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Record W4233530069 · doi:10.1504/ijcee.2018.088324

Testing for multi-fractality and efficiency in selected sovereign bond markets: a multi-fractal detrended moving average (MF-DMA) analysis

2017· article· en· W4233530069 on OpenAlexaboutno aff
Selçuk Bayracı

Bibliographic record

VenueInternational Journal of Computational Economics and Econometrics · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicComplex Systems and Time Series Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMultifractal systemBondGovernment bondBond marketDetrended fluctuation analysisFinancial marketEconometricsEconomicsFractalFinancial economicsMonetary economicsMathematicsFinance

Abstract

fetched live from OpenAlex

This study examines the multifractality and relative efficiency in sovereign bond markets, using the multifractal detrended moving average (MF-DMA) approach to quantify the degree of multifractality in the international sovereign bond yields. We use the daily values of the 2-year maturity government bond yields for 12 countries between 2003 and 2014 for empirical analysis. Our results document that all bond markets show multifractal characteristics in various levels. High degree of multifractality is seen in the Spanish, Portuguese and Italian bond markets while low multifractality features belong to the Canadian and the US bond market. The source of multifractality is mainly due to the structure of the bond markets where the recent Eurozone debt crisis has manifested itself as extreme observations.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.431
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.058
GPT teacher head0.275
Teacher spread0.217 · 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.

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
Published2017
Admission routes1
Has abstractyes

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