COVID-19 Pandemic and Global Financial Market Interlinkages: A Dynamic Temporal Network Analysis

Chakrabarti, Prasenjit; Jawed, Mohammad Shameem; Sarkhel, Manish

doi:10.2139/ssrn.3621610

Cited by 3 publications

(7 citation statements)

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“…86.94% “+ve” and 13.06% “−ve”) and lowest for frontier financial markets (346 out of 605 i.e. 57.19% “+ve” and 42.81% “−ve”), which is consistent with the findings of Chakrabarti et al. (2020).…”

Section: Results Analysis and Discussionsupporting

confidence: 89%

“…In the fourth subsample period the number of positive correlation was 3,025, which means all the market indices included in the analysis became 100% positively correlated. Such surge of linkages during the crisis period of COVID-19 is also reported by Chakrabarti et al (2020) and Aslam et al (2020). This state of positive correlation remained evident in subsample periods from fourth till eighth.…”

Section: Results Analysis and Discussionsupporting

confidence: 71%

“…(2020) studied the influence of COVID-19 on stock markets through network analysis. Chakrabarti et al . (2020), studied the impact of COVID-19 on stock markets using MST.…”

Section: Introductionmentioning

confidence: 99%

“…Aslam et al (2020) studied the influence of COVID-19 on stock markets through network analysis. Chakrabarti et al (2020), studied the impact of COVID-19 on stock markets using MST. It is possible to assist to an increasing amount of literature exploring the impact of the COVID-19 worldwide on various aspects including financial markets' network.…”

Section: Introductionmentioning

confidence: 99%

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Temporal changes in global stock markets during COVID-19: an analysis of dynamic networks

Zaheer

Aslam

Mohmand

et al. 2022

CFRI

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PurposeCOVID-19 evolved from a local health crisis to a pandemic and affected countries worldwide accordingly. Similarly, the impacts of the pandemic on the performance of global stock markets could be time-varying. This study applies a dynamic network analysis approaches to evaluate the evolution over time of the impact of COVID-19 on the stock markets' network.Design/methodology/approachDaily closing prices of 55 global stock markets from August 1, 2019 to September 10, 2020 were retrieved. This sample period was further divided into nine subsample periods for dynamic analysis purpose. Distance matrix based on long-range correlations was calculated, using rolling window's length of 100 trading days, rolled forward at an interval of one month's working days. These distance matrices than used to construct nine minimum spanning trees (MSTs). Network characteristics were figured out, community detection and network rewiring techniques were also used for extracting meaningful from these MSTs.FindingsThe findings are, with the evolution of COVID-19, a change in co-movements amongst stock markets' indices occurred. On the 100th day from the date of reporting of the first cluster of cases, the co-movement amongst the stock markets become 100% positively correlated. However, the international investor can still get better portfolio performance with such temporal correlation structure either avoiding risk or pursuing profits. A little change is observed in the importance of authoritative node; however, this central node changed multiple times with change of epicenters. During COVID-19 substantial clustering and less stable network structure is observed.Originality/valueIt is confirmed that this work is original and has been neither published elsewhere, nor it is currently under consideration for publication elsewhere.

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Section: Results Analysis and Discussionsupporting

confidence: 89%

Section: Results Analysis and Discussionsupporting

confidence: 71%

“…(2020) studied the influence of COVID-19 on stock markets through network analysis. Chakrabarti et al . (2020), studied the impact of COVID-19 on stock markets using MST.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Temporal changes in global stock markets during COVID-19: an analysis of dynamic networks

Zaheer

Aslam

Mohmand

et al. 2022

CFRI

View full text Add to dashboard Cite

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“…In the same context, however, Conlon and McGee (2020) found that bitcoin could not provide a safe haven during the decrease of S&P500 prices. Chakrabarti et al (2021) studied the changing dynamics of networks between G20 financial markets nations and found that the outbreak of the virus changes the economic outlook forming a strong coupled network between all the G20 financial markets except for China, which remains outside this network. Akhtaruzzaman et al (2021) examined the contagion spillovers (from one country to another) aris from financial and non-financial firms among China, from G7 countries, or from both during the COVID-19 period.…”

Section: Literature Reviewmentioning

confidence: 99%

The dynamic network connectedness and hedging strategies across stock markets and commodities: COVID-19 pandemic effect

Mezghani

Hamadou

Abbes

2021

APJBA

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PurposeThe aim of this study was to investigate the dynamic network connectedness between stock markets and commodity futures and its implications on hedging strategies. Specifically, the authors studied the impact of the 2014 oil price drop and coronavirus disease 2019 (COVID-19) pandemic on risk spillovers and portfolio allocation among stock markets (United States (SP500), China (SSEC), Japan (Nikkei 225), France (CAC40) and Germany (DAX)) and commodities (oil and gold).Design/methodology/approachIn this study, the authors used the Baba, Engle, Kraft and Kroner–generalized autoregressive conditional heteroskedasticity (BEKK–GARCH) model to estimate shock transmission among the five financial markets and the two commodities. The authors rely on Diebold and Yılmaz (2014, 2015) methodology to construct network-associated measures.FindingsRelying on the BEKK–GARCH, the authors found that the recent health crisis of COVID-19 intensified the volatility spillovers among stock markets and commodities. Using the dynamic network connectedness, the authors showed that at the 2014 oil price drop and the COVID-19 pandemic shock, the Nikkei225 moderated the transmission of volatility to the majority of markets. During the COVID-19 pandemic, the commodity markets are a net receiver of volatility shocks from stock markets. In addition, the SP500 stock market dominates the network connectedness dynamic during the COVID-19 pandemic, while DAX index is the weakest risk transmitter. Regarding the portfolio allocation and hedging strategies, the study showed that the oil market is the most vulnerable and risky as it was heavily affected by the two crises. The results show that gold is a hedging tool during turmoil periods.Originality/valueThis study contributes to knowledge in this area by improving our understanding of the influence of fluctuations in oil prices on the dynamics of the volatility connection between stock markets and commodities during the COVID-19 pandemic shock. The study’s findings provide more implications regarding portfolio management and hedging strategies that could help investors optimize their portfolios.

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Further evidence of contagion effect between the Chinese and the G20 stock markets during the COVID-19 pandemic: A time-varying copula approach

Sghaier

Kouki

Messaoud

2023

Cogent Economics & Finance

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COVID-19 Pandemic and Global Financial Market Interlinkages: A Dynamic Temporal Network Analysis

Cited by 3 publications

References 0 publications

Temporal changes in global stock markets during COVID-19: an analysis of dynamic networks

Temporal changes in global stock markets during COVID-19: an analysis of dynamic networks

The dynamic network connectedness and hedging strategies across stock markets and commodities: COVID-19 pandemic effect

Further evidence of contagion effect between the Chinese and the G20 stock markets during the COVID-19 pandemic: A time-varying copula approach

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