The equity risk premium and the low frequency of the term spread : international evidence
Bibliographic record
Abstract
Nesta tese analisamos o poder de previsão out-of-sample do term-spread e dos seus domínios de frequência sobre prémios de risco de mercado. A variável term spread representa a diferença entre taxas de juro de longo e curto prazo de obrigações do governo e a sua decomposição em domínio de frequência é feita através do método Maximum Overlap Discrete Wavelet Transform. Foi comprovado pela literatura que no mercado dos Estados Unidos da América a componente de baixa frequência do term spread tem uma performance forte e robusta em exercícios out-of-sample sobre prémios de risco de mercado. Nesta tese, abordamos a possibilidade de este indicador ter a mesma performance em mercados internacionais (Alemanha, França, Japão, Reino Unido, Canada, África do Sul e Australia). Até então, esta alternativa ainda não foi abordada na literatura, e consideramos muito importante a sua análise para os mais diversos investidores, tanto locais como internacionais. A principal conclusão desta tese é que a série original em domínio temporal e a componente de baixa frequência do term spread tem uma performance out-of-sample forte e robusta a prever prémios de risco de mercado para além dos Estados Unidos da América, na Alemanha, na França e no Canada.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".