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Record W4212998156 · doi:10.31410/eraz.s.p.2021.13

The 2020 Oil Price War Has Increased Integration Between G7 Stock Markets and Crude Oil WTI

2021· article· en· W4212998156 on OpenAlexaboutno aff
Rui Dias, Paulo Alexandre, Paula Heliodoro, Hortense Santos, Ana Rita Farinha, Márcia C. Santos

Bibliographic record

VenueInternational Scientific Conference ERAZ. Knowledge Based Sustainable Development · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
Fundersnot available
KeywordsWest Texas IntermediateStock (firearms)Diversification (marketing strategy)PortfolioCrude oilOil priceIndex (typography)EconomicsStock market indexFinancial economicsStock priceOil-storage tradeBusinessMonetary economicsStock marketGeographyComputer sciencePetroleum engineeringEngineeringSeries (stratigraphy)Biology

Abstract

fetched live from OpenAlex

This paper aims to examine whether the oil price war between Saudi Arabia and Russia has increased integration between the Crude Oil WTI Spot oil index and the G7 stock markets, namely France (CAC 40), Germany (DAX 30), USA (DOW JONES), UK (FTSE 100), Italy (FTSE MID), Japan (Nikkei 225), Canada (S&P TSX), from January 2018 to January 2021. The results show that in the period before the oil price war, the G7 stock markets and the WTI index had 29 integrations (out of 56 possible). The WTI index is integrated with the UK stock markets (FTSE 100), and Japan (NIKKEI 225), and is integrated into the Japanese market. In the period of the oil price war, the G7’s stock markets and the Crude Oil WTI Spot index had 43 integrations (out of 56 possible), namely the WTI, Dow Jones, and Nikkei 225 indexes, with all their peers (7 out of 7 possible). When comparing the period before and during the 2020 oil crash, we found that integrations increased significantly from 29 to 43 (out of 56 possible); we also found that the Crude Oil WTI Spot index is no longer a safe haven for portfolio diversification in G7 stock markets. These findings validate our research issue, i.e., the oil price war between Saudi Arabia and Russia had increased integrations, and this evidence could question portfolio diversification.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.774
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0020.000
Open science0.0010.000
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 head0.028
GPT teacher head0.238
Teacher spread0.210 · 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 designNot applicable
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

Citations3
Published2021
Admission routes1
Has abstractyes

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