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Record W4289466175 · doi:10.31014/aior.1992.01.03.24

The Financial Crisis and its Impact on Comovements of Financial Markets: Evidence from Exchange-Traded Funds

2018· article· en· W4289466175 on OpenAlexaff
Rachid Ghilal, Ahmed Maghfor

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsUniversité du Québec à Rimouski
Fundersnot available
KeywordsFinancial crisisBusinessFinancial systemFinanceEconomicsMacroeconomics

Abstract

fetched live from OpenAlex

The two-fold objective of this paper is, on one hand, to study the comovements of international financial markets before and after the “subprime” financial crisis and, on the other hand, to determine their impact on international diversification, using substitutes for investable country indices, that is, exchange-traded funds. These new instruments are highly prized by investors. Three main categories of comovements are analyzed: short-term comovements as studied by contagion and dynamic conditional correlations; long-term comovements as studied by cointegration; and, finally comovements induced by the transmission of extreme values. In studying these comovements between the American market and 21 other developed and emerging markets, our results suggest that, after the financial crisis, the interdependencies and transmission of extreme values between the American market and the other markets studied increased significantly in the short term and, thus, reduced the advantages of international diversification in the short term. However, our analyses of contagion and cointegration suggest that the benefits of international diversification persist over the long term, even in times of crisis.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.052
GPT teacher head0.269
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2018
Admission routes1
Has abstractyes

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