Methodology for Assigning Credit Ratings to Sovereigns
Bibliographic record
Abstract
The investment of foreign exchange reserves or other asset portfolios requires an assessment of the credit quality of investment counterparties. Traditionally, foreign exchange reserve and asset managers have relied on credit rating agencies (CRAs) as the main source for credit assessments. The Financial Stability Board issued principles to reduce reliance on CRA ratings in standards, laws and regulations, in support of financial stability. Moreover, best practices in the asset management industry suggest that investors should understand the credit risks they are exposed to and, more broadly, that internal credit assessments be relied upon to inform investment decisions. In support of efforts by market participants to establish stronger internal credit assessment practices, as well as to solicit feedback, this paper provides a detailed technical description of the methodology developed to assign internal credit ratings to sovereigns, using publicly available data only. This methodology proposes three key innovations: (i) a quantitative approach to assess political risks, (ii) a framework to assess the government’s potential contingent liabilities related to the banking sector, and (iii) a framework to determine the presence of asset price imbalances in the country. The methodology presented relies on fundamental credit analysis that produces a forward-looking and “through-the-cycle” assessment of the investment entity’s capacity and willingness to pay its financial obligations, resulting in an opinion on the relative credit standing or likelihood of default. The methodology presented is currently used to assess eligibility and inform investment decisions in the management of Canada’s foreign exchange reserves. The methodology is a key component of the joint Bank of Canada and Department of Finance Canada initiative to develop internal credit assessment capabilities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".