Bibliographic record
Abstract
Abstract The GARCH class of models for dynamic conditional covariances trades off flexibility with parameter parsimony. The unrestricted BEKK GARCH dominates its restricted scalar and diagonal versions in terms of model fit, but its parameter dimensionality increases quickly with the number of variables. Covariance targeting has been proposed as a way of reducing parameter dimensionality, but for the BEKK with targeting the imposition of positive definiteness on the conditional covariance matrices presents a significant challenge. In this article, we suggest an approach based on Constrained Hamiltonian Monte Carlo that can deal effectively both with the nonlinear constraints resulting from BEKK targeting and the complicated nature of the BEKK likelihood in relatively high dimensions. We perform a model comparison of the full BEKK and the BEKK with targeting, indicating that the latter dominates the former in terms of marginal likelihood. Thus, we show that the BEKK with targeting presents an effective way of reducing parameter dimensionality without compromising the model fit, unlike the scalar or diagonal BEKK. The model comparison is conducted in the context of an application concerning a multivariate dynamic volatility analysis of a foreign exchange rate returns portfolio.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".