Specification and Calibration Errors in Measures of Portfolio Credit Risk: The Case of the ASRF Model
Bibliographic record
Abstract
This paper focuses on the asymptotic single-risk-factor (ASRF) model in order to analyze the impact of specification and calibration errors on popular measures of portfolio credit risk. Violations of key assumptions of this model are found to be virtually inconsequential, especially for large, welldiversified portfolios. By contrast, flaws in the calibrated interdependence of credit risk across exposures, caused by plausible small-sample estimation errors or rule-of-thumb values of asset return correlations, can lead to significant inaccuracies in measures of portfolio credit risk. Similar inaccuracies arise under standard assumptions regarding the tails of the distribution of asset returns. JEL Codes: G21, G28, G13, C15. We are indebted to Michael Gordy and Kostas Tsatsaronis for their encouragement and useful comments on an earlier version of this paper. We thank Klaus Düllmann, Wenying Jiangli, and participants at a BIS workshop, the 3rd International Conference on Credit and Operational Risk at HEC Montréal, and the 17th Annual Derivatives Securities and Risk Management Conference at the FDIC for insightful suggestions. We are also grateful to Marcus Jellinghaus for his valuable help with the data. The views expressed in this paper are our own and do not necessarily represent those of the Bank for International Settlements.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".