The analysis of volatility of gold coin price fluctuations in Iran using ARCH & VAR models
Bibliographic record
Abstract
The aim of this study is to investigate the changes in gold price and modeling of its return volatility and conditional variance model.The study gathers daily prices of gold coins as the dependent variable and the price of gold in world market, the price of oil in OPEC, exchange rate USD to IRR and index of Tehran Stock Exchange from March 2007 to July 2013 and using ARCH family models and VAR methods, the study analysis the data.The study first examines whether the data are stationary or not and then it reviews the household stability, Arch and Garch models.The proposed study investigates the causality among variables, selects different factors, which could be blamed of uncertainty in the coin return.The results indicate that the effect of sudden changes of standard deviation and after a 14-day period disappears and gold price goes back to its initial position.In addition, in this study we observe the so-called leverage effect in Iran's Gold coin market, which means the good news leads to more volatility in futures market than bad news in an equal size.Finally, the result of analysis of variance implies that in the short-term, a large percentage change in uncertainty of the coin return is due to changes in the same factors and volatility of stock returns in the medium term, global gold output, oil price and exchange rate fluctuation to some extent will show the impact.In the long run, the effects of parameters are more evident.
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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 | 0.004 |
| 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) | 0.001 | 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".