The Impact of Regulation on Corporate Hedging Activities and the Response of Corporates – A Preliminary Conceptual Framework
Bibliographic record
Abstract
Following the financial crisis of 2007/2008 regulators intensified the regulation of financial derivatives through (i) the implementation of the European Markets Infrastructure Directive (EMIR) to increase transparency of over-the-counter (OTC) derivatives and (ii) the implementation of Basel III to increase capital underpinning. Non-financial corporates, who mainly hedge with OTC derivatives, are seeing tendencies of increasing costs and decreasing availability of required OTC derivatives but fail to have a full concept of the impact and possible responses to manage the impact. Also, theoretical research did not consider reguation as an influencing factor and thus does not offer theories to analyse the impact of regulation on corporate hedging activities (defined as the willingness and ability of NFCs to conduct hedging in an optimal way). Given this gap, this paper reviews existing theories and based on that pre-conceptualises a model that helps to analyse the impact of regulation on corporate hedging activities and provides a preliminary conceptual framework that includes corporate responses to manage the regulatory impact.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".