Forecasting the conditional heteroscedasticity of stock returns usingasymmetric models based on empirical evidence from Eastern Europeancountries: Will there be an impact on other industries?
Bibliographic record
Abstract
This empirical study investigates the leverage effect in six Eastern European countries under normal and non-normaldistribution densities for the sample period from January 2020 to August 2020. We find three countries, Bulgaria, CzechRepublic and Russia which are subject to ARCH effect whereas Poland, Romania and Hungary do not exhibit ARCHeffect in daily stock returns. Further, our study finds leverage effect, where past bad news affects is asymmetrical, pastnegative returns cause more volatility in current stock returns as compared to past positive returns, in three EasternEuropean countries. Based on the AIC and BIC model selection criteria we find that the non-normal student t-distributionand GED produce reliable estimates for Bulgaria, Czech Republic and Poland, respectively. The autocorrelation functionQ1 statistic confirms the insignificance of autocorrelation in residuals of TGARCH model. The impact of stock marketdynamics on other industries, such as pharmaceutical industry, textile and clothing industry, automotive industry issignificant, especially in the conditions of COVID-19 pandemic
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".