Evidence map›Paper›PMID 35967840›Full record

ArticleAnnals of operations research2022

Modelling systemic risk of energy and non-energy commodity markets during the COVID-19 pandemic.

Zaheer Anwer, Ashraf Khan, Muhammad Abubakr Naeem, Aviral Kumar Tiwari

Open access · bronzeAbstract read
In one paragraph

Article in Annals of operations research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
12.3field-weighted citation impact, top 2% of its field
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

0 citing papers in PubMed, 49 citations in OpenAlex.

No citing paper in PubMed yet.

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 at 3 institutions in 6 countries.

Zaheer AnwerDepartment of Economics and Finance, Sunway University Business School, Sunway University, Bandar Sunway, Malaysia.ORCID 0000-0003-3698-7073
Ashraf KhanInstitute of Business Administration, Karachi, Pakistan.ORCID 0000-0001-9758-3625
Muhammad Abubakr NaeemAccounting and Finance Department, Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates.ORCID 0000-0001-6962-3175
Aviral Kumar TiwariRajagiri Business School, Rajagiri Valley Campus, Kochi, India.ORCID 0000-0002-1822-9263
South Ural State University · RUSunway University · MYUniversity of Verona · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 led restrictions make it imperative to study how pandemic affects the systemic risk profile of global commodities network. Therefore, we investigate the systemic risk profile of global commodities network as represented by energy and nonenergy commodity markets (precious metals, industrial metals, and agriculture) in pre- and post-crisis period. We use neural network quantile regression approach of Keilbar and Wang (Empir Econ 62:1-26, 2021) using daily data for the period 01 January 2018-27 October 2021. The findings suggest that at the onset of COVID-19, the two firm-specific risk measures namely value at risk and conditional value of risk explode pointing to increasing systemic risk in COVID-19 period. The risk spillover network analysis reveals moderate to high lower tail connectedness of commodities within each sector and low tail connectedness of energy commodities with the other sectors for both pre- and post-COVID-19 periods. The Systemic Network Risk Index reveals an abrupt increase in systemic risk at the start of pandemic, followed by gradual stabilization. We rank commodities in terms of systemic fragility index and observe that in post COVID-19 period, gold, silver, copper, and zinc are the most fragile commodities while wheat and sugar are the least fragile commodities. We use Systemic Hazard Index to rank commodities with respect to their risk contribution to global commodities network. During post COVID-19 period, the energy commodities (except natural gas) contribute most to the systemic risk. Our study has important implications for policymakers and the investment industry.

Indexed as

CommoditiesCoVaRCOVID-19EnergyNeural network quantile regression

Identifiers

PMID35967840
PMCPMC9362964
OpenAlexW4292411053

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.