The G20 Over-the-Counter Derivative Markets \nReforms: More Harm than Good? \nA Theoretical Perspective
Bibliographic record
Abstract
The Great Financial Crisis (GFC) has revealed that financial theory influences the manner in which financial markets are conceptualised and consequently regulated as evidenced by the deregulation that took place in over-the-counter derivative markets (OTC-DMs) pre-GFC – attributable to the economic ideology proselytised by theories of modern finance. Operating on the premise that theory matters for how we regulate, this thesis explores post-GFC reforms in OTC-DMs. Specifically, this thesis explores the central counterparty prescription, the reporting obligation, and the centralised trading requirement to determine whether there is any congruence between regulatory reforms in OTC-DMs and theories of modern finance. In addition, this thesis assesses these reforms utilising alternative theories of finance, which it argues are better suited for the operation and regulation of real-world financial markets namely behavioural finance, Minsky’s financial instability hypothesis, and imperfect knowledge economics as an evaluative framework. This analysis reveals that the endogenous risk attributable to fundamental uncertainty, irrationality, and the imperfect knowledge constraint is not fully accounted for in current regulatory reforms. Consequently, this thesis argues that regulatory reforms in OTC-DMs may prove ineffective in environments of financial stress. Finally, this thesis makes the case for an approach towards financial regulation that recognises the primacy of endogenous risk in financial markets.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".