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3,636 results · page 51 of 152
Blanka Łęt, Konrad Sobański, Wojciech Świder, Katarzyna Włosik
No abstract is available for this record.
Crina Anina Bejan, Dominic Bucerzan, Mihaela Crăciun
No abstract is available for this record.
Myriam Ben Osman, Emilios Galariotis, Khaled Guesmi, Haykel Hamdi · 5 authors
No abstract is available for this record.
Katarzyna Kryńska, Robert Ślepaczuk
No abstract is available for this record.
Chavan Rajkumar Dhaku, Senthil Kumar Arumugam
No abstract is available for this record.
Anusha Bansal, Aakanksha Singh, Sakshi Vats, Khyati Ahlawat
No abstract is available for this record.
Rareş Chelmuş, Daniela Gîfu, Adrian Iftene
No abstract is available for this record.
Mehmet Canayaz, Charles Cao, Giang Nguyen, Qiang Wang
No abstract is available for this record.
Milan Fičura
No abstract is available for this record.
Adnan Branković
Due to economic uncertainty and the financial crisis of 2008, a desire for an unregulated currency arose, leading to the invention of Bitcoin. Using a pseudonym called Satoshi Nakamoto, Bitcoin was created in 2009, anonymously or by a group of unknown individuals. Since Bitcoin has been the most valuable cryptocurrency in recent years, its prices have fluctuated dramatically, making it difficult to predict their prices. Investors, businesses, risk managers, and market analysts can all benefit from being able to predict Bitcoin prices. By using the Bitcoin transaction data obtained from the Bitstamp website in this study, several different Machine Learning models are employed to determine the most accurate model for predicting Bitcoin prices. These models are based on 1-minute interval exchange rates in USD from January 1, 2012, to January 8, 2022. Analysis was performed primarily with Python, but it was also used and Hadoop, a distributed data storage and processing framework that uses the map-reduce programming model to allow efficient parallel processing of Big Data. Based on the results of our research, comprising three experiments, autoregres-sive-integrated moving average (ARIMA) makes the most accurate prediction of Bitcoin prices, with a 95.98% success rate.
Roberto Moro Visconti, Andrea Cesaretti
No abstract is available for this record.
Prodromos E. Tsinaslanidis, Francisco Guijarro
No abstract is available for this record.
Negar Fazlollahi, Saeed Ebrahimijam
No abstract is available for this record.
Wang Chun Wei, Dimitrios Koutmos
No abstract is available for this record.
D. Siddharth, Jitendra Kaushik
No abstract is available for this record.
Eric M. Kimani, Anthony Ngunyi, Joseph Mung’atu
Cryptocurrencies are considered to be among the most disruptive innovations done in the financial sector within the last decade. It is a digital asset that is designed to serve as a medium of exchange using cryptography. Financial modeling of cryptocurrencies is needed in order to determine the presence of dependence between currencies. Copulas functions assist in modeling dependency structure by making it possible to separate marginal distributions of a given multivariate distribution. The purpose of the study was to model dependencies of cryptocurrencies using copula Garch. The study proposed the use of copula Garch model to model the dependence of cryptocurrency price data. Bivariate copula was extended to Bivariate Copula Garch in order to model prices and measure the cryptocurrency dependence. Prices of the four cryptocurrencies (Bitcoin, Binance, Litecoin and Dogecoin) were analyzed to establish whether there exists any dependency. The results showed standard Garch (1,1) under the highly flexible ARMA-GARCH model was appropriate to identify the true patterns of index returns. Fitting the copula standard Garch (1,1) model to the currencies, it was observed that the pair Litecoin and Bitcoin has the highest tail dependence among the selected cryptocurrencies, which implies that change in prices of Litecoin will influence the prices of Bitcoin and vice versa is true. Optimization of the cryptocurrencies showed that Dogecoin has the best optimization. The results of this study indicate that investing on Dogecoin significantly reduces risk irrespective of significant correlation among Litecoin, Bitcoin and Binance. Standard Garch (1,1) is the best in identifying dependence between the cryptocurrencies.
Viviane de Senna, Adriano Mendonça Souza
ABSTRACT Cryptocurrencies are assets with transactions managed by new methods compared to traditional transactions mediated by Stock Exchanges. The insertion of these assets can change the economic system. The objective of the study is to analyze a set of articles published in international databases of scientific content on cryptocurrencies and the relations with the Stock Exchanges to understand the evolution of the theme over time. The consultation was carried out in the Scopus and Web of Science databases, where 196 articles were analyzed, these indicated learning algorithms, electronic trading, financial and digital markets thematic evolution. The main studies focused on investigating the behavior of cryptocurrencies in the face of market variables, cryptocurrencies as a safe haven or diversification, analysis of prices and the impact of emotional value on cryptocurrencies. The most relevant articles, the citations and co-citations network of these, provided insights into not yet known literature, such authors are Baur et al., 2018; Ji et al., 2020; Peng et al., 2018; Symitsi & Chalvatzis, 2019; Urquhart, 2017.
Juliane Proelss, Stéphane Sévigny, Denis Schweizer
No abstract is available for this record.
Kristof Lommers, Jack Kim, Boris Skidan, Viktor Smits
No abstract is available for this record.
Fernando Henrique Antunes de Araujo, Leonardo H.S. Fernandes, JOSÉ W. L. SILVA, Kleber E. S. Sobrinho · 5 authors
This paper has investigated the predictability of the top 10 cryptocurrencies’ price dynamics, ranked by their daily market capitalization and trade volume, via the information theory quantifiers. Our analysis considers the Complexity-entropy causality plane to study the temporal evolution of the price of these cryptocurrencies and their respective locations along this 2D map, bearing in mind after and during the Russia–Ukraine war. Moreover, we apply the permutation entropy and the Jensen–Shannon statistical complexity measure to rank these cryptocurrencies similarly to a complexity hierarchy. Our findings reflect that the Russian–Ukraine war affects the informational efficiency of cryptocurrency dynamics. Specifically, the cryptocurrencies notably showed a decrease in informational inefficiency (USD-coin, Binance-USD, BNB, Dogecoin, and XRP). At the same time, the cryptocurrencies with more expressiveness for the financial market, considering the volume traded and the capitalized market, were strongly impacted, presenting an increase in informational inefficiency (Tether, Cardano, Ethereum, and Bitcoin). It clarifies the potential of cryptocurrencies to mitigate exogenous shocks and their capability to use with portfolio selection, risk diversification and herding behavior.
Cal Abel
No abstract is available for this record.
Bao Doan, Dulani Jayasuriya, John B. Lee, Jonathan J. Reeves
In this study, we analyse systematic risk associated with the two leading cryptocurrencies - Bitcoin and Ethereum, from 2015 to 2023. Our findings show a significant escalation in the systematic risk levels, with beta estimates rising from 0.032 to 0.834 for Bitcoin, and from 0.087 to 1.003 for Ethereum. This hike in risk levels has dramatically reduced the diversification benefits of cryptocurrency that were documented in prior studies. In addition, we also identify increased autocorrelation of cryptocurrency systematic risk.
Olga Klein, Roman Kozhan, Ganesh Viswanath-Natraj, Junxuan Wang
No abstract is available for this record.
Sinda Hadhri
No abstract is available for this record.