Essays on Collateral and Central Counterparties
Bibliographic record
Abstract
The collateral systems commonly employed by many derivatives central counterparties (CCPs), such as the Standard Portfolio Analysis of Risk (SPAN) or the Value-at-Risk (VaR) approach, fail to consider the loss dependence of their clearing members. As a consequence, CCPs are often left exposed to simultaneous extreme losses that could undermine their stability and that of the entire financial system. In this context, this thesis proposes two new collateral methodologies that address this problem. Chapter 2 uses copulas to develop a methodology that accounts for the tail dependence of market participants. This method allows individual margins to increase when clearing firms are more likely to suffer simultaneous extreme losses; thus, reducing the probability and shortfall associated with joint margin exceedances. Chapter 3 proposes a collateral methodology, called CoMargin, which generalizes the VaR approach to a multivariate setting. This method targets and stabilizes the conditional probability of financial distress across clearing members, can be generalized to any number of market participants and can be backtested using formal statistical tests. The empirical sections of Chapter 3 use proprietary data from the Canadian Derivatives Clearing Corporation (CDCC), which include daily observations of the actual trading positions of all of its members from 2003 to 2011. This dataset is the first one of its kind in the economics and finance literature and opens the door to the development of new models that do not have to rely on the strong assumptions made in the past about the trading behaviour of market participants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".