Bibliographic record
Abstract
In this research I examine the effectiveness of cross hedging the USD/TWD spot exchange rate changes with various futures contracts, including those of Euro, Japanese Yen, Australian Dollar, British Pound and Canadian Dollar, all relative to US Dollar. This study applies the minimum-variance hedge model and the hedge ratio is the slope coefficient estimated from the regression of spot price changes on futures price changes. Both simple cross hedges and portfolio cross hedges with two futures contracts are tested based on in-sample and out-of-sample data. Major conclusions are listed as follows: 1.The hedging effectiveness of in-sample strategies is, as expected, much higher than those of out-of-sample strategies. 2.The effectiveness of dynamic hedging performs better than that of static hedging. 3.The hedging effectiveness changes with the length of hedge period, ranging from 3 months to 9 months, irregardless of whether in- sample or out-of-sample hedge ratios are applied. 4.Japanese Yen futures, due to its higher correlation with US dollar, consistently yield the greatest hedging effectiveness as being used to cross hedge USD/TWD exchange rate. Cross hedging with multiple hedges generally performs better than that with single hedges.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.015 |
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; both teacher heads agree on what is shown here.
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".