Bibliographic record
Abstract
This dissertation comprises three separate chapters on both risk-neutral and physical probability spaced equity tail risk for both the market index and in the cross-section of individual stocks. The first chapter is titled “Does VIX Truly Measure Return Volatility?” This chapter studies the bias of the VIX index as a volatility measure. Particularly, VIX undervalues (overvalues) volatility when market return is expected to be negatively (positively) skewed. Alternatively, we develop a model-free generalized volatility index (GVIX). This chapter further derives the risk-neutral tail risk estimated from the VIX index. The second chapter is titled “Decomposing the VIX: Implications for the Predictability of Stock Returns” This chapter studies the tail risk for the market index (S&P 500 index) in both risk-neutral and physical probability space and subsequently quantifies the market tail risk premium. Market tail risk premium also is a driving force of the VIX index, especially during a nervous market condition. The VIX decomposed market tail risk premium possesses significant prediction power for the equity market index (S&P500 index), Fama and French style portfolios, and industry portfolios with a prediction range that varies from one month to 12 months. The third chapter is titled “The Predictive Power of Tail Risk Premia on Individual Stock Returns” This chapter studies both the risk-neutral and physical probability space tail risk for the cross-section of individual stocks and examines the characteristics of this premium in the cross-section of stock returns. The tail risk premium for individual stocks is statistically and economically priced in the cross-section of individual stock returns. Specifically, the existence of a premium for bearing negative tail risk is significantly associated with negative returns up to one month in the future. In contrast, the premium for bearing positive tail risk has no significant predictive power. This phenomenon cannot be explained by size, book-to-market ratio, market beta, idiosyncratic volatility, momentum, illiquidity, or lottery effect (maximum and minimum monthly returns). Overall, the results from the three chapters indicate that equity tail risk is an important factor for the market index in both risk-neutral and physical probability spaces, and its premium carries strong return predictability for multiple market-level portfolio assets. Furthermore, equity tail risk and its premium carry significant return prediction power in the cross-section of individual stock returns. This phenomenon is robust to previously documented asset pricing factors.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".