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3,636 results · page 143 of 152
Aviral Kumar Tiwari, Rabin K. Jana, Debojyoti Das, David Roubaud
No abstract is available for this record.
Serguei Popov, Olivia Saa, Paulo Finardi
We analyse the Tangle --- a DAG-valued stochastic process where new vertices get attached to the graph at Poissonian times, and the attachment's locations are chosen by means of random walks on that graph. These new vertices, also thought of as "transactions", are issued by many players (which are the nodes of the network), independently. The main application of this model is that it is used as a base for the IOTA cryptocurrency system (www.iota.org). We prove existence of "almost symmetric" Nash equilibria for the system where a part of players tries to optimize their attachment strategies. Then, we also present simulations that show that the "selfish" players will nevertheless cooperate with the network by choosing attachment strategies that are similar to the "recommended" one.
Yaohao Peng, Pedro Henrique Melo Albuquerque, Jader Martins Camboim de Sá, ANA JULIA AKAISHI PADULA · 5 authors
No abstract is available for this record.
Hio Loi
This paper studies the liquidity of Bitcoin using the time series daily data over the period 1/1/2014 to 12/31/2015. Based on the available data for Bitcoin, five liquidity measures are chosen to compare the liquidities among five Bitcoin exchanges and the liquidities of different sizes of stocks. The results suggest that the liquidity of Bitcoin depends on the choice of the Bitcoin exchanges, and Bitfinex, one of the Bitcoin exchanges, provides the highest liquidity for Bitcoin trading. Moreover, the results indicate that, on average, stocks are more liquid than Bitcoin.
Wenjun Feng, Yiming Wang, Zhengjun Zhang
No abstract is available for this record.
Pradipta Kumar Sahoo
This paper examines the comprehensive idea about the growth and future sustainability of bitcoin as a cryptocurrency. The transaction volume of bitcoin is used as the growth of the bitcoin and the bitcoin log return is used for testing the volatility which is helpful for the future sustainability of bitcoin. The study period says that the growth of bitcoin’s transaction volume is an increasing trend as more day to day transaction is minting with the exchange of Bitcoin. The study also uses ARCH & GARCH methodology to know the volatility of this emerging digital currency, and the GARCH result shows that it is a highly volatile currency. As a result, most of the governments have not given their legal status for the use of bitcoin in their country. But if bitcoin will be stable in the future, then it is easily accepted through worldwide and in the long run, people will have more faith in the cryptocurrency technology and its usability.
Josef Kurka
Large stream of literature studies interconnectedness among various assets that are relevant in current global markets. Transmission of shocks between cryptocurrencies and traditional asset classes is, however, not understood at all, but should not be ignored due to increasing influence of cryptocurrencies in recent years. In this paper, we study how shocks between the most liquid representatives of the traditional asset classes including commodities, foreign exchange, stocks, financials, and cryptocurrencies are being transmitted. Generally, we document very low level of connectedness between the main cryptocurrency and other studied assets. The only exception is gold which receives substantial amount of shocks from cryptocurrency market. Our findings are important since we show that cryptocurrencies play role in global markets, and the results could also be useful in portfolio diversification schemes. Moreover, we find significant positive asymmetry in spillovers between the studied assets, which is in contradiction to previous studies conducted on assets from a single asset class.
Leonardo Ermann, Klaus M. Frahm, Dima L. Shepelyansky
We construct and study the Google matrix of Bitcoin transactions during the time period from the very beginning in 2009 till April 2013. The Bitcoin network has up to a few millions of bitcoin users and we present its main characteristics including the PageRank and CheiRank probability distributions, the spectrum of eigenvalues of Google matrix and related eigenvectors. We find that the spectrum has an unusual circle-type structure which we attribute to existing hidden communities of nodes linked between their members. We show that the Gini coefficient of the transactions for the whole period is close to unity showing that the main part of wealth of the network is captured by a small fraction of users.
Andrew Phillip, Jennifer Chan, Shelton Peiris
No abstract is available for this record.
J. Alvarez-Ramirez, Eduardo Rodríguez, Carlos Ibarra-Valdez
No abstract is available for this record.
Pedro Jorge Melgo Vieira
No abstract is available for this record.
Salim Lahmiri, Stelios Bekiros
No abstract is available for this record.
Ross C. Phillips, Denise Gorse
Financial price bubbles have previously been linked with the epidemic-like spread of an investment idea; such bubbles are commonly seen in cryptocurrency prices. This paper aims to predict such bubbles for a number of cryptocurrencies using a hidden Markov model previously utilised to detect influenza epidemic outbreaks, based in this case on the behaviour of novel online social media indicators. To validate the methodology further, a trading strategy is built and tested on historical data. The resulting trading strategy outperforms a buy and hold strategy. The work demonstrates both the broader utility of epidemic-detecting hidden Markov models in the identification of bubble-like behaviour in time series, and that social media can provide valuable predictive information pertaining to cryptocurrency price movements.
Dirk G. Baur, Thomas Dimpfl, Konstantin Kuck
No abstract is available for this record.
Kirichenko Lyudmyla, Bulakh Vitalii, Radivilova Tamara
In the work, a comparative correlation and fractal analysis of time series of Bitcoin crypto currency rate and community activities in social networks associated with Bitcoin was conducted. A significant correlation between the Bitcoin rate and the community activities was detected. Time series fractal analysis indicated the presence of self-similar and multifractal properties. The results of researches showed that the series having a strong correlation dependence have a similar multifractal structure.
Nhi N.Y. Vo, Guandong Xu
The 2008 financial crisis had scattered incredulity around the globe regarding traditional financial systems, which made investors and non-financial customers turn to other alternative such as digital banking systems. The existence and development of blockchain technology make cryptocurrency in recent years believably become a complete alternative to traditional ones. Bitcoin is the world's first peer-to-peer and decentralized digital cash system initiated by Nakamoto [1]. Though being the most prominent cryptocurrency, Bitcoin has not been a legal trading currency in various countries. Its exchange rate has appeared to be an exceptionally high-risk portfolio with extreme volatility, which requires a more detailed evaluation before making any decision. This paper utilizes knowledge of statistics for financial time series and machine learning to (i) fit the parametric distribution and (ii) model and forecast the volatility of Bitcoin returns, and (iii) analyze its correlation to other financial market indicators. The fitted parametric time series model significantly outperforms other standard models in explaining the stylized facts and statistical variances in the behavior of Bitcoin returns. The model forecast also outperforms some machine learning methodologies, which would benefit policy makers, banks and financial investors in trading activities for both long-term and short-term strategies.
Arief Radityo, Qorib Munajat, Indra Budi
Cryptocurrency trade is now a popular type of investment. Cryptocurrency market has been treated similar to foreign exchange and stock market. However, because of its volatility, there's a need for a prediction tool for investors to help them consider investment decisions for cryptocurrency trade. Nowadays, Artificial Neural Network (ANN) computing based tools are commonly used in stock and foreign exchange market predictions. There has been much research about ANN predictor on stocks and foreign exchange as case studies but none on cryptocurrency. Therefore, this research studied variety of ANN method to predict the market value of one of the most used cryptocurrency, Bitcoin. The ANN methods will be used to develop model to predict the close value of Bitcoin in the next day (next day prediction). This study compares four ANN methods, namely backpropagation neural network (BPNN), genetic algorithm neural network (GANN), genetic algorithm backpropagation neural network (GABPNN), and neuro-evolution of augmenting topologies (NEAT). The methods are evaluated based on accuracy and complexity. The result of the experiment showed that BPNN is the best method with MAPE 1.998 ± 0.038 % and training time 347 ± 63 seconds.
Damiano Di Francesco Maesa, Andrea Marino, Laura Ricci
No abstract is available for this record.
Osamu Kodama, Lukáš Pichl, Taisei Kaizoji
Bitcoin time series dataset recording individual transactions denominated in Euro at the COINBASE market between April 23, 2015 and August 15, 2016 is analyzed. Markov switching model is applied to classify the regions of varying volatility represented by three hidden state regimes using univariate autoregressive model and dependent mixture model. Causality extraction and price prediction of daily BTCEUR exchange rates is performed by means of a recurrent neural network using the standard Elman model. Strong correlations is found between the normalized mean squared error of the Elman network (out-of-sample 5-day-ahead prediction) and the realized volatility (sum of minute returns squared throughout the trading day). The present approach is calibrated using simulated regime change in standard econometric models. Our results clearly demonstrate the applicability of recurrent neural networks to causality extraction even in the case of highly volatile cryptocurrency exchange rate time series data.
Aurelio F. Bariviera
This letter revisits the informational efficiency of the Bitcoin market. In particular we analyze the time-varying behavior of long memory of returns on Bitcoin and volatility 2011 until 2017, using the Hurst exponent. Our results are twofold. First, R/S method is prone to detect long memory, whereas DFA method can discriminate more precisely variations in informational efficiency across time. Second, daily returns exhibit persistent behavior in the first half of the period under study, whereas its behavior is more informational efficient since 2014. Finally, price volatility, measured as the logarithmic difference between intraday high and low prices exhibits long memory during all the period. This reflects a different underlying dynamic process generating the prices and volatility.
Veni Madhavan C. E., Kumar Swamy H. V.
In the last few years the phenomenon of cryptocurrencies has captured the imagination of people in two dominant sectors, information technology and financial technology. These two sectors have been brought much closer due to the recent developments in both sectors. We provide an analytic view of various interconnected issues, drawing upon tenets from cryptography, digital cash, cryptocurrencies, blockchains and socio-economics of money. We discuss the underlying scientific and technical principles behind the extant methodologies, in particular the artefact Bitcoin.
Hermann Elendner, Simon Trimborn, Bobby Ong, Teik Ming Lee
No abstract is available for this record.
Shi Chen, Cathy Yi‐Hsuan Chen, Wolfgang Karl Härdle, Taehyun Lee · 5 authors
No abstract is available for this record.
Thomas Kim
No abstract is available for this record.