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3,636 results · page 117 of 152
Douglas Eduardo Turatti, Fernando Henrique P.S. Mendes, João F. Caldeira
No abstract is available for this record.
Dehua Shen, Andrew Urquhart, Pengfei Wang
No abstract is available for this record.
Jying‐Nan Wang, Hung‐Chun Liu, Yuan‐Teng Hsu
No abstract is available for this record.
Vasily Derbentsev, Liubov Kibalnyk, Yu. Radzihovska
The paper focuses on the study of the effect of long memory and the analysis of the multifractal properties of the time series of the most capitalized cryptocurrencies for the period from 2010 to 2018. To do this, the Hurst exponent is calculated by both R/S analysis and the Detrended Fluctuation Analysis being more stable in the case of non-stationary time series. Our results show that time series of cryptocurrencies to be persistent during almost the whole study period that do not allow accepting the hypothesis concerning the efficiency of the cryptocurrency market. We also found that (i) time series became anti-persistent during the periods of market crisis phenomena and turbulence; (ii) the Hurst exponents showed significant fluctuations about the value of 0.5. In addition, we conduct a multifractal analysis of cryptocurrency time series that allows us to assess the state and stability of the market.The calculated spectrum of multifractality shows that the cryptocurrency market comes out of a crisis state, since the width of the multifractality spectrum has the maximum value for all cryptocurrencies.
Paolo Giudici, Paolo Pagnottoni
Abstract Bitcoins are traded on various exchange platforms and, therefore, prices may differ across trading venues. We aim to investigate return connectedness across eight of the major exchanges of Bitcoin, both from a static and a dynamic viewpoint. To this end, we employ an extension of the order‐invariant forecast error variance decomposition proposed by Diebold and Yilmaz (2012) to a generalized vector error correction framework. Our results suggest that there is strong connectedness among the exchanges, as expected, although some of them behave dissimilarly. We identify Bitfinex and Coinbase as leading exchanges during the considered period, while Kraken as a follower exchange. We also obtain that connectedness across exchanges is strongly dynamic, as it evolves over time.
Alessandra Cretarola, Gianna Figà‐Talamanca
No abstract is available for this record.
Luis A. Gil‐Alana, Emmanuel Joel Aikins Abakah, María Fátima Romero Rojo
No abstract is available for this record.
Jinan Liu, Apostolos Serletis
How do cryptocurrency prices evolve? Is there any interdependence among cryptocurrency returns and/or volatilities? Are there any return spillovers and volatility spillovers between the cryptocurrency market and other financial markets? To answer these questions, we use GARCH-in-mean models to examine the relationship between volatility and returns of leading cryptocurrencies, to investigate spillovers within the cryptocurrency market, and also from the cryptocurrency market to other financial markets. Overall, we find statistically significant transmission of shocks and volatilities among the leading cryptocurrencies. We also find statistically significant spillover effects from the cryptocurrency market to other financial markets in the United States, as well as in other leading economies (Germany, the United Kingdom, and Japan).
Anoop Kumar, Taufeeq Ajaz
We study the time varying co-movement patterns of the crypto-currency prices with the help of wavelet-based methods; employing daily bilateral exchange rate of four major crypto-currencies namely Bitcoin, Ethereum, Lite and Dashcoin. First, we identify Bitcoin as potential market leader using Wavelet multiple correlation and Cross correlation. Further, Wavelet Local Multiple Correlation for the given crypto-currency prices are estimated across different time-scales. From the results, it is found that that the correlation follows an aperiodic cyclical nature, and the crypto-currency prices are driven by Bitcoin price movements. Based on the results obtained, we suggest that constructing a portfolio based on crypto-currencies may be risky at this point of time as the other crypto-currency prices are mainly driven by Bitcoin prices, and any shocks in the latter is immediately transformed to the former.
Li Liu
No abstract is available for this record.
Paul Gatabazi, J.C. Mba, Edson Pindza
No abstract is available for this record.
Gülin Vardar, Berna Aydoğan
Purpose With a substantial return and volatility characteristic of Bitcoin, which may be seen as a new category of investment assets, better understanding of the nature of return and volatility spillover can help investors and regulators in achieving the potential goal from portfolio diversification. The paper aims to discuss these issues. Design/methodology/approach This paper explores the return and volatility transmission between the Bitcoin, as the largest cryptocurrency, and other traditional asset classes, namely stock, bond and currencies from the standpoint of Turkey over the period July, 2010–June, 2018 using the newly developed multivariate econometric technique, VAR–GARCH, in mean framework with the BEKK representation. Findings The empirical results reveal the existence of the positive unilateral return spillovers from the bond market to Bitcoin market. Regarding the results of shock and volatility spillovers, there exists strong evidence of bidirectional cross-market shock and volatility spillover effects between Bitcoin and all other financial asset classes, except US Dollar exchange rate. Originality/value The important extention is the adoption of a newly developed multivariate econometric technique, VAR–GARCH, in mean framework with the BEKK representation, proposed by Engle and Kroner (1995), which is employed for the first time specifically to examine the extent of integration in terms of volatility and return between Bitcoin and key asset classes. Second, Bitcoin has experienced a rapid growth since around a decade and a number of investors are showing interest in its potential as an integrative part of portfolio diversification. The information provided by empirical results gives empirical bases from which to address topics concerning hedging purposes and optimal portfolio allocation. It is also increasingly important to analyze the current behavior of Bitcoin in relation to other assets to provide policy makers and regulatory bodies with guidance on the role of the Bitcoin as an investment asset in Turkey. Thus, this is the first serious attempt at exploring the potential for Bitcoin to offer diversification opportunities in the context of Turkey.
Paulo Ferreira, Éder Johnson de Area Leão Pereira
The rapid development of cryptocurrencies has drawn attention to this particular market, with investors trying to understand its behaviour and researchers trying to explain it. The evolution of cryptocurrencies’ prices showed a kind of bubble and a crash at the end of 2017. Based on this event, and on the fact that Bitcoin is the most recognized cryptocurrency, we propose to evaluate the contagion effect between Bitcoin and other major cryptocurrencies. Using the Detrended Cross-Correlation Analysis correlation coefficient (ΔρDCCA) and comparing the period after and before the crash, we found evidence of a contagion effect, with this particular market being more integrated now than in the past—something that should be taken into account by current and potential investors.
Rangga Handika, Gatot Soepriyanto, Shinta Amalina Hazrati Havidz
No abstract is available for this record.
Alexandre Bovet, Carlo Campajola, Francesco Mottes, Valerio Restocchi · 7 authors
Cryptocurrencies (the most paradigmatic blockchain-based systems) are distributed systems that allow to exchange tokens among participants.These cryptocurrencies can also be acquired in exchange markets.The availability of the historical bookkeeping of cryptocurrency transfers in a public ledger opens up the possibility of understanding the relationship between aggregate users' behaviour and the cryptocurrency pricing in exchange markets.This paper analyses the properties of the transaction network of Bitcoin.We consider different representations over a period of nine years since its creation and involving 16 million users and 283 million transactions.Importantly, these transactions do not include orders filled in exchange markets, which are settled outside of the blockchain, and ultimately determine Bitcoin price.By analysing these networks, we show the existence of Granger causal relationships between Bitcoin price movements and changes of its transaction network topology.Our results reveal the interplay between structural quantities, indicative of the collective behaviour of Bitcoin users, and price movements, showing that, during price drops, the system is characterised by a larger heterogeneity of users' activity.
V. Dimitrova, M. Fernández–Martínez, M.A. Sánchez-Granero, Juan Evangelista Trinidad Segovia
In this paper, we explore the (in)efficiency of the continuum Bitcoin-USD market in the period ranging from mid 2010 to early 2019. To deal with, we dynamically analyse the evolution of the self-similarity exponent of Bitcoin-USD daily returns via accurate FD4 approach by a 512 day sliding window with overlapping data. Further, we define the memory indicator by the difference between the self-similarity exponent of Bitcoin-USD series and the self-similarity index of its shuffled series. We also carry out additional analyses via FD4 approach by sliding windows of sizes equal to 64, 128, 256, and 1024 days, and also via FD algorithm for values of q equal to 1 and 2 (and sliding windows equal to 512 days). Moreover, we explored the evolution of the self-similarity exponent of actual S&P500 series via FD4 algorithm by sliding windows of sizes equal to 256 and 512 days. In all the cases, the obtained results were found to be similar to our first analysis. We conclude that the self-similarity exponent of the BTC-USD (resp., S&P500) series stands above 0.5. However, this is not due to the presence of significant memory in the series but to its underlying distribution. In fact, it holds that the self-similarity exponent of BTC-USD (resp., S&P500) series is similar or lower than the self-similarity index of a random series with the same distribution. As such, several periods with significant antipersistent memory in BTC-USD (resp., S&P500) series are distinguished.
Marc Gronwald
No abstract is available for this record.
Cynthia Miglietti, Zdenka Kubosova, Nicole Škuláňová
Purpose This paper aims to empirically investigate the volatility of Bitcoin, Litecoin and the Euro. Design/methodology/approach The authors use quantitative methodologies to assess the annualized volatility of two cryptocurrencies and one international fiat currency. The exchange rate of the currencies is monitored on a daily basis using 1,460 observations from January 1, 2014 to December 31, 2017. The models used include the augmented Dickey–Fuller test, Akaike Information Criteria, autocorrelation function and exchange rate changes determining which currency is the most volatile. Findings The findings indicate, based on the statistical measures used, including the standard deviation of selected currencies and annualized volatility, that Litecoin is more volatile than Bitcoin and the Euro and that Bitcoin is more volatile than the Euro. This furthers previous research on cryptocurrency volatility. Originality/value The paper provides compelling evidence about the volatility of Litecoin and Bitcoin. The volatility of cryptocurrencies is furthered with data that are more current. The findings are important for investors, financial markets and central banks.
Hélder Sebastião, Pedro Godinho
No abstract is available for this record.
Paolo Pagnottoni
Despite the current growing interest in Bitcoins-and cryptocurrencies in general-financial instruments, as well as studies related to them, are quite underdeveloped. Therefore, this article aims to provide a suitable pricing model for options written on this peculiar underlying. This is done through an artificial neural network approach, where classical pricing models-namely the trinomial tree, Monte Carlo simulation, and explicit finite difference method-are used as input layers. Results show that options written on Bitcoin turn out to be systematically overpriced when considering classical methods, whereas a noticeable improvement in price predictions is achieved by means of the proposed neural network model.
Emre Esat Topaloğlu
Sanal ve kripto para niteliğinde olan Bitcoin, dijital formata sahip, teknik olarak blok zinciri olarak ifade edilen işlemleri kapsayan ve merkezi para sistemine dahil olmayan bir para birimidir. Çalışmada, kripto para Bitcoin ile döviz kurları arasındaki ilişkiyi ortaya çıkarmak amaçlanmıştır. ABD Doları bazında Bitcoin kuru ile Euro, Japon Yeni, İngiliz Sterlini, Avustralya Doları, Kanada Doları, İsviçre Frankı, Yuan Renminbisi ve İsveç Kronu döviz kurları arasındaki ilişki, 3.02.2012-04.10.2017 dönemindeki günlük kur değerleri esas alınarak, yapısal kırılmalı Gregory ve Hansen eşbütünleşme ve Granger nedensellik analizleri ile incelenmiştir. Analiz sonucunda, BTC/USD döviz kurunda yapısal kırılmaların, 2013 yılı Nisan ve Aralık aylarında gerçekleştiği belirlenmiştir. Ayrıca çalışmada, döviz kurlarına ilişkin zaman serileri arasında uzun dönemli eşbütünleşme ilişkisi tespit edilirken, CNY/USD döviz kuru ile BTC/USD döviz kuru arasında tek yönlü pozitif nedensellik ilişkisi tespit edilmiştir.
Maria Glenski, Tim Weninger, Svitlana Volkova
Social media signals have been successfully used to develop large-scale predictive and anticipatory analytics. For example, forecasting stock market prices and influenza outbreaks. Recently, social data has been explored to forecast price fluctuations of cryptocurrencies, which are a novel disruptive technology with significant political and economic implications. In this paper we leverage and contrast the predictive power of social signals, specifically user behavior and communication patterns, from multiple social platforms GitHub and Reddit to forecast prices for three cyptocurrencies with high developer and community interest - Bitcoin, Ethereum, and Monero. We evaluate the performance of neural network models that rely on long short-term memory units (LSTMs) trained on historical price data and social data against price only LSTMs and baseline autoregressive integrated moving average (ARIMA) models, commonly used to predict stock prices. Our results not only demonstrate that social signals reduce error when forecasting daily coin price, but also show that the language used in comments within the official communities on Reddit (r/Bitcoin, r/Ethereum, and r/Monero) are the best predictors overall. We observe that models are more accurate in forecasting price one day ahead for Bitcoin (4% root mean squared percent error) compared to Ethereum (7%) and Monero (8%).
Keke Wu, Bo Peng, Hua Xie, Zhen Huang
Decentralization is a key selling point of most public blockchain platforms. However, despite the widely acknowledged importance of this property, most researches on this topic lack quantification, and none of them performs a calculation on the degrees of decentralization they achieve in practice. In this paper, taking Bitcoin and Ethereum for instances, we propose an entropy method in information theory to quantify the decentralization for them. Using the information entropy, we calculate the discrete degrees of blocks mined and address balances to quantify the degrees of decentralization for Bitcoin and Ethereum systems, and the results of calculations indicate that Bitcoin's mining is more approximately 12% decentralized than Ethereum with full samples, and Bitcoin's wealth is more approximately 9.2% decentralized than Ethereum with 10,000 samples. Our method can be used to evaluate the degree of decentralization for any blockchain system.
Paul Gatabazi, J. C. Mba, Edson Pindza
The Fractional Gray Lotka-Volterra Model (FGLVM) is introduced and used for modeling the transaction counts of three cryptocurrencies, namely, Bitcoin, Litecoin, and Ripple. The 2-dimensional study is on Bitcoin and Litecoin, while the 3-dimensional study is on Bitcoin, Litecoin, and Ripple. Dataset from 28 April 2013 to 10 February 2018 provides forecasting values for Bitcoin and Litecoin through the 2-dimensional FGLVM study, while dataset from 7 August 2013 to 10 February 2018 provides forecasting values of Bitcoin, Litecoin, and Ripple through the 3-dimensional FGLVM study. Forecasting values of cryptocurrencies for the n-dimensional FGLVM study, n={2,3} along 100 days of study time, are displayed. The graph and Lyapunov exponents of the 2-dimensional Lotka-Volterra system using the results of FGLVM reveal that the system is a chaotic dynamical system, while the 3-dimensional Lotka-Volterra system displays parabolic patterns in spite of the chaos indicated by the Lyapunov exponents. The mean absolute percentage error indicates that 2-dimensional FGLVM has a good accuracy for the overall forecasting values of Bitcoin and a reasonable accuracy for the last 300 forecasting values of Litecoin, while the 3-dimensional FGLVM has a good accuracy for the overall forecasting values of Bitcoin and a reasonable accuracy for the last 300 forecasting values of both Litecoin and Ripple. Both 2- and 3-dimensional FGLVM analyses evoke a future constant trend in transacting Bitcoin and a future decreasing trend in transacting Litecoin and Ripple. Bitcoin will keep relatively higher transaction counts, with Litecoin transaction counts everywhere superior to that of Ripple.