MIXED EXPONENTIAL POWER ASYMMETRIC CONDITIONAL HETEROSKEDASTICITY
Bibliographic record
Abstract
To match the stylized facts of high frequency financial time series precisely and\nparsimoniously, this paper presents a finite mixture of conditional exponential power\ndistributions where each component exhibits asymmetric conditional heteroskedasticity. We\nprovide stationarity conditions and unconditional moments to the fourth order. We apply this\nnew class to Dow Jones index returns. We find that a two-component mixed exponential\npower distribution dominates mixed normal distributions with more components, and more\nparameters, both in-sample and out-of-sample. In contrast to mixed normal distributions, all\nthe conditional variance processes become stationary. This happens because the mixed\nexponential power distribution allows for component-specific shape parameters so that it can\nbetter capture the tail behaviour. Therefore, the more general new class has attractive features\nover mixed normal distributions in our application: Less components are necessary and the\nconditional variances in the components are stationary processes. Results on NASDAQ index\nreturns are similar.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".