Option Prices, Preferences, and State Variables
Bibliographic record
Abstract
This paper surveys recent developments in the theory of option pricing. The emphasis is on the interplay between option prices and investors' impatience and their aversion to risk. The traditional view, steeped in the risk-neutral approach to derivative pricing, has been that these preferences play no role in the determination of option prices. However, the usual lognormality assumption required to obtain preference-free option pricing formulas is at odds with the empirical properties of financial assets. The lognormality assumption is easily reconcilable with those properties by the introduction of a latent state variable whose values can be interpreted as the states of the economy. The presence of a covariance risk with the state variable makes option prices depend explicitly on preferences. Generalized option pricing formulas, in which preferences matter, can explain several well-known empirical biases associated with preference-free models such as that of Black and Scholes (1973) and the stochastic volatility extensions of Hull and White (1987) and Heston (1993).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".