Determinant of the Relationship between Natural Gas Prices and Leading Natural Gas Countries’ Stock Exchange
Bibliographic record
Abstract
Over the recent decades, natural sources of energy have become an interesting topic to investigate for researchers. Sources of energy play a crucial role in all industrial segments such as export revenue, exchange rate and stock market. One of the major sources is natural gas which its price affects many countries’ economy. This paper investigates the effect of natural gas price on the three leading natural gas exporting countries’ stock market: Russia, Norway and Qatar. This paper employs monthly data observations including natural gas price and stock exchange market index on Russia, Norway and Qatar from January 2005 to November 2013. This study uses Unrestricted Vector Autoregressive model (VAR) to apply Granger Causality test, Impulse Response functions and Variance Decomposition. Findings show that there are two-way causality relationship between natural gas prices and stock exchanges of Russia and Norway, though natural gas prices affected Russia stock exchange index at 10% significance level and Norway stock exchange index at 5% significance. However, there is not causality relationship between Qatar stock exchange and natural gas prices. Moreover, outcomes of impulse response function present that natural gas price shock does not have significant impact on all three countries’ stock exchange. The variance decomposition test also reinforces the results from impulse response function since Russia, Norway and Qatar’s stock exchange variance are not significantly due to natural gas price.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".