Operational Hedging and Exchange Rate Risk: A Cross-sectional Examination of Canada’s Hotel Industry
Bibliographic record
Abstract
Rather than engage in expensive and complicated currency hedging, hotels operating in an international environment can gain similar benefits from their normal operations, including revenue management. An analysis of 1,032 Canadian hotels over a period of over two and one-half years shows that due to exhange rate interactions, ADR, occupancy, and RevPAR increase in weak currency environments, while they decrease in strong environments. As a local currency fluctuates in relation to the dollar, euro, or yen, changes in ADR, occupancy, and (thus) RevPAR offset losses from currency translation in weak environments and modify gains when the currency is strong. When a local currency loses value against the dollar, for instance, travelers consider hotels priced in that currency to be less expensive, even though the nominal price hasn?t changed. Additional travelers who are attracted by "bargain" prices increase occupancy and cause the hotel?s revenue management system to recommend higher rates. Even with higher rates, the hotel?s rates might still be favorable, and the hotel?s revenue per available room would be augmented by both higher room rates and higher occupancy. The implication is that multinational hotel chains have significantly less exposure to foreign exchange risk than implied by traditional hedging practices.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".