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Record W2123563488 · doi:10.5897/jeif.9000120

Transmission of returns between the U.S. stock market and four other major international stock market indexes

2011· article· en· W2123563488 on OpenAlexaboutno aff
Eun S. Ahn, Franklin T. Kudo

Bibliographic record

VenueJournal of Economics and International Finance · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Risk and Volatility Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsMultivariate adaptive regression splinesStock marketMars Exploration ProgramEconometricsStock (firearms)Financial economicsMultivariate statisticsEconomicsBusinessGeographyStatisticsMathematicsNonparametric regression

Abstract

fetched live from OpenAlex

This paper explored the transmission of returns between the U.S. stock market and four international (Canada, Japan, France, U.K.) stock market indexes from January 1996 to December 2006, using a nonlinear model which focused on threshold effects. The nonlinear, MARS (multivariate adaptive regression spline) model was applied to study this relationship at the general as well as industry specific levels. The nonlinear characteristic of the MARS model was able to provide valuable detailed information with its unique ability to capture and highlight the asymmetric nature of the international financial markets. The findings displayed evidence of asymmetric influence from the previous market from both extreme positive and negative price movements. This paper posits that there are advantages of applying the MARS model to international financial markets, as the results revealed significant information which may be beneficial for managing risk and hedging portfolios.   Key words: Transmission of returns, multivariate adaptive regression spline (MARS), stock market.

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 score0.134
Threshold uncertainty score0.518

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 communication0.0000.000
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 head0.062
GPT teacher head0.241
Teacher spread0.179 · 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

Citations3
Published2011
Admission routes1
Has abstractyes

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