Testing for changes in option-implied risk aversion
Bibliographic record
Abstract
Purpose – The purpose of this paper is to investigate and test for changes in investor risk aversion and the stochastic discount factor (SDF) using options data on the West Texas Intermediate crude oil futures contract during the 2007-2011 period. Design/methodology/approach – Risk aversion functions and SDFs are estimated using parametric approaches before and after four specific dates of interest. The dates are: the summer 2008 end of the bull market regime; the late 2008 credit freeze trough; the BP Deepwater Horizon explosion; and the Libyan uprising. Findings – Absolute risk aversion functions and SDFs are significantly flatter (less decreasing in wealth) after the end of the bull market and the credit freeze trough. After these two market reversals, oil market participants were less risk-averse for low levels of wealth but more risk-averse for high wealth levels. Oil market investors also increased their valuation of anticipated future wealth in average states of nature relative to very high or very low-asset return states after reversals. The BP explosion and the Libyan uprising led to steeper risk aversion functions (decreasing more rapidly in wealth) and SDF. Oil market investors were more risk-averse for lower future wealth, but less risk-averse for higher future wealth. Oil market investors increased their valuation of anticipated future wealth in extreme states of nature relative to average states of nature after both dates. Originality/value – Documenting statistically and economically significant changes in oil market investors’ attitude toward risk and inter-temporal appetite for risk in relation to changes in financial and political conditions.
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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 | 0.005 | 0.016 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".