Co-Jumps, Co-Jump Tests, and Volatility Forecasting: Monte Carlo and Empirical Evidence
Bibliographic record
Abstract
This study classifies jumps into idiosyncratic jumps and co-jumps to quantitatively identify systematic risk and idiosyncratic risk by utilizing high-frequency data. We found that systematic risk occurs more frequently and has larger magnitudes than the idiosyncratic risk in an individual asset, which indicates that volatilities from one sector are largely derived from the contagious effect of other sectors. We further investigated the importance of idiosyncratic jumps and co-jumps to predict the sector-level S&P500 exchange-traded fund (ETF) volatility. It was found that the predictive content of co-jumps is higher than that of idiosyncratic jumps, suggesting that systematic risk is more informative than idiosyncratic risk in volatility forecasting. Additionally, we carried out Monte Carlo experiments designed to examine the relative performances of the four co-jump tests. The findings indicate that the BLT test and the co-exceedance rule of the LM test outperform other tests, while the co-exceedance rule of the LM test has larger power and a smaller empirical size than that of the BLT test.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".