Emulating the Standard Initial Margin Model: initial margin forecasting with a stochastic cross-currency basis
Bibliographic record
Abstract
A common shortcut for forecasting initial margin requirements and margin valuation adjustments that are aligned with the International Swaps and Derivatives Association’s Standard Initial Margin Model relies on simulating and recalibrating value-at-risk quantiles. Doing so largely avoids costly sensitivity calculations but works only if the relevant risks are appropriately represented in the simulation model. In this paper we highlight the impact of missing cross-currency basis risk factors on estimating initial margin and margin valuation adjustments for instruments with a cross-currency basis sensitivity. We propose a parsimonious, consistent and efficient stochastic cross-currency basis model extension as remedy and provide illustrative examples. The examples cover vanilla interest rate swaps and resetting and non-resetting cross-currency basis swaps in Canadian dollars, euros, Japanese yen and US dollars. In addition to initial margin and margin valuation adjustment, we also compute and compare the impact on residual credit valuation adjustment.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".