Laeding and Following Variance Risk Premiums: Evidence from S&P500 and KOSPI200 Options
Bibliographic record
Abstract
This study aims to examine the return predictability of variance risk premium, which is defined as the difference between risk-neutral variance and expected realized variance, on KOSPI 200 index returns. Although extant literature shows that variance risk premium estimated from U.S. index options has a predictive power on underlying returns, little study has been conducted in KOSPI 200 index returns. In addition, there is no conclusion for the predictive power of variance risk premium in other financial markets. In this paper, we can find the predictive power of S&P500 variance risk premium on KOSPI200 index returns as well as on S&P500 index returns, but cannot find the predictive power of KOSPI200 variance risk premium on both indices. These results are consistent to Londono (2012) and Bollerslev et al. (2013). The poor performance of KOSPI200 variance risk premium is explained by the assumption that U.S. economy is a leader economy, while Korea economy is a follower economy. To support this conclusion, we conduct Vector Auto-Regression (VAR) using two variance risk premiums. Two premiums have bi-directional lead-lag relationship but S&P500 variance risk premium is informationally superior to KOSPI200 variance risk premium regarding return predictions.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".