Basel Requirement of Downturn Lgd: Modeling and Estimating Pd & Lgd Correlations
Bibliographic record
Abstract
Basel II requires that banks use downturn loss given default (LGD) estimates in regulatory capital calculations, citing the fact that the probability of default (PD) and LGD correlations are not captured. We show that the lack of correlation can be taken care of by incorporating certain degree of conservatism in cyclical LGD in a point-in-time (PIT) framework. We examine a model which can capture the PD and LGD correlation in its entirety, differentiating the different components of correlations in question. Using historical LGD and default data of a loan portfolio, we calibrate our model and, through the simulation of economic capital, we show the mean LGD needs to be increased by about 35% to 41% in order to compensate for the lack of correlations. Our hope is to provide a framework that the banks can use based on their internal data to estimate and justify their LGD choices for different portfolios. Although the paper is presented within the context of Basel II, the applications could be much wider including structured finance, credit derivatives, economic capital and portfolio modeling in general, where PD and LGD correlations need to be estimated and modeled.
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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) | 0.000 | 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".