Follow blockchain research across journals, conferences, and preprint repositories.
3,636 results · page 111 of 152
Vasily Derbentsev, Andriy Matviychuk, Vladimir Soloviev
No abstract is available for this record.
Daniele Bianchi, Mykola Babiak
No abstract is available for this record.
Guglielmo Maria Caporale, Woo-Young Kang, Fabio Spagnolo, Nicola Spagnolo
This paper examines mean and volatility spillovers between three major cryptocurrencies (Bitcoin, Litecoin and Ethereum) and the role played by cyber-attacks. Specifically, trivariate GARCH-BEKK models are estimated which include suitably defined dummies corresponding to different types, targets and number per day of cyber-attacks. Significant dynamic linkages (interdependence) between the three cryptocurrencies under investigation are found in most cases when cyber-attacks are taken into account, Bitcoin appearing to be the dominant cryptocurrency. Further, Wald tests for parameter shifts during episodes of turbulence resulting from cyber-attacks provide evidence that the latter affect the transmission mechanism between cryptocurrency returns and volatilities (contagion). More precisely, cyber-attacks appear to strengthen cross-market linkages, thereby reducing portfolio diversification opportunities for cryptocurrency investors. Finally, the conditional correlation analysis confirms the previous findings.
Klaus Grobys, Juha-Pekka Junttila
This is the first paper that explores lottery-like demand in cryptocurrency markets. Since recent research provides evidence that cryptocurrency returns appear to be short-memory processes, we modify Bali, Cakici and Whitelaw’s (2011) and Bali, Brown, Murray, and Tang’s (2017) MAX measure and employ a weekly forecast horizon and daily log-returns from the previous week to calculate the metric for our portfolio sorts. From an econometric point of view, this study proposes statistical tests that are robust to unknown dynamic dependency structures in the cryptocurrency data. Our results show that average raw and risk-adjusted return differences between cryptocurrencies in the lowest and highest MAX quintiles exceed 1.50% per week. These results are robust after controlling for Bitcoin risk or potential microstructure effects. Our findings are important also from a theoretical point of view because they suggest that parallel to stock markets, similar behavioral mechanisms of underlying investor behavior are present also in new virtual currency markets.
David Y. Aharon, Ender Demir, Chi Keung Marco Lau, Adam Zaremba
No abstract is available for this record.
Angelo Aspris, Sean Foley, Jiří Švec, Leqi Wang
No abstract is available for this record.
Νikolaos Kyriazis, Stephanos Papadamou, Shaen Corbet
No abstract is available for this record.
Hanna Hałaburda, Guillaume Haeringer, Joshua S. Gans, Neil Gandal
This chapter focuses on how bitcoin performs the functions of money. A better understanding of where cryptocurrencies fall short of fiat money might allow for a better design and might possibly decrease price volatility. The medium of exchange function means a generally accepted form of payment. The Haitian gourde, for example, is fiat money in Haiti. General acceptance of various forms of fiat money is limited. To function as a medium of exchange, a currency needs a low transaction cost. Transaction costs have both domestic and international dimensions. Cryptocurrency is faster and sometimes cheaper for international and long-distance domestic transactions, whereas fiat money is cheaper for local domestic transactions. The Lightning Network technology reduces transaction costs for parties that can pool bitcoin transactions without converting into and out of fiat currency each time. Bitcoin provides users with other valuable features, such as financial privacy. Fiat money in the form of physical cash offers excellent privacy.
Jay Patel, Vasu Kalariya, Pushpendra Parmar, Sudeep Tanwar · 6 authors
Over the past few years, with the advent of blockchain technology, there has been a massive increase in the usage of Cryptocurrencies. However, Cryptocurrencies are not seen as an investment opportunity due to the market's erratic behavior and high price volatility. Most of the solutions reported in the literature for price forecasting of Cryptocurrencies may not be applicable for real-time price prediction due to their deterministic nature. Motivated by the aforementioned issues, we propose a stochastic neural network model for Cryptocurrency price prediction. The proposed approach is based on the random walk theory, which is widely used in financial markets for modeling stock prices. The proposed model induces layer-wise randomness into the observed feature activations of neural networks to simulate market volatility. Moreover, a technique to learn the pattern of the reaction of the market is also included in the prediction model. We trained the Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) models for Bitcoin, Ethereum, and Litecoin. The results show that the proposed model is superior in comparison to the deterministic models.
Marcin Wkatorek, Stanislaw Dro.zd.z, Jarosław Kwapień, Ludovico Minati · 6 authors
The review introduces the history of cryptocurrencies, offering a description of the blockchain technology behind them. Differences between cryptocurrencies and the exchanges on which they are traded have been shown. The central part surveys the analysis of cryptocurrency price changes on various platforms. The statistical properties of the fluctuations in the cryptocurrency market have been compared to the traditional markets. With the help of the latest statistical physics methods the non-linear correlations and multiscale characteristics of the cryptocurrency market are analyzed. In the last part the co-evolution of the correlation structure among the 100 cryptocurrencies having the largest capitalization is retraced. The detailed topology of cryptocurrency network on the Binance platform from bitcoin perspective is also considered. Finally, an interesting observation on the Covid-19 pandemic impact on the cryptocurrency market is presented and discussed: recently we have witnessed a "phase transition" of the cryptocurrencies from being a hedge opportunity for the investors fleeing the traditional markets to become a part of the global market that is substantially coupled to the traditional financial instruments like the currencies, stocks, and commodities. The main contribution is an extensive demonstration that structural self-organization in the cryptocurrency markets has caused the same to attain complexity characteristics that are nearly indistinguishable from the Forex market at the level of individual time-series. However, the cross-correlations between the exchange rates on cryptocurrency platforms differ from it. The cryptocurrency market is less synchronized and the information flows more slowly, which results in more frequent arbitrage opportunities. The methodology used in the review allows the latter to be detected, and lead-lag relationships to be discovered.
Carol Alexander, Daniel F. Heck
No abstract is available for this record.
John W. Goodell, Stéphane Goutte
Co-movement, COVID-19, Bitcoin, Wavelet, Safe haven
Mustafa Özyeşil
In this study, the relationship between the popularity of cryptocurrencies and their price, return and trading volumes are examined through time series analysis. The popularity variable is determined according the frequency of cryptocurrencies being searched on the internet. Stationarity of series is examined by Vogelsang and Perron (1998) structural breaks ADF unit root test. According to the test results, all series are found to be stationary at level values. VAR analyses and impulse-response functions are performed to reveal dynamic interaction between the series. According to impulse - response test results, returns of BITCOIN decreased against a decreasing shock in the number searches on the internet and its price and trading volume followed a fluctuating course. In order to see the causality relationship between variables the Granger causality test is conducted. Regression analyses are performed using ordinary least squares (OLS) method through three different equations. According to the result of the regression analysis, an increase in the number of internet searches for cryptocurrencies was found to positively affect prices, returns and trading volumes of all cryptocurrencies. The highest impact on prices and trading volume is observed in BITCOIN, while the highest effect on returns is observed in LITECOIN. According to the findings, popularity can be considered an important determinant for price, returns and trading volumes of cryptocurrencies.
Baykar Silahli, Kemal Dinçer Dingeç, Atilla Çifter, Nezir Aydın
No abstract is available for this record.
Jeffrey Chu, Stephen Chan, Yuanyuan Zhang
No abstract is available for this record.
Andrei Shynkevich
This study explores whether pricing inefficiency, or imperfect tracking in the market value of shares of a bitcoin fund of the fund’s net asset value (NAV), makes a significant impact on the fund’s market efficiency relative to the retail bitcoin market. Two bitcoin funds whose shares are traded at the exchanges which impose more stringent criteria for transparency compared to cryptocurrency exchanges are considered. The fund whose shares have been trading without significant premium or a discount relative to its NAV is found as weak-form efficient. The fund, whose shares have been trading at a significant premium over its NAV is found inefficient due to the presence of persistent and strong positive autocorrelation in its returns. Trading of shares in the inefficiently priced fund appears to be even more emotion-driven than the already volatile and emotional trading of bitcoin and exhibits a strong herding behaviour.
Walid M.A. Ahmed
No abstract is available for this record.
Reilly White, Yorgos Marinakis, Nazrul Islam, Steven T. Walsh
Cryptocurrencies such as Bitcoin have fascinated technologists and investors alike. They have become prevalent, with over 2,000 Bitcoin-like cryptocurrencies now in use. Most jurisdictions have not regulated cryptocurrencies. Whether existing regulations apply to cryptocurrency turns ultimately on if we classify cryptocurrencies as currencies, securities, or derivatives, or a money services (transfer) vehicle. In this set of exploratory analyses we seek to classify Bitcoin. We utilize a variety of methods to compare aspects of its behavior to: currencies, asset classes such as derivatives, technology-based products and possible technology-based products such as Ether and the security SPY, and speculative financial bubbles. We find that Bitcoin's behavior more closely resembles a technology-based product, an emerging asset class, or a bubble event, rather than a currency or a security; such that it is correct that existing currency and security laws should not apply to cryptocurrencies.
Ibrahim Mert
The major reason of performing this study is to examine volatility of the Bitcoin prices. As known, Bitcoin became more popular when its price movements changed radically. It has been increasing for years since the date of first issuance in 2010 and reached highest level in its history by testing 19,345 USD. Based on this price movement, risk and returns are taken together for making investment in Bitcoin since huge decreasing observed in respond to the these increases. Methodology -In this study, Bitcoin prices are analyzed monthly basis through the time series analysis. Data related to closing prices of Bitcoin are obtained from investing.com web site. We established analysis based on sample consist of Bitcoin prices for the period between 2016 and 2019. Augmented Dickey Fuller (ADF) and Phillips Perron (PP) unit root test is applied to find out whether series are stationary or not. Findings-According to test results, the series of Bitcoin prices are not stationary yet. Although different fluctuating degree can be seen by years, generally it can be stated that Bitcoin prices are still volatile. Conclusion-Based on findings, Bitcoin prices may still be considered as volatile instrument. Therefore, investors should be careful when they want to include this investment tool to the portfolio since it represents risky instrument properties.
Roman Matkovskyy, Akanksha Jalan, Michael Dowling, Taoufik Bouraoui
No abstract is available for this record.
Sujin Pyo, Jaewook Lee
No abstract is available for this record.
Silivanxay Phetsouvanh, Anwitaman Datta, Frédérique Oggier
Summary Distinct transactions among different and unrelated users are combined together to create a single Bitcoin transaction (mixing transaction) to obfuscate the relationships among the actual participants (more specifically, the wallet addresses used for the transactions). We consider multi‐input multi‐output transactions with at least two inputs and three outputs as proxy, to analyze four characteristic periods of ∼50 days each, representing periods before the introduction of mixing, in its early days, during its growth, and after the volume of such multi‐input multi‐output transactions became more or less stabile. Structural properties and characteristics of the transaction and wallet address networks are computed and compared, through standard tools, but also via the introduction of two novel techniques that provide indicators of mixing‐like behaviors: (1) an entropy characterization to detect abnormally uniform inputs and/or outputs and (2) a connected component analysis of subgraphs formed by only multi‐input multi‐output transactions (showing cascades of such transactions). The contributions of this exploratory Bitcoin network analysis paper can thus be seen as two‐fold. At a macroscopic level, the growth and stabilization periods are shown to stand out with respect to most considered metrics, while at a microscopic level, chains of multi‐input multi‐output transactions, and transactions with outlier behavior in terms of input/output entropies are identified for further investigation.
Paulo Ferreira, Ladislav Krištoufek, Éder Johnson de Area Leão Pereira
No abstract is available for this record.
Maurice Omane‐Adjepong, Paul Alagidede
No abstract is available for this record.