors exhibit stronger flight-to-quality when their sentiment is low. Thus, low investor sentiment leads to flightto-quality behavior and, ultimately, high bond correlation.JEL classification: G11; G12

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confidence: 89%

Section: Risk Factor Correlation and Investor Sentimentmentioning

confidence: 99%

Investor sentiment, flight-to-quality, and corporate bond comovement

Bethke

Gehde-Trapp

Kempf

2017

Journal of Banking & Finance

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“…Despite the evidence for a non-default component in credit spreads few papers have tried to explain the credit spread puzzle as arising from the difference in liquidity between corporate bonds and Treasury bonds. A notable exception is Ericsson and Renault (2006). They develop a structural bond valuation model capturing both liquidity and credit risk.…”

Section: Literature Reviewmentioning

confidence: 99%

The Same Bond at Different Prices: Identifying Search Frictions and Selling Pressures

Feldhütter¹

2011

Rev. Financ. Stud.

346

113

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I model how corporate bond prices are affected by search frictions and occasional selling pressures and test my predictions empirically. A key prediction is that in a distressed market with more sellers than buyers, prices paid by institutional investors decrease more than those of retail investors. Using a structural estimation, the model is able to identify liquidity crises (i.e. high number of forced sellers) based on the relative prices of institutional and retail investors. New light is shed on two crises, the downgrade of GM and Ford in 2005 and the current crisis. The model also explains why the spread between corporate bond yields and Treasury yields is so large, the so-called credit spread puzzle. * I am very grateful to David Lando and Lasse Heje Pedersen for many helpful comments and discussions and to Jens Dick-Nielsen for discussions regarding the TRACE database. I also thank Viral

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“…We classify the reference companies along three dimensions: (i) industry sectors (financial and non-financial), (ii) credit 1 Many researchers strive to identify and distinguish the different components of corporate bond yields. Prominent examples include Fisher (1959), Jones, Mason, and Rosenfeld (1984), Longstaff and Schwartz (1995), Duffie and Singleton (1997), Duffee (1999), Elton, Gruber, Agrawal, and Mann (2001), Collin-Dufresne, Goldstein, and Martin (2001), Delianedis and Geske (2001), Liu, Longstaff, and Mandell (2000), Eom, Helwege, and Huang (2004), Huang and Huang (2003), Collin-Dufresne, Goldstein, and Helwege (2003), Ericsson and Renault (2005), and Longstaff, Mithal, and Neis (2005). ratings (A and BBB), and (iii) quote updating frequency (high and low liquidity). 2 We also download from Bloomberg the eurodollar libor and swap rates of matching maturities and sample periods.…”

Section: Dynamic Interactions Between Interest Rate Credit and Liqumentioning

confidence: 99%

Dynamic Interactions Between Interest Rate, Credit, and Liquidity Risks: Theory and Evidence from the Term Structure of Credit Default Swap Spreads

Chen

Cheng

2005

SSRN Journal

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Using a large data set on credit default swaps, we study how default risk interacts with interest-rate risk and liquidity risk to jointly determine the term structure of credit spreads. We classify the reference companies into two broad industry sectors, two broad credit rating classes, and two liquidity groups. We develop a class of dynamic term structure models that include (i) two benchmark interest-rate factors to capture the libor and swap rates term structure, (ii) two credit-risk factors to capture the credit swap spreads of high-liquidity group of each industry and rating class, and (iii) both an additional credit-risk factor and a liquidity-risk factor to capture the difference between the high-and low-liquidity groups.Estimation shows that companies in different industry and credit rating classes have different credit-risk dynamics. Nevertheless, in all cases, credit risks exhibit intricate dynamic interactions with the interestrate factors. Interest-rate factors both affect credit spreads simultaneously, and impact subsequent moves in the credit-risk factors. Within each industry and credit rating class, we also find that the average credit default swap spreads for the high-liquidity group are significantly higher than for the low-liquidity group. Estimation shows that the difference is driven by both credit risk and liquidity differences. The low-liquidity group has a lower default arrival rate and also a much heavier discounting induced by the liquidity risk.JEL CLASSIFICATION CODES: E43, G12, G13, C51.KEY WORDS: Credit default swap; credit risk; credit premium; term structure; interest rate risk; liquidity risk; liquidity premium; maximum likelihood estimation. Dynamic Interactions Between Interest Rate, Credit, and Liquidity Risks: Theory and Evidence from the Term Structure of Credit Default Swap SpreadsIt is important to understand how credit risk interacts with interest-rate risk and liquidity risk in determining the term structure of credit spreads on different reference entities. Nevertheless, limited data availability has severely hindered the understanding. Since defaults are rare events that often lead to termination or restructuring of the underlying reference entity, researchers need to rely heavily on cross-sectional averages of different entities over a long history to obtain any reasonable estimates of statistical default probabilities.Although corporate bond prices contain useful information on the default probability and the price of credit risk, the information is often mingled with the pricing of the underlying interest-rate risk and other factors such as liquidity and tax. 1 The recent development in credit derivatives provides us with an excellent opportunity to better understand the pricing of credit risk, its interactions with interest-rate risk and liquidity, and the impacts on the term structure of credit spreads. The most widely traded credit derivative is in the form of credit default swap (CDS), written on a reference entity such as a sovereign country or a cor...

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Liquidity and Credit Risk

Cited by 84 publications

References 29 publications

Investor sentiment, flight-to-quality, and corporate bond comovement

Investor sentiment, flight-to-quality, and corporate bond comovement

The Same Bond at Different Prices: Identifying Search Frictions and Selling Pressures

Dynamic Interactions Between Interest Rate, Credit, and Liquidity Risks: Theory and Evidence from the Term Structure of Credit Default Swap Spreads

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