IDEAS home Printed from https://ideas.repec.org/a/ris/buecrj/0397.html
   My bibliography  Save this article

Comparison of Country Ratings of Credit Rating Agencies with MOORA Method

Author

Listed:
  • Guneren Genc, Elif

    (Istanbul Commerce University)

  • Deniz Basar, Ozlem

    (Istanbul Commerce University)

Abstract

The three main credit rating agencies, Standard and Poor’s, Moody’s and Fitch, use a combination of economic, social and political factors to determine the capacity and current and future debt obligation of countries. This study contributes to literature in two ways. The first one is comparison of results obtained by rating countries on various macroeconomic variables using credit scores given by the three main credit rating agencies and using the MOORA method. The second one is the determination of how optimistic or pessimistic country-based results the three main credit rating agencies yield according to their estimates by macroeconomic variables. Among the three rating agencies, Moody’s make the most optimistic estimates in terms of its rating of countries. Moody’s is followed by Standard and Poor’s, and the credit rating agencies that make the most pessimistic estimates is observed to be Fitch.

Suggested Citation

  • Guneren Genc, Elif & Deniz Basar, Ozlem, 2019. "Comparison of Country Ratings of Credit Rating Agencies with MOORA Method," Business and Economics Research Journal, Uludag University, Faculty of Economics and Administrative Sciences, vol. 10(2), pages 391-404, April.
    • Handle: RePEc:ris:buecrj:0397
    as

    Download full text from publisher

    File URL: https://www.berjournal.com/comparison-of-country-ratings-of-credit-rating-agencies-with-moora-method
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bertrand Candelon & Mr. Amadou N Sy & Mr. Rabah Arezki, 2011. "Sovereign Rating News and Financial Markets Spillovers: Evidence from the European Debt Crisis," IMF Working Papers 2011/068, International Monetary Fund.
    2. Stefanos Ioannou, 2018. "Sovereign ratings, macroeconomic dynamics, and fiscal policy. Interactions within a stock flow consistent framework," Metroeconomica, Wiley Blackwell, vol. 69(1), pages 151-177, February.
    3. Davor Kunovac & Rafael Ravnik, 2017. "Are Sovereign Credit Ratings Overrated?," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 59(2), pages 210-242, June.
    4. Lennart Freitag, 2015. "Procyclicality and Path Dependence of Sovereign Credit Ratings: The Example of Europe," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 44(2), pages 309-332, July.
    5. Matthias Bodenstedt & Daniel R�sch & Harald Scheule, 2013. "The path to impairment: do credit-rating agencies anticipate default events of structured finance transactions?," The European Journal of Finance, Taylor & Francis Journals, vol. 19(9), pages 841-860, October.
    6. Baghai, Ramin P. & Becker, Bo, 2018. "Non-rating revenue and conflicts of interest," Journal of Financial Economics, Elsevier, vol. 127(1), pages 94-112.
    7. Hill, Paula & Brooks, Robert & Faff, Robert, 2010. "Variations in sovereign credit quality assessments across rating agencies," Journal of Banking & Finance, Elsevier, vol. 34(6), pages 1327-1343, June.
    8. Burhan Kabadayi & Ahmet Alkan elik, 2015. "Determinants of Sovereign Ratings in Emerging Countries: A Qualitative, Dependent Variable Panel Data Analysis," International Journal of Economics and Financial Issues, Econjournals, vol. 5(3), pages 656-662.
    9. Huseyin Ozturk, 2016. "Reliance of Sovereign Credit Ratings on Governance Indicators," The European Journal of Development Research, Palgrave Macmillan;European Association of Development Research and Training Institutes (EADI), vol. 28(2), pages 184-212, April.
    10. Alexander M. Karminsky & Ella Khromova, 2016. "Modelling banks’ credit ratings of international agencies," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 6(3), pages 341-363, December.
    11. Vanja Bozic & Cosimo Magazzino, 2013. "Credit Rating Agencies: The Importance of Fundamentals in the Assessment of Sovereign Ratings," Economic Analysis and Policy, Elsevier, vol. 43(2), pages 157-176, September.
    12. Baum, Anja & Checherita-Westphal, Cristina & Rother, Philipp, 2013. "Debt and growth: New evidence for the euro area," Journal of International Money and Finance, Elsevier, vol. 32(C), pages 809-821.
    13. Abad, Pilar & Robles, M. Dolores, 2014. "Credit rating agencies and idiosyncratic risk: Is there a linkage? Evidence from the Spanish Market," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 152-171.
    14. Huseyin Ozturk & Ersin Namli & Halil Ibrahim Erdal, 2016. "Reducing Overreliance on Sovereign Credit Ratings: Which Model Serves Better?," Computational Economics, Springer;Society for Computational Economics, vol. 48(1), pages 59-81, June.
    15. Constantin Mellios & Eric Paget-Blanc, 2006. "Which factors determine sovereign credit ratings?," The European Journal of Finance, Taylor & Francis Journals, vol. 12(4), pages 361-377.
    16. Erdem, Orhan & Varli, Yusuf, 2014. "Understanding the sovereign credit ratings of emerging markets," Emerging Markets Review, Elsevier, vol. 20(C), pages 42-57.
    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. Zsuzsanna Banász & Zsolt T. Kosztyán & Vivien V. Csányi & András Telcs, 2023. "University leagues alongside rankings," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(1), pages 721-736, February.

    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. Srđan Jelinek & Pavle Milošević & Aleksandar Rakićević & Ana Poledica & Bratislav Petrović, 2022. "A Novel IBA-DE Hybrid Approach for Modeling Sovereign Credit Ratings," Mathematics, MDPI, vol. 10(15), pages 1-21, July.
    2. Oliver Takawira & John W. Muteba Mwamba, 2022. "Sovereign Credit Ratings Analysis Using the Logistic Regression Model," Risks, MDPI, vol. 10(4), pages 1-24, March.
    3. Zoran Ivanovic & Sinisa Bogdan & Suzana Baresa, 2015. "Modeling and Estimating Shadow Sovereign Ratings," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 9(3), September.
    4. Oliver Takawira & John W. Muteba Mwamba, 2020. "Determinants of Sovereign Credit Ratings: An Application of the Naïve Bayes Classifier," Eurasian Journal of Economics and Finance, Eurasian Publications, vol. 8(4), pages 279-299.
    5. Zamira Oskonbaeva, 2020. "Determinants of credit ratings: evidence from panel discrete model," Economics and Business Letters, Oviedo University Press, vol. 9(3), pages 240-247.
    6. Aras, Osman Nuri & Öztürk, Mustafa, 2018. "The Effect of the Macroeconomic Determinants on Sovereign Credit Rating of Turkey," MPRA Paper 86642, University Library of Munich, Germany.
    7. Gabriel Caldas Montes & Diego Silveira Pacheco Oliveira, 2019. "Central bank transparency and sovereign risk ratings: a panel data approach," International Economics and Economic Policy, Springer, vol. 16(2), pages 417-433, April.
    8. Bernal, Oscar & Girard, Alexandre & Gnabo, Jean-Yves, 2016. "The importance of conflicts of interest in attributing sovereign credit ratings," International Review of Law and Economics, Elsevier, vol. 47(C), pages 48-66.
    9. Cuadros-Solas, Pedro Jesús & Salvador Muñoz, Carlos, 2022. "Disentangling the sources of sovereign rating adjustments: An examination of changes in rating policies following the GFC," Research in International Business and Finance, Elsevier, vol. 59(C).
    10. Carlos Uribe-Teran & Santiago Mosquera, 2019. "Structural factors, global shocks and sovereign debt credit ratings," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 43(1), pages 104-126, January.
    11. Duygun, Meryem & Ozturk, Huseyin & Shaban, Mohamed, 2016. "The role of sovereign credit ratings in fiscal discipline," Emerging Markets Review, Elsevier, vol. 27(C), pages 197-216.
    12. Balima, Hippolyte Weneyam, 2020. "Coups d’état and the cost of debt," Journal of Comparative Economics, Elsevier, vol. 48(3), pages 509-528.
    13. Diego Silveira Pacheco de Oliveira & Gabriel Caldas Montes, 2020. "Sovereign credit news and disagreement in expectations about the exchange rate: evidence from Brazil," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 48(3), pages 660-698, August.
    14. El-Shagi, Makram & Schweinitz, Gregor von, 2018. "The joint dynamics of sovereign ratings and government bond yields," Journal of Banking & Finance, Elsevier, vol. 97(C), pages 198-218.
    15. Teixeira, João C.A. & Silva, Francisco J.F. & Ferreira, Manuel B.S. & Vieira, José A.C., 2018. "Sovereign credit rating determinants under financial crises," Global Finance Journal, Elsevier, vol. 36(C), pages 1-13.
    16. Bart H. L. Overes & Michel van der Wel, 2021. "Modelling Sovereign Credit Ratings: Evaluating the Accuracy and Driving Factors using Machine Learning Techniques," Papers 2101.12684, arXiv.org, revised Jul 2021.
    17. Chen, Sheng-Syan & Chen, Hsien-Yi & Chang, Chong-Chuo & Yang, Shu-Ling, 2016. "The relation between sovereign credit rating revisions and economic growth," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 90-100.
    18. Athari, Seyed Alireza & Kondoz, Mehmet & Kirikkaleli, Dervis, 2021. "Dependency between sovereign credit ratings and economic risk: Insight from Balkan countries," Journal of Economics and Business, Elsevier, vol. 116(C).
    19. Chiwitt, Ulrich, 2014. "Ratingagenturen - Fluch oder Segen? Eine kritische Bestandsaufnahme," Arbeitspapiere der FOM 48, FOM Hochschule für Oekonomie & Management.
    20. Afonso, António & Furceri, Davide & Gomes, Pedro, 2012. "Sovereign credit ratings and financial markets linkages: Application to European data," Journal of International Money and Finance, Elsevier, vol. 31(3), pages 606-638.

    More about this item

    Keywords

    MOORA; Sovereign Credit Rating; Rating Agencies;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • O57 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Comparative Studies of Countries

    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:ris:buecrj:0397. 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: Adem Anbar (email available below). General contact details of provider: https://edirc.repec.org/data/iiulutr.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.