Evidence map›Paper›PMID 36092850›Full record

ArticleResources policy2022

Storm after the Gloomy days: Influences of COVID-19 pandemic on volatility of the energy market.

Le Thanh Ha

Open access · greenAbstract read
In one paragraph

Article in Resources policy, 2022. 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
3.8field-weighted citation impact, top 6% 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

1 citing paper in PubMed, 14 citations in OpenAlex.

  1. 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

1 author at 1 institution in 1 country.

Le Thanh HaFaculty of Economics, National Economics University, Hanoi, Viet Nam.
National Economics University · VN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Volatility is a common phenomenon in the energy market, but COVID-19 has cast a dark shadow over this characteristic. In light of this observation, individuals might have an incorrect impression of the impact of this shock on the energy markets. By applying a time-varying parameter vector autoregression (TVP-VAR) in combination with an extended joint connectedness approach to identify the sources of the energy market's volatility, we characterize the influences of COVID-19 health crisis and the volatility of the crude oil and precious metals (including gold and silver) market on the volatility of the energy market starting from January 1, 2020, to December 31, 2021. The total connectedness index, the net total, and pairwise directional connectedness measures obtained from the extended TVP-VAR allow us to monitor interlinkages from various variables in a designed network. The novel method has the benefit of distinguishing between a net recipient and a net transmitter. Our results demonstrate that the COVID-19 pandemic shocks first absorb the volatility from the energy and precious market to cause lagged but more severe consequences returning to these markets. Furthermore, there is a time-variant of system-wide interlinkages. Net total directional connectedness suggests that the oil and gold markets consistently appear to be a net transmitter of spillover shocks in the energy market. The COVID-19 pandemic shock first plays the role of shock receiver from other markets. However, this uncertainty shock acts as a shock transmitter, and its effects seem to be delayed but persistent for an extended period, thus making the energy and precious metal markets more volatile.

Indexed as

COVID-19 pandemic, TVP-VARjoint and dynamic connectedness, joint connectednessVolatility, energy market

Identifiers

PMID36092850
PMCPMC9444895
OpenAlexW4294740912

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.