Explaining Forward Rate Unbiasedness Hypothesis: The Risk Premium Approach
Bibliographic record
Abstract
The hypothesis that the forward rate is an unbiased predictor of the future spot rate has been questioned time and again. In majority of the cases empirical evidence suggests that forward rates are neither efficient nor rational forecasts of future spot rates. The rejection of the forward rate unbiasedness hypothesis can be attributed to a misspecified theoretical model. In this paper we consider the misspecification to be in the form of exclusion of an explanatory variable, the risk premium. We test the unbiasedness hypothesis by including a time-varying risk premium using the GARCH-M representation. The risk premium is modeled by extending the Domowitz and Hakkio (1985) ARCH framework and by applying a GARCH (1, 1) specification. The exchange rates data are from January 1991 to February 2008 for U.K., Canada, Australia and Japan, the four advanced economies and for India, the emerging market economy, the data ranges from January 1999 to February 2008.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".