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Record W4366003056 · doi:10.5539/ijef.v15n5p37

Interrelationships Between the Brazilian Financial Market and Foreign Financial Markets: New Evidence During and After the Subprime Crisis

2023· article· en· W4366003056 on OpenAlexvenueno aff
Edson Zambon Monte, Renzo Caliman Souza, Ricardo Ramalhe Moreira

Bibliographic record

VenueInternational Journal of Economics and Finance · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRisk Management in Financial Firms
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa e Inovação do Espírito SantoConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsSubprime crisisFinancial crisisSubprime mortgage crisisFinancial marketFinancial systemStock marketGranger causalityEquity (law)EconomicsStock exchangeChinaExchange rateStock market indexMonetary economicsBusinessFinanceGeographyMacroeconomics

Abstract

fetched live from OpenAlex

This study analyzed the financial interrelations between Brazil and selected foreign economies (United States (US), Germany, United Kingdom (UK), Japan and China) during and after the Subprime crisis, using three financial market indicators: stock market index, exchange rate and interest rate. The Vector Autoregressive approach and the Granger causality test were used, with daily data. The periods considered were: i) period of crisis (03/14/2007 to 03/31/2010); and ii) post-crisis period (04/01/2010 to 12/30/2019). The results revealed that in the Subprime crisis, the interrelations were intense, especially in the stock and exchange markets. IBOVESPA and Brazilian exchange rate were predominantly affected by the US, German and UK equity markets. Evidence in the post-crisis period showed considerably lesser interrelationships between the Brazilian financial market and foreign financial markets. Thus, the results confirmed that the crisis significantly intensified interrelations, with the main contagion channels as the stock markets and the foreign exchange markets.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.164
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.230
Teacher spread0.209 · 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.

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

Citations2
Published2023
Admission routes1
Has abstractyes

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