MétaCan
Menu
Back to cohort
Record W3043782679 · doi:10.3390/jrfm13070156

Time-Frequency Based Dynamics of Decoupling or Integration between Islamic and Conventional Equity Markets

2020· article· en· W3043782679 on OpenAlexvenueno aff
Muhammad Anas, Ghulam Mujtaba, Sadaf Nayyar, Saira Ashfaq

Bibliographic record

VenueJournal of risk and financial management · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
Fundersnot available
KeywordsEmerging marketsEquity (law)Financial economicsEconomicsDiversification (marketing strategy)Time horizonStock (firearms)PortfolioAsset allocationEconometricsGeographyBusinessMacroeconomicsFinancePolitical science

Abstract

fetched live from OpenAlex

This paper investigates the decoupling and integration between the region-wise (Asia, Europe, Africa and the Americas) developed and emerging market’s equity pairs of Islamic and conventional stock returns with the focus on multi-horizons. In doing so, daily wavelet and ADCC-based stock returns correlations are estimated to capture the dynamics of time-frequency and the time-domain based correlations, respectively. The findings of this study indicate that at the short-term horizon, the all selected emerging and developed Islamic and conventional equity markets across all regions depict a high positive correlation, suggesting a rejection of the decoupling hypothesis. However, it is accepted for some of the developed markets of the Pacific region (Hong Kong and New Zealand), Europe (Ireland, Denmark and Spain) and emerging markets of Asia (China), Europe (Czech Republic) and Americas (Argentina and Peru) at a medium-term horizon. Moreover, in an examination of the comparative behaviors of the wavelet and ADCC-based Islamic-conventional correlations, the observed transitional behavior has been exemplified as the difference between the time-frequency and time-domain analysis. This study provides fruitful insights for investors who opt for cross-asset allocation and seek maximum portfolio diversification benefits.

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 score0.307
Threshold uncertainty score0.432

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 head0.021
GPT teacher head0.236
Teacher spread0.214 · 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

Citations17
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of risk and financial managementSame topicMarket Dynamics and VolatilityFrench-language works237,207