IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v12y2019i3p124-d250681.html
   My bibliography  Save this article

Empirical Credit Risk Ratings of Individual Corporate Bonds and Derivation of Term Structures of Default Probabilities

Author

Listed:
  • Takeaki Kariya

    (Graduate School of International Administration, Josai International University, Tokyo 102-0094, Japan)

  • Yoshiro Yamamura

    (Graduate School of Global Business, Meiji University, Tokyo 101-8301, Japan)

  • Koji Inui

    (School of Interdisciplinary Mathematical Science, Meiji University, Tokyo 164-8525, Japan)

Abstract

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.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jjrfmx:v:12:y:2019:i:3:p:124-:d:250681
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/12/3/124/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1911-8074/12/3/124/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Diebold, Francis X. & Li, Canlin, 2006. "Forecasting the term structure of government bond yields," Journal of Econometrics, Elsevier, vol. 130(2), pages 337-364, February.
    3. Friewald, Nils & Jankowitsch, Rainer & Subrahmanyam, Marti G., 2012. "Illiquidity or credit deterioration: A study of liquidity in the US corporate bond market during financial crises," Journal of Financial Economics, Elsevier, vol. 105(1), pages 18-36.
    4. 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..
    5. Nelson, Charles R & Siegel, Andrew F, 1987. "Parsimonious Modeling of Yield Curves," The Journal of Business, University of Chicago Press, vol. 60(4), pages 473-489, October.
    6. Duffie, Darrell, 2011. "Measuring Corporate Default Risk," OUP Catalogue, Oxford University Press, number 9780199279234.
    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.
    8. 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.
    9. Takeaki Kariya & Yoshiro Yamamura & Yoko Tanokura & Zhu Wang, 2015. "Credit Risk Analysis on Euro Government Bonds-Term Structures of Default Probabilities," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 22(4), pages 397-427, November.
    10. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhehao Huang & Zhenghui Li & Zhenzhen Wang, 2020. "Utility Indifference Valuation for Defaultable Corporate Bond with Credit Rating Migration," Mathematics, MDPI, vol. 8(11), pages 1-26, November.
    2. Seyyide Doğan & Yasin Büyükkör & Murat Atan, 2022. "A comparative study of corporate credit ratings prediction with machine learning," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 32(1), pages 25-47.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Takeaki Kariya & Yoshiro Yamamura & Yoko Tanokura & Zhu Wang, 2015. "Credit Risk Analysis on Euro Government Bonds-Term Structures of Default Probabilities," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 22(4), pages 397-427, November.
    3. Krishnan, C.N.V. & Ritchken, Peter H. & Thomson, James B., 2010. "Predicting credit spreads," Journal of Financial Intermediation, Elsevier, vol. 19(4), pages 529-563, October.
    4. Schuster, Philipp & Uhrig-Homburg, Marliese, 2012. "The term structure of bond market liquidity conditional on the economic environment: An analysis of government guaranteed bonds," Working Paper Series in Economics 45, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    5. Tu, Anthony H. & Chen, Cathy Yi-Hsuan, 2018. "A factor-based approach of bond portfolio value-at-risk: The informational roles of macroeconomic and financial stress factors," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 243-268.
    6. Atkeson, Andrew G. & Eisfeldt, Andrea L. & Weill, Pierre-Olivier, 2017. "Measuring the financial soundness of U.S. firms, 1926–2012," Research in Economics, Elsevier, vol. 71(3), pages 613-635.
    7. Lucélia Vaz & Rodrigo Raad, 2021. "Functional data analysis for brazilian term structure of interest rate," Textos para Discussão Cedeplar-UFMG 638, Cedeplar, Universidade Federal de Minas Gerais.
    8. Duan, Jin-Chuan & Sun, Jie & Wang, Tao, 2012. "Multiperiod corporate default prediction—A forward intensity approach," Journal of Econometrics, Elsevier, vol. 170(1), pages 191-209.
    9. Anthony H. Tu & Cathy Yi-Hsuan Chen, 2016. "What Derives the Bond Portfolio Value-at-Risk: Information Roles of Macroeconomic and Financial Stress Factors," SFB 649 Discussion Papers SFB649DP2016-006, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    10. Kearney, Fearghal & Shang, Han Lin & Sheenan, Lisa, 2019. "Implied volatility surface predictability: The case of commodity markets," Journal of Banking & Finance, Elsevier, vol. 108(C).
    11. Eric Hillebrand & Huiyu Huang & Tae-Hwy Lee & Canlin Li, 2018. "Using the Entire Yield Curve in Forecasting Output and Inflation," Econometrics, MDPI, vol. 6(3), pages 1-27, August.
    12. Matsumura, Marco & Moreira, Ajax & Vicente, José, 2011. "Forecasting the yield curve with linear factor models," International Review of Financial Analysis, Elsevier, vol. 20(5), pages 237-243.
    13. Ruey-Ching Hwang, 2013. "Forecasting credit ratings with the varying-coefficient model," Quantitative Finance, Taylor & Francis Journals, vol. 13(12), pages 1947-1965, December.
    14. Chen, Peimin & Wu, Chunchi, 2014. "Default prediction with dynamic sectoral and macroeconomic frailties," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 211-226.
    15. Giordani, Paolo & Jacobson, Tor & Schedvin, Erik von & Villani, Mattias, 2014. "Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial Ratios," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(4), pages 1071-1099, August.
    16. Lily Y. Liu, 2017. "Estimating Loss Given Default from CDS under Weak Identification," Supervisory Research and Analysis Working Papers RPA 17-1, Federal Reserve Bank of Boston.
    17. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    18. Bowsher, Clive G. & Meeks, Roland, 2008. "The Dynamics of Economic Functions: Modeling and Forecasting the Yield Curve," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1419-1437.
    19. Dick Dijk & Siem Jan Koopman & Michel Wel & Jonathan H. Wright, 2014. "Forecasting interest rates with shifting endpoints," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 693-712, August.
    20. Gary S. Anderson & Alena Audzeyeva, 2019. "A Coherent Framework for Predicting Emerging Market Credit Spreads with Support Vector Regression," Finance and Economics Discussion Series 2019-074, Board of Governors of the Federal Reserve System (U.S.).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jjrfmx:v:12:y:2019:i:3:p:124-:d:250681. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.