Evidence map›Paper›PMID 37361958›Full record

ArticleEmpirical economics2023

The impact of pandemic on dynamic volatility spillover network of international stock markets.

Tingting Lan, Liuguo Shao, Hua Zhang, Caijun Yuan

Open access · bronzeAbstract read
In one paragraph

Article in Empirical economics, 2023. 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
8.6field-weighted citation impact, top 3% 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, 15 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 2 institutions in 1 country.

Tingting LanSchool of Business, Central South University, Changsha, 410083 China.
Liuguo ShaoSchool of Business, Central South University, Changsha, 410083 China.
Hua ZhangSchool of Business, Central South University, Changsha, 410083 China.ORCID 0000-0002-7258-7125
Caijun YuanState Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, 100024 China.
Central South University · CNCommunication University of China · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since the beginning of the twenty-first century, several pandemics, including SARS and COVID-19, have spread faster and on a broader scale. Not only do they harm people's health, but they can also cause significant damage to the global economy within a short period of time. This study uses the infectious disease EMV tracker index to investigate the impact of pandemics on the volatility spillover effects of global stock markets. Spillover index model estimation is conducted using the time-varying parameter vector autoregressive approach, and the maximum spanning tree and threshold filtering techniques are combined to construct the dynamic network of volatility spillovers. The conclusion from the dynamic network is that when a pandemic occurs, the total volatility spillover effect increases sharply. In particular, the total volatility spillover effect historically peaked during the COVID-19 pandemic. Moreover, when pandemics occur, the density of the volatility spillover network increases, while the diameter of the network decreases. This indicates that global financial markets are increasingly interconnected, speeding up the transmission of volatility information. The empirical results further reveal that volatility spillovers among international markets have a significant positive correlation with the severity of a pandemic. The study's findings are expected to help investors and policymakers understand volatility spillovers during pandemics.

Indexed as

Dynamic networkEMVIDPandemicVolatility spillover

Identifiers

PMID37361958
PMCPMC10123483
OpenAlexW4366824031

What OpenQuestion holds

Textmetadata
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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.