Evaluated the Success of Fractionally Integrated-GARCH Models on Prediction Stock Market Return Volatility in Gulf Arab Stock Markets
Bibliographic record
Abstract
This paper evaluated the different Fractionally Integrated-GARCH Models (FIGARCH BBM's, FIGARCH Chung, FIEGARCH, FIAPARCH BBM's, FIAPARCH Chung, and HYGARCH). This is the first research to use six different Fractionally Integrated-GARCH Models. Most research compares one of Fractionally Integrated-GARCH Models with the traditional GARCH, EGARCH, GJG-GARCH, IGARCH, and APGARCH. To do so, daily returns of Gulf Cooperation Council (GCC) Stock Markets analyzed, covering the period 1995 to 2015. Both the Superior Predictive Ability and the Model Confidence Set tests were used to identify the best fitting models of each country. The results reveal that FIGARCH BBM is the best fitting model for UAE, KSA, and Bahrain. FIEGARCH is the best fitting model for Kuwait. FIGARCH Chung is the best fitting model for Qatar. Only the results for Oman were mixed between FIGARCH BBM and FIAPARCH BBM models.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".