The compatibility of one‐factor market models in caps and swaptions markets: Evidence from their dynamic hedging performance
Bibliographic record
Abstract
Abstract This study examines the dynamic hedging performance of the one‐factor LIBOR and swap market models in both caps and swaptions markets, using a procedure similar to the way that these models are used in practice. The effects of different calibration methods on model performance are investigated as well. The LIBOR market models and the swap market models are calibrated to the cross‐sectional Black implied volatilities for caps and swaptions respectively; the test is based on their effectiveness in hedging floors and swaptions that are not used in the calibration. We find that the LIBOR market models outperform the swap market models in hedging floors and perform as well as the swap market models in hedging swaptions. Our results also show that incorporating a humped volatility structure into these models does not significantly improve their hedging performance. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:109–130, 2008
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".