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Sovereign Credit Ratings Analysis Using the Logistic Regression Model

Risks (Basel), 2022-04, Vol.10 (4), p.70 [Peer Reviewed Journal]

2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2227-9091 ;EISSN: 2227-9091 ;DOI: 10.3390/risks10040070

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  • Title:
    Sovereign Credit Ratings Analysis Using the Logistic Regression Model
  • Author: Takawira, Oliver ; Mwamba, John W. Muteba
  • Subjects: Bond ratings ; Borrowing ; Budget deficits ; Central banks ; Costs ; Credit ratings ; Credit risk ; Credit scoring ; Decision making ; Default ; Financial services ; Foreign exchange rates ; Government ; Inflation ; Interest rates ; Investments ; logistic regression ; macroeconomic indicators ; Macroeconomics ; National debt ; Politics ; Rating services ; Ratings & rankings ; Regression analysis ; sovereign credit ratings ; Tax increases
  • Is Part Of: Risks (Basel), 2022-04, Vol.10 (4), p.70
  • Description: This study is an empirical analysis of sovereign credit ratings (SCR) in South Africa (SA) using Logistic Regression (LR) to identify their determinants and forecast SCRs. Data of macroeconomic indicators including SCRs from 1999 to 2020 in quarterly format were classified and analyzed to identify indicators utilized by Credit Rating Agencies (CRAs) and then predict future ratings CRAs take various information from political, infrastructure, financial, economic, regional, local, and other factors pertaining to a country and assess the ability of that country to pay its debt. This information is then presented through a grading scale termed rating, with the highest rating country being highly creditworthy and lowest rating likely to default. There are three major CRAs, namely, Fitch, Moodys and Standard and Poors. The study identified the use of different macroeconomic indicators by CRAs as well as different techniques in assessing and assigning sovereign credit ratings. The study points out that Household Debt to Disposable Income Ratio (HDDIR) was the most influential variable on SCRs. HDDIR, exchange rates and the inflation rate were the most crucial variables for guessing credit ratings. Policymakers should aim to reduce household debt in relation to disposable income, implement policies that strengthen the local currency and stabilize as well as lower inflation. Investors should watch out on nations that have high household debt levels as this may spill over into credit risk.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2227-9091
    EISSN: 2227-9091
    DOI: 10.3390/risks10040070
  • Source: DOAJ Directory of Open Access Journals
    Coronavirus Research Database
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
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