In this study, we conduct a network analysis with centrality measures, using historical daily close prices of top 120 cryptocurrencies between 2013 and 2020, to study and understand the dynamic evolution and characteristics of the cryptocurrency market. Our study has two primary findings: (1) the overall cross-return correlation among the cryptocurrencies is weakening from 2013 to 2016 and then strengthening thereafter; (2) cryptocurrencies that are primarily used for transaction payment, notably BTC, dominate the market until mid-2016, followed by those developed for applications using blockchain as the underlying technology, particularly data storage and recording such as MAID and FCT, between mid-2016 and mid-2017. Since then, ETH, alongside with its strongly correlated cryptocurrencies have replaced BTC to become the benchmark cryptocurrencies. Furthermore, during COVID-19, QTUM and BNB have intermittently replaced ETH to take the leading positions due to their active community engagement during the pandemic.
Purpose While monetary autonomy is self-explanatory for cryptocurrencies such as Bitcoin with predetermined supply path, it is of great interest to probe into the monetary structures of Stablecoins. In these supply contracts and expands and capital restrictions apply due to the existence of reserves as the exchange rate arrangement adheres to a price rule. Design/methodology/approach Ever since the launch of Bitcoin and its offspring, examination of cryptocurrencies' trading activity from the empirical finance viewpoint has received much attention and continues to do so. The particular monetary arrangements found in Stable cryptocurrencies (colloquially referred to as Stablecoins), however, have not been properly (1) classified and (2) studied within an empirical international finance and banking context. This paper provides an empirical framework analogous to Impossible Trinity for exploring monetary arrangements across Stablecoins wherein reserves are held as price stability is targeted. Findings The study findings of existence of the degree of achievement along the three dimensions of the Impossible Trinity hypothesis, namely monetary independence, exchange rate stability and financial openness for a representative sample able to cover all varieties of Stablecoins, provide fresh empirical insights and arguments to this growing literature with respect to the success of their embedded exchange rate stabilization mechanisms. While the hypothesis can be supported for all cryptocurrencies in question, the trade-off combination among exchange rate stability, capital openness and monetary independence varies with the categorical types of Stablecoins. Research limitations/implications If Stable cryptocurrencies, therefore, claim the role of global monetary assets freed from sovereign limits and national boundaries, it is critical to explore whether they adhere to traditional monetary frameworks. It goes without saying that in this work the author does not use a complete catalogue of all the available Stablecoins, rather a complete catalogue of all the possible asset classes of Stablecoins. While there is a significant difficulty in finding Algorithmic Stablecoins and, so far, there is plethora of Stable Token initiatives, a broader sample to further examine these under this paper's empirical framework is suggested. Enrichment of the robustness analysis by constructing additional proxies, possibly building time series for the proposed cmo1 subindex and using additional estimation methods is encouraged. Practical implications Stablecoins have been developed aiming to address the issue of excessive price variation in cryptocurrencies such as Bitcoin. Holders of Stablecoins enjoy the combined advantages of using a blockchain-based digital infrastructure in fulfilling the functions of store of value and media of exchange and of using a traditional currency, which merely plays the role of the unit of account (and in some circumstances the trusted reserve to which is convertible to). Understanding the varieties of Stablecoins and quantifying the components for success of their price stabilization may result in designing better Stablecoins. Social implications Blockchain and cryptocurrencies have introduced new challenges to money and banking. Cryptocurrencies, which independently float such as Bitcoin, have gained the interest so far due to price variation that allows for gains. But these should be by far not considered to be a substitute to traditional means of payment. Lately, Stablecoins have increasingly gained attention for that USD Tether/Bitcoin pair (a Stablecoin pegged to the US dollar at parity) has outrun the US dollar/Bitcoin pair as the most traded pair in digital exchanges marking the strong position and high demand for Stablecoins. Originality/value This approach uncovers the varieties of Stablecoins with respect to their monetary constraints compared to the rest of the cryptocurrencies, which independently float. In this paper, the author provides a conceptual framework for the analysis of the exchange rate mechanisms conditional on Stablecoin asset classes accompanied with an empirical study from the monetary viewpoint. This is the first work in this attempt. The empirical framework employed is analogous to the traditional theory of international monetary economics referred to as Impossible Trinityz. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/JES-06-2020-0279
In this paper, we explore the volatility spillovers across different Bitcoin markets. We decompose the realized volatility into common and idiosyncratic volatilities, as well as the good and bad volatilities. Then the asymmetry in volatility spillovers between Bitcoin markets is measured by the DY (Diebold and Yilmaz) index. In addition, we construct statistics to test the asymmetry in volatility spillovers between different Bitcoin markets. The results are achieved as follows. The spillovers of systematic and idiosyncratic volatilities dominate the connectedness among different Bitcoin markets. In addition, the idiosyncratic volatility spillovers are more easily influenced by policies. Good volatility spillovers dominate the Bitcoin markets and change over time. The further results suggest that there is significant asymmetry between systematic and idiosyncratic volatility spillovers in the Bitcoin markets, while the asymmetries between good and bad volatility spillovers are heterogeneous in different markets. The findings in this paper can provide some suggestions for regulators controlling market stability and investors generating investment strategies.
Salim Lahmiri, Raafat George Saadé, Danielle Morin, Fassil Nebebe
Cryptocurrencies are digital assets gaining popularity and generating huge transactions on electronic platforms. We develop an ensemble predictive system based on artificial neural networks to forecast Bitcoin daily trading volume level. Indeed, although ensemble forecasts are increasingly employed in various forecasting tasks, developing an intelligent predictive system for Bitcoin trading volume based on ensemble forecasts has not been addressed yet. Ensemble Bitcoin trading volume are forecasted using two specific artificial neural networks; namely, radial basis function neural networks (RBFNN) and generalized regression neural networks (GRNN). They are adopted to respectively capture local and general patterns in Bitcoin trading volume data. Finally, the feedforward artificial neural network (FFNN) is implemented to generate Bitcoin final trading volume after having aggregated the forecasts from RBFNN and GRNN. In this regard, FFNN is executed to merge local and global forecasts in a nonlinear framework. Overall, our proposed ensemble predictive system reduced the forecasting errors by 18.81% and 62.86% when compared to its components RBFNN and GRNN, respectively. In addition, the ensemble system reduced the forecasting error by 90.49% when compared to a single FFNN used as a basic reference model. Thus, the empirical outcomes show that our proposed ensemble predictive model allows achieving an improvement in terms of forecasting. Regarding the practical results of this work, while being fast, applying the artificial neural networks to develop an ensemble predictive system to forecast Bitcoin daily trading volume is recommended to apply for addressing simultaneously local and global patterns used to characterize Bitcoin trading data. We conclude that the proposed artificial neural networks ensemble forecasting model is easy to implement and efficient for Bitcoin daily volume forecasting.
The present study is on the five cryptocurrency daily mean return time series linearity dynamics during the Covid-19 period. These cryptocurrencies were chosen based on their influence on the market, primarily driven by its market capitalisation. Tether is included as the most important stable coin on the market, nominally pegged to the U.S. dollar (USD). The reason to investigate it is that there are some inconsistencies in its behaviour as opposed to the other four cryptocurrencies. This study found that the behaviour of Tether cryptocurrency daily average return time series pattern is highly nonlinear and chaotic in nature, whereas the other four cryptocurrencies (namely Bitcoin, Ethereum, XRP and Bitcoin Cash) daily average return time series were found to be linear in nature. To further study Tether’s nonlinear time series rich dynamics, this study deployed one category of the regime switching models popularly known as the threshold regressions. The study estimates fairly suggest that both the threshold autoregression (TAR) and smooth transition autoregressive (STAR) models with lag 1 are adequate to capture the rich nonlinear and chaotic dynamics of Tether’s daily average return time series.
This paper investigates asymmetry and local leverage behaviour in individual and aggregate markets of leading cryptocurrencies, and compares such characteristics to diverse traditional emerging asset classes. Generally different from the cryptocurrencies, the results show diffuse evidence of asymmetry and a significant presence of local leverage in the emerging markets. New findings indicate that mega‐size cryptocurrencies like Bitcoin and Ripple exhibit return‐volatility behaviour whereby volatility changes in their markets increase rather by a response to positive shocks than by a response to negative shocks. Akin to safe net assets, particularly gold, the inverse asymmetric reactions of the cryptocurrency markets position them distinctively from the existing emerging markets, suggestive that the digital assets stand to offer potentials beyond being diversifiers.
This article aims to analyse the hedging, diversifier and safe-haven properties of Bitcoin for the US Dollar Index (USDI). We explore the long-term relationship between USDI and Bitcoin by estimating a Markov-switching autoregressive (MS-AR) model with two regimes. Thus, the data used cover the period from 18 January 2010 to 30 June 2017 for both USDI and Bitcoin. The empirical findings based on the analysis of the MS-AR model report that investing in Bitcoin involves more benefits than USDI even if the economy is in a recession. However, by examining Bitcoin and USDI volatility, the research findings underline positive dependency between the two. Such results denote that Bitcoin does not act as a hedge, and not even as a safe haven, against USDI. We found that Bitcoin is merely a diversifier for USDI. Accordingly, the outcomes will help investors and portfolio risk managers to make more up-to-date investment analyses and decisions.
Can cryptocurrencies price variations be explained by exogenous classical market prices? We evaluate this issue by using daily data on some of the most important asset prices and indexes in Thailand i.e. Gold, Oil, SET50 index, Tourism index, Mutual fund, and THB/USD exchange rate in comparison with digital asset prices i.e. Bitcoin, Ethereum, Litecoin, Ripple, DASH, and Stellar. By performing both direct and inverse relationships using correlation matrix to find distance relationship and using minimum spanning tree to find the closest path between assets, we found strong direct relationship among cryptocurrencies in digital market with SET50 index and oil price in classical markets. We also found that THB-USD exchange rate has inverse relationship with Bitcoin price, SET50 index and oil price. There is a link between cryptocurrencies asset price and some classical assets' market price.
As one of the most important and famous applications of blockchain technology, cryptocurrency has attracted extensive attention recently. Empowered by blockchain technology, all the transaction records of cryptocurrencies are irreversible and recorded in the blocks. These transaction records containing rich information and complete traces of financial activities are publicly accessible, thus providing researchers with unprecedented opportunities for data mining and knowledge discovery in this area. Networks are a general language for describing interacting systems in the real world, and a considerable part of existing work on cryptocurrency transactions is studied from a network perspective. This survey aims to analyze and summarize the existing literature on analyzing and understanding cryptocurrency transactions from a network perspective. Aiming to provide a systematic guideline for researchers and engineers, we present the background information of cryptocurrency transaction network analysis and review existing research in terms of three aspects, i.e., network modeling, network profiling, and network-based detection. For each aspect, we introduce the research issues, summarize the methods, and discuss the results and findings given in the literature. Furthermore, we present the main challenges and several future directions in this area.
Abstract The Innovation Bank is a novel business method that integrates and capitalizes knowledge assets. The Innovation Bank is an application of game theory, actuarial math and a simple native “proof-of-stake” blockchain. The system aims to unify the global engineering and scientific disciplines by incentivizing individual practitioners to form knowledge asset networks among each other by producing claims and validations related to observable and measurable events. Each claim and associated validation forms a node in a network for which each participant is awarded a cryptographic token memorializing earned stake (equity) in the system. A secure, validated, and decentralized knowledge repository and access management system is secured by a simple native blockchain. Revenue is generated through the liquidation of earned tokens on an external market to third parties seeking access to network metadata for business intelligence. The intrinsic value of the network grows as the number of participants increases. As participation increases, the quantity and quality of the transaction records also increases. Third-party buyers may include banks, insurance companies, and private enterprise.
Xi He, Zhang Fan, Shenwen Lin, Mao Hongliang · 5 authors
Bitcoin is a decentralized cryptocurrency that has led to a new trading model. It allows people to trade directly without going through financial institutions such as banks. This model results in many transactions that occur outside the law and beyond ethical constraints. In such an anonymous environment, the large number of entities using Bitcoin, and the huge scale of the Bitcoin trading network make it difficult for users to have a rough idea of the entire trading network before transaction. Thus, it is of great theoretical and practical significance to summarize the research problems, achievements and possible research trends based on Bitcoin data analysis. Therefore, in this paper we review the literatures about data analysis on Bitcoin transaction entities. Starting from the relevant conceptual framework of Bitcoin, this paper divides the existing research models into three categories, heuristic algorithm identification of entities, transaction descriptive statistics and network analysis, and visual system analysis. By analyzing the transaction entity, Bitcoin transaction data can be processed in a manner which is similar to an account, such as a bank or credit card, thereby achieving the purpose of in-depth analysis of all transaction activities related to the account entity. Finally, we summarize the data analysis results of Bitcoin transaction network and prospects of the future research directions.
Abstract 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.
Jeremy Eng‐Tuck Cheah, Di Luo, Zhuang Zhang, Ming‐Chien Sung
This paper comprehensively examines the performance of a host of popular variables to predict Bitcoin returns. We show that time-series momentum, economic policy uncertainty, and financial uncertainty outperform other predictors in all in-sample, out-of-sample, and asset allocation tests. Bitcoin returns have no exposure to common stock and bond market factors but rather are affected by Bitcoin-specific and external uncertainty factors.
Abstract This study aims to explain price movements in the two largest cryptocurrencies that represent the majority of cryptocurrency market capitalization—Bitcoin and Ethereum. A VAR‐GARCH‐BEKK model is estimated to analyze how Google search interest, number of tweets and active addresses on the blockchain impact prices of Bitcoin and Ethereum over time. We find solid evidence that the amount of active addresses is the most significant variable among others influencing price movements in Bitcoin and Ethereum. Based on spillover effects and GIRFs, Google searches and tweets, to a certain extent, have impacts on the Bitcoin and Ethereum prices, but the impacts are weaker than that of active addresses in terms of magnitude and significance.
Otabek Sattarov, Heung Seok Jeon, Ryum-Duck Oh, Jun Dong Lee
Bitcoin is one of the main phenomena in recent times together with other cryptocurrencies due to the redefinition of the money term and its price fluctuations. Moreover, scientists are increasingly recognizing Twitter's predictive power for a wide range of events, and particularly for financial markets. This article examines to what degree Bitcoin returns can be estimated using public opinion on Twitter. Using a sentiment analyzer on Bitcoin-related tweets and financial data, the Twitter sentiment was found to have predictive power for Bitcoin's results. Once again, our findings confirm the presence of a correlation between them. We observed 62.48% accuracy when making predictions based on bitcoin-related tweet sentiment and historical bitcoin price.
Maria Letizia Guerra, Laerte Sorini, Luciano Stefanini
Sentiment analysis to characterize the properties of Bitcoin prices and their forecasting is here developed thanks to the capability of the Fuzzy Transform (F-transform for short) to capture stylized facts and mutual connections between time series with different natures. The recently proposed Lp-norm F-transform is a powerful and flexible methodology for data analysis, non-parametric smoothing and for fitting and forecasting. Its capabilities are illustrated by empirical analyses concerning Bitcoin prices and Google Trend scores (six years of daily data): we apply the (inverse) F-transform to both time series and, using clustering techniques, we identify stylized facts for Bitcoin prices, based on (local) smoothing and fitting F-transform, and we study their time evolution in terms of a transition matrix. Finally, we examine the dependence of Bitcoin prices on Google Trend scores and we estimate short-term forecasting models; the Diebold–Mariano (DM) test statistics, applied for their significance, shows that sentiment analysis is useful in short-term forecasting of Bitcoin cryptocurrency.
This essay brings insights from the academic literature on foreign exchange rate determination to the analysis of cryptocurrency markets. We present a simple framework to summarize the factors that determine exchange rates. To the extent that cryptocurrencies are like national currencies issued by central banks, their pricing can be analyzed using these models of exchange rates…