Blockchain Papers

David Krause

No abstract is available for this record.

Toshiko Matsui, William J. Knottenbelt

No abstract is available for this record.

Ziying Liu, Xu Chen, Xuan Wang

No abstract is available for this record.

N. Gowri Sree Lakshmi, V. Ajaykumar, Baldi Ashish, K. Hemalatha · 6 authors

The cryptocurrency market is known for its inherent volatility, making accurate predictions a challenging endeavor.In this research study, investigate the efficacy of logistic regression, support vector machines (SVM), decision trees and random forests for the task of Bitcoin price prediction.To address this, conduct a thorough analysis and comparison of these machine learning models using historical Bitcoin price data.By rigorously assessing their performance and predictive capabilities, this study aims to provide valuable insights for both cryptocurrency traders and researchers operating in the dynamic digital asset landscape.These results illuminate the strengths and weaknesses of each model, shedding light on their respective abilities to forecast Bitcoin price movements.Through this research, contribute to the growing body of knowledge surrounding cryptocurrency market analysis and prediction techniques.This analysis can inform traders' decision-making processes and assist researchers in developing more robust models in the exciting and rapidly evolving realm of cryptocurrency investment and analysis.

Mortaza Ojaghlou

No abstract is available for this record.

Chia‐Hsien Tang, Yen‐Hsien Lee, Ya‐Ling Huang, You-Xuan Liu

This study examines the relationship between E-mini S&P 500 futures' crash risk and Bitcoin futures' returns and volatility using data from 2017 to 2021. While E-mini S&P 500's crash risk doesn't significantly influence Bitcoin returns, it correlates with its volatility, especially during events like the COVID-19 pandemic and U.S. elections. Furthermore, as global and emerging market indices rise, Bitcoin futures volatility decreases, suggesting its role as a hedging tool. These findings are pivotal for investors aiming to construct informed trading strategies, leverage Bitcoin futures as a hedging asset during economic instability, and keep tabs on traditional market indicators like E-mini S&P 500 crash risk for anticipating fluctuations in Bitcoin futures.

Minhao Leong, Simon Kwok, Vitali Alexeev

No abstract is available for this record.

Cong Zhang, Zhenzhi He

No abstract is available for this record.

Noor Azryani Auzairy, Ahmad Ibn Ibrahimy

No abstract is available for this record.

Putra Sadewa, Andrian Dolfriandra Huruta

This study aims to examine the spillover effects of return and volatility between three different assets (Bitcoin, Gold, and Nasdaq) using GARCH-ARMA models. The data is taken from monthly closing prices from January 2015 to February 2024 through Investing.com. The analysis focuses on understanding how these three assets interact regarding the spillover effect of return and volatility, particularly during periods o f economic uncertainty. Our findings indicate that spillover effects o f return are visible from Bitcoin to Nasdaq, Nasdaq to Bitcoin, and Nasdaq to Gold. In addition, spillover effects o f volatility are visible from Gold to Bitcoin, Bitcoin to Nasdaq, Nasdaq to Bitcoin, and Nasdaq to Gold. Our finding highlights the dynamic relationship between traditional and digital assets, emphasizing Bitcoin's potential role as a financial hedge likely to Gold and Nasdaq.

Thong Li Yi, Ricky Chia Chee Jiun, Mohd Fahmi Ghazali

Among all the cryptocurrencies in the market, Bitcoin is the most widely discussed and most popular cryptocurrency in the cryptocurrency market. This study aims to review and summarize the existing literature findings pertaining to the impact of geopolitical risk and economic policy uncertainty on Bitcoin. The results shown geopolitical risk and economic policy uncertainty have predictive power on Bitcoin prices. Both economic policy uncertainty and geopolitical risk have positive and negative effects on Bitcoin. The geopolitical risk and economic policy uncertainty able serve as a hedging instrument against Bitcoin. Bitcoin also can act as a safe haven against geopolitical risk and economic policy uncertainty. A summary of further implication from previous study suggested utilizing other uncertainty measures, applying other cryptocurrency, exploring Bitcoin’s relationship with other financial assets, and employing alternative methodologies.

Mustafa Özer, Darko Vuković, Michael Frömmel, Melik Kamışlı

No abstract is available for this record.

A. Abuzov, M.E. Konovalova

Целью исследования является определение особенностей функционирования, роли и места рынка криптовалют в системе финансового рынка в современных условиях, его структуры.Источниками информации послужили научные труды, опубликованные в международных базах данных и российской электронной библиотеке.Рынок криптовалют, возникший чуть более десятилетия назад, претерпел стремительное развитие и трансформацию, став значимой частью глобальной финансовой системы.На сегодняшний день криптовалютный рынок представляет собой сложную систему, включающую тысячи различных монет и токенов, блокчейн-платформы, криптовалютные биржи, кошельки и прочие инфраструктурные элементы.Объем капитализации этого рынка достигает нескольких сотен миллиардов долларов, а ежедневные объемы торгов сопоставимы с традиционными фондовыми биржами.В результате исследования определены особенности функционирования, атрибутивные признаки и функции рынка криптовалют в сравнении с рынком денег и рынком финансового капитала.Это позволило определить роль и обособленное место рынку криптовалют в системе финансов наряду с рынком денег и финансового капитала.Структура рынка криптовалют представлена его основными элементами.Выявлено, что попытки урегулирования и стабилизации цен на рынке криптовалют посредством внедрения деривативов не решили полностью поставленную задачу.Рынок попрежнему зависим в больше степени от спроса и предложений на рынке

Isik Akin, Meryem Akın, Zafer Ozturk, Affan Hameed · 6 authors

Abstract This research employs a vector autoregression (VAR) analysis to explore the volatility and dynamic interactions between stock, commodity, and cryptocurrency markets. It focuses on the returns of the S&P 500, gold, crude oil, and Bitcoin to analyse their interconnections. Our results indicate that Bitcoin returns positively affect S&P 500 and crude oil, but negatively impact gold. Conversely, crude oil returns have a positive influence on gold but lead to decreased returns for Bitcoin and the S&P 500. Similarly, higher gold returns correspond to increased returns in crude oil and S&P 500 but decreased returns in Bitcoin. The rise of the S&P 500 negatively influences Bitcoin and crude oil returns, while gold returns remain unaffected. However, these relationships exhibit weak and limited strength. Including these assets in a portfolio can help risk mitigation, as Bitcoin diversifies crude oil, gold, and S&P 500, and crude oil diversifies S&P 500. These findings contribute to our understanding of global financial dynamics and inform decision-making in risk assessment, portfolio management, risk mitigation, and diversification strategies.

Gönül Çolak, Joshua Della Vedova, Sean Foley, Sinh Thoi

No abstract is available for this record.

Riccardo Cosenza, Simon Stalder

No abstract is available for this record.

Daniel Tut

No abstract is available for this record.

Surachai Chancharat, Parichat Sinlapates

The shifting nature of volatility spillovers among Bitcoin, WTI oil, and the MSCI global index between two financial crises, the COVID-19 outbreak, and the Russia-Ukraine war, is examined in this paper. The BEKKGARCH model is applied to daily data for Bitcoin, crude oil, and the world stock index. Our empirical results suggest that the return spillovers for the Bitcoin-WTI, Bitcoin-MSCI, and WTI-MSCI pairings change among the four different periods. However, the volatility transmissions for the Bitcoin-WTI and Bitcoin-MSCI pairings change throughout the four study periods. These findings offer policymakers and portfolio managers useful information about risk management, forecasting, hedging, and portfolio diversification.

Rihab Qasim Abdulkadhim, Hasanen S. Abdullah, Mustafa Jasim Hadi

Abstract Decentralized cryptocurrencies have received much attention over the last few years. Bitcoin (BTC) has enabled straight online expenditures without the need for centralized financial institutions. Cryptocurrencies are used not only for online payments but are also increasingly used as financial assets. With the rise in the number of cryptocurrencies, including BTC, Ethereum (ETH), and Ripple (XRP), and the millions of daily trades through different exchange services, cryptocurrency trading is prone to challenges similar to those seen in the traditional financial industry, such as price and trend forecasting, volatility forecasting, portfolio building, and fraud detection. This study examines the use of Recurrent neural networks (RNNs) for predicting BTC, ETH, and XRP prices. Accurate price prediction is essential for investors and traders in this volatile market. Machine learning techniques, including RNNs, Long-Short-Term Memory (LSTM), and convolutional neural networks, have been employed to forecast cryptocurrency prices with varying degrees of success. The aim of this study is to evaluate the effectiveness of RNNs in predicting cryptocurrency prices and compare their performance with other established methods. The results indicate that RNNs, particularly LSTMs and Gated Recurrent Units, demonstrate excellent capabilities in accurately predicting currency prices and providing insights to investors and traders in the cryptocurrency market.

Xudong Wang, Xiaofeng Hui

Bitcoin futures exchange‐traded funds (ETFs) are recent innovations in cryptocurrency investment. This article studies the price‐volume relationship in this market from an information perspective. We first propose effective mutual information which has better estimation accuracy to analyze the contemporaneous relationship. Using half‐hourly trading data of the world’s largest Bitcoin futures ETF, we find that trading volume changes and returns contain information about each other and are contemporaneously dependent. Then, we employ effective transfer entropy to examine the intertemporal relationship. The results show that there exists information transfer from volume changes to returns in most of our sample period, suggesting the presence of return predictability and market inefficiency. However, information transfer in the opposite direction occurs much less frequently, and the amount is typically smaller.

Kiana Kia, Bo Liu, Qian Li, Victor Song · 5 authors

ABSTRACT In this study, we explore price discovery across the following three Bitcoin markets: spot, futures, and exchange‐traded funds (ETFs). Employing the fractionally cointegrated vector autoregressive (FCVAR) model, we estimate price discovery in each market using minute‐level price data from October 19, 2021, the launch date of the first US Bitcoin futures‐based Bitcoin ETF, to December 30, 2022. The trivariate FCVAR analysis reveals that the three markets are pairwise cointegrated. In the spot‐futures pair, the spot market emerges as the dominant force in price discovery, while in the spot–ETF pair, the ETF market assumes a leading role. Our paper is the first to show the importance of the newly introduced Bitcoin ETF market in the price discovery process. Extending the analysis to the more recent period, we find that the approval of spot‐based Bitcoin ETFs has weakened the price discovery contribution of the futures‐based ETF and Bitcoin spot market has since become the dominant venue for price discovery.

Maria-Cristina Zwak-Cantoriu, Lucian Claudiu Anghel

The main purpose of this paper is to investigate the volatility in the cryptocurrency market and the relationships between them, using the cryptocurrencies: Bitcoin, Cardano and Stellar. The results obtained suggest that volatility in cryptocurrency prices is influenced by previous events and the level of past volatility. The propagation of volatility and shocks between the three analyzed cryptocurrencies, and the observation of the interconnection between them was also followed in this paper. By determining the degree of correlation between the analyzed cryptocurrencies, we observed that in the selected period there are quite strong positive correlations. Also, we noticed that the volatility of cryptocurrencies was strongly influenced by certain economically uncertain periods, a fact that caused their prices to have a strong fluctuation, especially in the period 2021-2023. The results obtained show an interdependence between the three cryptocurrencies, which is significant in the decision-making of investors. News and events play a significant role in the cryptocurrency market, especially those in the financial, technological, and political worlds that can have a considerable impact on cryptocurrency prices and volatility. In the case of Bitcoin, there was an increase in interest in this cryptocurrency when there were investments from large companies and financial institutions.

Hind Alnafisah, Bashar Yaser Almansour, Wajih Elabed, Ahmed Jeribi

No abstract is available for this record.

Leili Pour Rostami

No abstract is available for this record.