IDEAS home Printed from https://ideas.repec.org/a/bdd/journl/v15y2021i2p227-259.html
   My bibliography  Save this article

Structuring Key Credit Risk Parameters for Regulated Electric and Gas Utilities under Alternative Moody’s Rating Methodologies: A Case Study for a Natural Gas Distribution Utility

Author

Listed:
  • Vahit Ferhan BENLI
  • Feyzullah YETGIN

Abstract

This paper elaborates the relevancy issue of a rating model in the context of credit rating analysis process of a natural gas distribution company. Against this background, we have analysed the Moody’s Analytics Risk Calc™ v3.1 Emerging Markets and the Regulated Electric and Gas Rating Methodology of Moody’s Investor Services dated from March the 16th, 2017. Methodologically, the article relies on case studies namely the Enron case and a case from regulated natural gas distribution company in Turkey. In terms of findings, Enron case highlights the importance of point-in-time rating models over agency based rating models in terms of default prediction. The EDF model provided a PD value of 0.65%, which corresponds to Baa3 level in Moody’s rating agency terms. On the other hand, the REGU Model indicates the Company with “Ba” rating, which is a “Speculative Grade”. This result indicates us a severe difference in default probabilities for the same entity. This is consequent and in line with the informational needs of different users and if different models are used respective to their needs. In summary, each rating model is developed by rating agencies for different purposes and we need to choose the appropriate rating model to make accurate analysis.

Suggested Citation

  • Vahit Ferhan BENLI & Feyzullah YETGIN, 2021. "Structuring Key Credit Risk Parameters for Regulated Electric and Gas Utilities under Alternative Moody’s Rating Methodologies: A Case Study for a Natural Gas Distribution Utility," Journal of BRSA Banking and Financial Markets, Banking Regulation and Supervision Agency, vol. 15(2), pages 227-259.
  • Handle: RePEc:bdd:journl:v:15:y:2021:i:2:p:227-259
    as

    Download full text from publisher

    File URL: https://www.bddk.org.tr/Content/docs/bddkDergiTr/dergi_0030_05.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Crouhy, Michel & Galai, Dan & Mark, Robert, 2000. "A comparative analysis of current credit risk models," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 59-117, January.
    Full references (including those not matched with items on IDEAS)

    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. Bonfim, Diana, 2009. "Credit risk drivers: Evaluating the contribution of firm level information and of macroeconomic dynamics," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 281-299, February.
    2. Suzan Hol, 2006. "The influence of the business cycle on bankruptcy probability," Discussion Papers 466, Statistics Norway, Research Department.
    3. Kern, Markus & Rudolph, Bernd, 2001. "Comparative analysis of alternative credit risk models: An application on German middle market loan portfolios," CFS Working Paper Series 2001/03, Center for Financial Studies (CFS).
    4. Pesaran, M. Hashem & Schuermann, Til & Treutler, Bjorn-Jakob & Weiner, Scott M., 2006. "Macroeconomic Dynamics and Credit Risk: A Global Perspective," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1211-1261, August.
    5. Paul S. Calem & Michael LaCour-Little, 2001. "Risk-based capital requirements for mortgage loans," Finance and Economics Discussion Series 2001-60, Board of Governors of the Federal Reserve System (U.S.).
    6. Kiff, J. & Michaud, F L. & Mitchell, J., 2003. "An analytical review of credit risk tranfer instruments," Financial Stability Review, Banque de France, issue 2, pages 106-131, June.
    7. Christian Gouriéroux & Alain Monfort, 2017. "Composite Indirect Inference with Application," Working Papers 2017-07, Center for Research in Economics and Statistics.
    8. Greta Falavigna, 2006. "Models for Default Risk Analysis: Focus on Artificial Neural Networks, Model Comparisons, Hybrid Frameworks," CERIS Working Paper 200610, CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY.
    9. Correia, Maria & Kang, Johnny & Richardson, Scott, 2018. "Asset volatility," LSE Research Online Documents on Economics 84405, London School of Economics and Political Science, LSE Library.
    10. Kexue Liu & Jean Salvati & Mr. Renzo G Avesani & Mr. Alin T Mirestean, 2006. "Review and Implementation of Credit Risk Models of the Financial Sector Assessment Program (FSAP)," IMF Working Papers 2006/134, International Monetary Fund.
    11. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.
    12. Lutz Johanning & Björn Döhrer, 2010. "Produktrating im Anlagemarkt für Privatkunden: Konzeption, Backtesting und Akzeptanz eines Zertifikateratings," Schmalenbach Journal of Business Research, Springer, vol. 62(61), pages 166-184, January.
    13. Bag, Pinaki, 2010. "Exposure at Default Model for Contingent Credit Line," MPRA Paper 20387, University Library of Munich, Germany.
    14. Patrick Gagliardini, 2005. "Stochastic Migration Models with Application to Corporate Risk," Journal of Financial Econometrics, Oxford University Press, vol. 3(2), pages 188-226.
    15. repec:onb:oenbwp:y:2002:i:3:b:3 is not listed on IDEAS
    16. Kanak Patel & Ricardo Pereira, 2007. "Expected Default Probabilities in Structural Models: Empirical Evidence," The Journal of Real Estate Finance and Economics, Springer, vol. 34(1), pages 107-133, January.
    17. Georges Dionne, 2003. "The Foundationsof Banks' Risk Regulation: A Review of Literature," THEMA Working Papers 2003-46, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    18. Harald Hau & Sam Langfield & David Marques-Ibanez, 2013. "Bank ratings: what determines their quality? [Bank risk during the financial crisis: do business models matter?]," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 28(74), pages 289-333.
    19. Yan, Alice Xie & Shi, Jian & Wu, Chunchi, 2008. "Do macroeconomic variables matter for pricing default risk?," International Review of Economics & Finance, Elsevier, vol. 17(2), pages 279-291.
    20. Georges Dionne & Sadok Laajimi & Sofiane Mejri & Madalina Petrescu, 2006. "Estimation of the Default Risk of Publicly Traded Canadian Companies," Staff Working Papers 06-28, Bank of Canada.
    21. Ephraim Clark & Geeta Lakshmi, 2003. "Controlling the risk: a case study of the Indian liquidity crisis 1990-92," Journal of International Development, John Wiley & Sons, Ltd., vol. 15(3), pages 285-298.

    More about this item

    Keywords

    Rating; Credit risk modelling; Moody’s.;
    All these keywords.

    JEL classification:

    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C - Mathematical and Quantitative Methods
    • C - Mathematical and Quantitative Methods
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

    Statistics

    Access and download statistics

    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:bdd:journl:v:15:y:2021:i:2:p:227-259. 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: Sumeyye Azize CENGIZ (email available below). General contact details of provider: https://edirc.repec.org/data/bddgvtr.html .

    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.