Quantifying the spillover effect in the cryptocurrency market

Moratis, George

doi:10.1016/j.frl.2020.101534

Cited by 80 publications

(39 citation statements)

References 21 publications

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“…Studying the interdependence of digital currencies in the cryptocurrency market has become crucial for risk management, forecasting and pricing purposes (Moratis, 2021). Moreover, governments seek to limit the intermarket movement of shocks and have recently increased literature examining the nature of their decisions to invest in volatile instruments such as cryptocurrencies (Gillaizeau et al , 2019).…”

Section: Introductionmentioning

confidence: 99%

Causality-in-mean and causality-in-variance among Bitcoin, Litecoin, and Ethereum

Gemici

Polat

2021

SEF

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Purpose This study aims to examine the volatility spillovers between Bitcoin (BTC), Litecoin (LTC) and Ethereum (ETH) as they are related to structural breaks. Design/methodology/approach This study examines the daily period from August 7, 2015 to July 10, 2018 by conducting causality-in-mean and causality-in-variance tests among cryptocurrencies. Findings The findings showed that there was one-way causality-in-mean from BTC to LTC and ETH, but there was no causality-in-mean from LTC and ETH to BTC. On the other hand, considering the structural breaks included in the variance equations, the estimation results showed that there were short-term causality-in-variance from LTC to BTC and long-term causality-in-variance from BTC to LTC. Originality/value This study fills the gap by contributing in two ways. First, to the best of the authors’ knowledge, this is the first study that used the cross-correlation function (CCF) of causality to explore causality-in-variance among cryptocurrencies. Second, this study considers the structural breaks in variance in the return series.

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Section: Introductionmentioning

confidence: 99%

Causality-in-mean and causality-in-variance among Bitcoin, Litecoin, and Ethereum

Gemici

Polat

2021

SEF

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“…Authors consider more macro variables or markets, such as stock market and public expenditure [22,50,51]. Under the new situation, Moratis proposed the use of a rolling window Bayesian vector autoregressive model measured the risk spillover of the cryptocurrency market and discovered the important role of external driving factors [52]. Based on the structural mutations brought about by the financial crisis, Zhou selected Sweden's 10-year industrial production index modeling, while Timo et al selected 47 macrovariables of the G7 economy to test the effectiveness of the STVAR model and concluded that this STVAR model is better than the linear model [53,54].…”

Section: Literature Reviewmentioning

confidence: 99%

Multiscale Systemic Risk and Its Spillover Effects in the Cryptocurrency Market

Zhang

Ding

2021

Complexity

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Since the advent of Bitcoin, the cryptocurrency market has become an important financial market. However, due to the existence of the cryptocurrency bubble, investors face more difficulties in risk portfolios. We adopt wavelet packet decomposition, nonlinear Granger causality test, risk spillover network, and STVAR model; retain the mature research of multiscale systemic risk based on time and frequency; and thus extend systemic risk to different regimes. We found that when frequency is combined with regimes, the risk spillover center will undergo subversive changes in the long run. We also proposed that BTC will be more robust at extreme values (like longest and shortest periods), while cryptocurrencies with smaller market capitalization will be stronger in the medium term. At the same time, the recession period will also spur on it.

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“…As for the methods in the research of Bitcoin issues, the GARCH model is the most widely used (see Dyhrberg 2016a, b;Bouri et al 2017a, b;Katsiampa 2017;Catania and Grassi 2017;Chu et al 2017;Corbet et al 2018;Aftab et al 2019;Wu et al 2019;Das et al 2020;etc.). Then the VAR model and variance decomposition based on VAR model are also widely used (Hoang et al 2020;Moratis 2021;Rehman 2020;Urom et al 2020). Other methods such as Copula-type models (Garcia-Jorcano and Muela 2020), wavelet analysis (Qureshi et al 2018) etc., are gradually applied.…”

Section: Literature Reviewmentioning

confidence: 99%

Co-movement and return spillover: evidence from Bitcoin and traditional assets

2021

SN Bus Econ

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To analyze the asset attribute and hedge effect of Bitcoin, we investigate the relationship between Bitcoin and several kinds of traditional financial assets by the univariate GARCH and multivariate GARCH models. We find that Bitcoin has a unique risk-return characteristic and volatility clustering performance, its high volatility persistence similar to Gold, but different from currency. In addition, Bitcoin exhibits a significant one-way spillover effect with other variables, without a two-way spillover effect. Bitcoin is much more affected by other market shocks than other markets are affected by the impact of Bitcoin shocks, which could not be a haven but a weak hedge. From the dynamic linkage perspective, Bitcoin and Gold have different connectedness to other markets, Gold exhibits a stronger movement to other markets, especially during extreme situations. To summarize, we classify Bitcoin as a high speculative financial asset between Gold and currency, but not Gold or currency. Our study has important implications for investors, policymakers, and risk managers who are interested in Bitcoin.

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Quantifying the spillover effect in the cryptocurrency market

Cited by 80 publications

References 21 publications

Causality-in-mean and causality-in-variance among Bitcoin, Litecoin, and Ethereum

Causality-in-mean and causality-in-variance among Bitcoin, Litecoin, and Ethereum

Multiscale Systemic Risk and Its Spillover Effects in the Cryptocurrency Market

Co-movement and return spillover: evidence from Bitcoin and traditional assets

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