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Sovereign Debt Ratings: A Judgmental Model Based on the Analytic Hierarchy Process

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  • Ronald A Johnson

    (Northeastern University)

  • Venkat Srinivasan

    (Northeastern University)

  • Paul J Bolster

    (Northeastern University)

Abstract

The major rating agencies assign quality rating to international security offerings. These ratings serve as indicators of perceived sovereign risk associated with such offerings. A review of the ratings process followed by the major rating agencies (in particular, the Standard & Poor’s Corporation) reveals that ratings are assigned with considerable reliance on informal judgment. Informal judgmental systems, however, can induce serious inconsistencies in the relative importance assigned to criteria used in assigning rating and lead to biased ratings.This paper presents a formal judgmental model for assigning sovereign debt ratings that avoids the potential inconsistencies of informal systems. Model use is illustrated by relying on descriptions of the rating process published by Standard and Poor’s Corporation. While the model proposed in this paper provides a framework for the rating agencies to ensure consistency in their ratings process, we suggest a complementary relationship between multivariate statistical classification models typically used to assess economic and political risk and the proposed model which assigns country risk ratings.© 1990 JIBS. Journal of International Business Studies (1990) 21, 95–117

Suggested Citation

  • Ronald A Johnson & Venkat Srinivasan & Paul J Bolster, 1990. "Sovereign Debt Ratings: A Judgmental Model Based on the Analytic Hierarchy Process," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 21(1), pages 95-117, March.
  • Handle: RePEc:pal:jintbs:v:21:y:1990:i:1:p:95-117
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    Cited by:

    1. Tennant, David F. & Tracey, Marlon R. & King, Damien W., 2020. "Sovereign credit rating: Evidence of bias against poor countries," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    2. Levary, Reuven R. & Wan, Ke, 1999. "An analytic hierarchy process based simulation model for entry mode decision regarding foreign direct investment," Omega, Elsevier, vol. 27(6), pages 661-677, December.
    3. Olga Porro & Francesc Pardo-Bosch & Núria Agell & Mónica Sánchez, 2020. "Understanding Location Decisions of Energy Multinational Enterprises within the European Smart Cities’ Context: An Integrated AHP and Extended Fuzzy Linguistic TOPSIS Method," Energies, MDPI, vol. 13(10), pages 1-29, May.
    4. Yucel, Eray, 2011. "A Review and Bibliography of Early Warning Models," MPRA Paper 32893, University Library of Munich, Germany.
    5. Timothy J Sinclair, 2000. "Reinventing Authority: Embedded Knowledge Networks and the New Global Finance," Environment and Planning C, , vol. 18(4), pages 487-502, August.
    6. Hanwen Chen & Wang Dong & Hongling Han & Nan Zhou, 2017. "A comprehensive and quantitative internal control index: construction, validation, and impact," Review of Quantitative Finance and Accounting, Springer, vol. 49(2), pages 337-377, August.
    7. Andr??s Ramirez & Hakan Saraoglu, 2009. "An Analytic Approach To Selecting A Nonprofit," William Davidson Institute Working Papers Series wp951, William Davidson Institute at the University of Michigan.
    8. Markus Behn & Rainer Haselmann & Jonas Sobott & Rüdiger Weber & Dorje Wulf, 2013. "Welche Aussagekraft haben Länderratings? Eine empirische Modellierung der Ratingvergabe während der europäischen Staatsschuldenkrise," Schmalenbach Journal of Business Research, Springer, vol. 65(1), pages 2-31, February.
    9. Paudyn, Bartholomew, 2013. "Credit rating agencies and the sovereign debt crisis: performing the politics of creditworthiness through risk and uncertainty," LSE Research Online Documents on Economics 59626, London School of Economics and Political Science, LSE Library.

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