Stochastic Conditional Duration Models with
Bibliographic record
Abstract
This article proposes stochastic conditional duration (SCD) models with leverage effect for financial transaction data, which extends both the autoregressive conditional duration (ACD) model (Engle and Russell, 1998, Econometrica, 66, 1127-1162) and the existing SCD model (Bauwens and Veredas, 2004, Journal of Econometrics, 119, 381-412). The proposed models belong to a class of linear nongaussian state-space models, where the observation equation for the duration process takes an additive form of a latent process and a noise term. The latent process is driven by an autoregressive component to characterize the transition property and a term associated with the observed duration. The inclusion of such a term allows the model to capture the asymmetric behavior or leverage effect of the expected duration. The Monte Carlo maximum-likelihood (MCML) method is employed for consistent and efficient parameter estimation with applications to the transaction data of IBM and other stocks. Our analysis suggests that trade intensity is correlated with stock return volatility and modeling the duration process with leverage effect can enhance the forecasting performance of intraday volatility.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".