Modified Volatility Conditional Heteroscedastic Models using Binary Variable
Bibliographic record
Abstract
This study modified some of the existing conditional heteroscedastic models by introducing binary variable to sort out categorical data into mutually exclusive categories and compared the existing conditional heteroscedastic models with the modified conditional heteroscedastic models. Jarque-Bera statistic was used for normality test, Augmented dickey-fuller (ADF) test was used to test the stationarity of the return series, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) was used for model selection, and root mean square error was for model fitness. The parameters of these models were estimated using the Marquardt’s numerical optimization algorithm in the Econometric view software. Time series behaviour of daily closing stocks returns of seven oil companies listed in the Nigerian stocks market from 4th January, 2017 to 30th June, 2023 was considered. ARCH (1), ARCH (2), GARCH (1,1), GARCH (2,1), EGARCH (1,1), EGARCH (2,1), GJRGARCH (1,1) and GJRGARCH (2,1) with generalized error distribution were used for the analysis. Results revealed that all the oil stocks returns were all stationary and not normally distributed. These shows the evidence of volatility clustering, negative skewness, leptokurtic and leverage effect which are usually observed in financial time series. Also, the modified GJRGARCH (1,1) outperformed better than other models for forecast evaluation and fitness performance respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".