Gamma stochastic volatility models
Bibliographic record
Abstract
This paper presents gamma stochastic volatility models and investigates its distributional\nand time series properties. The parameter estimators obtained by the\nmethod of moments are shown analytically to be consistent and asymptotically\nnormal. The simulation results indicate that the estimators behave well. The insample\nanalysis shows that return models with gamma autoregressive stochastic\nvolatility processes capture the leptokurtic nature of return distributions and\nthe slowly decaying autocorrelation functions of squared stock index returns\nfor the USA and UK. In comparison with GARCH and EGARCH models, the\ngamma autoregressive model picks up the persistence in volatility for the US\nand UK index returns but not the volatility persistence for the Canadian and\nJapanese index returns. The out-of-sample analysis indicates that the gamma\nautoregressive model has a superior volatility forecasting performance compared\nto GARCH and EGARCH models.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.018 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".