Bibliographic record
Abstract
A major issue in financial economics is the behaviour of stock market returns over long horizons. This article provides an empirical investigation of the long-range dependence in the emerging stock markets of Egypt, Jordan, Morocco and Turkey. We use the modified rescaled range statistic (R/S) proposed by Lo (1991 Lo, AW. 1991. Long-term memory in stock market prices. Econometrica, 59: 1279–313. [Crossref], [Web of Science ®] , [Google Scholar]) and the rescaled variance statistic (V/S) developed by Giraitis et al . (2003 Giraitis, L, Kokoszka, PS, Leipus, R and Teyssiere, G. 2003. Rescaled variance and related tests for long memory in volatility and levels. Journal of Econometrics, 112: 265–94. [Crossref], [Web of Science ®] , [Google Scholar]) to investigate the long memory in the returns and volatility. Significant long memory is demonstrated in the series and implies a fractal market structure in the Middle East and North African (MENA) equity markets. We further investigate whether the long memory is caused by a shift in variance. Interestingly, our findings indicate that the presence of long memory in volatility due to shifts in variance cannot be confirmed for these markets and are consistent with those results obtained by Lobato and Savin (1998 Lobato, IN and Savin, NE. 1998. Real and spurious long memory properties of stock-market data. Journal of Business and Economics Statistics, 16: 261–8. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) on other markets. Thus, our results should be useful to regulators, practitioners and derivative market participants in the MENA region, whose success depends on the ability to forecast stock price movements over long horizons.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".