Artificial Intelligence Development and Sovereign Bond Ratings
Bibliographic Data
| ID | 24232193 |
|---|---|
| Authors | Lance Y Hunter (0000-0002-6918-2952), Glen Biglaiser |
| Year | 2026 |
| Volume | 29 |
| Issue | 3 |
| Pages | 284-310 |
| Publication date | 2026-09-30 |
| Open Access | No |
| Type | ARTICLE |
| Venue | International Area Studies Review (JOURNAL) |
| Journal identifiers | ISSN: 2049-1123 • E-ISSN: 2233-8659 |
| Publisher | Center for International Area Studies, Hankuk University of Foreign Studies (PUBLISHER) |
| DOI | 10.69473/iasr.2026.29.3.284 |
| OpenAlex | W7221528134 |
| Language | EN |
| References cited | 44 |
The study presented here is a follow-up study to Rowland and Torres (2004), who used a panel data framework together with data from 16 emerging market issuers to identify the determinants of the spread and the creditworthiness.Since many new issuers of emerging market sovereign debt have emerged recently, we can by using data from one single point in time, end of July 2003, expand our country set to 29 for the analysis of the spread and around 50 for the analysis of the credit ratings and the creditworthiness.We will use an OLS regression framework for the empirical analysis.The study identifies some seven variables that play a role in determining ratings, creditworthiness and spreads.These include the GDP per capita, the economic growth rate, the inflation rate, external-debt ratios, debt-service ratios, the level of international reserves, and the openness of the economy.Emerging market policy makers and investors should pay extra attention to these variables when defining economic policies and evaluating bond issues.
public debt · sovereign credit ratings · Artificial Intelligence · Artificial Intelligence Applications · Credit Risk and Financial Regulations · Ethics and Social Impacts of AI
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| Highly cited | No |
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