Modeling Term Structure of Corporate Bond and Credit Risk Spreads

2009 ◽  
Vol 38 (2) ◽  
pp. 69-86
Author(s):  
Akihiro Kawada ◽  
Takayuki Shiohama
2015 ◽  
Vol 14 (2) ◽  
pp. 186-201 ◽  
Author(s):  
DAVID F. BABBEL

AbstractThe Pension Benefit Guaranty Corporation's (PBGC) Pension Insurance Modeling System model has taken on the Herculean task of modeling in detail and under many scenarios the cash outflows associated with the pension obligations, they have assumed. This paper's comments are focused almost entirely upon the PBGC's termination liabilities, and address four pressing issues: (1) the need to discount the liability stream by current riskless interest rates instead of using corporate bond rates that reflect credit risk, call risk, and other risks, or using some ad hoc prescribed average of past rates; (2) the need to use a term structure of interest rates; (3) the need to employ more useful investment management benchmarks; and (4) how to implement a relevant and rigorous liability benchmark.


2019 ◽  
Vol 12 (3) ◽  
pp. 124 ◽  
Author(s):  
Takeaki Kariya ◽  
Yoshiro Yamamura ◽  
Koji Inui

Undoubtedly, it is important to have an empirically effective credit risk rating method for decision-making in the financial industry, business, and even government. In our approach, for each corporate bond (CB) and its issuer, we first propose a credit risk rating (Crisk-rating) system with rating intervals for the standardized credit risk price spread (S-CRiPS) measure presented by Kariya et al. (2015), where credit information is based on the CRiPS measure, which is the difference between the CB price and its government bond (GB)-equivalent CB price. Second, for each Crisk-homogeneous class obtained through the Crisk-rating system, a term structure of default probability (TSDP) is derived via the CB-pricing model proposed in Kariya (2013), which transforms the Crisk level of each class into a default probability, showing the default likelihood over a future time horizon, in which 1545 Japanese CB prices, as of August 2010, are analyzed. To carry it out, the cross-sectional model of pricing government bonds with high empirical performance is required to get high-precision CRiPS and S-CRiPS measures. The effectiveness of our GB model and the S-CRiPS measure have been demonstrated with Japanese and United States GB prices in our papers and with an evaluation of the credit risk of the GBs of five countries in the EU and CBs issued by US energy firms in Kariya et al. (2016a, b). Our Crisk-rating system with rating intervals is tested with the distribution of the ratings of the 1545 CBs, a specific agency’s credit rating, and the ratings of groups obtained via a three-stage cluster analysis.


Author(s):  
Patrizia Beraldi ◽  
Giorgio Consigli ◽  
Francesco De Simone ◽  
Gaetano Iaquinta ◽  
Antonio Violi

2020 ◽  
Vol 21 (4) ◽  
pp. 399-422
Author(s):  
Amira Abid ◽  
Fathi Abid ◽  
Bilel Kaffel

Purpose This study aims to shed more light on the relationship between probability of default, investment horizons and rating classes to make decision-making processes more efficient. Design/methodology/approach Based on credit default swaps (CDS) spreads, a methodology is implemented to determine the implied default probability and the implied rating, and then to estimate the term structure of the market-implied default probability and the transition matrix of implied rating. The term structure estimation in discrete time is conducted with the Nelson and Siegel model and in continuous time with the Vasicek model. The assessment of the transition matrix is performed using the homogeneous Markov model. Findings The results show that the CDS-based implied ratings are lower than those based on Thomson Reuters approach, which can partially be explained by the fact that the real-world probabilities are smaller than those founded on a risk-neutral framework. Moreover, investment and sub-investment grade companies exhibit different risk profiles with respect of the investment horizons. Originality/value The originality of this study consists in determining the implied rating based on CDS spreads and to detect the difference between implied market rating and the Thomson Reuters StarMine rating. The results can be used to analyze credit risk assessments and examine issues related to the Thomson Reuters StarMine credit risk model.


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