Systematic risk in cryptocurrency market: Evidence from DCC-MGARCH model

Nguyen, Canh Phuc; Wongchoti, Udomsak; Thanh, Su Dinh; Nguyen, Thong Trung

doi:10.1016/j.frl.2019.03.011

Cited by 100 publications

(49 citation statements)

References 31 publications

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“…Referring to Panel C, the constant conditional correlations ( ) are significantly positive during both sample periods, consistent with the findings of Katsiampa et al ( 2019b ) and Canh et al ( 2019 ). Moreover, correlations are observed to be higher during the COVID-19 period compared to the pre-COVID-19 period.…”

Section: Resultssupporting

confidence: 88%

“…In the related literature, numerous studies have examined the return/volatility spillover between different cryptocurrencies (Chu et al 2017 ; Yi et al 2018 ; Koutmos 2018 ; Baur and Dimpfl 2018 ; Ji et al 2019 ; Katsiampa 2019 ; Katsiampa et al 2019a , b ; Canh et al 2019 ; Beneki et al 2019 ; Liu and Serletis 2019 ). For example, Yi et al ( 2018 ) analyze the volatility connectedness between the 52 cryptocurrencies and find a volatility transmission from Bitcoin to other cryptocurrencies.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, bidirectional volatility transmissions are observed between the Bitcoin–Ethereum, Bitcoin–Litecoin, and Ethereum–Litecoin pairs. Canh et al ( 2019 ) investigate volatility dynamics across the seven major cryptocurrencies by employing the DCC-MGARCH model and find significant volatility transmission between all cryptocurrencies. Liu and Serletis ( 2019 ) employ the GARCH in mean model and find significant shock and volatility transmission between Bitcoin, Ethereum, and Litecoin.…”

Section: Introductionmentioning

confidence: 99%

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Discovering interlinkages between major cryptocurrencies using high-frequency data: new evidence from COVID-19 pandemic

Yousaf

Ali

2020

Financ Innov

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Through the application of the VAR-AGARCH model to intra-day data for three cryptocurrencies (Bitcoin, Ethereum, and Litecoin), this study examines the return and volatility spillover between these cryptocurrencies during the pre-COVID-19 period and the COVID-19 period. We also estimate the optimal weights, hedge ratios, and hedging effectiveness during both sample periods. We find that the return spillovers vary across the two periods for the Bitcoin–Ethereum, Bitcoin–Litecoin, and Ethereum–Litecoin pairs. However, the volatility transmissions are found to be different during the two sample periods for the Bitcoin–Ethereum and Bitcoin–Litecoin pairs. The constant conditional correlations between all pairs of cryptocurrencies are observed to be higher during the COVID-19 period compared to the pre-COVID-19 period. Based on optimal weights, investors are advised to decrease their investments (a) in Bitcoin for the portfolios of Bitcoin/Ethereum and Bitcoin/Litecoin and (b) in Ethereum for the portfolios of Ethereum/Litecoin during the COVID-19 period. All hedge ratios are found to be higher during the COVID-19 period, implying a higher hedging cost compared to the pre-COVID-19 period. Last, the hedging effectiveness is higher during the COVID-19 period compared to the pre-COVID-19 period. Overall, these findings provide useful information to portfolio managers and policymakers regarding portfolio diversification, hedging, forecasting, and risk management.

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Section: Resultssupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Discovering interlinkages between major cryptocurrencies using high-frequency data: new evidence from COVID-19 pandemic

Yousaf

Ali

2020

Financ Innov

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“…Not only about their most differentiating characteristics [45][46][47][48] but also for the analysis of volatility models [43,[49][50][51][52][53] and risk forecasts [36]. Their behavior for hedging has been studied in portfolios with other kinds of assets [54,55], or with others cryptos [29,[56][57][58]. Our application fits in this latter framework, since GC models are applied to a portfolio with the three best known and most representative crypto assets: Bitcoin, Litecoin and Ripple.…”

Section: Cryptocurrenciesmentioning

confidence: 99%

Portfolio Risk Assessment under Dynamic (Equi)Correlation and Semi-Nonparametric Estimation: an Application to Cryptocurrencies

Jiménez¹,

Mora–Valencia²,

Ñíguez³

et al. 2020

Preprint

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The semi-nonparametric (SNP) modeling of the return distribution has been proved to be a flexible and accurate methodology for portfolio risk management that allows two-step estimation of the dynamic conditional correlation (DCC) matrix. For this SNP-DCC model, we propose a stepwise procedure to compute pairwise conditional correlations under bivariate marginal SNP distributions, overcoming the curse of dimensionality. The procedure is compared to the assumption of Dynamic Equicorrelation (DECO), which is a parsimonious model when correlations among the assets are not significantly different but requires joint estimation of the multivariate SNP model. The risk assessment of both methodologies is tested for a portfolio on cryptocurrencies by implementing backtesting techniques and for different risk measures: Value-at-Risk, Expected Shortfall and Median Shortfall. The results support our proposal showing that the SNP-DCC model has better performance for a smaller confidence level than the SNP-DECO model, although both models perform similarly for higher confidence levels.

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“…Furthermore, there exist literature discussing the spillover effect and systematic risk among the cryptocurrency markets. In [ 34 ] it is found that the structural breaks are universally present in seven of the largest cryptocurrencies, whereas it is spreads from the smallest to the largest currencies, in order of capitalization. This finding is done by implementing the Granger causality test, as well as a test for the ARCH and the Dynamic Conditional Correlation MGARCH to the selected coins.…”

Section: Introductionmentioning

confidence: 99%

Network Analysis of Multivariate Transfer Entropy of Cryptocurrencies in Times of Turbulence

García-Medina

Hernandez

2020

Entropy

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We investigate the effects of the recent financial turbulence of 2020 on the market of cryptocurrencies taking into account the hourly price and volume of transactions from December 2019 to April 2020. The data were subdivided into time frames and analyzed the directed network generated by the estimation of the multivariate transfer entropy. The approach followed here is based on a greedy algorithm and multiple hypothesis testing. Then, we explored the clustering coefficient and the degree distributions of nodes for each subperiod. It is found the clustering coefficient increases dramatically in March and coincides with the most severe fall of the recent worldwide stock markets crash. Further, the log-likelihood in all cases bent over a power law distribution, with a higher estimated power during the period of major financial contraction. Our results suggest the financial turbulence induce a higher flow of information on the cryptocurrency market in the sense of a higher clustering coefficient and complexity of the network. Hence, the complex properties of the multivariate transfer entropy network may provide early warning signals of increasing systematic risk in turbulence times of the cryptocurrency markets.

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Systematic risk in cryptocurrency market: Evidence from DCC-MGARCH model

Cited by 100 publications

References 31 publications

Discovering interlinkages between major cryptocurrencies using high-frequency data: new evidence from COVID-19 pandemic

Discovering interlinkages between major cryptocurrencies using high-frequency data: new evidence from COVID-19 pandemic

Portfolio Risk Assessment under Dynamic (Equi)Correlation and Semi-Nonparametric Estimation: an Application to Cryptocurrencies

Network Analysis of Multivariate Transfer Entropy of Cryptocurrencies in Times of Turbulence

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