Follow blockchain research across journals, conferences, and preprint repositories.
2,964 results · page 98 of 124
Gbenga Ibikunle, Frank McGroarty, Khaladdin Rzayev
No abstract is available for this record.
S. Alonso Monsalve, Andrés L. Suárez‐Cetrulo, Alejandro Cervantes, David Quintana
No abstract is available for this record.
Samuel Asante Gyamerah
High frequency Bitcoin price series are often non-linear and non-stationary and hence forecasting the price of Bitcoin directly or by transformation using statistical models is subject to large errors. This paper presents an ensemble model using variational mode decomposition (VMD) and Generalized additive model (GAM) to forecast intraday Bitcoin price. To evaluate the performance of the constructed model, it is compared with an ensemble of empirical mode decomposition (EMD) and GAM. The results showed that VMD-GAM model performed better than the EMD-GAM ensemble model in terms of three evaluation metrics (root mean square error, mean absolute percentage error, and bias) used.
Andrew Burnie, Emine Yılmaz, Tomaso Aste
The recent extreme volatility in cryptocurrency prices occurred in the setting of popular social media forums devoted to the discussion of cryptocurrencies. We develop a framework that discovers potential causes of phasic shifts in the price movement captured by social media discussions. This draws on principles developed in healthcare epidemiology where, similarly, only observational data are available. Such causes may have a major, one-off effect or recurring effects on the trend in the price series. We find a one-off effect of regulatory bans on bitcoin, the repeated effects of rival innovations on ether and the influence of technical traders, captured through discussion of market price, on both cryptocurrencies. The results for Bitcoin differ from Ethereum, which is consistent with the observed differences in the timing of the highest price and the price phases. This framework could be applied to a wide range of cryptocurrency price series where there exists a relevant social media text source. Identified causes with a recurring effect may have value in predictive modelling, whilst one-off causes may provide insight into unpredictable black swan events that can have a major impact on a system.
Anwar Hasan Abdullah Othman, Salina Kassim, Romzie Rosman, Nur Harena Redzuan
No abstract is available for this record.
Dilek Teker, Suat Teker
No abstract is available for this record.
Meihua Xie, Haiyan Li, Yuanjun Zhao
No abstract is available for this record.
Samet Evci
Kripto para piyasası kısa dönemde çok hızlı bir gelişim göstermiş hem yatırımcıların hem de akademisyenlerin ilgisini çekmiştir. Bu piyasada en fazla piyasa değerine sahip kripto para birimi Bitcoin’dir. Gerek geleneksel finansal piyasaların işleyişinden farklı bir piyasa işleyişine sahip olması gerekse para yaratma sürecinde farklı bir sistemi kullanması yatırımcılar açısından Bitcoin fiyatlarında değişime yol açan faktörleri anlamayı gerekli kılmaktadır. Bu çalışma ile Bitcoin fiyatlarında haftanın günü anomalisinin varlığının araştırılması amaçlanmıştır. Bitcoin getirilerinde haftanın günü anomalisi, 2013-2019 yıllarına ait günlük fiyatlar kullanılarak asimetrik GARCH modeliyle incelenmiştir. Çalışmadan elde edilen bulgular Bitcoin getirileri üzerinde Pazartesi, Perşembe ve Pazar günlerinin negatif etkileri olduğunu ve en fazla kaybın Perşembe günü gerçekleştiğini ortaya koymuştur.
Don Gunasekera, Ernesto Valenzuela
Recent analysis of Blockchain use has highlighted considerable potential productivity gains arising from lower transaction costs between buyers and sellers of goods. This has been shown by recent examples of Blockchain use in the Australian grains sector. In this paper, we have further developed and quantified this concept of productivity gain by undertaking several illustrative scenarios using a general equilibrium model of the global economy. Our analysis indicates that an assumed modest growth (five per cent) in productivity due to Blockchain use in the grains sector could raise output by eight per cent over the medium term. If this is accompanied by Blockchain use in the Australian finance sector, grains output could reach ten per cent. This reflects the effect of reduction in transaction costs due to the use of Blockchain technology as a “distributed ledger technology” in grain trading. Further, it is anticipated that the wider effects of Blockchain‐driven productivity enhancement of the Australian finance sector could contribute to approximately 2.5 per cent increase in GDP in the medium term, relative to what would otherwise be.
Nicola Uras, Lodovica Marchesi, Michele Marchesi, Roberto Tonelli
This paper studies how to forecast daily closing price series of Bitcoin,\nusing data on prices and volumes of prior days. Bitcoin price behaviour is\nstill largely unexplored, presenting new opportunities. We compared our results\nwith two modern works on Bitcoin prices forecasting and with a well-known\nrecent paper that uses Intel, National Bank shares and Microsoft daily NASDAQ\nclosing prices spanning a 3-year interval. We followed different approaches in\nparallel, implementing both statistical techniques and machine learning\nalgorithms. The SLR model for univariate series forecast uses only closing\nprices, whereas the MLR model for multivariate series uses both price and\nvolume data. We applied the ADF -Test to these series, which resulted to be\nindistinguishable from a random walk. We also used two artificial neural\nnetworks: MLP and LSTM. We then partitioned the dataset into shorter sequences,\nrepresenting different price regimes, obtaining best result using more than one\nprevious price, thus confirming our regime hypothesis. All the models were\nevaluated in terms of MAPE and relativeRMSE. They performed well, and were\noverall better than those obtained in the benchmarks. Based on the results, it\nwas possible to demonstrate the efficacy of the proposed methodology and its\ncontribution to the state-of-the-art.\n
Ying Chen, Paolo Giudici, Branka Hadji Misheva, Simon Trimborn
We aim to understand the dynamics of Bitcoin blockchain trading volumes and, specifically, how different trading groups, in different geographic areas, interact with each other. To achieve this aim, we propose an extended Vector Autoregressive model, aimed at explaining the evolution of trading volumes, both in time and in space. The extension is based on network models, which improve pure autoregressive models, introducing a contemporaneous contagion component that describes contagion effects between trading volumes. Our empirical findings show that transactions activities in bitcoins is dominated by groups of network participants in Europe and in the United States, consistent with the expectation that market interactions primarily take place in developed economies.
Christian Hafner
Alternative assets, defined by their low correlation with classical financial assets, have become an important investment vehicle in times of negative interest rates and in the aftermath of the global economic and financial crisis. Hedge funds increasingly invest in physical assets such as fine art, wine, or diamonds. Although digital and not physical, cryptocurrencies share many features of alternative assets, but are hampered by high volatility, sluggish commercial acceptance, and regulatory uncertainties. This special issue covers a broad variety of topics in financial technology, and provides a state-of-the-art overview of cryptocurrencies from economic, financial, statistical and technical points of view.
Yuanyuan Zhang, Stephen Chan, Jeffrey Chu, Hana Sulieman
The market for cryptocurrencies has experienced extremely turbulent conditions in recent times, and we can clearly identify strong bull and bear market phenomena over the past year. In this paper, we utilise algorithms for detecting turnings points to identify both bull and bear phases in high-frequency markets for the three largest cryptocurrencies of Bitcoin, Ethereum, and Litecoin. We also examine the market efficiency and liquidity of the selected cryptocurrencies during these periods using high-frequency data. Our findings show that the hourly returns of the three cryptocurrencies during a bull market indicate market efficiency when using the detrended-fluctuation-analysis (DFA) method to analyse the Hurst exponent with a rolling window. However, when conditions turn and there is a bear-market period, we see signs of a more inefficient market. Furthermore, our results indicated differences between the cryptocurrencies in terms of their liquidity during the two market states. Moving from a bull to a bear market, Ethereum and Litecoin appear to become more illiquid, as opposed to Bitcoin, which appears to become more liquid. The motivation to study the high-frequency cryptocurrency market came from the increasing availability of higher-frequency cryptocurrency-pricing data. However, it also comes from a movement towards higher-frequency trading of cryptocurrency. In addition, the efficiency of cryptocurrency markets relates not only to whether prices are predictable and arbitrage opportunities exist, but, more widely, to topics such as testing the profitability of trading strategies and determining the maturity of cryptocurrency markets.
Vangelis Malamas, Thomas K. Dasaklis, Veni Arakelian, Gregory Chondrokoukis
No abstract is available for this record.
Antonis Ballis, Κωνσταντίνος Δράκος
No abstract is available for this record.
Ryuta Sakemoto
This study proposes a method to enhance cryptocurrency portfolios constructed by forecast models. This study forecasts returns on four liquid cryptocurrencies (Bitcoin, Litecoin, Ripple, and Dash) and determines the weights on the cryptocurrencies based upon a dynamic allocation framework. We assess the performances of the portfolios using the performance fee measure. Our results present that the proposed portfolios outperform the benchmark portfolio with the conventional level of the risk aversion parameter. The economic gain for an investor is equivalent to 12% per week. The economic gain is sensitive to a change in the risk aversion parameter, which contrasts with the studies of exchange rates which is due to the high volatility on the cryptocurrencies. Our predictors are related to the price momentum effects and they outperform widely used network factors.
Claude B. Erb
Bitcoin has been described as digital gold. Bitcoin is exactly like gold except when it isn’t. Over millennia, gold has gained a questionable reputation as an inflation hedge, a store of value and a safe haven. Gold’s price can arguably be decomposed into a “golden constant” fair price and a fair price deviation. Bitcoin has no track record as an inflation hedge, a store of value and a safe haven. Bitcoin’s price can arguably be decomposed into a questionable “bitcoin network” fair price and a fair price deviation. Both bitcoin and gold are about 50% above their “fair prices”.
Guglielmo Maria Caporale, Alex Plastun, Viktor Oliinyk
This paper investigates the relationship between Bitcoin returns and the frequency of daily abnormal returns over the period from June 2013 to February 2020 using a number of regression techniques and model specifications including standard OLS, weighted least squares (WLS), ARMA and ARMAX models, quantile regressions, Logit and Probit regressions, piecewise linear regressions, and non-linear regressions. Both the in sample and out-of-sample performance of the various models are compared by means of appropriate selection criteria and statistical tests. These suggest that, on the whole, the piecewise linear models are the best, but in terms of forecasting accuracy they are outperformed by a model that combines the top five to produce “consensus” forecasts. The finding that there exist price patterns that can be exploited to predict future price movements and design profitable trading strategies is of interest both to academics (since it represents evidence against the EMH) and to practitioners (who can use this information for their investment decisions).
Afees A. Salisu, Ahamuefula E. Ogbonna, Tirimisiyu F. Oloko
This study examines the effect of a pandemic-induced uncertainty on cryptocurrencies (specifically, Bitcoin, Ethereum and Ripple). It employs a predictive model by Westerlund and Narayan (2012, 2015) to examine the predictability of a pandemic-induced uncertainty as a predictor, as well as the forecast performance of our predictive model for cryptocurrency returns. We examine the role of asymmetry in uncertainty and the sensitivity of our results to alternative measures of uncertainty due to pandemics, using the recently developed Global Fear Index (GFI) by Salisu and Akanni (2020). Our results indicate that cryptocurrencies could act as hedge against uncertainty due to pandemics, albeit with reduced hedging effectiveness in the COVID-19 period. Accounting for asymmetry is found to improve the predictability and forecast performance of the model, which indicates that failure to account for asymmetry in modeling the effect of a pandemic-induced uncertainty on cryptocurrency may lead to incorrect conclusion. The results seem to be sensitive to the choice of measure of pandemic-induced uncertainty.
Almero de Villiers, Paul Cuffe
Ever since the invention of Bitcoin by the pseudonymous Satashi Nakamoto, cryptocurrency has provoked debate in banking and finance sectors, and is sometimes considered a potential successor to fiat currency. Blockchain, the new technology underpinning decentralised and immutable databases, has seen much discussion as a potentially game-changing development. Although many industries are exploring its value, the technology has thus far made only minor impacts. A rapidly expanding base of research has emerged on blockchain's role as a potential disruptor in the electrical energy industry. However, it may be difficult to distinguish hype from more imminently plausible impacts. This paper attempts to serve as a guide for engineering management wishing to make sense of blockchain's potential in electricity. This is accomplished by formulating a novel blockchain industry disruption framework, which exists across three tiers. These tiers extend from ideas with the least effect on an industry to total revolutionary concepts that could completely transform an industry. This taxonomy is constructed by examining existing research into disruption hierarchies and blockchain classification methods. Through the lens of this taxonomy, a literature review is performed on blockchain's role in energy to draw out themes and ideas characterising each tier. The potential likelihood of real-world application of various ideas are discussed, giving consideration to how established industries may be affected or disrupted. The authors provide some conjecture here. Finally, courses of action are suggested for those whose sector may be affected by blockchain.
Guglielmo Maria Caporale, Woo-Young Kang
No abstract is available for this record.
Dominique Guégan, Marius Frunza, Rostislav Haliplii
No abstract is available for this record.
Carlos César Trucíos Maza, James W. Taylor
No abstract is available for this record.
Mieszko Mazur
Bitcoin market capitalization has recently surpassed $1 trillion. According to the popular belief one of the key characteristics of bitcoin is its excessive volatility. This paper provides evidence that high volatility of bitcoin is largely a misperception. We show that bitcoin return fluctuations are lower than those of roughly 900 different stocks in the S&P1500 and 190 stocks in the S&P500. Moreover, we find that bitcoin is less volatile than commodities such as oil and silver, US Treasuries, AAA-rated corporate bonds, EU carbon credits, and some of the most popular technology and media stocks: Apple, Twitter, and Netflix. Equally important, we find that during the March 2020 stock market crash triggered by COVID-19, bitcoin volatility was lower than most of the above-mentioned asset classes. Significant decline in bitcoin volatility over the last decade renders it more “investable” by conservative investors.