Bibliographic record
Abstract
We argue that a payment’s risk approaches zero as maturity approaches zero, and that the central bank’s short-term rate best captures the risk-free rate of various assets. We employ two factors to model the expected risk-free rate that the market expects the current monetary policy to move towards the neutral rate over a certain period. Expecting that the T-bill risk (i.e., the macrorisk) largely reflects a country’s inflation risk, we measure the risk as a 5-year payment’s risk to be comparable across assets. To solve the model factors, we use repeated trials to minimize the prediction errors. Our models thus split US and Canada T-bill yields into the risk and risk-free rate, on average explaining 98.7% of the returns. The models assuming independence of the two returns show similar power in predicting T-bill returns, which can significantly simplify the formulas. We also find that the inclusion of a risk constant over maturity, which has a small value of several basis points, significantly reduces the prediction errors. The risk and the risk-free rate is the gateway to corporate the risk of various assets in the country.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".