A search-theoretic model of the term premium
Bibliographic record
Abstract
A consistent empirical feature of bond yields is that term premia are, on average, positive. The majority of theoretical explanations for this observation have viewed the term premia through the lens of the consumption based capital asset pricing model. In contrast, we harken to an older empirical literature that attributes the term premium to the idea that short maturity bonds are inherently more liquid. The goal of this paper is to provide a theoretical justification of this concept. To that end, we employ a monetary-search model extended to include assets of different maturities. Short term assets mature in time to take advantage of random consumption opportunities. Long term assets cannot be used directly to purchase consumption, but agents may liquidate them in a secondary asset market characterized by search and bargaining frictions. Our model delivers three results that are consistent with empirical facts. First, long term assets have higher rates of return in steady state to compensate agents for their relative lack of liquidity. Second, since the difference in the yield of short and long term assets reflects asset market frictions, our model predicts a steeper yield curve for assets that trade in less liquid secondary markets. Third, our model predicts that freshly issued (“on-the-run”) assets will sell at higher prices than previously issued (“off-the-run”) assets that mature in nearby dates, because sellers of the latter have a more urgent need for liquidity.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".