Effects of Oil Prices and Exchange Rates Movements on JSE Stock Return Volatility
Bibliographic record
Abstract
South Africa has targeted the oil and gas sector for investment through the industrial action plan as a special economic zone. This paper focussed on the effects of oil prices and exchange rate movements in the oil and gas stock returns using the GARCH - GED model to incorporate volatility. Additionally, the paper estimates causality effects through the pairwise Granger causality techniques using secondary monthly data for the period 2007 - 2015. The findings where that change in oil prices had a positive and significant mean effect on oil and gas sector stock returns. Furthermore, changes in exchange rates had a negative and significant mean effect on the sector returns. Volatility clustering was found to be present in the sector stock returns, but volatilities associated with each of the significant variables do not last for long before it fades away. It is highly recommended that market players or investors and portfolio managers should have a keen interest on the exchange rate. While policy makers and regulators should strive to have stable exchange rate movements to offset unexpected or sudden decline of the exchange rate, depreciation. This has the detriment of additional costs through oil prices purchase by companies in the sector.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".