Using daily BTC-USD data from September 19,2014 to January 21,2024,this paper re-examines whether weekends differ from weekdays for Bitcoin along three margins:average returns,close-to-close volatility,and trading act...Using daily BTC-USD data from September 19,2014 to January 21,2024,this paper re-examines whether weekends differ from weekdays for Bitcoin along three margins:average returns,close-to-close volatility,and trading activity.We implement Welch mean comparisons and HAC-robust OLS with month fixed effects(bandwidths 5,7,and 14).In the full sample and across subsamples(2016–2019;2020–2023;early 2024),we find no detectable weekend–weekday gap in average returns,while volatility and trading activity are lower on weekends.The patterns are robust to using squared returns as a volatility proxy.The joint evidence is consistent with liquidity and attention mechanisms—quieter weekends rather than compensating return premia.Replication files reproduce all tables and figures.展开更多
This paper employs Granger causality analysis and the generalized impulse response function(GIRF)to study the higher-order moment spillover effects among Bitcoin,stock markets,and foreign exchange markets in the U.S.U...This paper employs Granger causality analysis and the generalized impulse response function(GIRF)to study the higher-order moment spillover effects among Bitcoin,stock markets,and foreign exchange markets in the U.S.Using intraday high-frequency data,the research focuses on the interactions across higher-order moments,including volatility,jumps,skewness,and kurtosis.The results reveal significant bidirectional spillover effects between Bitcoin and traditional financial assets,particularly in terms of volatility and jump behavior,indicating that the cryptocurrency market has become a crucial component of global financial risk transmission.This study provides new theoretical perspectives and policy recommendations for global asset allocation,market regulation,and risk management,underscoring the importance of proactive management measures in addressing the complex risk interactions between cryptocurrencies and traditional financial markets.展开更多
Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges,gambling,marketplaces,and also scams such as high-yield investment projects.Identifying the services ope...Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges,gambling,marketplaces,and also scams such as high-yield investment projects.Identifying the services operated by a Bitcoin address can help determine the risk level of that address and build an alert model accordingly.Feature engineering can also be used to flesh out labeled addresses and to analyze the current state of Bitcoin in a small way.In this paper,we address the problem of identifying multiple classes of Bitcoin services,and for the poor classification of individual addresses that do not have significant features,we propose a Bitcoin address identification scheme based on joint multi-model prediction using the mapping relationship between addresses and entities.The innovation of the method is to(1)Extract as many valuable features as possible when an address is given to facilitate the multi-class service identification task.(2)Unlike the general supervised model approach,this paper proposes a joint prediction scheme for multiple learners based on address-entity mapping relationships.Specifically,after obtaining the overall features,the address classification and entity clustering tasks are performed separately,and the results are subjected to graph-basedmaximization consensus.The final result ismade to baseline the individual address classification results while satisfying the constraint of having similarly behaving entities as far as possible.By testing and evaluating over 26,000 Bitcoin addresses,our feature extraction method captures more useful features.In addition,the combined multi-learner model obtained results that exceeded the baseline classifier reaching an accuracy of 77.4%.展开更多
Due to its anonymity and decentralization,Bitcoin has long been a haven for various illegal activities.Cybercriminals generally legalize illicit funds by Bitcoin mixing services.Therefore,it is critical to investigate...Due to its anonymity and decentralization,Bitcoin has long been a haven for various illegal activities.Cybercriminals generally legalize illicit funds by Bitcoin mixing services.Therefore,it is critical to investigate the mixing services in cryptocurrency anti-money laundering.Existing studies treat different mixing services as a class of suspicious Bitcoin entities.Furthermore,they are limited by relying on expert experience or needing to deal with large-scale networks.So far,multi-class mixing service identification has not been explored yet.It is challenging since mixing services share a similar procedure,presenting no sharp distinctions.However,mixing service identification facilitates the healthy development of Bitcoin,supports financial forensics for cryptocurrency regulation and legislation,and provides technical means for fine-grained blockchain supervision.This paper aims to achieve multi-class Bitcoin Mixing Service Identification with a Graph Classification(BMSI-GC)model.First,BMSI-GC constructs 2-hop ego networks(2-egonets)of mixing services based on their historical transactions.Second,it applies graph2vec,a graph classification model mainly used to calculate the similarity between graphs,to automatically extract address features from the constructed 2-egonets.Finally,it trains a multilayer perceptron classifier to perform classification based on the extracted features.BMSI-GC is flexible without handling the full-size network and handcrafting address features.Moreover,the differences in transaction patterns of mixing services reflected in the 2-egonets provide adequate information for identification.Our experimental study demonstrates that BMSI-GC performs excellently in multi-class Bitcoin mixing service identification,achieving an average identification F1-score of 95.08%.展开更多
COVID-19 has significantly influenced global financial markets,including Bitcoin.Recent studies have focused on investigating the first wave of the COVID-19 outbreak and accounting for market changes,which were mostly...COVID-19 has significantly influenced global financial markets,including Bitcoin.Recent studies have focused on investigating the first wave of the COVID-19 outbreak and accounting for market changes,which were mostly due to the pandemic.This research not only analyzes the contagion effects of COVID-19 but also considers aftermath events beyond the first pandemic wave to examine spillovers of Bitcoin.The study employs Diebold and Yilmaz’s method to explore the static and dynamic spillovers of the selected variables and identifies several major global events,including crypto-specific affairs,macroeconomic policies,and geopolitical conflicts,to explain the new market dynamics of Bitcoin using network analysis.The findings identify a few high-contagion periods related to Bitcoin.The paper also found that Bitcoin is more likely to produce extreme returns and is more connected to other markets.Contagion effects“from”and“to”other markets are asymmetrical in terms of arrival time and market response.Bitcoin is more likely to be affected by other markets in extreme situations and receives spillovers from them sooner than it transmits spillovers to others.In the context of various global events,impacts arising from developed countries are stronger.China still has some impact on cryptocurrency markets,but they are waning.Bitcoin is thus not a safe haven from the shocks of global events,but can sometimes work as a hedge or diversifier.The results offer alternative explanations for Bitcoin’s different market dynamics and enrich our understanding of Bitcoin’s safe haven,hedge,and diversifier properties within a diversified portfolio.展开更多
This paper applies deep learning models to predict Bitcoin price directions and the subsequent profitability of trading strategies based on these predictions.The study compares the performance of the convolutional neu...This paper applies deep learning models to predict Bitcoin price directions and the subsequent profitability of trading strategies based on these predictions.The study compares the performance of the convolutional neural network-long short-term memory(CNN–LSTM),long-and short-term time-series network,temporal convolutional network,and ARIMA(benchmark)models for predicting Bitcoin prices using on-chain data.Feature-selection methods—i.e.,Boruta,genetic algorithm,and light gradient boosting machine—are applied to address the curse of dimensionality that could result from a large feature set.Results indicate that combining Boruta feature selection with the CNN-LSTM model consistently outperforms other combinations,achieving an accuracy of 82.44%.Three trading strategies and three investment positions are examined through backtesting.The long-and-short buy-and-sell investment approach generated an extraordinary annual return of 6654% when informed by higher-accuracy price-direction predictions.This study provides evidence of the potential profitability of predictive models in Bitcoin trading.展开更多
Since its inception in 2009,Bitcoin has become and is currently the most successful and widely used cryptocurrency.It introduced blockchain technology,which allows transactions that transfer funds between users to tak...Since its inception in 2009,Bitcoin has become and is currently the most successful and widely used cryptocurrency.It introduced blockchain technology,which allows transactions that transfer funds between users to take place online,in an immutable manner.No real-world identities are needed or stored in the blockchain.At the same time,all transactions are publicly available and auditable,making Bitcoin a pseudo-anonymous ledger of transactions.The volume of transactions that are broadcast on a daily basis is considerably large.We propose a set of features that can be extracted from transaction data.Using this,we apply a data processing pipeline to ultimately cluster transactions via a k-means clustering algorithm,according to the transaction properties.Finally,according to these properties,we are able to characterize these clusters and the transactions they include.Our work mainly differentiates from previous studies in that it applies an unsupervised learning method to cluster transactions instead of addresses.Using the novel features we introduce,our work classifies transactions in multiple clusters,while previous studies only attempt binary classification.Results indicate that most transactions fall into a cluster that can be described as common user transactions.Other clusters include transactions made by online exchanges and lending services,those relating to mining activities as well as smaller clusters,one of which contains possibly illicit or fraudulent transactions.We evaluated our results against an online database of addresses that belong to known actors,such as online exchanges,and found that our results generally agree with them,which enhances the validity of our methods.展开更多
Most previous studies on the market efficiency of cryptocurrencies consider time evolution but do not provide insights into the potential driving factors.This study addresses this limitation by examining the time-vary...Most previous studies on the market efficiency of cryptocurrencies consider time evolution but do not provide insights into the potential driving factors.This study addresses this limitation by examining the time-varying efficiency of the two largest cryptocurrencies,Bitcoin and Ethereum,and the factors that drive efficiency.It uses daily data from August 7,2016,to February 15,2023,the adjusted market inefficiency magnitude(AMIMs)measure,and quantile regression.The results show evidence of time variation in the levels of market(in)efficiency for Bitcoin and Ethereum.Interestingly,the quantile regressions indicate that global financial stress negatively affects the AMIMs measures across all quantiles.Notably,cryptocurrency liquidity positively and significantly affects AMIMs irrespective of the level of(in)efficiency,whereas the positive effect of money flow is significant when the markets of both cryptocurrencies are efficient.Finally,the COVID-19 pandemic positively and significantly affected cryptocurrency market inefficiencies across most quantiles.展开更多
In the blockchain world,proof-of-work is the dominant protocol mechanism that determines the consensus of the ledger.The hashrate,a measure of the computational power directed toward securing a blockchain through proo...In the blockchain world,proof-of-work is the dominant protocol mechanism that determines the consensus of the ledger.The hashrate,a measure of the computational power directed toward securing a blockchain through proof-of-work consensus,is a fundamental measure of preventing various attacks.This study tests the causal relationship between the hashrate and the security outcome of the Bitcoin blockchain.We use vector error correction modeling to analyze the endogenous relationships between the hashrate,Bitcoin price,and transaction fee,revealing the need for an additional variable to achieve our aim.Employing a measure summarizing the growth of demand factors in the Bitcoin ecosystem indicates that hashrate fluctuations significantly influence security level changes.This result underscores the importance of the hashrate in ensuring the security of the Bitcoin blockchain.展开更多
In the FinTech era,we contribute to the literature by studying the pricing of Bitcoin options,which is timely and important given that both Nasdaq and the CME Group have started to launch a variety of Bitcoin derivati...In the FinTech era,we contribute to the literature by studying the pricing of Bitcoin options,which is timely and important given that both Nasdaq and the CME Group have started to launch a variety of Bitcoin derivatives.We find pricing errors in the presence of market smiles in Bitcoin options,especially for short-maturity ones.Long-maturity options display more of a“smirk”than a smile.Additionally,the ARJI-EGARCH model provides a better overall fit for the pricing of Bitcoin options than the other ARJI-GARCH type models.We also demonstrate that the ARJI-GARCH model can provide more precise pricing of Bitcoin and its options than the SVCJ model in term of the goodness-of-fit in forecasting.Allowing for jumps is crucial for modeling Bitcoin options as we find evidence of time-varying jumps.Our empirical results demonstrate that the realized jump variation can describe the volatility behavior and capture the jump risk dynamics in Bitcoin and its options.展开更多
The spread of the coronavirus has reduced the value of stock indexes,depressed energy and metals commodities prices including oil,and caused instability in financial markets around the world.Due to this situation,inve...The spread of the coronavirus has reduced the value of stock indexes,depressed energy and metals commodities prices including oil,and caused instability in financial markets around the world.Due to this situation,investors should consider investing in more secure assets,such as real estate property,cash,gold,and crypto assets.In recent years,among secure assets,cryptoassets are gaining more attention than traditional investments.This study compares the Bitcoin market,the gold market,and American stock indexes(S&P500,Nasdaq,and Dow Jones)before and during the COVID-19 pandemic.For this purpose,the dynamic conditional correlation exponential generalized autoregressive conditional heteroskedasticity model was used to estimate the DCC coefficient and compare this model with the artificial neural network approach to predict volatility of these markets.Our empirical findings showed a substantial dynamic conditional correlation between Bitcoin,gold,and stock markets.In particular,we observed that Bitcoin offered better diversification opportunities to reduce risks in key stock markets during the COVID-19 period.This paper provides practical impacts on risk management and portfolio diversification.展开更多
This study aims to identify the factors that robustly contribute to Bitcoin liquidity,employing a rich range of potential determinants that represent unique characteristics of the cryptocurrency industry,investor atte...This study aims to identify the factors that robustly contribute to Bitcoin liquidity,employing a rich range of potential determinants that represent unique characteristics of the cryptocurrency industry,investor attention,macroeconomic fundamentals,and global stress and uncertainty.To construct liquidity metrics,we compile 60-min high-frequency data on the low,high,opening,and closing exchange rates of Bitcoin against the US dollar.Our empirical investigation is based on the extreme bounds analysis(EBA),which can resolve model uncertainty issues.The results of Leamer’s version of the EBA suggest that the realized volatility of Bitcoin is the sole variable relevant to explaining liquidity.With the Sala-i-Martin’s variant of EBA,however,four more variables,(viz.Bitcoin’s negative returns,trading volume,hash rates,and Google search volume)are also labeled as robust determinants.Accordingly,our evidence confirms that Bitcoin-specific factors and developments,rather than global macroeconomic and financial variables,matter for explaining its liquidity.The findings are largely insensitive to our proxy of liquidity and to the estimation method used.展开更多
The recently developed Bitcoin futures and options contracts in cryptocurrency derivatives exchanges mark the beginning of a new era in Bitcoin price risk hedging.The need for these tools dates back to the market cras...The recently developed Bitcoin futures and options contracts in cryptocurrency derivatives exchanges mark the beginning of a new era in Bitcoin price risk hedging.The need for these tools dates back to the market crash of 1987,when investors needed better ways to protect their portfolios through option insurance.These tools provide greater flexibility to trade and hedge volatile swings in Bitcoin prices effectively.The violation of constant volatility and the log-normality assumption of the Black–Scholes option pricing model led to the discovery of the volatility smile,smirk,or skew in options markets.These stylized facts;that is,the volatility smile and implied volatilities implied by the option prices,are well documented in the option literature for almost all financial markets.These are expected to be true for Bitcoin options as well.The data sets for the study are based on short-dated Bitcoin options(14-day maturity)of two time periods traded on Deribit Bitcoin Futures and Options Exchange,a Netherlandsbased cryptocurrency derivative exchange.The estimated results are compared with benchmark Black–Scholes implied volatility values for accuracy and efficiency analysis.This study has two aims:(1)to provide insights into the volatility smile in Bitcoin options and(2)to estimate the implied volatility of Bitcoin options through numerical approximation techniques,specifically the Newton Raphson and Bisection methods.The experimental results show that Bitcoin options belong to the commodity class of assets based on the presence of a volatility forward skew in Bitcoin option data.Moreover,the Newton Raphson and Bisection methods are effective in estimating the implied volatility of Bitcoin options.However,the Newton Raphson forecasting technique converges faster than does the Bisection method.展开更多
As an extension of the traditional encryption technology,information hiding has been increasingly used in the fields of communication and network media,and the covert communication technology has gradually developed.T...As an extension of the traditional encryption technology,information hiding has been increasingly used in the fields of communication and network media,and the covert communication technology has gradually developed.The blockchain technology that has emerged in recent years has the characteristics of decentralization and tamper resistance,which can effectively alleviate the disadvantages and problems of traditional covert communication.However,its combination with covert communication thus far has been mostly at the theoretical level.The BLOCCE method,as an early result of the combination of blockchain and covert communication technology,has the problems of low information embedding efficiency,the use of too many Bitcoin addresses,low communication efficiency,and high costs.The present research improved on this method,designed the V-BLOCCE which uses base58 to encrypt the plaintext and reuses the addresses generated by Vanitygen multiple times to embed information.This greatly improves the efficiency of information embedding and decreases the number of Bitcoin addresses used.Under the premise of ensuring the order,the Bitcoin transaction OP_RETURN field is used to store the information required to restore the plaintext and the transactions are issued at the same time to improve the information transmission efficiency.Thus,a more efficient and feasible method for the application of covert communication on the blockchain is proposed.In addition,this paper also provides a more feasible scheme and theoretical support for covert communication in blockchain.展开更多
Background:Bitcoin,the most innovate digital currency as of now,created since 2008,even through experienced its ups and downs,still keeps drawing attentions to all parts of society.It relies on peer-to-peer network,ac...Background:Bitcoin,the most innovate digital currency as of now,created since 2008,even through experienced its ups and downs,still keeps drawing attentions to all parts of society.It relies on peer-to-peer network,achieved decentralization,anonymous and transparent.As the most representative digital currency,people curious to study how Bitcoin’price changes in the past.Methods:In this paper,we use monthly data from 2011 to 2016 to build a VEC model to exam how economic factors such as Custom price index,US dollar index,Dow jones industry average,Federal Funds Rate and gold price influence Bitcoin price.Result:From empirical analysis we find that all these variables do have a long-term influence.US dollar index is the biggest influence on Bitcoin price while gold price influence the least.Conclusion:From our result,we conclude that for now Bitcoin can be treated as a speculative asset,however,it is far from being a proper credit currency.展开更多
This paper analyzes the selfish-mine strategy in the Bitcoin blockchain introduced in 2013 by I.Eyal and E.G.Sirer.This strategy could be used by a colluding pool of miners to earn more than their fair share of the mi...This paper analyzes the selfish-mine strategy in the Bitcoin blockchain introduced in 2013 by I.Eyal and E.G.Sirer.This strategy could be used by a colluding pool of miners to earn more than their fair share of the mining revenue and in consequence to force other honest miners to join them to decrease the variance of their revenues and make their monthly revenues more predictable.It is a very dangerous dynamic that could allow the rogue pool of miners to go toward a majority by accumulating powers of news adherents and control the entire network.Considering that the propagation delay of information between any two miners in the network,which is not negligible and follows a normal distribution with mean proportional to the physical distance between the two miners,and a constant variance independent of others'delays,we prove that no guarantee can be given about the success or failure of the selfish-mine attack because of the variability of information propagation in the network.展开更多
This study examines the connectedness between the US yield curve components(i.e.,level,slope,and curvature),exchange rates,and the historical volatility of the exchange rates of the main safe-haven fiat currencies(Can...This study examines the connectedness between the US yield curve components(i.e.,level,slope,and curvature),exchange rates,and the historical volatility of the exchange rates of the main safe-haven fiat currencies(Canada,Switzerland,EURO,Japan,and the UK)and the leading cryptocurrency,the Bitcoin.Results of the static analysis show that the level and slope of the yield curve are net transmitters of shocks to both the exchange rate and its volatility.The exchange rate of the Euro and the volatility of the Euro and the Canadian dollar exchange rate are net transmitters of shocks.Meanwhile,the curvature of the yield curve and the Japanese Yen,Swiss Franc,and British Pound act mainly as net receivers.Our static connectedness analysis shows that Bitcoin is mainly independent of shocks from the yield curve’s level,slope,and curvature,and from any main currency investigated.These findings hint that Bitcoin might provide hedging benefits.However,similar to the static analysis,our dynamic analysis shows that during different periods and particularly in stressful times,Bitcoin is far from being isolated from other currencies or the yield curve components.The dynamic analysis allows us to observe Bitcoin’s connectedness in times of stress.Evidence supporting this contention is the substantially increased connectedness due to policy shocks,political uncertainty,and systemic crisis,implying no empirical support for Bitcoin’s safe-haven property during stress times.The increased connectedness in the dynamic analysis compared with the static approach implies that in normal times and especially in stressful times,Bitcoin has the property of a diversifier.The results may have important implications for investors and policymakers regarding their risk monitoring and their assets allocation and investment strategies.展开更多
Bitcoin is currently the leading global provider of cryptocurrency.Cryptocurrency allows users to safely and anonymously use the Internet to perform digital currency transfers and storage.In recent years,the Bitcoin n...Bitcoin is currently the leading global provider of cryptocurrency.Cryptocurrency allows users to safely and anonymously use the Internet to perform digital currency transfers and storage.In recent years,the Bitcoin network has attracted investors,businesses,and corporations while facilitating services and product deals.Moreover,Bitcoin has made itself the dominant source of decentralized cryptocurrency.While considerable research has been done concerning Bitcoin network analysis,limited research has been conducted on predicting the Bitcoin price.The purpose of this study is to predict the price of Bitcoin and changes therein using the grey system theory.The first order grey model(GM(1,1))is used for this purpose.It uses a firstorder differential equation to model the trend of time series.The results show that the GM(1,1)model predicts Bitcoin’s price accurately and that one can earn a maximum profit confidence level of approximately 98%by choosing the appropriate time frame and by managing investment assets.展开更多
文摘Using daily BTC-USD data from September 19,2014 to January 21,2024,this paper re-examines whether weekends differ from weekdays for Bitcoin along three margins:average returns,close-to-close volatility,and trading activity.We implement Welch mean comparisons and HAC-robust OLS with month fixed effects(bandwidths 5,7,and 14).In the full sample and across subsamples(2016–2019;2020–2023;early 2024),we find no detectable weekend–weekday gap in average returns,while volatility and trading activity are lower on weekends.The patterns are robust to using squared returns as a volatility proxy.The joint evidence is consistent with liquidity and attention mechanisms—quieter weekends rather than compensating return premia.Replication files reproduce all tables and figures.
文摘This paper employs Granger causality analysis and the generalized impulse response function(GIRF)to study the higher-order moment spillover effects among Bitcoin,stock markets,and foreign exchange markets in the U.S.Using intraday high-frequency data,the research focuses on the interactions across higher-order moments,including volatility,jumps,skewness,and kurtosis.The results reveal significant bidirectional spillover effects between Bitcoin and traditional financial assets,particularly in terms of volatility and jump behavior,indicating that the cryptocurrency market has become a crucial component of global financial risk transmission.This study provides new theoretical perspectives and policy recommendations for global asset allocation,market regulation,and risk management,underscoring the importance of proactive management measures in addressing the complex risk interactions between cryptocurrencies and traditional financial markets.
基金sponsored by the National Natural Science Foundation of China Nos.62172353,62302114 and U20B2046Future Network Scientific Research Fund Project No.FNSRFP-2021-YB-48Innovation Fund Program of the Engineering Research Center for Integration and Application of Digital Learning Technology of Ministry of Education No.1221045。
文摘Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges,gambling,marketplaces,and also scams such as high-yield investment projects.Identifying the services operated by a Bitcoin address can help determine the risk level of that address and build an alert model accordingly.Feature engineering can also be used to flesh out labeled addresses and to analyze the current state of Bitcoin in a small way.In this paper,we address the problem of identifying multiple classes of Bitcoin services,and for the poor classification of individual addresses that do not have significant features,we propose a Bitcoin address identification scheme based on joint multi-model prediction using the mapping relationship between addresses and entities.The innovation of the method is to(1)Extract as many valuable features as possible when an address is given to facilitate the multi-class service identification task.(2)Unlike the general supervised model approach,this paper proposes a joint prediction scheme for multiple learners based on address-entity mapping relationships.Specifically,after obtaining the overall features,the address classification and entity clustering tasks are performed separately,and the results are subjected to graph-basedmaximization consensus.The final result ismade to baseline the individual address classification results while satisfying the constraint of having similarly behaving entities as far as possible.By testing and evaluating over 26,000 Bitcoin addresses,our feature extraction method captures more useful features.In addition,the combined multi-learner model obtained results that exceeded the baseline classifier reaching an accuracy of 77.4%.
基金supported in part by National Key R&D Program of China under Grant 2023YFB3106801in part by Jiangsu Province Natural Science Foundation Project under Grant BK20231413+1 种基金in part by the National Natural Science Foundation of China under Grants 61602114 and 62172093in part by the Special Funds for Basic Scientific Research Operations of Central Universities under Grant 2242024K30021。
文摘Due to its anonymity and decentralization,Bitcoin has long been a haven for various illegal activities.Cybercriminals generally legalize illicit funds by Bitcoin mixing services.Therefore,it is critical to investigate the mixing services in cryptocurrency anti-money laundering.Existing studies treat different mixing services as a class of suspicious Bitcoin entities.Furthermore,they are limited by relying on expert experience or needing to deal with large-scale networks.So far,multi-class mixing service identification has not been explored yet.It is challenging since mixing services share a similar procedure,presenting no sharp distinctions.However,mixing service identification facilitates the healthy development of Bitcoin,supports financial forensics for cryptocurrency regulation and legislation,and provides technical means for fine-grained blockchain supervision.This paper aims to achieve multi-class Bitcoin Mixing Service Identification with a Graph Classification(BMSI-GC)model.First,BMSI-GC constructs 2-hop ego networks(2-egonets)of mixing services based on their historical transactions.Second,it applies graph2vec,a graph classification model mainly used to calculate the similarity between graphs,to automatically extract address features from the constructed 2-egonets.Finally,it trains a multilayer perceptron classifier to perform classification based on the extracted features.BMSI-GC is flexible without handling the full-size network and handcrafting address features.Moreover,the differences in transaction patterns of mixing services reflected in the 2-egonets provide adequate information for identification.Our experimental study demonstrates that BMSI-GC performs excellently in multi-class Bitcoin mixing service identification,achieving an average identification F1-score of 95.08%.
文摘COVID-19 has significantly influenced global financial markets,including Bitcoin.Recent studies have focused on investigating the first wave of the COVID-19 outbreak and accounting for market changes,which were mostly due to the pandemic.This research not only analyzes the contagion effects of COVID-19 but also considers aftermath events beyond the first pandemic wave to examine spillovers of Bitcoin.The study employs Diebold and Yilmaz’s method to explore the static and dynamic spillovers of the selected variables and identifies several major global events,including crypto-specific affairs,macroeconomic policies,and geopolitical conflicts,to explain the new market dynamics of Bitcoin using network analysis.The findings identify a few high-contagion periods related to Bitcoin.The paper also found that Bitcoin is more likely to produce extreme returns and is more connected to other markets.Contagion effects“from”and“to”other markets are asymmetrical in terms of arrival time and market response.Bitcoin is more likely to be affected by other markets in extreme situations and receives spillovers from them sooner than it transmits spillovers to others.In the context of various global events,impacts arising from developed countries are stronger.China still has some impact on cryptocurrency markets,but they are waning.Bitcoin is thus not a safe haven from the shocks of global events,but can sometimes work as a hedge or diversifier.The results offer alternative explanations for Bitcoin’s different market dynamics and enrich our understanding of Bitcoin’s safe haven,hedge,and diversifier properties within a diversified portfolio.
文摘This paper applies deep learning models to predict Bitcoin price directions and the subsequent profitability of trading strategies based on these predictions.The study compares the performance of the convolutional neural network-long short-term memory(CNN–LSTM),long-and short-term time-series network,temporal convolutional network,and ARIMA(benchmark)models for predicting Bitcoin prices using on-chain data.Feature-selection methods—i.e.,Boruta,genetic algorithm,and light gradient boosting machine—are applied to address the curse of dimensionality that could result from a large feature set.Results indicate that combining Boruta feature selection with the CNN-LSTM model consistently outperforms other combinations,achieving an accuracy of 82.44%.Three trading strategies and three investment positions are examined through backtesting.The long-and-short buy-and-sell investment approach generated an extraordinary annual return of 6654% when informed by higher-accuracy price-direction predictions.This study provides evidence of the potential profitability of predictive models in Bitcoin trading.
基金co-financed by the European Union Horizon Europe Research and Innovation Programme under Grant Agreements No.101058174 and No 101091895.
文摘Since its inception in 2009,Bitcoin has become and is currently the most successful and widely used cryptocurrency.It introduced blockchain technology,which allows transactions that transfer funds between users to take place online,in an immutable manner.No real-world identities are needed or stored in the blockchain.At the same time,all transactions are publicly available and auditable,making Bitcoin a pseudo-anonymous ledger of transactions.The volume of transactions that are broadcast on a daily basis is considerably large.We propose a set of features that can be extracted from transaction data.Using this,we apply a data processing pipeline to ultimately cluster transactions via a k-means clustering algorithm,according to the transaction properties.Finally,according to these properties,we are able to characterize these clusters and the transactions they include.Our work mainly differentiates from previous studies in that it applies an unsupervised learning method to cluster transactions instead of addresses.Using the novel features we introduce,our work classifies transactions in multiple clusters,while previous studies only attempt binary classification.Results indicate that most transactions fall into a cluster that can be described as common user transactions.Other clusters include transactions made by online exchanges and lending services,those relating to mining activities as well as smaller clusters,one of which contains possibly illicit or fraudulent transactions.We evaluated our results against an online database of addresses that belong to known actors,such as online exchanges,and found that our results generally agree with them,which enhances the validity of our methods.
文摘Most previous studies on the market efficiency of cryptocurrencies consider time evolution but do not provide insights into the potential driving factors.This study addresses this limitation by examining the time-varying efficiency of the two largest cryptocurrencies,Bitcoin and Ethereum,and the factors that drive efficiency.It uses daily data from August 7,2016,to February 15,2023,the adjusted market inefficiency magnitude(AMIMs)measure,and quantile regression.The results show evidence of time variation in the levels of market(in)efficiency for Bitcoin and Ethereum.Interestingly,the quantile regressions indicate that global financial stress negatively affects the AMIMs measures across all quantiles.Notably,cryptocurrency liquidity positively and significantly affects AMIMs irrespective of the level of(in)efficiency,whereas the positive effect of money flow is significant when the markets of both cryptocurrencies are efficient.Finally,the COVID-19 pandemic positively and significantly affected cryptocurrency market inefficiencies across most quantiles.
基金supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2022S1A5A2A01044485).
文摘In the blockchain world,proof-of-work is the dominant protocol mechanism that determines the consensus of the ledger.The hashrate,a measure of the computational power directed toward securing a blockchain through proof-of-work consensus,is a fundamental measure of preventing various attacks.This study tests the causal relationship between the hashrate and the security outcome of the Bitcoin blockchain.We use vector error correction modeling to analyze the endogenous relationships between the hashrate,Bitcoin price,and transaction fee,revealing the need for an additional variable to achieve our aim.Employing a measure summarizing the growth of demand factors in the Bitcoin ecosystem indicates that hashrate fluctuations significantly influence security level changes.This result underscores the importance of the hashrate in ensuring the security of the Bitcoin blockchain.
文摘In the FinTech era,we contribute to the literature by studying the pricing of Bitcoin options,which is timely and important given that both Nasdaq and the CME Group have started to launch a variety of Bitcoin derivatives.We find pricing errors in the presence of market smiles in Bitcoin options,especially for short-maturity ones.Long-maturity options display more of a“smirk”than a smile.Additionally,the ARJI-EGARCH model provides a better overall fit for the pricing of Bitcoin options than the other ARJI-GARCH type models.We also demonstrate that the ARJI-GARCH model can provide more precise pricing of Bitcoin and its options than the SVCJ model in term of the goodness-of-fit in forecasting.Allowing for jumps is crucial for modeling Bitcoin options as we find evidence of time-varying jumps.Our empirical results demonstrate that the realized jump variation can describe the volatility behavior and capture the jump risk dynamics in Bitcoin and its options.
基金supported by the Department of Economics and Management,University of Luxembourgfinancial support from the Department of Economics and Management,University of Luxembourg.
文摘The spread of the coronavirus has reduced the value of stock indexes,depressed energy and metals commodities prices including oil,and caused instability in financial markets around the world.Due to this situation,investors should consider investing in more secure assets,such as real estate property,cash,gold,and crypto assets.In recent years,among secure assets,cryptoassets are gaining more attention than traditional investments.This study compares the Bitcoin market,the gold market,and American stock indexes(S&P500,Nasdaq,and Dow Jones)before and during the COVID-19 pandemic.For this purpose,the dynamic conditional correlation exponential generalized autoregressive conditional heteroskedasticity model was used to estimate the DCC coefficient and compare this model with the artificial neural network approach to predict volatility of these markets.Our empirical findings showed a substantial dynamic conditional correlation between Bitcoin,gold,and stock markets.In particular,we observed that Bitcoin offered better diversification opportunities to reduce risks in key stock markets during the COVID-19 period.This paper provides practical impacts on risk management and portfolio diversification.
文摘This study aims to identify the factors that robustly contribute to Bitcoin liquidity,employing a rich range of potential determinants that represent unique characteristics of the cryptocurrency industry,investor attention,macroeconomic fundamentals,and global stress and uncertainty.To construct liquidity metrics,we compile 60-min high-frequency data on the low,high,opening,and closing exchange rates of Bitcoin against the US dollar.Our empirical investigation is based on the extreme bounds analysis(EBA),which can resolve model uncertainty issues.The results of Leamer’s version of the EBA suggest that the realized volatility of Bitcoin is the sole variable relevant to explaining liquidity.With the Sala-i-Martin’s variant of EBA,however,four more variables,(viz.Bitcoin’s negative returns,trading volume,hash rates,and Google search volume)are also labeled as robust determinants.Accordingly,our evidence confirms that Bitcoin-specific factors and developments,rather than global macroeconomic and financial variables,matter for explaining its liquidity.The findings are largely insensitive to our proxy of liquidity and to the estimation method used.
文摘The recently developed Bitcoin futures and options contracts in cryptocurrency derivatives exchanges mark the beginning of a new era in Bitcoin price risk hedging.The need for these tools dates back to the market crash of 1987,when investors needed better ways to protect their portfolios through option insurance.These tools provide greater flexibility to trade and hedge volatile swings in Bitcoin prices effectively.The violation of constant volatility and the log-normality assumption of the Black–Scholes option pricing model led to the discovery of the volatility smile,smirk,or skew in options markets.These stylized facts;that is,the volatility smile and implied volatilities implied by the option prices,are well documented in the option literature for almost all financial markets.These are expected to be true for Bitcoin options as well.The data sets for the study are based on short-dated Bitcoin options(14-day maturity)of two time periods traded on Deribit Bitcoin Futures and Options Exchange,a Netherlandsbased cryptocurrency derivative exchange.The estimated results are compared with benchmark Black–Scholes implied volatility values for accuracy and efficiency analysis.This study has two aims:(1)to provide insights into the volatility smile in Bitcoin options and(2)to estimate the implied volatility of Bitcoin options through numerical approximation techniques,specifically the Newton Raphson and Bisection methods.The experimental results show that Bitcoin options belong to the commodity class of assets based on the presence of a volatility forward skew in Bitcoin option data.Moreover,the Newton Raphson and Bisection methods are effective in estimating the implied volatility of Bitcoin options.However,the Newton Raphson forecasting technique converges faster than does the Bisection method.
基金This work is sponsored by the Natural Science Foundation of Heilongjiang Province of China under Grant No.LC2016024Natural Science Foundation of the Jiangsu Higher Education Institutions Grant No.17KJB520044Six Talent Peaks Project in Jiangsu Province No.XYDXX-108.
文摘As an extension of the traditional encryption technology,information hiding has been increasingly used in the fields of communication and network media,and the covert communication technology has gradually developed.The blockchain technology that has emerged in recent years has the characteristics of decentralization and tamper resistance,which can effectively alleviate the disadvantages and problems of traditional covert communication.However,its combination with covert communication thus far has been mostly at the theoretical level.The BLOCCE method,as an early result of the combination of blockchain and covert communication technology,has the problems of low information embedding efficiency,the use of too many Bitcoin addresses,low communication efficiency,and high costs.The present research improved on this method,designed the V-BLOCCE which uses base58 to encrypt the plaintext and reuses the addresses generated by Vanitygen multiple times to embed information.This greatly improves the efficiency of information embedding and decreases the number of Bitcoin addresses used.Under the premise of ensuring the order,the Bitcoin transaction OP_RETURN field is used to store the information required to restore the plaintext and the transactions are issued at the same time to improve the information transmission efficiency.Thus,a more efficient and feasible method for the application of covert communication on the blockchain is proposed.In addition,this paper also provides a more feasible scheme and theoretical support for covert communication in blockchain.
基金This work was supported by the Key Plan of National Social Science Foundation of China under the Grant 14ZDA044.
文摘Background:Bitcoin,the most innovate digital currency as of now,created since 2008,even through experienced its ups and downs,still keeps drawing attentions to all parts of society.It relies on peer-to-peer network,achieved decentralization,anonymous and transparent.As the most representative digital currency,people curious to study how Bitcoin’price changes in the past.Methods:In this paper,we use monthly data from 2011 to 2016 to build a VEC model to exam how economic factors such as Custom price index,US dollar index,Dow jones industry average,Federal Funds Rate and gold price influence Bitcoin price.Result:From empirical analysis we find that all these variables do have a long-term influence.US dollar index is the biggest influence on Bitcoin price while gold price influence the least.Conclusion:From our result,we conclude that for now Bitcoin can be treated as a speculative asset,however,it is far from being a proper credit currency.
基金Author of this article,M.BA,would like to thank the laboratory MODAL’X of Universite Paris Nanterre to support this work。
文摘This paper analyzes the selfish-mine strategy in the Bitcoin blockchain introduced in 2013 by I.Eyal and E.G.Sirer.This strategy could be used by a colluding pool of miners to earn more than their fair share of the mining revenue and in consequence to force other honest miners to join them to decrease the variance of their revenues and make their monthly revenues more predictable.It is a very dangerous dynamic that could allow the rogue pool of miners to go toward a majority by accumulating powers of news adherents and control the entire network.Considering that the propagation delay of information between any two miners in the network,which is not negligible and follows a normal distribution with mean proportional to the physical distance between the two miners,and a constant variance independent of others'delays,we prove that no guarantee can be given about the success or failure of the selfish-mine attack because of the variability of information propagation in the network.
文摘This study examines the connectedness between the US yield curve components(i.e.,level,slope,and curvature),exchange rates,and the historical volatility of the exchange rates of the main safe-haven fiat currencies(Canada,Switzerland,EURO,Japan,and the UK)and the leading cryptocurrency,the Bitcoin.Results of the static analysis show that the level and slope of the yield curve are net transmitters of shocks to both the exchange rate and its volatility.The exchange rate of the Euro and the volatility of the Euro and the Canadian dollar exchange rate are net transmitters of shocks.Meanwhile,the curvature of the yield curve and the Japanese Yen,Swiss Franc,and British Pound act mainly as net receivers.Our static connectedness analysis shows that Bitcoin is mainly independent of shocks from the yield curve’s level,slope,and curvature,and from any main currency investigated.These findings hint that Bitcoin might provide hedging benefits.However,similar to the static analysis,our dynamic analysis shows that during different periods and particularly in stressful times,Bitcoin is far from being isolated from other currencies or the yield curve components.The dynamic analysis allows us to observe Bitcoin’s connectedness in times of stress.Evidence supporting this contention is the substantially increased connectedness due to policy shocks,political uncertainty,and systemic crisis,implying no empirical support for Bitcoin’s safe-haven property during stress times.The increased connectedness in the dynamic analysis compared with the static approach implies that in normal times and especially in stressful times,Bitcoin has the property of a diversifier.The results may have important implications for investors and policymakers regarding their risk monitoring and their assets allocation and investment strategies.
文摘Bitcoin is currently the leading global provider of cryptocurrency.Cryptocurrency allows users to safely and anonymously use the Internet to perform digital currency transfers and storage.In recent years,the Bitcoin network has attracted investors,businesses,and corporations while facilitating services and product deals.Moreover,Bitcoin has made itself the dominant source of decentralized cryptocurrency.While considerable research has been done concerning Bitcoin network analysis,limited research has been conducted on predicting the Bitcoin price.The purpose of this study is to predict the price of Bitcoin and changes therein using the grey system theory.The first order grey model(GM(1,1))is used for this purpose.It uses a firstorder differential equation to model the trend of time series.The results show that the GM(1,1)model predicts Bitcoin’s price accurately and that one can earn a maximum profit confidence level of approximately 98%by choosing the appropriate time frame and by managing investment assets.
