Forecasting volatility: Roles of sampling frequency and forecasting horizon
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This study empirically tests how and to what extent the choice of the sampling frequency, the realized volatility (RV) measure, the forecasting horizon and the time‐series model affect the quality of volatility forecasting. Using highly synchronous executable quotes retrieved from an electronic trading platform, the study avoids the influence of various market microstructure factors in measuring RV with high‐frequency intraday data and in inferring implied volatility (IV) from option prices. The study shows that excluding non‐trading‐time volatility produces significant downward bias of RV by as much as 36%. Quality of prediction is significantly affected by the forecasting horizon and RV model, but is largely immune from the choice of sampling frequency. Consistent with prior research, IV outperforms time‐series forecasts; however, the information content of historical volatility critically depends on the choice of RV measure. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark
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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 | 0.002 |
| 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 | 0.000 | 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 it