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Record W3044025017 · doi:10.1002/ijfe.1823

Time‐dependent intrinsic correlation analysis of crude oil and the <scp>US</scp> dollar based on <scp>CEEMDAN</scp>

2020· article· en· W3044025017 on OpenAlex
Qing Peng, Fenghua Wen, Xu Gong

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Finance & Economics · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsUniversity of Windsor
FundersNational Natural Science Foundation of China
KeywordsEconometricsEconomicsCorrelationDiversification (marketing strategy)Crude oilHilbert–Huang transformUs dollarLiberian dollarPortfolioNegative correlationMathematicsStatisticsFinancial economicsWhite noiseMonetary economicsExchange rate

Abstract

fetched live from OpenAlex

Abstract In this article, we analyze the dynamic linkage between crude oil price and the US dollar at multi‐scale frequencies using time‐dependent intrinsic correlation analysis based on the complete ensemble empirical mode decomposition with adaptive noise. After applying a refined method to extract the trend, we reveal that the overall correlation and the long‐term trend correlation exhibit very similar patterns. The correlation coefficients between crude oil and the US dollar are negative at most of the time; however, the coefficients become positive in certain periods, such as 2013–2014 and 2017–2018. Furthermore, the negative correlations in high frequency intrinsic mode functions (IMFs), with a shorter time horizon, are weaker and display time‐varying characteristics, whereas the correlation in low frequency IMFs, with a longer time horizon, are stronger and more static. The findings of this article may have important implications for investors to construct optimal portfolio diversification.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.620
Threshold uncertainty score0.830

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.012
GPT teacher head0.205
Teacher spread0.193 · 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