Modeling Term Structures of Defaultable Bonds

Duffie, Darrell; Singleton, Kenneth J.

doi:10.1093/rfs/12.4.687

Cited by 2,067 publications

(1,255 citation statements)

References 44 publications

Supporting

Mentioning

1,195

Contrasting

Unclassified

Order By: Relevance

“…24 I use the term reduced-form in the spirit of the work by Jarrow and Turnbull (1995) and Duffie and Singleton (1999). The popular alternative to this approach are so-called structural models of credit, a la Merton (1974).…”

Section: Risk Bearing Capital and The Level Of Cds Premiumsmentioning

confidence: 99%

Concentrated Capital Losses and the Pricing of Corporate Credit Risk

Siriwardane

2015

SSRN Journal

View full text Add to dashboard Cite

Section: Risk Bearing Capital and The Level Of Cds Premiumsmentioning

confidence: 99%

Concentrated Capital Losses and the Pricing of Corporate Credit Risk

Siriwardane

2015

SSRN Journal

View full text Add to dashboard Cite

“…In related studies, Skinner and Diaz (2003) look at early CDS prices from September 1997 to February 1999 for 31 CDS contracts. They compare the pricing results of the Duffie and Singleton (1999) and Jarrow and Turnbull (1995) models. Blanco, Brennan, and Marsh (2004) compare the CDS spreads with credit spreads derived from corporate bond yields and find that overall the two sources of spreads match each other well.…”

Section: Background Information On Credit Default Swap Spreadsmentioning

confidence: 99%

“…We value the credit default swap contract using the framework of Duffie and Singleton (1999), and Duffie, Pedersen, and Singleton (2003). First, we use r t to denote the instantaneous benchmark interest rate.…”

Section: A Dynamic Term Structure Model Of Interest Rate Default Anmentioning

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

View full text Add to dashboard Cite

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...

show abstract

“…Default time is simulated using the Inverse-CDF method offered in Duffie and Singleton (1999). For each firm i, we draw a uniform random number U .…”

Section: B Simulation Approachmentioning

confidence: 99%

A Little Knowledge Is a Dangerous Thing: Model Specification, Data History, and CDO (Mis)Pricing

Luo

Wang

Tang³

2009

SSRN Journal

View full text Add to dashboard Cite

The revaluation of collateralized debt obligations (CDOs) plays a significant role in the ongoing 2007-2009 credit crisis. Starting in August 2007, a large amount of initially AAA rated CDO securities are substantially downgraded, some directly to junk grade. This paper explores two structural sources of CDO mispricing: modeling difficulty and data limitation. Simulating the frailty correlated default model of Duffie, Eckner, Horel, and Saita (2008), we show that CDO mis-pricing can be partly attributed to model misspecifications, as well as limited availability of historical data on CDO collateral assets. This simulation result is consistent with empirical evidence on historical performance of a sample of 279 CDOs. The frailty model estimated with adequate historical data would have reduced the amount of AAA rated CDO securities by 12% on average. The frailty model has predictive power for the subsequent downgrading of AAA rated CDO tranches. Our study addresses practical issues on financial innovations and provides guidance for corresponding risk management. * , a large amount of initially AAA rated CDO securities are substantially downgraded, some directly to junk grade. This paper explores two structural sources of CDO mispricing: modeling difficulty and data limitation. Simulating the frailty correlated default model of Duffie, Eckner, Horel, and Saita (2008), we show that CDO mis-pricing can be partly attributed to model misspecifications, as well as limited availability of historical data on CDO collateral assets. This simulation result is consistent with empirical evidence on historical performance of a sample of 279 CDOs. The frailty model estimated with adequate historical data would have reduced the amount of AAA rated CDO securities by 12% on average. The frailty model has predictive power for the subsequent downgrading of AAA rated CDO tranches. Our study addresses practical issues on financial innovations and provides guidance for corresponding risk management.

show abstract

Modeling Term Structures of Defaultable Bonds

Cited by 2,067 publications

References 44 publications

Concentrated Capital Losses and the Pricing of Corporate Credit Risk

Concentrated Capital Losses and the Pricing of Corporate Credit Risk

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

A Little Knowledge Is a Dangerous Thing: Model Specification, Data History, and CDO (Mis)Pricing

Contact Info

Product

Resources

About