Predict GARCH Based Volatility of Shanghai Composite Index by Recurrent Relevant Vector Machines and Recurrent Least Square Support Vector Machines
Why this work is in the frame
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Bibliographic record
Abstract
A new machine learning method so called Relevant Vector Machine (RVM) is an efficiently learning technique for classificationand regression problems, including financial time series forecasting. One of the main advantages is that the modelis treated by Bayesian approach and its functional form is identical to a powerful prediction tool Support Vector Machine.In this paper, we propose a new recurrent algorithm of the relevant vector machine to predict GARCH (1,1) based volatilityof Shanghai composite index. The recurrent support vector machine, recurrent least square support vector machine andnormal GARCH (1,1) models are also employed to make a comparison with the proposed model. Our empirical resultsshow that the proposed approach generates superior forecasting performance.
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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 | 0.043 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 it