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Record W2125364302 · doi:10.1002/fut.10092

Looking for contagion in currency futures markets

2003· article· en· W2125364302 on OpenAlexaboutno aff
Chu‐Sheng Tai

Bibliographic record

VenueJournal of Futures Markets · 2003
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Risk and Volatility Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsFutures contractCurrencyVolatility (finance)Financial economicsMonetary economicsPound (networking)Liberian dollar

Abstract

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Abstract This article tests whether there are pure contagion effects in both conditional means and volatilities among British pound, Canadian dollar, Deutsche mark, and Swiss franc futures markets during the 1992 ERM crisis. A conditional version of international capital asset pricing model (ICAPM) in the absence of purchasing power parity (PPP) is used to control for economic fundamentals. The empirical results indicate that overall there are no mean spillovers among those futures markets, but they are detected during the crisis period. That is, past return shocks originating in any one of the four markets have no impact on the other three markets during the entire sample period, suggesting that these markets are weak‐form efficient. However, this weak‐form market efficiency fails to hold during the market turmoil, especially for British pound and Swiss franc, and the sources of contagion‐in‐mean effects are mainly due to the return shocks originating in three European currency futures markets. As for the contagion‐in‐volatility, it is detected for British pound only because its conditional volatility is influenced by the negative volatility shocks from Canadian dollar, Deutsche mark, and Swiss franc, with Deutsche mark playing the dominant role in generating these shocks. JEL Classifications: C32; F31; G12. © 2003 Wiley Periodicals, Inc. Jrl Fut Mark 23:957–988, 2003

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)0.003
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
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 head0.024
GPT teacher head0.247
Teacher spread0.223 · 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 designSimulation or modeling
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

Citations14
Published2003
Admission routes1
Has abstractyes

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