This article analyzes the predictability of market betas concerning cryptocurrency assets and evaluates the efficiency of beta-hedged,market-neutral portfolios.We forecast 1-year-ahead market betas using various estim...This article analyzes the predictability of market betas concerning cryptocurrency assets and evaluates the efficiency of beta-hedged,market-neutral portfolios.We forecast 1-year-ahead market betas using various estimating methods,including ordinary least squares(OLS)and Vasicek’s Bayesian shrinkage estimator,and assess their impact on portfolio variance reduction across cryptomarket indices.Our findings indicate that while standard OLS betas explain significantly less of the variation in future betas for cryptoassets compared to US stocks,slope winsorization and Bayesian shrinkage improve prediction accuracy.The results suggest that beta-hedged portfolios reduce variance for approximately 17%of the universe,with the Broad Digital Market Index demonstrating the best hedging efficiency.These findings underscore the significant challenges of developing effective hedging strategies in the cryptocurrency market,emphasizing the importance of idiosyncratic risk in crypto returns and the need for appropriate market index representation.展开更多
The aim of this study is to examine the extreme return spillovers among the US stock market sectors in the light of the COVID-19 outbreak.To this end,we extend the now-traditional Diebold-Yilmaz spillover index to the...The aim of this study is to examine the extreme return spillovers among the US stock market sectors in the light of the COVID-19 outbreak.To this end,we extend the now-traditional Diebold-Yilmaz spillover index to the quantiles domain by building networks of generalized forecast error variance decomposition of a quantile vector autoregressive model specifically for extreme returns.Notably,we control for common movements by using the overall stock market index as a common factor for all sectors and uncover the effect of the COVID-19 outbreak on the dynamics of the network.The results show that the network structure and spillovers differ considerably with respect to the market state.During stable times,the network shows a nice sectoral clustering structure which,however,changes dramatically for both adverse and beneficial market conditions constituting a highly connected network structure.The pandemic period itself shows an interesting restructuring of the network as the dominant clusters become more tightly connected while the rest of the network remains well separated.The sectoral topology thus has not collapsed into a unified market during the pandemic.展开更多
The driving forces behind cryptoassets’price dynamics are often perceived as being dominated by speculative factors and inherent bubble-bust episodes.Fundamental components are believed to have a weak,if any,role in ...The driving forces behind cryptoassets’price dynamics are often perceived as being dominated by speculative factors and inherent bubble-bust episodes.Fundamental components are believed to have a weak,if any,role in the price-formation process.This study examines five cryptoassets with different backgrounds,namely Bitcoin,Ethereum,Litecoin,XRP,and Dogecoin between 2016 and 2022.It utilizes the cusp catastrophe model to connect the fundamental and speculative drivers with possible price bifurcation characteristics of market collapse events.The findings show that the price and return dynamics of all the studied assets,except for Dogecoin,emerge from complex interactions between fundamental and speculative components,includ-ing episodes of price bifurcations.Bitcoin shows the strongest fundamentals,with on-chain activity and economic factors driving the fundamental part of the dynam-ics.Investor attention and off-chain activity drive the speculative component for all studied assets.Among the fundamental drivers,the analyzed cryptoassets present their coin-specific factors,which can be tracked to their protocol specifics and are economi-cally sound.展开更多
We examine the interactions between stablecoins,Bitcoin,and a basket of altcoins to uncover whether stablecoins represent the investors’demand for trading and investing into cryptoassets or rather play a role as boos...We examine the interactions between stablecoins,Bitcoin,and a basket of altcoins to uncover whether stablecoins represent the investors’demand for trading and investing into cryptoassets or rather play a role as boosting mechanisms during cryptomarkets price rallies.Using a set of instruments covering the standard cointegration framework as well as quantile-specific and non-linear causality tests,we argue that stablecoins mostly reflect an increasing demand for investing in cryptoassets rather than serve as a boosting mechanism for periods of extreme appreciation.We further discuss some specificities of 2017,even though the dynamic patterns remain very similar to the general behavior.Overall,we do not find support for claims about stablecoins being bubble boosters in the cryptoassets ecosystem.展开更多
The cryptomarket has evolved into a complex system of different types of cryptoassets,each playing an important role within the system.With specific features,opportunities,and risks.Studying their apparent and hidden ...The cryptomarket has evolved into a complex system of different types of cryptoassets,each playing an important role within the system.With specific features,opportunities,and risks.Studying their apparent and hidden linkages and general connectedness not only inside the system but also the linkages to the outer markets,being it either the traditional financial markets or the macroeconomic and monetary indicators and variables,plays a crucial role in understanding the market,managing risks,and aiming for profitable opportunities.The cryptomarkets are far from being simply Bitcoin or even just the most popular and capitalised cryptocurrencies and tokens which might have been the case just a few years back.展开更多
基金financial support from the Czech Science Foundation under the project`Deep dive into decentralized finance:Market microstructure,and behavioral and psychological patterns’[Grant No.23-06606S]supported by Charles University Research Centre program No.24/SSH/020+1 种基金the Cooperatio Program at Charles University,research area Economicsfinancial support from the Charles University Specific University Research scheme[Grant No.SVV 260843].
文摘This article analyzes the predictability of market betas concerning cryptocurrency assets and evaluates the efficiency of beta-hedged,market-neutral portfolios.We forecast 1-year-ahead market betas using various estimating methods,including ordinary least squares(OLS)and Vasicek’s Bayesian shrinkage estimator,and assess their impact on portfolio variance reduction across cryptomarket indices.Our findings indicate that while standard OLS betas explain significantly less of the variation in future betas for cryptoassets compared to US stocks,slope winsorization and Bayesian shrinkage improve prediction accuracy.The results suggest that beta-hedged portfolios reduce variance for approximately 17%of the universe,with the Broad Digital Market Index demonstrating the best hedging efficiency.These findings underscore the significant challenges of developing effective hedging strategies in the cryptocurrency market,emphasizing the importance of idiosyncratic risk in crypto returns and the need for appropriate market index representation.
基金Ladislav Kristoufek gratefully acknowledges financial support of the Czech Science Foundation(project 20-17295S)the Charles University PRIMUS program(project PRIMUS/19/HUM/17).
文摘The aim of this study is to examine the extreme return spillovers among the US stock market sectors in the light of the COVID-19 outbreak.To this end,we extend the now-traditional Diebold-Yilmaz spillover index to the quantiles domain by building networks of generalized forecast error variance decomposition of a quantile vector autoregressive model specifically for extreme returns.Notably,we control for common movements by using the overall stock market index as a common factor for all sectors and uncover the effect of the COVID-19 outbreak on the dynamics of the network.The results show that the network structure and spillovers differ considerably with respect to the market state.During stable times,the network shows a nice sectoral clustering structure which,however,changes dramatically for both adverse and beneficial market conditions constituting a highly connected network structure.The pandemic period itself shows an interesting restructuring of the network as the dominant clusters become more tightly connected while the rest of the network remains well separated.The sectoral topology thus has not collapsed into a unified market during the pandemic.
基金financial support from the Czech Science Foundation under the 20-17295S“Cryptoassets:Pricing,Interconnectedness,Mining,and their Interactions”project and from the Charles University PRIMUS program(project PRIMUS/19/HUM/17)Jiri Kukacka gratefully acknowledges financial support from the Charles University UNCE program(project UNCE/HUM/035)supported by the Cooperatio Program at Charles University,research area Economics.
文摘The driving forces behind cryptoassets’price dynamics are often perceived as being dominated by speculative factors and inherent bubble-bust episodes.Fundamental components are believed to have a weak,if any,role in the price-formation process.This study examines five cryptoassets with different backgrounds,namely Bitcoin,Ethereum,Litecoin,XRP,and Dogecoin between 2016 and 2022.It utilizes the cusp catastrophe model to connect the fundamental and speculative drivers with possible price bifurcation characteristics of market collapse events.The findings show that the price and return dynamics of all the studied assets,except for Dogecoin,emerge from complex interactions between fundamental and speculative components,includ-ing episodes of price bifurcations.Bitcoin shows the strongest fundamentals,with on-chain activity and economic factors driving the fundamental part of the dynam-ics.Investor attention and off-chain activity drive the speculative component for all studied assets.Among the fundamental drivers,the analyzed cryptoassets present their coin-specific factors,which can be tracked to their protocol specifics and are economi-cally sound.
基金Support from the Charles University PRIMUS program(Project PRIMUS/19/HUM/17)the Czech Science Foundation(Project 20-17295S)is highly appreciated.
文摘We examine the interactions between stablecoins,Bitcoin,and a basket of altcoins to uncover whether stablecoins represent the investors’demand for trading and investing into cryptoassets or rather play a role as boosting mechanisms during cryptomarkets price rallies.Using a set of instruments covering the standard cointegration framework as well as quantile-specific and non-linear causality tests,we argue that stablecoins mostly reflect an increasing demand for investing in cryptoassets rather than serve as a boosting mechanism for periods of extreme appreciation.We further discuss some specificities of 2017,even though the dynamic patterns remain very similar to the general behavior.Overall,we do not find support for claims about stablecoins being bubble boosters in the cryptoassets ecosystem.
文摘The cryptomarket has evolved into a complex system of different types of cryptoassets,each playing an important role within the system.With specific features,opportunities,and risks.Studying their apparent and hidden linkages and general connectedness not only inside the system but also the linkages to the outer markets,being it either the traditional financial markets or the macroeconomic and monetary indicators and variables,plays a crucial role in understanding the market,managing risks,and aiming for profitable opportunities.The cryptomarkets are far from being simply Bitcoin or even just the most popular and capitalised cryptocurrencies and tokens which might have been the case just a few years back.
