Measuring volatility persistence in leveraged loan markets in the presence of structural breaks
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Bibliographic record
Abstract
This paper examines volatility persistence in leverage loan market price series for Australia, Canada, Europe, Japan, Singapore, UK and USA in the presence of structural breaks. To the best of our knowledge, this is the first empirical study to examine volatility persistence in the leveraged loan markets. To this end, using fractional integration methods, the results indicate that both absolute and squared returns display long memory features, with orders of integration confirming the long memory hypothesis. However, after accounting for structural breaks, we find a reduction in the degree of persistence in the leveraged loan market. The evidence of persistence in volatility implies that market participants who want to make gains across trading scales need to factor the persistence properties of leveraged loan price series in their valuation and forecasting models since that will help improve long-term volatility market forecasts and optimal hedging decisions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it