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Record W1920252535 · doi:10.22059/ier.2015.55163

Reinvestigation of Oil Price-Stock Market Nexus in Iran: A SVAR Approach

2015· article· en· W1920252535 on OpenAlexaboutno aff
Eisa Maboudian, Khashayar Seyyed Shokri

Bibliographic record

VenueIranian economic review · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
Fundersnot available
KeywordsVariance decomposition of forecast errorsOil supplyStock marketEconomicsStock (firearms)EconometricsShock (circulatory)Oil priceStock market indexIndex (typography)Supply shockMonetary economicsFinancial economicsMonetary policyGeography

Abstract

fetched live from OpenAlex

In this paper we investigate the effect of oil price shocks on stock market index in Iran, by using of a structural VAR (SVAR) approach. We used four variables in the model namely Kilian index, global oil supply, real oil price and real stock market index. The data are monthly and spanning the period 1997M10-2014M12. We identify the effect of four different shocks on stock market including oil supply shock, aggregate demand shock, other oil-specific shock and other stockspecific shock. Empirical evidences from impulse response functions (IRFs) indicate that oil supply shock is not significant, and the impact of other three shocks persists for about 3, 6 and 2 months respectively. Variance decomposition (VD) of stock market index indicates “other stock-specific shock” is the most important explainer of its variations. These findings are consistent with the findings of other oil-exporting countries including Saudi Arabia, Kuwait, Mexico, Norway, Russia, Venezuela and Canada except the effect of oil supply shock in variance decomposition of stock market index.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.853
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.081
GPT teacher head0.254
Teacher spread0.173 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2015
Admission routes1
Has abstractyes

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