Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the uncertainty of acquiring the lowest possible airfare when contemplating the purchase of a ticket. A real option model is applied to value insurance contracts that could be offered to passengers to cope with price risk. Furthermore, the premiums charged for such airfare price insurance contracts can augment airline carrier revenues. Design/methodology/approach Prices on 14 airfares were collected for 79 consecutive days on an assortment of US domestic and international flights from four airline carriers. Volatility in airfares was shown using the price range and SD. The Black‐Scholes‐Merton model was employed to value the call and put options representing different airfare price insurance contracts. Findings Airfare price insurance contracts affordability was demonstrated ranging from 1.55 to 11.28 percent of the average dollar ticket price. Research limitations/implications The valuations in the paper were based on ex post data that would not be available to the customer purchaser. Nonetheless, the airline carriers that sell the insurance would have better estimates of the price volatility and therefore could price the contracts to make a profit. Practical implications Airline passengers would have an opportunity to reduce the ticket price risk they face when buying their tickets. Airline carrier could increase revenues by offering such products. Social implications The opportunity to manage price risk contributes to the completeness of markets. Originality/value The paper shows that airfare price insurance contracts are a viable tool in the management of price risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".