We provide empirical evidence supporting the economic reasoning behind the impossibility of diversification benefits and the hedge attributes of cryptocurrencies remaining in force during the downside trends observed ...We provide empirical evidence supporting the economic reasoning behind the impossibility of diversification benefits and the hedge attributes of cryptocurrencies remaining in force during the downside trends observed in bearish financial markets.We employ a spillover connectedness model driven by time-varying parameter vector autoregressions on daily data covering January 2018 to November 2022 to analyze spillover transmissions between conventional and digital markets,focusing on the role of stablecoin issuances.We study the stock,bond,cryptocurrency,and stablecoin markets and find very high connectedness,which varies over time in response to up/down trends in financial markets.The results show that during financial turmoil,cryptocurrencies amplify downside risks rather than serve as diversifiers.In addition to risky assets from conventional financial markets,cryptocurrencies champion the transmission of spillovers to digital and conventional markets.In contrast,changes in stablecoin issuances produce few shocks because of their pegged prices,but they facilitate investors’switch from volatile cryptos to more stable digital instruments;that is,we observe a phenomenon designated by us as the“flight-to-cryptosafety.”We draw insightful conclusions,provoking new thinking regarding portfolio hedge strategies that could potentially benefit investors when searching for less volatile investment performance.展开更多
This study examines blockchain technologies and their pivotal role in the evolving Metaverse,shedding light on topics such as how to invest in cryptocurrency,the mechanics behind crypto mining,and strategies to effect...This study examines blockchain technologies and their pivotal role in the evolving Metaverse,shedding light on topics such as how to invest in cryptocurrency,the mechanics behind crypto mining,and strategies to effectively buy and trade cryptocurrencies.While it contextualises the common queries of"why is crypto crashing?"and"why is crypto down?",the research transcends beyond the frequent market fluctuations to unravel how cryptocurrencies fundamentally work and the step-by-step process on how to create a cryptocurrency.Contrasting existing literature,this comprehensive investigation encompasses both the economic and cybersecurity risks inherent in the blockchain and fintech spheres.Through an interdisciplinary approach,the research transitions from the fundamental principles of fintech investment strategies to the overarching implications of blockchain within the Metaverse.Alongside exploring machine learning potentials in financial sectors and risk assessment methodologies,the study critically assesses whether developed or developing nations are poised to reap greater benefits from these technologies.Moreover,it probes into both enduring and dubious crypto projects,drawing a distinct line between genuine blockchain applications and Ponzi-like schemes.The conclusion resolutely affirms the staying power of blockchain technologies,underlined by a profound exploration of their intrinsic value and a reflective commentary by the author on the potential risks confronting individual investors.展开更多
This paper examines the dynamics of the asymmetric volatility spillovers across four major cryptocurrencies comprising nearly 61% of cryptocurrency market capitalization and covering both conventional(Bitcoin and Ethe...This paper examines the dynamics of the asymmetric volatility spillovers across four major cryptocurrencies comprising nearly 61% of cryptocurrency market capitalization and covering both conventional(Bitcoin and Ethereum)and Islamic(Stellar and Ripple)cryptocurrencies.Using a novel time-varying parameter vector autoregression(TVP-VAR)asymmetric connectedness approach combined with a high frequency(hourly)dataset ranging from 1st June 2018 to 22nd July 2022,we find that(i)good and bad spillovers are time-varying;(ii)bad volatility spillovers are more pronounced than good spillovers;(iii)a strong asymmetry in the volatility spillovers exists in the cryptocurrency market;and(iv)conventional cryptocurrencies dominate Islamic cryptocurrencies.Specifically,Ethereum is the major net transmitter of positive volatility spillovers while Stellar is the main net transmitter of negative volatility spillovers.展开更多
Systematic risks in cryptocurrency markets have recently increased and have been gaining a rising number of connections with economics and financial markets;however,in this area,climate shocks could be a new kind of i...Systematic risks in cryptocurrency markets have recently increased and have been gaining a rising number of connections with economics and financial markets;however,in this area,climate shocks could be a new kind of impact factor.In this paper,a spillover network based on a time-varying parametric-vector autoregressive(TVP-VAR)model is constructed to measure overall cryptocurrency market extreme risks.Based on this,a second spillover network is proposed to assess the intensity of risk spillovers between extreme risks of cryptocurrency markets and uncertainties in climate conditions,economic policy,and global financial markets.The results show that extreme risks in cryptocurrency markets are highly sensitive to climate shocks,whereas uncertainties in the global financial market are the main transmitters.Dynamically,each spillover network is highly sensitive to emergent global extreme events,with a surge in overall risk exposure and risk spillovers between submarkets.Full consideration of overall market connectivity,including climate shocks,will provide a solid foundation for risk management in cryptocurrency markets.展开更多
This study uses high-frequency(1-min)price data to examine the connectedness among the leading cryptocurrencies(i.e.Bitcoin,Ethereum,Binance,Cardano,Litecoin,and Ripple)at volatility and high-order(third and fourth or...This study uses high-frequency(1-min)price data to examine the connectedness among the leading cryptocurrencies(i.e.Bitcoin,Ethereum,Binance,Cardano,Litecoin,and Ripple)at volatility and high-order(third and fourth orders in this paper)moments based on skewness and kurtosis.The sample period is from February 10,2020,to August 20,2022,which captures a pandemic,wartime,cryptocurrency market crashes,and the full collapse of a stablecoin.Using a time-varying parameter vector autoregressive(TVP-VAR)connectedness approach,we find that the total dynamic connectedness throughout all realized estimators grows with the time frequency of the data.Moreover,all estimators are time dependent and affected by significant events.As an exception,the Russia-Ukraine War did not increase the total connectedness among cryptocurrencies.Analysis of third-and fourth-order moments reveals additional dynamics not captured by the second moments,highlighting the importance of analyzing higher moments when studying systematic crash and fat-tail risks in the cryptocurrency market.Additional tests show that rolling-window-based VAR models do not reveal these patterns.Regarding the directional risk transmissions,Binance was a consistent net transmitter in all three connectedness systems and it dominated the volatility connectedness network.In contrast,skewness and kurtosis connectedness networks were dominated by Litecoin and Bitcoin and Ripple were net shock receivers in all three networks.These findings are expected to serve as a guide for portfolio optimization,risk management,and policy-making practices.展开更多
This study explores whether the COVID-19 outbreak and Russian-Ukrainian(R-U)conflict have impacted the efficiency of cryptocurrencies.The novelty of this study is the use of the Cramér-von Mises test to examine c...This study explores whether the COVID-19 outbreak and Russian-Ukrainian(R-U)conflict have impacted the efficiency of cryptocurrencies.The novelty of this study is the use of the Cramér-von Mises test to examine cryptocurrency efficiency.We used a sample of daily prices for the six largest cryptocurrencies,covering the period from September 11,2017,to September 30,2022.Cryptocurrencies are found to be weakly efficient but exhibit heterogeneous levels of efficiency across currencies.Extraordinary events(COVID-19 and R-U)play a vital role in the degree of efficiency,where a trend toward inefficiency appears in all cryptocurrencies except for Ethereum Classic and Ripple.During the COVID-19 pandemic,the degree of inefficiency was higher than the level of inefficiency during R-U.This study provides useful guidance for investors and portfolio diversifiers to adjust their asset allocations during normal and stressful market periods.展开更多
The rapid rise of Bitcoin and its increasing global adoption has raised concerns about its impact on traditional markets,particularly in periods of economic turmoil and uncertainty such as the COVID-19 pandemic.This s...The rapid rise of Bitcoin and its increasing global adoption has raised concerns about its impact on traditional markets,particularly in periods of economic turmoil and uncertainty such as the COVID-19 pandemic.This study examines the extent of the volatility contagion from the Bitcoin market to traditional markets,focusing on gold and six major stock markets(Japan,USA,UK,China,Germany,and France)using daily data from January 2,2011,to June 2,2022,with 2958 daily observations.We employ DCC-GARCH,wavelet coherence,and cascade-correlation network models to analyze the relationship between Bitcoin and those markets.Our results indicate long-term volatility contagion between Bitcoin and gold and short-term contagion during periods of market turmoil and uncertainty.We also find evidence of long-term contagion between Bitcoin and the six stock markets,with short-term contagion observed in Chinese and Japanese markets during COVID-19.These results suggest a risk of uncontrollable threats from Bitcoin volatility and highlight the need for measures to prevent infection transmission to local stock markets.Hedge funds,mutual funds,and individual and institutional investors can benefit from using our findings in their risk management strategies.Our research confirms the utility of the cascade-correlation network model as an innovative method to investigate intermarket contagion across diverse conditions.It holds significant implications for stock market investors and policymakers,providing evidence for potentially using cryptocurrencies for hedging,for diversification,or as a safe haven.展开更多
We analyze the connectedness between major cryptocurrencies and nonfungible tokens(NFTs)for different quantiles employing a time-varying parameter vector autoregression approach.We find that lower and upper quantile s...We analyze the connectedness between major cryptocurrencies and nonfungible tokens(NFTs)for different quantiles employing a time-varying parameter vector autoregression approach.We find that lower and upper quantile spillovers are higher than those at the median,meaning that connectedness augments at extremes.For normal,bearish,and bullish markets,Bitcoin Cash,Bitcoin,Ethereum,and Litecoin consistently remain net transmitters,while NFTs receive innovations.However,spillover topology at both extremes becomes simpler—from cryptocurrencies to NFTs.We find no markets useful for mitigating BTC risks,whereas BTC is capable of reducing the risk of other digital assets,which is a valuable insight for market players and investors.展开更多
This study examines the market efficiency in the prices and volumes of transactions of 41 cryptocurrencies.Specifically,the correlation dimension(CD),Lyapunov Exponent(LE),and approximate entropy(AE)were estimated bef...This study examines the market efficiency in the prices and volumes of transactions of 41 cryptocurrencies.Specifically,the correlation dimension(CD),Lyapunov Exponent(LE),and approximate entropy(AE)were estimated before and during the COVID-19 pandemic.Then,we applied Student’s t-test and F-test to check whether the estimated nonlinear features differ across periods.The empirical results show that(i)the COVID-19 pandemic has not affected the means of CD,LE,and AE in prices,(ii)the variances of CD,LE,and AE estimated from prices are different across pre-pandemic and during pandemic periods,and specifically(iii)the variance of CD decreased during the pandemic;however,the variance of LE and the variance of AE increased during the pandemic period.Furthermore,the pandemic has not affected all three features estimated from the volume series.Our findings suggest that investing in cryptocurrencies is advantageous during a pandemic because their prices become more regular and stable,and the latter has not affected the volume of transactions.展开更多
The cryptocurrency market is a complex and rapidly evolving financial landscape in which understanding the inter-and intra-asset dependencies among key financial variables,such as return and liquidity,is crucial.In th...The cryptocurrency market is a complex and rapidly evolving financial landscape in which understanding the inter-and intra-asset dependencies among key financial variables,such as return and liquidity,is crucial.In this study,we analyze daily return and liquidity data for six major cryptocurrencies,namely Bitcoin,Ethereum,Ripple,Binance Coin,Litecoin,and Dogecoin,spanning the period from June 3,2020,to November 30,2022.Liquidity is estimated using three low-frequency proxies:the Amihud ratio and the Abdi and Ranaldo(AR)and Corwin and Schultz(CS)estimators.To account for autoregressive and persistent effects,we apply the autoregressive integrated moving average-generalized autoregressive conditional heteroscedasticity(ARIMA-GARCH)model and subsequently utilize the copula method to examine the interdependent relationships between the return on and liquidity of the six cryptocurrencies.Our analysis reveals strong cross-asset lower-tail dependence in return and significant cross-asset upper-tail dependence in illiquidity measures,with more pronounced dependence observed in specific cryptocurrency pairs,primarily involving Bitcoin,Ethereum,and Litecoin.We also observe that returns tend to be higher when liquidity is lower in the cryptocurrency market.Our findings have significant implications for portfolio diversification,asset allocation,risk management,and trading strategy development for investors and traders,as well as regulatory policy-making for regulators.This study contributes to a deeper understanding of the cryptocurrency marketplace and can help inform investment decision making and regulatory policies in this emerging financial domain.展开更多
This study examines the volatility spillovers in four representative exchanges and for six liquid cryptocurrencies.Using the high-frequency trading data of exchanges,the heterogeneity of exchanges in terms of volatili...This study examines the volatility spillovers in four representative exchanges and for six liquid cryptocurrencies.Using the high-frequency trading data of exchanges,the heterogeneity of exchanges in terms of volatility spillover can be examined dynamically in the time and frequency domains.We find that Ripple is a net receiver on Coinbase but acts as a net contributor on other exchanges.Bitfinex and Binance have different net spillover effects on the six cryptocurrency markets.Finally,we identify the determinants of total connectedness in two types of volatility spillover,which can explain cryptocurrency or exchange interlinkage.展开更多
This study examines the nexus between the good and bad volatilities of three technological revolutions—financial technology(FinTech),the Internet of Things,and artificial intelligence and technology—as well as the t...This study examines the nexus between the good and bad volatilities of three technological revolutions—financial technology(FinTech),the Internet of Things,and artificial intelligence and technology—as well as the two main conventional and Islamic cryptocurrency platforms,Bitcoin and Stellar,via three approaches:quantile cross-spectral coherence,quantile-VAR connectedness,and quantile-based non-linear causality-in-mean and variance analysis.The results are as follows:(1)under normal market conditions,in long-run horizons there is a significant positive cross-spectral relationship between FinTech's positive volatilities and Stellar’s negative volatilities;(2)Stellar’s negative and positive volatilities exhibit the highest net spillovers at the lower and upper tails,respectively;and(3)the quantile-based causality results indicate that Bitcoin’s good(bad)volatilities can lead to bad(good)volatilities in all three smart technologies operating between normal and bull market conditions.Moreover,the Bitcoin industry’s negative volatilities have a bilateral cause-and-effect relationship with FinTech’s positive volatilities.By analyzing the second moment,we found that Bitcoin's negative volatilities are the only cause variable that generates FinTech's good volatility in a unidirectional manner.As for Stellar,only bad volatilities have the potential to signal good volatilities for cutting-edge technologies in some middle quantiles,whereas good volatilities have no significant effect.Hence,the trade-off between Bitcoin and cutting-edge technologies,especially FinTech-related advancements,appear more broadly and randomly compared with the Stellar-innovative technologies nexus.The findings provide valuable insights for FinTech companies,blockchain developers,crypto-asset regulators,portfolio managers,and high-tech investors.展开更多
This study investigates volatility spillovers and network connectedness among four cryptocurrencies(Bitcoin,Ethereum,Tether,and BNB coin),four energy companies(Exxon Mobil,Chevron,ConocoPhillips,and Nextera Energy),an...This study investigates volatility spillovers and network connectedness among four cryptocurrencies(Bitcoin,Ethereum,Tether,and BNB coin),four energy companies(Exxon Mobil,Chevron,ConocoPhillips,and Nextera Energy),and four mega-technology companies(Apple,Microsoft,Alphabet,and Amazon)in the US.We analyze data for the period November 15,2017-October 28,2022 using methodologies in Diebold and Yilmaz(Int J Forecast 28(1):57-66,2012)and Baruník and Krehlík(J Financ Economet 16(2):271-2962018).Our analysis shows the COVID-19 pandemic amplified volatility spillovers,thereby intensifying the impact of financial contagion between markets.This finding indicates the impact of the pandemic on the US economy heightened risk transmission across markets.Moreover,we show that Bitcoin,Ethereum,Chevron,ConocoPhilips,Apple,and Microsoft are net volatility transmitters,while Tether,BNB,Exxon Mobil,Nextera Energy,Alphabet,and Amazon are net receivers Our results suggest that short-term volatility spillovers outweigh medium-and long-term spillovers,and that investors should be more concerned about short-term repercussions because they do not have enough time to act quickly to protect themselves from market risks when the US market is affected.Furthermore,in contrast to short-term dynamics,longer term patterns display superior hedging efficiency.The net-pairwise directional spillovers show that Alphabet and Amazon are the highest shock transmitters to other companies.The findings in this study have implications for both investors and policymakers.展开更多
The aim of this study is to examine the daily return spillover among 18 cryptocurrencies under low and high volatility regimes,while considering three pricing factors and the effect of the COVID-19 outbreak.To do so,w...The aim of this study is to examine the daily return spillover among 18 cryptocurrencies under low and high volatility regimes,while considering three pricing factors and the effect of the COVID-19 outbreak.To do so,we apply a Markov regime-switching(MS)vector autoregressive with exogenous variables(VARX)model to a daily dataset from 25-July-2016 to 1-April-2020.The results indicate various patterns of spillover in high and low volatility regimes,especially during the COVID-19 outbreak.The total spillover index varies with time and abruptly intensifies following the outbreak of COVID-19,especially in the high volatility regime.Notably,the network analysis reveals further evidence of much higher spillovers in the high volatility regime during the COVID-19 outbreak,which is consistent with the notion of contagion during stress periods.展开更多
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...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.展开更多
Analyzing comovements and connectedness is critical for providing significant implications for crypto-portfolio risk management.However,most existing research focuses on the lower-order moment nexus(i.e.the return and...Analyzing comovements and connectedness is critical for providing significant implications for crypto-portfolio risk management.However,most existing research focuses on the lower-order moment nexus(i.e.the return and volatility interactions).For the first time,this study investigates the higher-order moment comovements and risk connectedness among cryptocurrencies before and during the COVID-19 pandemic in both the time and frequency domains.We combine the realized moment measures and wavelet coherence,and the newly proposed time-varying parameter vector autoregression-based frequency connectedness approach(Chatziantoniou et al.in Integration and risk transmission in the market for crude oil a time-varying parameter frequency connectedness approach.Technical report,University of Pretoria,Department of Economics,2021)using intraday high-frequency data.The empirical results demonstrate that the comovement of realized volatility between BTC and other cryp-tocurrencies is stronger than that of the realized skewness,realized kurtosis,and signed jump variation.The comovements among cryptocurrencies are both time-dependent and frequency-dependent.Besides the volatility spillovers,the risk spillovers of high-order moments and jumps are also significant,although their magnitudes vary with moments,making them moment-dependent as well and are lower than volatility connectedness.Frequency connectedness demonstrates that the risk connectedness is mainly transmitted in the short term(1–7 days).Furthermore,the total dynamic connectedness of all realized moments is time-varying and has been significantly affected by the outbreak of the COVID-19 pandemic.Several practical implications are drawn for crypto investors,portfolio managers,regulators,and policymakers in optimizing their investment and risk management tactics.展开更多
Using an analogy between finance and astrophysics,this study aims to investigate whether there exists a mechanism that can describe the explosive increase in the number of traded cryptocurrencies and the cryptocurrenc...Using an analogy between finance and astrophysics,this study aims to investigate whether there exists a mechanism that can describe the explosive increase in the number of traded cryptocurrencies and the cryptocurrency market in general.In physics,the Schwarzschild radius indicates that black holes are constantly expanding because of their mass increase.Enriching this analogy,we consider the cryptocurrency market as a self-gravitational body whose mass is denoted by(1)the number of traded cryptocurrencies and(2)in terms of increasing market capitalization for a given number of traded cryptocurrencies.By analyzing weekly snapshot data of all traded cryptocurrencies from January 4,2009,to June 14,2020,we find evidence that the above-mentioned mechanism exists.The results clearly indicate the self-gravitational property of the cryptocurrency market,which is direct evidence toward the hypothesis that the changes in the traded cryptocurrencies are a positive function of the previous period’s number of traded cryptocurrencies.展开更多
The present work aims to implement two types of neural networks and an analysis of a multivariate time series model of VAR type to predict the price of cryptocurrencies like Bitcoin,Dash,Ethereum,Litecoin,and Ripple.T...The present work aims to implement two types of neural networks and an analysis of a multivariate time series model of VAR type to predict the price of cryptocurrencies like Bitcoin,Dash,Ethereum,Litecoin,and Ripple.This subject has been popular in recent years due to the rapid price fluctuations and the immense amount of money involved in the cryptocurrencies market.Several technologies have been developed around cryptocurrencies,with Blockchain rising as the most popular.Blockchain has been implementing other information technology projects which have helped to open a wide variety of job positions in some industries.A“New Economy”is emerging and it is important to study its basis in order to establish the pillars that help us to understand its behavior and be ready for a new era.展开更多
There is an urgent need to control global warming caused by humans to achieve a sustainable future.CO_(2) levels are rising steadily,and while countries worldwide are actively moving toward the sustainability goals pr...There is an urgent need to control global warming caused by humans to achieve a sustainable future.CO_(2) levels are rising steadily,and while countries worldwide are actively moving toward the sustainability goals proposed during the Paris Agreement in 2015,we are still a long way to go from achieving a sustainable mode of global operation.The increased popularity of cryptocurrencies since the introduction of Bitcoin in 2009 has been accompanied by an increasing trend in greenhouse gas emissions and high electrical energy consumption.Popular energy tracking studies(e.g.,Digiconomist and the Cambridge Bitcoin Energy Consumption Index(CBECI))have estimated energy consumption ranges from 29.96 TWh to 135.12 TWh and 26.41 TWh to 176.98 TWh,respectively for Bitcoin as of July 2021,which are equivalent to the energy consumption of countries such as Sweden and Thailand.The latest estimate by Digiconomist on carbon footprints shows a 64.18 MtCO_(2) emission by Bitcoin as of July 2021,close to the emissions by Greece and Oman.This review compiles estimates made by various studies from 2018 to 2021.We compare the energy consumption and carbon footprints of these cryptocurrencies with countries around the world and centralized transaction methods such as Visa.We identify the problems associated with cryptocurrencies and propose solutions that can help reduce their energy consumption and carbon footprints.Finally,we present case studies on cryptocurrency networks,namely,Ethereum 2.0 and Pi Network,with a discussion on how they can solve some of the challenges we have identified.展开更多
This paper examines the high frequency multiscale relationships and nonlinear multiscale causality between Bitcoin,Ethereum,Monero,Dash,Ripple,and Litecoin.We apply nonlinear Granger causality and rolling window wavel...This paper examines the high frequency multiscale relationships and nonlinear multiscale causality between Bitcoin,Ethereum,Monero,Dash,Ripple,and Litecoin.We apply nonlinear Granger causality and rolling window wavelet correlation(RWCC)to 15 min-data.Empirical RWCC results indicate mostly positive co-movements and long-term memory between the cryptocurrencies,especially between Bitcoin,Ethereum,and Monero.The nonlinear Granger causality tests reveal dual causation between most of the cryptocurrency pairs.We advance evidence to improve portfolio risk assessment,and hedging strategies.展开更多
基金Fundacão para a Ciencia e a Tecnologia(Grant No.UIDB/04521/2020).
文摘We provide empirical evidence supporting the economic reasoning behind the impossibility of diversification benefits and the hedge attributes of cryptocurrencies remaining in force during the downside trends observed in bearish financial markets.We employ a spillover connectedness model driven by time-varying parameter vector autoregressions on daily data covering January 2018 to November 2022 to analyze spillover transmissions between conventional and digital markets,focusing on the role of stablecoin issuances.We study the stock,bond,cryptocurrency,and stablecoin markets and find very high connectedness,which varies over time in response to up/down trends in financial markets.The results show that during financial turmoil,cryptocurrencies amplify downside risks rather than serve as diversifiers.In addition to risky assets from conventional financial markets,cryptocurrencies champion the transmission of spillovers to digital and conventional markets.In contrast,changes in stablecoin issuances produce few shocks because of their pegged prices,but they facilitate investors’switch from volatile cryptos to more stable digital instruments;that is,we observe a phenomenon designated by us as the“flight-to-cryptosafety.”We draw insightful conclusions,provoking new thinking regarding portfolio hedge strategies that could potentially benefit investors when searching for less volatile investment performance.
基金supported by the PETRAS National Centre of Excellence for IoT Systems Cybersecurity,which has been funded by the UK EPSRC[under grant number EP/S035362/1]the Software Sustainability Institute[grant number:EP/S021779/1]by the Cisco Research Centre[grant number CG1525381].
文摘This study examines blockchain technologies and their pivotal role in the evolving Metaverse,shedding light on topics such as how to invest in cryptocurrency,the mechanics behind crypto mining,and strategies to effectively buy and trade cryptocurrencies.While it contextualises the common queries of"why is crypto crashing?"and"why is crypto down?",the research transcends beyond the frequent market fluctuations to unravel how cryptocurrencies fundamentally work and the step-by-step process on how to create a cryptocurrency.Contrasting existing literature,this comprehensive investigation encompasses both the economic and cybersecurity risks inherent in the blockchain and fintech spheres.Through an interdisciplinary approach,the research transitions from the fundamental principles of fintech investment strategies to the overarching implications of blockchain within the Metaverse.Alongside exploring machine learning potentials in financial sectors and risk assessment methodologies,the study critically assesses whether developed or developing nations are poised to reap greater benefits from these technologies.Moreover,it probes into both enduring and dubious crypto projects,drawing a distinct line between genuine blockchain applications and Ponzi-like schemes.The conclusion resolutely affirms the staying power of blockchain technologies,underlined by a profound exploration of their intrinsic value and a reflective commentary by the author on the potential risks confronting individual investors.
文摘This paper examines the dynamics of the asymmetric volatility spillovers across four major cryptocurrencies comprising nearly 61% of cryptocurrency market capitalization and covering both conventional(Bitcoin and Ethereum)and Islamic(Stellar and Ripple)cryptocurrencies.Using a novel time-varying parameter vector autoregression(TVP-VAR)asymmetric connectedness approach combined with a high frequency(hourly)dataset ranging from 1st June 2018 to 22nd July 2022,we find that(i)good and bad spillovers are time-varying;(ii)bad volatility spillovers are more pronounced than good spillovers;(iii)a strong asymmetry in the volatility spillovers exists in the cryptocurrency market;and(iv)conventional cryptocurrencies dominate Islamic cryptocurrencies.Specifically,Ethereum is the major net transmitter of positive volatility spillovers while Stellar is the main net transmitter of negative volatility spillovers.
基金the support of a financial grant from the National Natural Science Foundation of China No.72348003,72022020,71974159,71974181the Fundamental Research Funds for the Central Universities and MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS.
文摘Systematic risks in cryptocurrency markets have recently increased and have been gaining a rising number of connections with economics and financial markets;however,in this area,climate shocks could be a new kind of impact factor.In this paper,a spillover network based on a time-varying parametric-vector autoregressive(TVP-VAR)model is constructed to measure overall cryptocurrency market extreme risks.Based on this,a second spillover network is proposed to assess the intensity of risk spillovers between extreme risks of cryptocurrency markets and uncertainties in climate conditions,economic policy,and global financial markets.The results show that extreme risks in cryptocurrency markets are highly sensitive to climate shocks,whereas uncertainties in the global financial market are the main transmitters.Dynamically,each spillover network is highly sensitive to emergent global extreme events,with a surge in overall risk exposure and risk spillovers between submarkets.Full consideration of overall market connectivity,including climate shocks,will provide a solid foundation for risk management in cryptocurrency markets.
文摘This study uses high-frequency(1-min)price data to examine the connectedness among the leading cryptocurrencies(i.e.Bitcoin,Ethereum,Binance,Cardano,Litecoin,and Ripple)at volatility and high-order(third and fourth orders in this paper)moments based on skewness and kurtosis.The sample period is from February 10,2020,to August 20,2022,which captures a pandemic,wartime,cryptocurrency market crashes,and the full collapse of a stablecoin.Using a time-varying parameter vector autoregressive(TVP-VAR)connectedness approach,we find that the total dynamic connectedness throughout all realized estimators grows with the time frequency of the data.Moreover,all estimators are time dependent and affected by significant events.As an exception,the Russia-Ukraine War did not increase the total connectedness among cryptocurrencies.Analysis of third-and fourth-order moments reveals additional dynamics not captured by the second moments,highlighting the importance of analyzing higher moments when studying systematic crash and fat-tail risks in the cryptocurrency market.Additional tests show that rolling-window-based VAR models do not reveal these patterns.Regarding the directional risk transmissions,Binance was a consistent net transmitter in all three connectedness systems and it dominated the volatility connectedness network.In contrast,skewness and kurtosis connectedness networks were dominated by Litecoin and Bitcoin and Ripple were net shock receivers in all three networks.These findings are expected to serve as a guide for portfolio optimization,risk management,and policy-making practices.
文摘This study explores whether the COVID-19 outbreak and Russian-Ukrainian(R-U)conflict have impacted the efficiency of cryptocurrencies.The novelty of this study is the use of the Cramér-von Mises test to examine cryptocurrency efficiency.We used a sample of daily prices for the six largest cryptocurrencies,covering the period from September 11,2017,to September 30,2022.Cryptocurrencies are found to be weakly efficient but exhibit heterogeneous levels of efficiency across currencies.Extraordinary events(COVID-19 and R-U)play a vital role in the degree of efficiency,where a trend toward inefficiency appears in all cryptocurrencies except for Ethereum Classic and Ripple.During the COVID-19 pandemic,the degree of inefficiency was higher than the level of inefficiency during R-U.This study provides useful guidance for investors and portfolio diversifiers to adjust their asset allocations during normal and stressful market periods.
文摘The rapid rise of Bitcoin and its increasing global adoption has raised concerns about its impact on traditional markets,particularly in periods of economic turmoil and uncertainty such as the COVID-19 pandemic.This study examines the extent of the volatility contagion from the Bitcoin market to traditional markets,focusing on gold and six major stock markets(Japan,USA,UK,China,Germany,and France)using daily data from January 2,2011,to June 2,2022,with 2958 daily observations.We employ DCC-GARCH,wavelet coherence,and cascade-correlation network models to analyze the relationship between Bitcoin and those markets.Our results indicate long-term volatility contagion between Bitcoin and gold and short-term contagion during periods of market turmoil and uncertainty.We also find evidence of long-term contagion between Bitcoin and the six stock markets,with short-term contagion observed in Chinese and Japanese markets during COVID-19.These results suggest a risk of uncontrollable threats from Bitcoin volatility and highlight the need for measures to prevent infection transmission to local stock markets.Hedge funds,mutual funds,and individual and institutional investors can benefit from using our findings in their risk management strategies.Our research confirms the utility of the cascade-correlation network model as an innovative method to investigate intermarket contagion across diverse conditions.It holds significant implications for stock market investors and policymakers,providing evidence for potentially using cryptocurrencies for hedging,for diversification,or as a safe haven.
基金supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2022S1A5A2A01038422)partly funded by the University of Economics Ho Chi Minh City,Vietnam.
文摘We analyze the connectedness between major cryptocurrencies and nonfungible tokens(NFTs)for different quantiles employing a time-varying parameter vector autoregression approach.We find that lower and upper quantile spillovers are higher than those at the median,meaning that connectedness augments at extremes.For normal,bearish,and bullish markets,Bitcoin Cash,Bitcoin,Ethereum,and Litecoin consistently remain net transmitters,while NFTs receive innovations.However,spillover topology at both extremes becomes simpler—from cryptocurrencies to NFTs.We find no markets useful for mitigating BTC risks,whereas BTC is capable of reducing the risk of other digital assets,which is a valuable insight for market players and investors.
文摘This study examines the market efficiency in the prices and volumes of transactions of 41 cryptocurrencies.Specifically,the correlation dimension(CD),Lyapunov Exponent(LE),and approximate entropy(AE)were estimated before and during the COVID-19 pandemic.Then,we applied Student’s t-test and F-test to check whether the estimated nonlinear features differ across periods.The empirical results show that(i)the COVID-19 pandemic has not affected the means of CD,LE,and AE in prices,(ii)the variances of CD,LE,and AE estimated from prices are different across pre-pandemic and during pandemic periods,and specifically(iii)the variance of CD decreased during the pandemic;however,the variance of LE and the variance of AE increased during the pandemic period.Furthermore,the pandemic has not affected all three features estimated from the volume series.Our findings suggest that investing in cryptocurrencies is advantageous during a pandemic because their prices become more regular and stable,and the latter has not affected the volume of transactions.
基金supported by the award of“Pioneering Innovator”from Guangzhou Tianhe Distinct government.
文摘The cryptocurrency market is a complex and rapidly evolving financial landscape in which understanding the inter-and intra-asset dependencies among key financial variables,such as return and liquidity,is crucial.In this study,we analyze daily return and liquidity data for six major cryptocurrencies,namely Bitcoin,Ethereum,Ripple,Binance Coin,Litecoin,and Dogecoin,spanning the period from June 3,2020,to November 30,2022.Liquidity is estimated using three low-frequency proxies:the Amihud ratio and the Abdi and Ranaldo(AR)and Corwin and Schultz(CS)estimators.To account for autoregressive and persistent effects,we apply the autoregressive integrated moving average-generalized autoregressive conditional heteroscedasticity(ARIMA-GARCH)model and subsequently utilize the copula method to examine the interdependent relationships between the return on and liquidity of the six cryptocurrencies.Our analysis reveals strong cross-asset lower-tail dependence in return and significant cross-asset upper-tail dependence in illiquidity measures,with more pronounced dependence observed in specific cryptocurrency pairs,primarily involving Bitcoin,Ethereum,and Litecoin.We also observe that returns tend to be higher when liquidity is lower in the cryptocurrency market.Our findings have significant implications for portfolio diversification,asset allocation,risk management,and trading strategy development for investors and traders,as well as regulatory policy-making for regulators.This study contributes to a deeper understanding of the cryptocurrency marketplace and can help inform investment decision making and regulatory policies in this emerging financial domain.
基金National Natural Science Foundation of China(Grant no.71771006)Science and Technology Support Plan of Guizhou(Grant no.2023-221).
文摘This study examines the volatility spillovers in four representative exchanges and for six liquid cryptocurrencies.Using the high-frequency trading data of exchanges,the heterogeneity of exchanges in terms of volatility spillover can be examined dynamically in the time and frequency domains.We find that Ripple is a net receiver on Coinbase but acts as a net contributor on other exchanges.Bitfinex and Binance have different net spillover effects on the six cryptocurrency markets.Finally,we identify the determinants of total connectedness in two types of volatility spillover,which can explain cryptocurrency or exchange interlinkage.
文摘This study examines the nexus between the good and bad volatilities of three technological revolutions—financial technology(FinTech),the Internet of Things,and artificial intelligence and technology—as well as the two main conventional and Islamic cryptocurrency platforms,Bitcoin and Stellar,via three approaches:quantile cross-spectral coherence,quantile-VAR connectedness,and quantile-based non-linear causality-in-mean and variance analysis.The results are as follows:(1)under normal market conditions,in long-run horizons there is a significant positive cross-spectral relationship between FinTech's positive volatilities and Stellar’s negative volatilities;(2)Stellar’s negative and positive volatilities exhibit the highest net spillovers at the lower and upper tails,respectively;and(3)the quantile-based causality results indicate that Bitcoin’s good(bad)volatilities can lead to bad(good)volatilities in all three smart technologies operating between normal and bull market conditions.Moreover,the Bitcoin industry’s negative volatilities have a bilateral cause-and-effect relationship with FinTech’s positive volatilities.By analyzing the second moment,we found that Bitcoin's negative volatilities are the only cause variable that generates FinTech's good volatility in a unidirectional manner.As for Stellar,only bad volatilities have the potential to signal good volatilities for cutting-edge technologies in some middle quantiles,whereas good volatilities have no significant effect.Hence,the trade-off between Bitcoin and cutting-edge technologies,especially FinTech-related advancements,appear more broadly and randomly compared with the Stellar-innovative technologies nexus.The findings provide valuable insights for FinTech companies,blockchain developers,crypto-asset regulators,portfolio managers,and high-tech investors.
文摘This study investigates volatility spillovers and network connectedness among four cryptocurrencies(Bitcoin,Ethereum,Tether,and BNB coin),four energy companies(Exxon Mobil,Chevron,ConocoPhillips,and Nextera Energy),and four mega-technology companies(Apple,Microsoft,Alphabet,and Amazon)in the US.We analyze data for the period November 15,2017-October 28,2022 using methodologies in Diebold and Yilmaz(Int J Forecast 28(1):57-66,2012)and Baruník and Krehlík(J Financ Economet 16(2):271-2962018).Our analysis shows the COVID-19 pandemic amplified volatility spillovers,thereby intensifying the impact of financial contagion between markets.This finding indicates the impact of the pandemic on the US economy heightened risk transmission across markets.Moreover,we show that Bitcoin,Ethereum,Chevron,ConocoPhilips,Apple,and Microsoft are net volatility transmitters,while Tether,BNB,Exxon Mobil,Nextera Energy,Alphabet,and Amazon are net receivers Our results suggest that short-term volatility spillovers outweigh medium-and long-term spillovers,and that investors should be more concerned about short-term repercussions because they do not have enough time to act quickly to protect themselves from market risks when the US market is affected.Furthermore,in contrast to short-term dynamics,longer term patterns display superior hedging efficiency.The net-pairwise directional spillovers show that Alphabet and Amazon are the highest shock transmitters to other companies.The findings in this study have implications for both investors and policymakers.
基金The fourth author acknowledges that the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia funded this project,under Grant No.(FP-71-42)The third author acknowledges the support of the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2020S1A5B8103268).
文摘The aim of this study is to examine the daily return spillover among 18 cryptocurrencies under low and high volatility regimes,while considering three pricing factors and the effect of the COVID-19 outbreak.To do so,we apply a Markov regime-switching(MS)vector autoregressive with exogenous variables(VARX)model to a daily dataset from 25-July-2016 to 1-April-2020.The results indicate various patterns of spillover in high and low volatility regimes,especially during the COVID-19 outbreak.The total spillover index varies with time and abruptly intensifies following the outbreak of COVID-19,especially in the high volatility regime.Notably,the network analysis reveals further evidence of much higher spillovers in the high volatility regime during the COVID-19 outbreak,which is consistent with the notion of contagion during stress periods.
文摘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.
文摘Analyzing comovements and connectedness is critical for providing significant implications for crypto-portfolio risk management.However,most existing research focuses on the lower-order moment nexus(i.e.the return and volatility interactions).For the first time,this study investigates the higher-order moment comovements and risk connectedness among cryptocurrencies before and during the COVID-19 pandemic in both the time and frequency domains.We combine the realized moment measures and wavelet coherence,and the newly proposed time-varying parameter vector autoregression-based frequency connectedness approach(Chatziantoniou et al.in Integration and risk transmission in the market for crude oil a time-varying parameter frequency connectedness approach.Technical report,University of Pretoria,Department of Economics,2021)using intraday high-frequency data.The empirical results demonstrate that the comovement of realized volatility between BTC and other cryp-tocurrencies is stronger than that of the realized skewness,realized kurtosis,and signed jump variation.The comovements among cryptocurrencies are both time-dependent and frequency-dependent.Besides the volatility spillovers,the risk spillovers of high-order moments and jumps are also significant,although their magnitudes vary with moments,making them moment-dependent as well and are lower than volatility connectedness.Frequency connectedness demonstrates that the risk connectedness is mainly transmitted in the short term(1–7 days).Furthermore,the total dynamic connectedness of all realized moments is time-varying and has been significantly affected by the outbreak of the COVID-19 pandemic.Several practical implications are drawn for crypto investors,portfolio managers,regulators,and policymakers in optimizing their investment and risk management tactics.
基金This research is co-financed by Greece and the European Union(European Social Fund-ESF)through the Operational Programme《Human Resources Development,Education and Lifelong Learning》in the context of the project“Strengthening Human Resources Research Potential via Doctorate Research”(MIS-5000432),implemented by the State Scholarships Foundation(IKY).
文摘Using an analogy between finance and astrophysics,this study aims to investigate whether there exists a mechanism that can describe the explosive increase in the number of traded cryptocurrencies and the cryptocurrency market in general.In physics,the Schwarzschild radius indicates that black holes are constantly expanding because of their mass increase.Enriching this analogy,we consider the cryptocurrency market as a self-gravitational body whose mass is denoted by(1)the number of traded cryptocurrencies and(2)in terms of increasing market capitalization for a given number of traded cryptocurrencies.By analyzing weekly snapshot data of all traded cryptocurrencies from January 4,2009,to June 14,2020,we find evidence that the above-mentioned mechanism exists.The results clearly indicate the self-gravitational property of the cryptocurrency market,which is direct evidence toward the hypothesis that the changes in the traded cryptocurrencies are a positive function of the previous period’s number of traded cryptocurrencies.
文摘The present work aims to implement two types of neural networks and an analysis of a multivariate time series model of VAR type to predict the price of cryptocurrencies like Bitcoin,Dash,Ethereum,Litecoin,and Ripple.This subject has been popular in recent years due to the rapid price fluctuations and the immense amount of money involved in the cryptocurrencies market.Several technologies have been developed around cryptocurrencies,with Blockchain rising as the most popular.Blockchain has been implementing other information technology projects which have helped to open a wide variety of job positions in some industries.A“New Economy”is emerging and it is important to study its basis in order to establish the pillars that help us to understand its behavior and be ready for a new era.
基金supported by the SERB ASEAN project CRD/2020/000369 received by Dr.Vinay Chamolasupported by a 2021-2022 Fulbright U.S.scholar grant award administered by the U.S.
文摘There is an urgent need to control global warming caused by humans to achieve a sustainable future.CO_(2) levels are rising steadily,and while countries worldwide are actively moving toward the sustainability goals proposed during the Paris Agreement in 2015,we are still a long way to go from achieving a sustainable mode of global operation.The increased popularity of cryptocurrencies since the introduction of Bitcoin in 2009 has been accompanied by an increasing trend in greenhouse gas emissions and high electrical energy consumption.Popular energy tracking studies(e.g.,Digiconomist and the Cambridge Bitcoin Energy Consumption Index(CBECI))have estimated energy consumption ranges from 29.96 TWh to 135.12 TWh and 26.41 TWh to 176.98 TWh,respectively for Bitcoin as of July 2021,which are equivalent to the energy consumption of countries such as Sweden and Thailand.The latest estimate by Digiconomist on carbon footprints shows a 64.18 MtCO_(2) emission by Bitcoin as of July 2021,close to the emissions by Greece and Oman.This review compiles estimates made by various studies from 2018 to 2021.We compare the energy consumption and carbon footprints of these cryptocurrencies with countries around the world and centralized transaction methods such as Visa.We identify the problems associated with cryptocurrencies and propose solutions that can help reduce their energy consumption and carbon footprints.Finally,we present case studies on cryptocurrency networks,namely,Ethereum 2.0 and Pi Network,with a discussion on how they can solve some of the challenges we have identified.
文摘This paper examines the high frequency multiscale relationships and nonlinear multiscale causality between Bitcoin,Ethereum,Monero,Dash,Ripple,and Litecoin.We apply nonlinear Granger causality and rolling window wavelet correlation(RWCC)to 15 min-data.Empirical RWCC results indicate mostly positive co-movements and long-term memory between the cryptocurrencies,especially between Bitcoin,Ethereum,and Monero.The nonlinear Granger causality tests reveal dual causation between most of the cryptocurrency pairs.We advance evidence to improve portfolio risk assessment,and hedging strategies.
