Bør norske investorer valutasikre?
Bibliographic record
Abstract
Sammendrag\n\nFormålet med oppgaven er å undersøke hvordan valutasikring påvirker avkastning og risiko over tid for norske investorer. I oppgaven konstruerer vi valutasikrede investeringer i Euronext 100, S&P 500 og FTSE 100 ved bruk forwardkurser i perioden 2010-2022. Vi ser på investeringsperioder på 3 og 6 måneder. For å undersøke forholdet mellom forwardkurs og spotkurs har vi brukt minste kvadraters metode. Vi finner at forwardkurser systematisk undervurderer hvor svak spotkursen faktisk blir gjennom perioden for alle valutapar, både 3 og 6 månedlig. Dette er en indikator på at en norsk investor vil oppleve negativ meravkastning i forwardmarkedet.\n\n\n\nVidere når vi ser på de valutasikrede porteføljene finner vi negativ meravkastning og høyere risiko sammenlignet med å ikke sikre. Dette resulterte i en nedgang i risikojustert avkastning både på 3 og 6 månedlig basis. En optimal hedgerate indikerte at det var mer hensiktsmessig å kjøpe mer av den utenlandske valutaen enn å valutasikre.\n\n\n\nDenne studien gir innsikt i valutasikringststrategier og deres implikasjoner for norske investorer. Resultatene kan være nyttige for investorer i å ta informerte beslutninger om valutasikring og risikostyring.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.049 | 0.013 |
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".