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Record W4392246923 · doi:10.1016/j.najef.2024.102122

Unraveling the multiscale comovement of green bonds and structural shocks: An oil-driven analysis

2024· article· en· W4392246923 on OpenAlexaboutno aff
Mobeen Ur Rehman, Neeraj Nautiyal, Rami Zeitun, Xuan Vinh Vo, Wafa Ghardallou

Bibliographic record

VenueThe North American Journal of Economics and Finance · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
FundersĐại học Kinh tế Thành phố Hồ Chí Minh
KeywordsBivariate analysisBondEconomicsFutures contractAutoregressive modelEconometricsFinancial crisisBond marketFinancial economicsMultivariate statisticsMonetary economicsMacroeconomicsFinanceStatisticsMathematics

Abstract

fetched live from OpenAlex

This paper examines the multiscale comovement between the green bonds issued in developed countries and international oil-driven shocks. We extracted the oil shocks using a structural vector autoregressive model. The countries in our analysis comprised the UK, the US, Japan, Canada, Australia, and Europe, with the data being captured from November 28, 2008 to June 11, 2021. We applied the wavelet technique to examine the returns comovement across time and frequency as a form of bivariate and multivariate analysis. Our results highlight a limited connection between the returns of green bonds and the international oil market, with Norway and Sweden exhibiting strong comovement across low frequencies. We posit that during crisis periods, the correlation between green bond returns and oil returns varies. The findings of this study have several implications for policymakers and investors with an interest in both oil futures and green bonds.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.013
GPT teacher head0.218
Teacher spread0.204 · 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

Citations10
Published2024
Admission routes1
Has abstractyes

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