IDEAS home Printed from https://ideas.repec.org/a/eee/ecanpo/v43y2013i2p157-176.html
   My bibliography  Save this article

Credit Rating Agencies: The Importance of Fundamentals in the Assessment of Sovereign Ratings

Author

Listed:
  • Vanja Bozic

    (Faculty of Economics, University of Rome Tor Vergata, Via Columbia 2, 00133, Rome (RM), Italy)

  • Cosimo Magazzino

    (School of Political Sciences, Roma Tre University, Via G. Chiabrera 199, 00145, Rome (RM), Italy)

Abstract

The aim of this paper is to investigate the significance of a set of macroeconomic variables in the assessment of the sovereign ratings provided by the three main credit rating agencies in different periods in time and for countries belonging to different cate gorizations. Ratings have a great economic importance as they constitute the main drivers for attracting foreign investments and can influence the dynamics of interest rates. By grouping the countries according to levels of development and indebted ness, we provide the analysis of the weights attributed to each one of the macroeco- nomic indicators included in the analysis. Furthermore, it is of interest to examine how ratings are constructed and if they exhibit a historical coherence that goes be yond the economic cycles. The analysis rests on an unbalanced panel of 139 countries in the period 1975- 2010. In order to provide an answer to ratings’ historical coher- ence, we selected two sub-periods: 1975-1996 and from 1997 onwards. Static esti mates findings show that per capita GNI, inflation, unemployment, fiscal balance, government debt and default history significantly affect ratings, while GNI growth and current account balance are less relevant. Furthermore, Granger causality results underline that a one-way causality runs from average ratings to economic growth.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ecanpo:v:43:y:2013:i:2:p:157-176
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0313592613500166
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. 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.
    2. Elnaz Gholipour & B'ela Vizv'ari & Zolt'an Lakner, 2020. "Reconstruction Rating Model of Sovereign Debt by Logical Analysis of Data," Papers 2011.14112, arXiv.org.
    3. Zirong Zhuo & Jixiang Liu & Wenmin Luo, 2016. "Credit Default Risk Assessment of Local Government Debts Based on KMV Model," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 8(5), pages 230-240, May.
    4. 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.
    5. 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.
    6. Abdulkerim Karaaslan & Kürşat Özgür Özden, 2017. "Forecasting Turkey’s Credit Ratings with Multivariate Grey Model and Grey Relational Analysis," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 15(3), pages 583-610, September.
    7. Bart H. L. Overes & Michel Wel, 2023. "Modelling Sovereign Credit Ratings: Evaluating the Accuracy and Driving Factors using Machine Learning Techniques," Computational Economics, Springer;Society for Computational Economics, vol. 61(3), pages 1273-1303, March.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. Yang, Daecheon & Song, Jeongseok, 2018. "Impact of wage rigidity on sovereign credit rating," Emerging Markets Review, Elsevier, vol. 34(C), pages 25-41.
    13. Choy, Swee Yew & Chit, Myint Moe & Teo, Wing Leong, 2021. "Sovereign credit ratings: Discovering unorthodox factors and variables," Global Finance Journal, Elsevier, vol. 48(C).

    More about this item

    Keywords

    Credit rating agencies; Sovereign debt; Panel data; Granger causality;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • H63 - Public Economics - - National Budget, Deficit, and Debt - - - Debt; Debt Management; Sovereign Debt
    • G01 - Financial Economics - - General - - - Financial Crises
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage

    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:eee:ecanpo:v:43:y:2013:i:2:p:157-176. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/economic-analysis-and-policy .

    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.