Evidence map›Paper›PMID 39801961›Full record

ArticleHeliyon2025

Nexus of crude oil and clean energy stock indices: Evidence from time-vector-auto-regression in conjunction with conditional-autoregressive-value-at-risk.

Nader Trabelsi, Aviral Kumar Tiwari, Fahmi Ghallabi, Imen Khemakhem

Abstract read
In one paragraph

Article in Heliyon, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Integrated omics and functional validation identify StGDPD1 as a central regulator of salt stress tolerance in potato.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Nader TrabelsiImam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.
Aviral Kumar TiwariIndian Institute of Management Bodh Gaya (IIM Bodh Gaya), Bodh Gaya, India.
Fahmi GhallabiFaculty of Economics and Management of Sfax, University of Sfax, Tunisia.
Imen KhemakhemFaculty of Economics and Management of Sfax, University of Sfax, Tunisia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The current study aims to elicit information regarding the tail risk transmission mechanism between crude oil (CO) and selected clean energy (CE) stock indices across time and during certain economic events. A Time-Varying Parameter Vector Auto-Regressive model (TVP-VAR) paired with the conditional autoregressive value-at-risk (CAViaR) approach was used to investigate data from January 1, 2015 to December 29, 2022. Overall, we show that an increased vulnerability to tail risk and deficits might be linked to dynamic spillover over examined markets. We also provide evidence that connectedness rises during significant crisis situations, and the last epidemic has the potential to make a lasting impact on the various marketplaces of concern. According to the return and conditional variance time-series, CE stock indices are the most important source of return shocks to CO. However, the CO is the primary cause of volatility in CE stock indices. During the recent virus pandemic, the most significant volatility shock transmissions from CO to CE stock indices occurred. During the Russia-Ukraine war, volatility shocks to CO were mostly caused by CE stock indices. The results of our study offer concrete consequences and new perspectives to various market players in order to improve the management and understanding of risks.

Indexed as

CAViaRClean energyConnectednessCrisesCrude oilTVP-VAR

Identifiers

PMID39801961
PMCPMC11721245

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.