Bond Risk Premia and Gaussian Term Structure Models
Bibliographic record
Abstract
Existing results show that (i) lagged forward rates help predict bond returns and (ii) modern Markovian dynamic term structure models (DTSMs) cannot match the evidence [Cochrane JH, Piazzesi M (2005) Bond risk premia. Amer. Econom. Rev. 95(1):138–160]. We develop the family of conditional mean DTSMs where the dynamics depend on current yields and their history through a moving-average component. Our preferred conditional mean model combines one moving average with the usual three Gaussian risk factors, closely matches the bond risk premium measured from predictive regressions, and provides better forecasts of bond returns. Our framework nests Duffee’s models with a small “hidden” factor [Duffee G (2011) Information in (and not in) the term structure. Rev. Financial Stud. 24(9):2895–2934], and our results compare favorably with his five-factor model. Conditional mean models are easier to estimate than state-space term structure models based on Kalman estimates of latent factors. The online appendix is available at https://doi.org/10.1287/mnsc.2016.2602 . This paper was accepted by Lauren Cohen, finance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".