Option-Implied Measures of Equity Risk
Bibliographic record
Abstract
Equity risk measured by beta is of great interest to both academics and practitioners. Existing estimates of beta use historical returns. Many studies have found option-implied volatility to be a strong predictor of future realized volatility. We .nd that option-implied volatility and skewness are also good predictors of future realized beta. Motivated by this .nding, we establish a set of assumptions needed to construct a beta estimate from option-implied return moments using equity and index options. This beta can be computed using only option data on a single day. It is therefore potentially able to re.ect sudden changes in the structure of the underlying company. Le risque du marché des actions mesuré selon le coefficient bêta suscite un vif intérêt de la part des universitaires et des praticiens. Les estimations existantes du coefficient bêta utilisent les rendements historiques. De nombreuses études ont démontré que la volatilité implicite du prix des options constitue un indice solide de la volatilité future réalisée. Nous constatons que la volatilité implicite des options et leur caractère asymétrique sont aussi de bons facteurs prévisionnels du bêta futur réalisé. Motivés par ce constat, nous établissons un ensemble d'hypothèses nécessaires pour effectuer une estimation du bêta, à partir des moments de rendement implicite des options, en recourant aux actions et aux options sur indices boursiers. Ce bêta peut être calculé en utilisant seulement les données obtenues sur les options au cours d'une même journée. Il peut donc refléter les changements soudains de la structure de la société sous-jacente.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".