Estimating Network Connectedness of Financial Markets and Commodities

Bagheri, Ehsan; Ebrahimi, Seyed Babak

doi:10.1007/s11518-020-5465-1

Cited by 19 publications

(12 citation statements)

References 45 publications

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“…The DY approach has been popular in financial connectedness studies and scholars have estimated spillovers among financial markets. Studies have calculated connectedness in various markets, such as FX markets connectedness (Bub ak et al, 2011;Antonakakis, 2012;Baruník and K rehlík, 2018), stock markets connectedness (Tsai, 2014;Guimarães-Filho and Hong, 2016), debt markets connectedness (Antonakakis and Vergos, 2013;Le et al, 2020), cryptocurrency markets connectedness (Yi et al, 2018;Andrada-Félix et al, 2020;Bagheri and Ebrahimi, 2020).…”

Section: Financial Contagion and Cryptocurrency Connectednessmentioning

confidence: 99%

Cryptocurrency connectedness nexus the COVID-19 pandemic: evidence from time-frequency domains

Polat

GÜNAY

2021

SEF

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Purpose The purpose of this study is to investigate volatility connectedness between major cryptocurrencies by the virtue of market capitalization. In this context, this paper implements the frequency connectedness approach of Barunik and Krehlik (2018) and to measure short-, medium- and long-term connectedness between realized volatilities of cryptocurrencies. Additionally, this paper analyzes network graphs of directional TO/FROM spillovers before and after the announcement of the COVID-19 pandemic by the World Health Organization. Design/methodology/approach In this study, we examine the volatility connectedness among eight major cryptocurrencies by the virtue of market capitalization by using the frequency connectedness approach over the period July 26, 2017 and October 28, 2020. To this end, this paper computes short-, medium- and long-cycle overall spillover indexes on different frequency bands. All indexes properly capture well-known events such as the 2018 cryptocurrency market crash and COVID-19 pandemic and markedly surge around these incidents. Furthermore, owing to notably increased volatilities after the official announcement of the COVID-19 pandemic, this paper concentrates on network connectedness of volatility spillovers for two distinct periods, July 26, 2017–March 10, 2020 and March 11, 2020–October 28, 2020, respectively. In line with the related studies, major cryptocurrencies stand at the epicenter of the connectedness network and directional volatility spillovers dramatically intensify based on the network analysis. Findings Overall spillover indexes have fluctuated between 54% and 92% in May 2018 and April 2020. The indexes gradually escalated till November 9, 2018 and surpassed their average values (71.92%, 73.66% and 74.23%, respectively). Overall spillover indexes dramatically plummeted till January 2019 and reached their troughs (54.04%, 57.81% and 57.81%, respectively). Etherium catalyst the highest sum of volatility spillovers to other cryptocurrencies (94.2%) and is followed by Litecoin (79.8%) and Bitcoin (76.4%) before the COVID-19 announcement, whereas Litecoin becomes the largest transmitter of total volatility (89.5%) and followed by Bitcoin (89.3%) and Etherium (88.9%). Except for Etherium, the magnitudes of total volatility spillovers from each cryptocurrency notably increase after – COVID-19 announcement period. The medium-cycle network topology of pairwise spillovers indicates that the largest transmitter of total volatility spillover is Litecoin (89.5%) and followed by Bitcoin (89.3%) and Etherium (88.9%) before the COVID-19 announcement. Etherium keeps its leading role of transmitting the highest sum of volatility spillovers (89.4%), followed by Bitcoin (88.9%) and Litecoin (88.2%) after the COVID-19 announcement. The largest transmitter of total volatility spillovers is Etherium (95.7%), followed by Litecoin (81.2%) and Binance Coin (75.5%) for the long-cycle connectedness network in the before-COVID-19 announcement period. These nodes keep their leading roles in propagating volatility spillover in the latter period with the following sum of spillovers (Etherium-89.5%, Bitcoin-88.9% and Litecoin-88.1%, respectively). Research limitations/implications The study can be extended by including more cryptocurrencies and high-frequency data. Originality/value The study is original and contributes to the extant literature threefold. First, this paper identifies connectedness between major cryptocurrencies on different frequency bands by using a novel methodology. Second, this paper estimates volatility connectedness between major cryptocurrencies before and after the announcement of the COVID-19 pandemic and thereby to concentrate on its impact on the cryptocurrency market. Third, this paper plots network graphs of volatility connectedness and herewith picture the intensification of cryptocurrencies due to a major financial distress event.

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Section: Financial Contagion and Cryptocurrency Connectednessmentioning

confidence: 99%

Cryptocurrency connectedness nexus the COVID-19 pandemic: evidence from time-frequency domains

Polat

GÜNAY

2021

SEF

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“…Further, a recent study offers significant evidence for a nonlinear and asymmetric relationship between these two assets, especially during periods of oil crashes (Ming et al 2022). Many other studies offer different insights into the dynamic relationship between Oil prices and exchange rates (see Jammazi and Nguyen 2017;Li et al 2017;Kočenda and Moravcová 2019;Bagheri and Ebrahimi 2020;Wen et al 2020;Karatas and Unal 2021;Shang and Hamori 2021;etc. ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…This indicates that EUR/USD series movement is predictable to a certain extent by perceiving the varying GOLD returns. Although there is numerous research that offer varied viewpoints on the dynamic relationship between gold prices and exchange rates (see Bagheri and Ebrahimi 2020;Mensi et al 2021;Abdullah et al 2022, etc. ), our observation is a bit different.…”

Section: Eur/usd Andmentioning

confidence: 99%

Asymmetric Information Flow between Exchange Rate, Oil, and Gold: New Evidence from Transfer Entropy Approach

Maiti¹,

Kayal

2022

JRFM

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The present study used transfer entropy and effective transfer entropy to examine the asymmetric information flow between exchange rates, oil, and gold. The dataset is composed of daily data covering the period of 1 January 2018 to 31 December 2021. Further, the dataset is bifurcated for analysis for before and during COVID. The bidirectional information flow is observed between EUR/USD and Oil for the whole study period unlike before COVID. However, during COVID, there was a unidirectional information flow from OilàEUR/USD. The study finds a significant unidirectional information flow from GoldàEUR/USD. The study estimates also indicate that before COVID, the direction of information flow was from OilàGold. However, the direction of information flow reversed during COVID from GoldàOil. Overall, the direction of information flow among these three variables is asymmetric. The highest transfer entropy was observed for GoldàEUR/USD among all the pairs under consideration.

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“…Globalization along with financialization has led to strongly connected financial markets via various channels, which propels expeditious risk propagation during financial imbalance periods. In this context, a strand of literature focuses on financial contagion during turbulence times and detect intensified connectedness between financial indicators (Billio et al, 2012;Cimini, 2015;Singh et al, 2018;Gong et al, 2019;Bagheri & Ebrahimi, 2020;Balcılar et al, 2021).…”

Section: Related Literaturementioning

confidence: 99%

Time-Varying Network Connectedness of G-7 Economic Policy Uncertainties: A Locally Stationary TVP-VAR Approach

Polat

2021

WJAE

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This work analyzes the frequency-dependent network structure of Economic Policy Uncertainties (EPU) across G-7 countries between January 1998 and April 2021. We implement an approach that builds dynamic networks relying on a locally stationary Time-Varying Parameter-Vector Autoregressive model using Quasi-Bayesian Local Likelihood methods. We compute short-, medium-, and long-term network connectedness of G-7 EPUs over a period covering several economic/financial turmoils. Furthermore, we structure short-term network topologies for the Global Financial Crisis (GFC) and the COVID-19 pandemic periods. Findings of the study indicate amplified interdependencies between G-7 EPUs around well-known economic/geopolitical incidents, frequency-dependent connectedness networks among them, and stronger interdependencies than the medium-, and long-term linkages. Finally, we find that short-term spillovers are not persistent in the long-term for both turmoil periods.

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Estimating Network Connectedness of Financial Markets and Commodities

Cited by 19 publications

References 45 publications

Cryptocurrency connectedness nexus the COVID-19 pandemic: evidence from time-frequency domains

Cryptocurrency connectedness nexus the COVID-19 pandemic: evidence from time-frequency domains

Asymmetric Information Flow between Exchange Rate, Oil, and Gold: New Evidence from Transfer Entropy Approach

Time-Varying Network Connectedness of G-7 Economic Policy Uncertainties: A Locally Stationary TVP-VAR Approach

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