Measures of Aggregate Credit Conditions and Their Potential Use by Central Banks
Bibliographic record
Abstract
Understanding the nature of credit risk has important implications for financial stability. Since authorities – notably, central banks – focus on risks that have systemic implications, it is crucial to develop ways to measure these risks. The difficulty lies in finding reliable measures of aggregate credit risk in the economy, as opposed to firmlevel credit risk. In this paper, the authors examine two models recently developed for this purpose: a reduced-form model applied to credit default swap index tranches, and a structural model applied to the spread on U.S. corporate bond indexes. The authors find that these models provide information on the nature of credit events – that is, whether the event is systemic or not – and on the type of risk priced in corporate bonds (i.e., credit or liquidity risk). However, although the two models provide potentially useful information for policy-makers, at this stage it is difficult to corroborate the accuracy of the information obtained from them. Further work is needed before authorities can include conclusions drawn from the two models into their policy decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".