Skewness of the Volatility Smile and Stock Returns in Brazil
Bibliographic record
Abstract
Several studies suggest implied volatility and options trading volume as a proxy for risk analyses and forecast returns. The skewness of the volatility smirk also appears in this field. Xing, Zhang and Zhao (2010) demonstrated the effect of this skew on the stock returns in the U.S. market and attempt to explain the results by the activity of inside traders. Using the conclusions of this study as a starting point, we sought to assess its individual validity on a daily level for the two principle shares traded on BM&FBovespa, using the implied volatility skew as an external regressor in the AR-GARCH models for shares returns. The results showed predictable gains of the models with skew, but the effect, however, was varied according to company, in accordance with the time lag of the regressor. It is possible to say that the options market in Brazil contains information about future returns; however, this connection appears, initially, to be specific to each company.
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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 | 0.011 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".