Bibliographic record
Abstract
The bachelor´s thesis examines the gains from hedging the currency exposure from the perspectives of American and Canadian investors. It is shown that exchange rate risk is a largely nondiversifiable factor which might negatively affect the performance of equity portfolios. Therefore, it is necassary to effectively control the exchange rate risk. It is found that the effect of currency risk on total portfolio risk varies among different currency pairs depending predominantly on the correlation between equity and currency returns. For this reason, it is essential to choose a different approach for each currency pair. The hedging strategy, which is refered to as optimal currency hedging, aims at minimizing the volatility of currency hedged portfolio returns. The optimal hedge ratios for individual currencies are also estimated. Over the period from 2004 to 2015, hedging the currency exposure considerably reduced the volatility of returns in the case of American investor. From the perspective of Canadian investor, hedging the currency risk reduced the volatility of returns only to a limited degree. The reason is that Canadian dollar behaves in a pro-cyclical fashion, strenghtening when the world economy surges and weakening when the economy turns down. Therefore, foreign currency exposure tend to reduce the volatility of portfolio returns from the perspective of Canadian investor.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".