Stock Market Volatility in Saudi Arabia: An Application of Univariate GARCH Model
Bibliographic record
Abstract
Study of stock market volatility has been the focus of financial economics. Modelling stock market volatility has great contributions to make in the areas of portfolio management, asset allocation, risk management, etc. We estimate the conditional volatility of Saudi stock market by applying AR (1)-GARCH (1, 1) model to the daily stock returns data spanning from August 1, 2004 to October 31, 2013. We show that a linear symmetric GARCH (1, 1) model is adequate to estimate the volatility of the stock market of the country. We find that Saudi stock market returns are characterised by volatility clustering and follow a non-normal distribution. Saudi stock market returns show a time varying volatility, show persistence and are predictable. Past volatility impacts the current period volatility and past returns play a role in determining the current period return. Saudi stock market is nervous in its reactions to market fluctuations. This finding of the study offer important input into the decisions relating to asset allocation and risk management strategies of investors and treasury managers in Saudi stock market.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".