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Record W3109974399 · doi:10.32479/ijeep.10294

DOES GOLD RETAIN ITS HEDGE AND SAFE HAVEN ROLE FOR ENERGY SECTOR INDICES DURING COVID-19 PANDEMIC? A CROSSQUANTILOGRAM APPROACH

2020· article· en· W3109974399 on OpenAlexaboutno aff
Guntur Anjana Raju

Bibliographic record

VenueInternational Journal of Energy Economics and Policy · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
Fundersnot available
KeywordsSafe havenHedgeEconomicsPredictabilityBusinessFinancial crisisDevelopment economicsMonetary economicsFinancial economicsMacroeconomics

Abstract

fetched live from OpenAlex

The Outbreak of the COVID-19 Pandemic has caused unprecedented risk and uncertainty in the global financial markets. The shattered investor's faith in the Global Financial system has stimulated the need to explore safe haven assets to mitigate risk and safeguard wealth during such turmoil. Therefore, this paper addresses the widely mooted hedge and safe haven property of gold against extreme downturns in the stock market energy sector indices during COVID-19 distress. The sample countries considered comprises of the USA, Saudi Arabia, UAE, Russia, Canada, India and China being strategically linked to gold and oil commodities. Splitting the sample period into Pre-COVID period from 30th June 2019 to 30th December 2019 and During-COVID period from 31st December 2019 to 30th June 2020 the study employs bivariate cross-quantilogram of (Han, et al., 2016) to examine directional predictability in quantiles between gold and energy sector indices. The results confirm the inability of gold to showcase its pronounced hedge and safe haven role before the COVID-19 crises. Specifically, Countries such as Saudi Arabia, Russia and Canada show a significant negative predictability from energy sector indices to gold thereby indicating its safe haven role during COVID-19 crises.Keywords: Gold, Safe Haven, COVID-19, Cross-quantilogram, Energy sector Indices.JEL Classifications: G01, G11, G15, Q40DOI: https://doi.org/10.32479/ijeep.10294

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.041
GPT teacher head0.272
Teacher spread0.231 · 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 designTheoretical or conceptual
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

Citations16
Published2020
Admission routes1
Has abstractyes

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