Bibliographic record
Abstract
Estimating the representative agent's or the market's degree of risk aversion from securities prices has a long history. However, it is only since this century that scholars have begun using options data to do so. Options provide a particularly promising context for studying risk preferences. Embedded in the prices of traded options is rich information set relating to various aspects of the underlying asset. One piece of embedded information is the state price density. To the best of my knowledge, the implied risk aversion deriving from state price density or the "risk neutral" distribution has not been well studied in Canadian context. In this paper, the author investigates the volatility smile derived from call options on the Canadian S&P/TSX 60 Index, which is one of the most heavily traded index options in the Canadian market. Then from the option pricing function, the author recovers the risk neutral distribution by using the nonparametric approach--the regularization method proposed by Jackwerth and Rubinstein (1996). The final step is to compare the option inferred risk neutral distribution with the estimated actual distribution of index prices based on, for example, the historical price path over the same time interval. The relationship between these two distributions depends on the preference (utility) of investors about money, as they grow richer or poorer. Knowing both distributions allows us to infer what the preferences must be within an economy to be consistent with the option price and the historical returns.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.405 | 0.058 |
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".