VALUING REAL OPTIONS: CAN RISK‐ADJUSTED DISCOUNTING BE MADE TO WORK?
Bibliographic record
Abstract
This paper examines three alternative approaches to valuing real options: (1) the standard option pricing technique using “risk‐neutral” probabilities; (2) the use of risk‐adjusted discount rates; and (3) discounting certainty‐equivalent values with a riskless discount rate. As suggested by the title, a question of particular interest is whether an approach based on risk‐adjusted discount rates can be “made to work” for valuing options. The answer is yes. Indeed, the authors show that any of the three approaches will provide a correct valuation if properly employed. Nevertheless, there are important differences in the information requirements associated with each of the three methods. Another important issue is the relative degree of difficulty in calculating the correct option value. When these two considerations are taken into account, the risk‐neutral option pricing procedure generally proves to be the preferred method. It tends to be computationally more convenient—often much more convenient—and to require less information than either the risk‐adjusted discounting or certainty‐equivalent procedures.
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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 | 0.019 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.024 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".