A coherent economic framework to model correlations between PD, LGD and EaD, and its applications in EaD modelling and IFRS-9
Bibliographic record
Abstract
This paper proposes an economic framework recognising EaD as a stochastic variable and capturing the PD–LGD, PD–EaD and LGD–EaD correlations. It explains how these correlations can be estimated from historical data, and how PD, LGD and EaD can then be simulated in determining credit VaR. The framework allows credit losses to be more accurately captured, both in terms of the expected credit losses (ECL under IFRS-9 and CECL) and the unexpected tail events in measuring Credit VaR. The framework quantifies the potential underestimation of the tail risk in Credit VaR and the IFRS-9 ECL if the full correlation structure is not captured. By explicitly modelling EaD in a correlated fashion with PD and LGD, lenders can understand and model the increase in funding requirements during downturns. Application in back-testing IFRS-9 ECL is discussed and supplemented by a numerical example.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".