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Measuring Credit Risk of Individual Corporate Bonds in US Energy Sector

Author

Listed:
  • Takeaki Kariya

    (Josai International University)

  • Yoko Tanokura

    (Meiji University)

  • Hideyuki Takada

    (Toho University)

  • Yoshiro Yamamura

    (Meiji University)

Abstract

In this paper, using the measures of the credit risk price spread (CRiPS) and the standardized credit risk price spread (S-CRiPS) proposed in Kariya’s (A CB (corporate bond) pricing model for deriving default probabilities and recovery rates. Eaton, IMS Collection Series: Festschrift for Professor Morris L., 2013) corporate bond model, we make a comprehensive empirical credit risk analysis on individual corporate bonds (CBs) in the US energy sector, where cross-sectional CB and government bond price data is used with bond attributes. Applying the principal component analysis method to the S-CRiPSs, we also categorize individual CBs into three different groups and study on their characteristics of S-CRiPS fluctuations of each group in association with bond attributes. Secondly, using the market credit rating scheme proposed by Kariya et al. (2014), we make credit-homogeneous groups of CBs and show that our rating scheme is empirically very timely and useful. Thirdly, we derive term structures of default probabilities for each homogeneous group, which reflect the investors’ views and perspectives on the future default probabilities or likelihoods implicitly implied by the CB prices for each credit-homogeneous group. Throughout this paper it is observed that our credit risk models and the associated measures for individual CBs work effectively and can timely provide the market credit information evaluated by investors.

Suggested Citation

  • Takeaki Kariya & Yoko Tanokura & Hideyuki Takada & Yoshiro Yamamura, 2016. "Measuring Credit Risk of Individual Corporate Bonds in US Energy Sector," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 23(3), pages 229-262, September.
    • Handle: RePEc:kap:apfinm:v:23:y:2016:i:3:d:10.1007_s10690-016-9217-7
    DOI: 10.1007/s10690-016-9217-7
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    References listed on IDEAS

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    1. Sudheer Chava & Robert A. Jarrow, 2008. "Bankruptcy Prediction with Industry Effects," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 21, pages 517-549, World Scientific Publishing Co. Pte. Ltd..
    2. Koopman, Siem Jan & Lucas, Andre & Monteiro, Andre, 2008. "The multi-state latent factor intensity model for credit rating transitions," Journal of Econometrics, Elsevier, vol. 142(1), pages 399-424, January.
    3. Takeaki Kariya & Jingsui Wang & Zhu Wang & Eiichi Doi & Yoshiro Yamamura, 2012. "Empirically Effective Bond Pricing Model and Analysis on Term Structures of Implied Interest Rates in Financial Crisis," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 19(3), pages 259-292, September.
    4. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    5. Duffie, Darrell, 2011. "Measuring Corporate Default Risk," OUP Catalogue, Oxford University Press, number 9780199279234, Decembrie.
    6. Sreedhar T. Bharath & Tyler Shumway, 2008. "Forecasting Default with the Merton Distance to Default Model," The Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1339-1369, May.
    7. Takeaki Kariya & Yoshiro Yamamura & Zhu Wang, 2016. "Empirically effective bond pricing model for USGBs and analysis on term structures of implied interest rates in financial crisis," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(6), pages 1580-1606, March.
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    Cited by:

    1. Hong-Ming Yin & Jin Liang & Yuan Wu, 2018. "On a New Corporate Bond Pricing Model with Potential Credit Rating Change and Stochastic Interest Rate," JRFM, MDPI, vol. 11(4), pages 1-12, December.
    2. Takeaki Kariya & Yoshiro Yamamura & Koji Inui, 2019. "Empirical Credit Risk Ratings of Individual Corporate Bonds and Derivation of Term Structures of Default Probabilities," JRFM, MDPI, vol. 12(3), pages 1-29, July.

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