Systemic risk contagion in FX market: A frequency connectedness and network analysis

Polat, Onur

doi:10.1111/boer.12197

Cited by 15 publications

(8 citation statements)

References 83 publications

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“…The total connectedness index C H defined in (8) achieved a value of 38.31% for the full sample period of 2008-2019. This result is in line with the empirical findings of different studies, which obtained total connectedness indices of similar magnitudes when analyzing global equity markets, commodity markets, and foreign exchange markets [40][41][42].…”

Section: Full-sample Analysissupporting

confidence: 90%

Dynamics of Connectedness in Clean Energy Stocks

Fuentes

Herrera

2020

Energies

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This paper examines the dynamics of connectedness among the realized volatility indices of 16 clean energy stocks belonging to the SPGCE and the implied volatility indices of two important stock markets—the S&P 500 and the STOXX50—and two commodities markets—the crude oil and gold markets. The empirical results show a unidirectional connectedness from the implied volatility indices to the clean energy stocks. Our analysis further reveals similar volatility connectedness behaviors among companies in the same energy production subsector. However, there exists heterogeneous behavior between different energy production subsectors over time. Further, we identify pairwise directional connectedness clusters among related companies, indicating that there are few possibilities for portfolio diversification within the energy production subsectors. Finally, through an impulse–response analysis, we confirm that the expectation of future market volatility of the S&P 500 index and the gold price plays a leading role in volatility connectedness with clean energy stocks.

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Section: Full-sample Analysissupporting

confidence: 90%

Dynamics of Connectedness in Clean Energy Stocks

Fuentes

Herrera

2020

Energies

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“…This novel methodology is superior to mainframe connectedness measures and has gained overwhelming attention from scholars. More recent studies have concentrated on connectedness in several financial markets by using the frequency connectedness approach (Polat, 2019;Maghyereh et al, 2019;Le et al, 2020;Owusu Junior et al, 2020;Polat, 2020;Fousekis and Tzaferi, 2021).…”

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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“…This method estimates connectedness in different financial cycles (short, medium and long term) using the spectral representation of variance decompositions of an N‐variable VAR model. This new approach has attracted interest by scholars and utilised to compute frequency connectedness between financial markets (Ferrer et al ., 2018; Polat, 2019; Maghyereh et al ., 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Measuring dynamic connectedness networks in energy commodities: evidence from the D‐Y and frequency connectedness approaches

Polat

2020

OPEC Energy Review

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In this study, we examine the energy commodities connectedness between the period June 2006 and April 2020 by implementing the Diebold–Yilmaz and the frequency connectedness approaches. We estimate dynamic connectedness between WTI crude oil, the Henry Hub natural gas, ULS diesel and the gasoline prices over the analysed period. Overall spillover indexes estimated by both methodologies properly respond to prominent geopolitical events over the sample period. Additionally, we plot network graphs for directional spillovers reflecting two distinct periods, 2007:3–2019:12 and 2020:1–2020:4. Network analysis verifies that the directional spillovers between energy commodities have prominently surged due to the COVID‐19 outbreak. The findings of the study underline the importance of an effective regulatory framework for monitoring commodity price developments to avoid adverse effects of commodity price shocks. Additionally, the authorities should enact policy actions to counteract the detrimental effects of the COVID‐19 pandemic on the commodity markets.

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Systemic risk contagion in FX market: A frequency connectedness and network analysis

Cited by 15 publications

References 83 publications

Dynamics of Connectedness in Clean Energy Stocks

Dynamics of Connectedness in Clean Energy Stocks

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

Measuring dynamic connectedness networks in energy commodities: evidence from the D‐Y and frequency connectedness approaches

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