Long memory and the relation between implied and realized volatility
Bibliographic record
Abstract
We argue that the conventional predictive regression between implied volatility (regressor) and realized volatility over the remaining life of the option (regressand) is likely to be a fractional cointegrating relation. Since cointegration is associated with long-run comovements, this finding modifies the usual interpretation of such regression as a study towards assessing option market efficiency (given a certain option pricing model) and/or short-term unbiasedness of implied volatility as a predictor for realized volatility, thereby rendering the conventional tests invalid. We use spectral methods and exploit the long memory in the data to design an econometric methodology which is robust to the various issues that the literature on the relation between implied and realized volatility has proposed as plausible explanations for an estimated slope coefficient less than one, implying biasedness, in the standard predictive regression (measurement errors and presence of an unobservable time-varying risk premium, for instance). Even though little can be said about market efficiency and/or short-term unbiasedness, which were the objects of the previous studies, our evidence in favor of a long-run one-to-one correspondence between implied and realized volatility series is rather strong. Simulation results confirm our findings.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".