Information Shocks, Jumps, and Price Discovery - Evidence from the U.S. Treasury Market
Bibliographic record
Abstract
We examine large price changes, known as jumps, in the U.S. Treasury market. Using recently developed statistical tools, we identify price jumps in the 2-, 3-, 5-, 10-year notes and 30-year bond during the period of 2005-2006. Our results show that jumps mostly occur during prescheduled macroeconomic announcements or events. Nevertheless, market surprise based on preannouncement surveys is an imperfect predictor of bond price jumps. We find that a macroeconomic news announcement is often preceeded by an increase in market volatility and a withdrawal of liquidity, and that liquidity shocks play an important role for price jumps in U.S. Treasury market. More importantly, we present evidence that jumps serve as a dramatic form of price discovery in the sense that they help to quickly incorporate market information into bond prices.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".