Heterogeneous investment horizons, risk regimes, and realized jumps
Bibliographic record
Abstract
Abstract This paper introduces a new empirical framework to identify the regimes of jump‐type tail risk over multiple trading horizons. Our approach combines the hidden Markov regime‐switching model with realized jumps, which allows us to examine the tail risk exposure of investors at different investment scales. Applying our method to data on bonds, stocks, and currencies, we find evidence that market risk linked to jumps exhibits time‐varying regime shifts and horizon‐dependence. We show that high‐frequency trading does not contribute to market instability, as measured by jump risk. The European bond market appears to be more vulnerable to downside (left‐tail) jump risk, relative to the U.S. Treasury bond market. Market jump fear embedded in the VIX exhibits vertical clustering where both risk regimes at low frequencies remain unchanged at higher frequencies. These results suggest that the premium for jump risk not only depends on stress periods, but also on the frequency at which investors trade financial assets.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".