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Record W3152988907 · doi:10.5430/afr.v10n2p17

Multifractal Analysis of the Foreign Exchange Markets Application to MENA Countries

2021· article· en· W3152988907 on OpenAlexvenueno aff
Nabiha Haouas

Bibliographic record

VenueAccounting and Finance Research · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicComplex Systems and Time Series Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMultifractal systemForeign exchange marketCurrencyExchange rateEconomicsEconometricsCharacter (mathematics)Foreign exchangeFunction (biology)ScalingMathematicsMonetary economicsFractal

Abstract

fetched live from OpenAlex

The present study focus on the multifractal analysis of the exchange rate for Middle East North Africa (MENA) region from January 1999 to May 2017. The purpose of this paper is to examine the behavior of currency markets and to verify the efficiency hypothesis of FOREX market for these countries. We first estimate the scaling function to detect the multifractal character of each series and then the Hölder exponent, using the Generalized Quadratic Variation (GQV) method, as a function of time H(t). We conclude that there's a multifractal character for all these countries with a difference in the degree of persistence of each 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)0.002
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 score0.274
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
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 head0.039
GPT teacher head0.287
Teacher spread0.249 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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