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Record W2159823094 · doi:10.5539/ibr.v7n8p134

Transfer of Risk in Emerging Eastern European Stock Markets: A Sectoral Perspective

2014· article· en· W2159823094 on OpenAlexvenueno aff
Kashif Saleem, Елена Федорова

Bibliographic record

VenueInternational Business Research · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Risk and Volatility Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsAccessionCzechEquity (law)Emerging marketsBusinessStock (firearms)Financial crisisAutoregressive conditional heteroskedasticityPortfolioFinancial marketFinancial economicsInternational economicsEconomicsFinancial systemEuropean unionVolatility (finance)International tradeFinanceGeographyMacroeconomics

Abstract

fetched live from OpenAlex

This paper studies the market integration of Poland, Hungary and the Czech Republic by utilizing the multivariate GARCH analysis of Engle and Kroner (1995) for which a BEKK representation is adopted. We investigate the transmission of the US subprime crisis across Poland, Hungary and the Czech Republic in a sectoral setting. In addition, we attempt to identify whether the three emerging Eastern European countries have become more integrated after their EU accessions in 2004, in a regional setting. Our results clearly indicate the existence of direct linkages between different stock market sectors with respect to returns and volatilities. We found that the transmission of equity shocks between markets has increased after the EU accession in 2004. Notably, the intra-industry contagion in emerging Europe has increased after this accession. Our findings have significant implications for asset pricing and portfolio selection for international financial institutions and financial managers.

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)0.001
metaresearch head score (Gemma)0.003
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
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.080
GPT teacher head0.326
Teacher spread0.246 · 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
Published2014
Admission routes1
Has abstractyes

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