Procyclicality Mitigation for Initial Margin Models with Asymmetric Volatility
Bibliographic record
Abstract
We apply a variety of volatility models in setting the initial margin requirements for central clearing counterparties (CCPs) and show how to mitigate procyclicality using a three-regime threshold autoregressive model. In order to evaluate the initial margin models, we introduce a loss function with two competing objectives: risk sensitivity and procyclicality mitigation. The trade-off parameter between these objectives can be selected by the regulator or CCP, depending on the specific preferences. We also explore the properties of asymmetric generalized autoregressive conditional heteroscedasticity (asymmetric GARCH) models in the threshold GARCH family, including the spline-generalized threshold GARCH model, which captures high-frequency return volatility and low-frequency macroeconomic volatility as well as an asymmetric response to past negative news in both past innovations (ARCH) and volatility (GARCH) terms. We find that the more general asymmetric volatility model has a better fit, greater persistence of negative news, a higher degree of risk aversion and an important effect on macroeconomic variables for the low-frequency volatility component of the Standard & Poor’s 500 and S&P/Toronto Stock Exchange returns.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".