Abstract The growing literature on Bitcoin can be divided into two groups. One performs an economic analysis of Bitcoin focusing on its monetary characteristics. The other one takes a financial look at the price of Bitcoin. Interestingly, both of these groups have not given much more than passing comments to the problem of whether or not Bitcoin has the right monetary rule in order to become a well‐established currency. This paper argues that Bitcoin in particular, and cryptocurrencies in general, do not have a good monetary rule and that this shortcoming seriously limits its prospect of becoming widely used money.
Purpose The purpose of this paper is to investigate the short- and long-run dynamic linkages between selected cryptocurrencies, several major world currencies and major equity indices. The results show that despite sharing some common characteristics, the cryptocurrencies do not reveal any short- and long-term stochastic trends with exchange rates and/or equity returns. The dynamics of each cryptocurrency with the Chinese Yuan appears to be more turbulent than that with the other exchange rates. Each cryptocurrency appears to follow its own trend in the global financial market and is independent of the exchange rates or the global stock markets, thus making them suitable for inclusion in global investment portfolios. Design/methodology/approach The cryptocurrencies examined are Bitcoin, Dash, Ethereum, Monero, Stellar and XRP. In addition, data were collected on major exchange rates with respect to the US dollar, namely, the euro, British pound, Japanese yen and Chinese Yuan. Finally, the following major stock market indices were selected: SP500, DAX, DJIA, CAC, FTSE, NIKKEI, Hang Seng and Shanghai. The study applied vector autoregressive (VAR) model and Engle’s (2002) dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (DCC-GARCH) specification. Findings First, it was found that cryptocurrencies do not interact with each other because their correlations are weak and do not share a common long-run path; thus they are not cointegrated. Second, impulse response analysis from the VAR models indicate different reactions of each cryptocurrency to both exchange rate and equity shocks and that cryptocurrencies appear to be isolated from market-driven shocks. Third, the ups and downs in the cryptocurrencies’ dynamic conditional correlations (from the DCC-GARCH models) indicate that all cryptocurrencies were susceptible to speculative attacks and market events. Research limitations/implications This paper examines the dynamic linkages among the most important cryptocurrencies with major exchange rates and equity markets and, to the best of the authors’ knowledge, is the first paper to do so. Thus, interested market agents would gain valuable insights as to whether this new form of asset might be used for conducting monetary policies and portfolio construction on a global setting. Originality/value The paper contributes to the scant literature on the dynamic linkages among major cryptocurrencies and global financial assets. In general, given the differential relationships of each crypto with the equity markets, one could infer that they represent a decent short-run investment vehicle within a well-diversified, global asset portfolio (as they may increase the returns and reduce the overall risk of the portfolio).
Ayana T. Aspembitova, Ling Feng, Valentin Melnikov, Lock Yue Chew
Bitcoin is the earliest cryptocurrency and among the most successful ones to date. Recently, its dynamical evolution has attracted the attention of the research community due to its completeness and richness in historical records. In this paper, we focus on the detailed evolution of bitcoin trading with the aim of elucidating the mechanism that drives the formation of the bitcoin transaction network. Our empirical investigation reveals that although the temporal properties of the transaction network possesses scale-free degree distribution like many other networks, its formation mechanism is different from the commonly assumed models of degree preferential attachment or wealth preferential attachment. By defining the fitness value of each node as the ability of the node to attract new connections, we have instead uncovered that the observed scale-free degree distribution results from the intrinsic fitness of each node following a power-law distribution. Our finding thus suggests that the "good-get-richer" rather than the "rich-get-richer" paradigm operates within the bitcoin ecosystem. Based on these findings, we propose a model that captures the temporal generative process by means of a fitness preferential attachment and data-driven birth/death mechanism. Our proposed model is able to produce structural properties in good agreement with those obtained from the empirical bitcoin network.
Summary In this paper, we analyze the Ethereum blockchain using the complex networks modeling framework. Accounts acting on the blockchain are represented as nodes, while the interactions among these accounts, recorded on the blockchain, are treated as links in the network. Using this representation, it is possible to derive interesting mathematical characteristics that improve the understanding of the actual interactions happening in the blockchain. Not only, by looking at the history of the blockchain, it is possible to verify if radical changes in the blockchain evolution happened.
This paper investigates the level of liquidity of digital currencies during the very intense bearish phase in their markets. The data employed span the period from April 2018 until January 2019, which is the second phase of bearish times with almost constant decreases. The Amihud’s illiquidity ratio is employed in order to measure the liquidity of these digital assets. Findings indicate that the most popular cryptocurrencies exhibit higher levels of liquidity during stressed periods. Thereby, it is revealed that investors’ preferences for trading during highly risky times are favorable for well-known virtual currencies in the detriment of less-known ones. This enhances findings of relevant literature about strong and persistent positive or negative herding behavior of investors based on Bitcoin, Ethereum and highly-capitalized cryptocurrencies in general. Notably though, a tendency towards investing in the TrueUSD stablecoin has also emerged.
Damiano Di Francesco Maesa, Andrea Marino, Laura Ricci
The availability of the entire Bitcoin transaction history, stored in its public blockchain, offers interesting opportunities for analysing the transaction graph to obtain insight on users behaviour. This paper presents an analysis of the Bitcoin users graph, obtained by clustering the transaction graph, to highlight its connectivity structure and the economical meaning of the different obtained components. In fact, the bow tie structure, already observed for the graph of the web, is augmented, in the Bitocoin users graph, with the economical information about the entities involved. We study the connectivity components of the users graph individually, to infer their macroscopic contribution to the whole economy. We define and evaluate a set of measures of nodes inside each component to characterize and quantify such a contribution. We also perform a temporal analysis of the evolution of the resulting bow tie structure. Our findings confirm our hypothesis on the components semantic, defined in terms of their economical role in the flow of value inside the graph.
Vítor Nuno da Costa Azevedo Fonseca, Luís Pacheco, Júlio Lob�ão
Purpose The purpose of this paper is to study the existence of psychological barriers in cryptocurrencies. Design/methodology/approach To detect psychological barriers, the authors perform a uniformity test, a barrier hump test, a barrier proximity test and conditional effects test to a sample comprised by the daily closing quotes of six of the most liquid cryptocurrencies. Findings The results evidence the existence of psychological barriers in four of the cryptocurrencies under scrutiny, namely, Bitcoin, Dash, NEM and Ripple. Practical implications The fact that the cryptocurrency market has a high share of unexperienced investors and presents several cases of psychological barriers is consistent with the hypothesis that that class of investors is particularly prone to the behavioral biases which cause psychological barriers. Originality/value This paper studies, for the first time, the existence of psychological barriers in the market of cryptocurrencies.
Exploring dependence structures between financial time series has been important within a wide range of applications. The main aim of this paper is to examine dependence relationships among five well-known cryptocurrencies—Bitcoin, Ethereum, Litecoin, Ripple, and Stella—by a copula directional dependence (CDD). By employing a neural network autoregression model to avoid the serial dependence in each individual cryptocurrency, we generate residuals of the fitted models with time series of daily log-returns in percentage of the five cryptocurrencies and then we apply a Gaussian copula marginal beta regression model to the residuals to explore the CDD. The results show that the CDD from Bitcoin to Litecoin is highest among all ordered directional dependencies and the CDDs from Ethereum to the other four cryptocurrencies are relatively higher than the CDDs to Ethereum from those cryptocurrencies. This finding implies that the return shocks of Bitcoin have the most effect on Litecoin and the return shocks of Ethereum relatively influence the shocks on the other four cryptocurrencies instead of being affected by them. This allows investors to build the market-timing strategies by observing the directional flow of return shocks among cryptocurrencies.
Bitcoin jest jedną z ważnych innowacji finansowych ostatnich lat, będącą wynikiem rozwoju technologicznego i informatyzacji rynku finansowego. Mimo stosunkowo krótkiej historii tej kryptowaluty, wykształciło się wiele giełd wyspecjalizowanych w jej obrocie, a na rynku finansowym pojawiły się pierwsze próby utworzenia opartych na niej instrumentów – pasywnie i aktywnie zarządzanych exchange-traded funds. W artykule przedstawiono te fundusze oraz, ze względu na brak dywersyfikacji ich portfeli, podjęto próbę określenia, czy inwestycja za ich pomocą jest lepsza od bezpośredniego lokowania środków na rynku bitcoina. Ponadto zidentyfikowano podstawowe czynniki ryzyka, związane z inwestowaniem w analizowane fundusze oraz dokonano ich klasyfikacji.
Ángel Enrique Chico Frías, Edwin Javier Santamaría Freire
Market forces are not the only influence on currency exchange rates. They can change due to monetary and fiscal policies among other international repercussions. Bitcoin, for its independence from all the central banks worldwide, has a natural shield that will change the direction in the economic policy of the industrialized countries. The research aim is to analyze the influence that indicators and financial assets can have on Bitcoin. The study tries to confirm the reasons why it has begun to be the solution in economies with unstable currencies.The behavior of the different agents appears as the core of the study. It is creating a backward 5-year work horizon. The data are continuous values, and they are the numerical variables for Pearson correlation analysis. The time series in fixed periods are the basis for the study of projections. Besides, the Relative Strength Index or Relative Strength Index called Welles Wilder is useful in the research. Bitcoin does not get influenced by the Dow Jones, gold price, and Gross Domestic Product (GDP). The independence in the creation of this cryptocurrency could in the long term end up turning it into a currency of world use. As a result, the understanding and management of this cryptocurrency could generate new ways of building the future monetary system. The new direction of the economy will be registered in the blockchain and not in a central bank.
This paper is saddled with the task of investigating the Bitcoin market behaviour in the presence of a government risk. This is because both the institutional and retail investors' interests in the Bitcoin market is growing rapidly. Conversely, the seemingly unregulated nature of this market is a serious concern to most economies and results to the placement of ban on Initial Coin Offering (ICO) in some economies by the government. Daily series of return and volume within the window of the ICO ban in China was used for the Bitcoin market and S&P500 stock market to examine the effect of a government risk in the Bitcoin market and possible hedging capabilities. Empirical results show that the ban dampened Bitcoin returns and the returns from each market can predict the other. The Exogenous Dynamic Conditional Correlation (Exo-DCC) model result suggests that, yes! the S&P500 stocks is capable of hedging Bitcoin risk while Bitcoin can also hedge S&P500 stocks risks and vice versa. The Exogenous BEKK (Exo-BEKK) model result shows evidence of bidirectional volatility spill over between the two markets studied. In practice, investors (institutions and retailers) can comfortably form a robust investment portfolio with (at least) these two assets and develop a hedging strategy such that the impacts of risks on the portfolio's returns are safely hedged.
The purpose of this study is to explore the market maturity of cryptocurrency trading platforms based on the information transmission perspective of financial market price volatility. This study uses the volatility spillovers index proposed by Diebold and Yilmaz [1][2] and uses the bitcoin trading platform to measure the total price volatility of cryptocurrency trading platforms, and the directional spillovers among the trading platforms. The sample period is from January 1, 2015 to September 30, 2018. In the empirical process, each sub-sample is taken every three months. The argument of this research indicated that if the cryptocurrency trading platforms' total spillover effects, with the rolling of the sub-sample period, show the increasing trend, and the trading platform has a staggered spillover effect with each other, indicating that cryptocurrency trading platforms exist the chaotic phenomenon, and the cryptocurrency market is in a stage of low maturity. On the contrary, if the total spillover effects are showing a decreasing trend and the spillover effects are mainly from a certain minority trading platforms, indicating that the cryptocurrency trading platforms present the order phenomenon, and cryptocurrency market is at a stage of high maturity. The contribution of this research is to identify the market maturity of the cryptocurrency trading platform, and to promote policy makers to propose a market-building mechanism for the market situation, so that the cryptocurrency has the opportunity to become a mainstream trading tool.
Within the decision-making process, investors are interested in finding the most effective solutions that will allow them to obtain short-term benefits. Current economic environment is characterized by the emergence of new financial instruments that can assist investors to diversify their investment portfolio. Crypto-currencies represents a category of financial assets that can be used by investors to reduce risk and achieve significant returns. Therefore, the study intends to analyze the financial behavior of investors in the moment of publishing the financial statements. Financial statements could have a positive or negative influence on the investment portfolio and structure. The issue analyzed by this study is represented by the ability of the cryptocurrency Bitcoin to be considered as an alternative investment asset. The study is divided into two parts. In the first part, the study presents the review of literature about value-relevance, cryptocurrency term and speculative bubble. The second part presents the research methodology and results. The results of the study validate the hypothesis of this study, cryptocurrency Bitcoin being a financial asset that can be used as an alternative investment asset for diversification of investment portfolio.
Aug 1, 2019·CEUR Workshop Proceedings, Vol-2422: Proceedings of the Selected Papers of the 8th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-EEMLPEED 2019)
Vasily Derbentsev, Наталія Даценко, Olga Stepanenko, Vitalii Bezkorovainyi
This paper describes the construction of the short-term forecasting model of cryptocurrencies’ prices using machine learning approach. The modified model of Binary Auto Regressive Tree (BART) is adapted from the standard models of regression trees and the data of the time series. BART combines the classic algorithm classification and regression trees (C&RT) and autoregressive models ARIMA. Using the BART model, we made a short-term forecast (from 5 to 30 days) for the 3 most capitalized cryptocurrencies: Bitcoin, Ethereum and Ripple. We found that the proposed approach was more accurate than the ARIMA-ARFIMA models in forecasting cryptocurrencies time series both in the periods of slow rising (falling) and in the periods of transition dynamics (change of trend).
Abstract This paper provides a comprehensive overview of cryptocurrencies, including the origin of cryptocurrencies, how cryptocurrencies operate, and the current situation of cryptocurrencies. In addition, we also provide the performance comparison of major cryptocurrencies with the performance of the stock market indexes. All the cryptocurrencies exhibit higher average returns and volatility than the stock market indexes, which appeals to risk-taking investors. We then perform additional analysis on the determinants of cryptocurrencies returns. We show that major fundamental variables are less likely to affect the returns of cryptocurrencies except for the S&P 500 index returns and the exchange rates between U.S. dollars and Euros.
The increasing volatility in pricing and growing potential for profit in digital currency have made predicting the price of cryptocurrency a very attractive research topic. Several studies have already been conducted using various machine-learning models to predict crypto currency prices. This study presented in this paper applied a classic Autoregressive Integrated Moving Average(ARIMA) model to predict the prices of the three major cryptocurrencies âAT Bitcoin, XRP and Ethereum âAT using daily, weekly and monthly time series. The results demonstrated that ARIMA outperforms most other methods in predicting cryptocurrency prices on a daily time series basis in terms of mean absolute error (MAE), mean squared error (MSE) and root mean squared error(RMSE).
This study examines the volatility of certain cryptocurrencies and how they are influenced by the three highest capitalization digital currencies, namely the Bitcoin, the Ethereum and the Ripple. We use daily data for the period 1 January 2018-16 September 2018, which represents the bearish market of cryptocurrencies. The impact of the decline of these three cryptocurrencies on the returns of the other virtual currencies is examined with models of the ARCH and GARCH family, as well as the DCC-GARCH. The main conclusion of the study is that the majority of cryptocurrencies are complementary with Bitcoin, Ethereum and Ripple and that no hedging abilities exist among principal digital currencies in distressed times.
<h3>Practical Applications Summary</h3> In <b>Investments in Cryptocurrencies: <i>Handle with Care!</i></b> from the Summer 2019 issue of <b><i>The Journal of Alternative Investments</i></b>, author <b>Tobias N. Glas</b> (of the Department of Finance at the <b>University of Bremen, Germany</b>) uses an extensive data set to analyze the new asset class of cryptocurrencies, such as Bitcoin. He demonstrates that the investment performance of cryptocurrencies has little or no correlation with the performance of traditional markets and investments, or the macroeconomic environment in general. The mean monthly investment returns of cryptocurrencies are basically random, and only a few of the traditional investment styles produce positive results when applied to cryptocurrencies. Therefore, traditional market mechanics cannot yet be applied to cryptocurrency markets—so the author advises investors to handle cryptocurrencies with care. On the other hand, a few individual digital coins dominate the cryptocurrency market, and they have posted high average returns for investors who bought and held them. Also, the investment performance of cryptocurrencies is not influenced by that of stocks or other investments—and so cryptocurrencies will not necessarily go down if the stock market goes down. So adding cryptocurrencies to a portfolio can make it more diversified. <b>TOPICS:</b>Currency, portfolio construction, risk management, performance measurement
<h3>Practical Applications Summary</h3> In <b>Beyond Bitcoin: <i>A Statistical Comparison of Leading Cryptocurrencies and Fiat Currencies and Their Impact on Portfolio Diversification</i></b> from the Summer 2019 issue of <b><i>The Journal of Alternative Investments</i></b>, authors <b>Stefan Ehlers</b> and <b>Kolja Gauer</b> (both at <b>Volkswagen AG</b>) provide a first-of-its-kind analysis of whether traditional currencies (also known as fiat currencies) and cryptocurrencies act similarly or differently with respect to their fluctuations in value and total return. The authors also explore whether mixing cryptocurrencies and fiat currencies in an investment portfolio can help diversify it and reduce the portfolio’s variance. The authors find no correlation between the fluctuations in value and total return of cryptocurrencies and fiat currencies, so combining them in a mixed portfolio improves diversification. Also, only Bitcoin and XRP play an important role in reducing the variance of a pure cryptocurrency portfolio, while just a few cryptocurrencies and fiat currencies significantly reduce the variance of mixed portfolios. So, those who want to invest in cryptocurrencies and avoid major swings in value and returns should consider including a few specific currencies in their portfolio and should combine cryptocurrencies with fiat currencies in a mixed portfolio. <b>TOPICS:</b>Currency, statistical methods, portfolio construction