On a quantile autoregressive conditional duration model applied to\n high-frequency financial data
Bibliographic record
Abstract
Autoregressive conditional duration (ACD) models are primarily used to deal\nwith data arising from times between two successive events. These models are\nusually specified in terms of a time-varying conditional mean or median\nduration. In this paper, we relax this assumption and consider a conditional\nquantile approach to facilitate the modeling of different percentiles. The\nproposed ACD quantile model is based on a skewed version of Birnbaum-Saunders\ndistribution, which provides better fitting of the tails than the traditional\nBirnbaum-Saunders distribution, in addition to advancing the implementation of\nan expectation conditional maximization (ECM) algorithm. A Monte Carlo\nsimulation study is performed to assess the behavior of the model as well as\nthe parameter estimation method and to evaluate a form of residual. A real\nfinancial transaction data set is finally analyzed to illustrate the proposed\napproach.\n
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".