Default Risk on Derivatives Exchanges: Evidence from Clearing-House Data
Bibliographic record
Abstract
In this paper, we analyze empirically the clearing house exposure to the risk of default by a clearing house member. Using actual daily data on margins and variation margins for all clearing members of the Chicago Mercantile Exchange’s clearing house, we identify many occurrences when the member’s daily loss exceeds his posted margin. Furthermore, we …nd that the major source of default risk for a clearing member is proprietary trading and not trading by customers. In order to quantify the default risk exposure, we provide a characterization of the tail risk of the clearing house using Extreme Value Theory. We then design and price a realistic insurance contract covering the loss to the clearing house from default by one or several clearing members. We investigate the impact on the insurance premium of including data from the Black Monday of 1987 in our sample. Our empirical analysis also allows us to put a dollar amount on the service provided by the Federal Reserve, which is the implicit insurer of the clearing house. JEL classi…cation: G13, G18
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".