Follow blockchain research across journals, conferences, and preprint repositories.
4,843 results · page 136 of 202
María de la O González, Francisco Jareño, Frank S. Skinner
No abstract is available for this record.
Jozo Frankovic, Bin Liu, Sandy Suardi
No abstract is available for this record.
Kavita Saini
Blockchain as the name says, it&s;s a chain of multiple blocks linked together. As a distributed ledger, Blockchain built from a chain of blocks where each block consists of various exchanges or transactions and block header. Each blockchain has a block header which helps in differentiating the first block and helps in making the chain. Basically a Blockchain is a distributed ledger, distributed across systems and accessible to all and it is stored and updated across the world by many systems. The beautify of the Blockchain technology is that it does not need any central or any third party involvement to manage, approve or control the transaction. Now a days Blockchain technology has become the backbone not for the cryptocurrency but also for all kind of applications where security and transparency is at most priority. Blockchain technology is used where there is a need to gain trust of the application or technology users such as banking, supply chain, drug counterfeit detection, health care data as the entire word is now storing all information on computer system. This technology is helping in building the trust and transparency across the machines and the human. The chapter discuss how Blockchain Technology works, what are the Consensus protocols. The chapter also talks about the Decentralized and Peer-to-Peer transactions in detail. The Proof of Work, Proof of Stake, Proof of Elapsed Time (PoET), and Practical Byzantine Fault Tolerance (PBFT) also discussed in detail.
Ye Wang, Yan Chen, Haotian Wu, Liyi Zhou · 6 authors
Decentralized Exchanges (DEXes) enable users to create markets for exchanging any pair of cryptocurrencies. The direct exchange rate of two tokens may not match the cross-exchange rate in the market, and such price discrepancies open up arbitrage possibilities with trading through different cryptocurrencies cyclically. In this paper, we conduct a systematic investigation on cyclic arbitrages in DEXes. We propose a theoretical framework for studying cyclic arbitrage. With our framework, we analyze the profitability conditions and optimal trading strategies of cyclic transactions. We further examine exploitable arbitrage opportunities and the market size of cyclic arbitrages with transaction-level data of Uniswap V2. We find that traders have executed 292,606 cyclic arbitrages over eleven months and exploited more than 138 million USD in revenue. However, the revenue of the most profitable unexploited opportunity is persistently higher than 1 ETH (4,000 USD), which indicates that DEX markets may not be efficient enough. By analyzing how traders implement cyclic arbitrages, we find that traders can utilize smart contracts to issue atomic transactions and the atomic implementations could mitigate users' financial loss in cyclic arbitrage from the price impact.
Hongbiao Li, Fan Xiao, Lixin Yin, Fengtong Wu
As blockchain and energy trading have become hot topics in industry and academia, this paper presents a brief literature regarding the blockchain-based energy trading in the fields of energy trading with blockchain. At first, the background and development process is presented, and then the applications of blockchain in energy trading are surveyed and analyzed. Finally, conclusions are summarized and important directions are highlighted in this field.
Syafiqah Ismail, Mohamad Yazis Ali Basah
Cryptocurrency symbolizes of a new development in the financial sector since it is the world's first entirely decentralized digital payment system. The cryptocurrency known as virtual money is one of the most important innovations brought on by digitalization. The purpose of this study is to analyze the relationship between the cryptocurrency (Bitcoin, Monero, and Stellar) with macroeconomics variables known as stock price index (Dow Jones dan Nikkei), oil price (Brent Oil dan WTI), and exchange rates (Australian Dollar, Euro, and Pound Sterling). The data was obtained from investing.com on monthly basis for the period between January 2016 untuil December 2020. The analysis were conducted based on unit root test, co-integration and vector error correction model (VECM) in order to identify the relationship between the three selected cryptocurrencis with macroeconomic variables. The findings of this paper showed that there is cointegration between the variables. The Vector Error Correction Model (VECM) indicates that the Bitcoin model and Stellar model did not have a long-run relationship. While for the second model, Monero found to have a long-run relationship with the variables. This research contributes to the growing study on cryptocurrency while extend and complement the literature by sourcing the latest research paper on this related field.
Mahboob Ullah, Maria Shaikh, Imran Abbas Jadoon, Muhammad Azizullah Khan · 5 authors
Purpose of the Study: In this research, the association between the COVID-19 pandemic and cryptocurrencies' price volatility has been examined.
 Methodology: To check the contagion effects of the COVID-19 pandemic on the price volatility of cryptocurrencies: BITCOIN, LITECOIN, XRP(RIPPLE), and ETHEREUM, the prices of all four are deployed from 10th August 2016 to 10th August 2020. The exponential generalized autoregressive conditional heteroscedastic (EGARCH) model is used to check the leverage effect exists or not. Stata 16 has been used to execute all the tests.
 Main Findings: The study's findings indicated that the leverage effect on the price volatility is present for LITECOIN, XRP(RIPPLE), and ETHEREUM but not for BITCOIN.
 Applications of the study: This study is significant for investors to develop strategies for investments and secure the transactions and control the creation of additional currency units. Also, it gives insight to the policy and decision-makers to articulate proper guidelines to overcome or minimize the effect of COVID-19 on cryptocurrency.
 Novelty/Originality of this Study: The motive for taking the crypto market into account is that the crypto market is one of the emerging markets and has started to have significance worldwide, linking with financial markets and economic growth. The leverage effect of COVID-19 is considered in this study as the epidemic has affected the supply and demand of goods due to lockdowns, blockages, and disruptions in delivery chains that lead to undiminished economic growth.
Natkamon Tovanich, Nicolas Soulié, Petra Isenberg
We present our work on visual analytics tools to support the analysis of Bitcoin mining pool evolution. Mining blocks are a critical component of the Bitcoin ecosystem, helping to keep the system secure, valid, and stable. At the same time, mining is a resource-intensive activity that continues to get more and more difficult. Mining pools have emerged to address this issue and to ensure a more stable and predictable income by sharing computing power. Yet, increased centralization of the mining power is also not without dangers (e. g., the 51% attack), and, thus, it is important to better understand and analyze mining pool activities in Bitcoin. Here, we report three contributions: our extensive data collection on Bitcoin mining pools, our development of two custom visualizations, and our first exploratory data analysis leading to hypotheses and documented activities about pools' main features such as market share, reward rules, or location.
Khaoula Ghaiti
The purpose of this paper is to select the best GARCH-type model for modelling the volatility of Bitcoin, Bitcoin Cash, Litecoin, Dogecoin and Ethereum. GARCH (1,1), IGARCH(1,1), EGARCH(1,1), TGARCH(1,1) and CGARCH(1,1) are used on the cryptocurrencies closing day return. We select the model with the highest Maximum Likelihood and run an OLS regression on the conditional volatility to measure the day-of-the-week effect. The findings show that EGARCH(1,1) model best suits Bitcoin, Litecoin, Dogecoin and Ethereum data and that the GARCH(1,1) model suits best Bitcoin data. The results show a significant presence of day-of-the-week effects on the conditional volatility of some days for Bitcoin, Bitcoin Cash and Ethereum. Wednesday has a significant negative effect on Bitcoin conditional volatility. Friday, Saturday and Sunday are found to be significant and positive on Bitcoin Cash conditional volatility. Finally, Saturday is found to be significant and positive on Ethereum conditional volatility.
Afees A. Salisu, Ahamuefula E. Ogbonna
No abstract is available for this record.
Samet Gürsoy
Bitcoin, son yüzyılın en meydan okuyan girişim örneklerindendir. Bu doğrultuda Bitcoin’e olan ilgi de hem kripto borsalarda hem de akademik literatürde çokça yer almaktadır. Genelde yapılan çalışmalarda, Bitcoin fiyatı üzerinde etkili olabileceği düşünülen finansal varlık rasyoları dikkate alınmaktadır. Bu çalışmada ise gazete ve medya haberlerinde Para Politikası Belirsizliği (MPU) ile ilgili yer alan haberler dikkate alınarak oluşturulan endeksler kullanılmıştır.Bu çalışmada, Ağustos 2010-Ağustos 2020 dönemlerinde Bitcoin fiyatı ile ABD ve Japonya Para Politikası Belirsizliği (MPU) endeksleri arasında aylık veriler kullanılarak, Hatemi-J (2012) asimetrik nedensellik testi çalıştırılmıştır. Çalışmanın sonunda ABD ve Japonya para politikası belirsizliği ile Bitcoin fiyatları arasında ne tek yönlü ne de çift bir nedensellik ilişkisine rastlanmamıştır. Bu çalışmada yer alan veriler ve değişkenler göz önüne alındığında, ABD ve Japon para politikası belirsizliği ile ilgili haberler ile Bitcoin fiyatları arasında bir ilişki olmadığı sonucuna varılmıştır
Oliver Borgards
No abstract is available for this record.
Ashis Kumar Pradhan, Ishan Mittal, Aviral Kumar Tiwari
In this paper, we utilize the conditional value-at-risk to quantify the risk exposure and the generalized Pareto distribution copula technique to analyse extreme events which helps in finding out the efficient portfolio selection. The sample data covers nine cryptocurrencies covering the period from September 2016 to August 2018. Our results using the efficient frontier indicate that if a minimum variance portfolio is constructed using chosen cryptocurrencies, investment in Bitcoin is preferred being the least risky currency on the bottom of the efficient frontier. These results find prime importance for investors and risk managers.
Ittai Barkai, Tomer Shushi, Rami Yosef
In this article, the authors develop a new analytical lens through which to examine the risk–return profiles of bitcoin, litecoin, ripple, and ethereum. Their focus is to understand better the price behavior of individual cryptocurrencies and their influence on one another. To achieve this, they segment each cryptocurrency’s time series of returns into disparate bull and bear regimes. They then examine the nature and extent of overlap between these regimes and whether they change over time. They also collect and plot several indicative distributed-denial-of-service attacks against the time series to investigate their possible impact on regime change episodes. Their findings shed light on previously unexplored systemic risk indicators within the cryptomarket as a whole and on the relationship between specific cryptocurrency pairs. These findings enhance the risk management toolkit for investors by revealing potential price behavior contagion patterns between cryptocurrencies pertinent to blended portfolio management. Furthermore, the authors’ approach serves as a blueprint for additional research into regime-type overlap within the cryptomarket. <b>TOPICS:</b>Currency, exchanges/markets/clearinghouses, financial crises and financial market history <b>Key Findings</b> ▪ Periods of overlapping regimes increase over time. The increase indicates a rise in cryptomarket systemic risk and an associated reduction in the diversification value of a blended portfolio of cryptocurrencies. ▪ Bitcoin exhibits the most favorable risk measures across both bull and bear regimes, including the lowest proclivity for extreme events during bear regimes. In contrast, ripple displays the overall riskiest profile across both regime types. ▪ Bitcoin’s regime type has the most meaningful impact on the risk–return profile of other cryptocurrencies, namely, litecoin and ripple. However, this relationship does not hold in reverse, a likely consequence of bitcoin’s market dominance and relative maturity.
Serda Selin Öztürk, M. Emre Bilgiç
There is a vast amount of information flow in social media about bitcoin, which may affect investors’ decisions. This article investigates whether tweets may affect returns or trade volume changes of bitcoin and, more importantly, whether some Twitter accounts are more influential than other Twitter accounts. We conduct two separate analyses based first on all Twitter accounts and then on the most influential 50 Twitter accounts, which have been selected as such by Unitedtraders. We use the number of positive, negative and neutral tweets by Valence Aware Dictionary and Sentiment Reasoner (VADER) in a logistic model to analyse if tweets have any valuable information about the change in both return and trade volume of bitcoin. Our results indicate that tweets can be used to predict bitcoin returns. Notably, the most influential accounts are the drivers of returns, but all Twitter accounts simply introduce some noise in volatility. This result indicates that following only these 50 most influential accounts may provide the information needed for investors.
Miguel Ángel Echarte Fernández, Sergio Luis Náñez Alonso, Javier Jorge-Vázquez, Ricardo Francisco Reier Forradellas
This article analyzes the monetary policy of major central banks during the economic crisis generated by the COVID-19 pandemic. Rising public debt in many countries is being financed through asset purchases by monetary authorities. Although these stimulus policies predate the pandemic, they have been significantly boosted as many governments face large financing needs. We have been in a low interest rate environment for years and some governments have issued debt securities at negative rates. In addition, the rise of decentralized cryptocurrencies, based on blockchain technology, has created greater competition in the international monetary system and many governments have considered the creation of centralized virtual currencies, known as central bank digital currencies (CBDCs). We will analyze some relevant cases, with an emphasis on the digital euro project. The methodology is based on the analysis of the evolution of monetary variables. Pearson’s correlation will be used to establish some relationships between them. There is a strong similarity in the expansionary monetary policies of central banks. Although the growth of the money supply has not been passed on to the CPI, it has been passed on to the financial markets and the price of assets such as Bitcoin or gold.
N. Serap VURUR
The Covid 19 pandemic is the first major crisis facing cryptocurrencies. Therefore, the reaction of the cryptocurrency markets is important. News about epidemics affects investors' decisions. Panic index (PIndex) is an index created from news about the Covid 19 outbreak. In the study, it is used to measure the impact of decisions on the crypto money market. As cryptocurrencies, Bitcoin (BTC), Etherium (ETH), and Ripple (XRP), which have the highest transaction volume in the crypto money market, are included in the analysis. The relationship between Panic Index and the three major cryptocurrencies with the largest share in the cryptocurrency market was investigated by Ardl and Hatemi-J asymmetric causality test. Traditional causality tests acknowledge that the effects of positive and negative changes are the same. However, there may be asymmetric information and different investor behaviors in financial markets. In the study, Hatemi-J [ 1 ] Asymmetric Causality Test was conducted to examine the asymmetric relationship and symmetric relationship between Pindex and cryptocurrencies by separating them into positive and negative shocks. According to the results of the Hatemi-J causality analysis, positive shocks in the panic index are the cause of negative shocks for all cryptocurrencies. In other words, increases in the panic index are caused to fall the value of Bitcoin, Ethereum, and Ripple cryptocurrencies decrease. The results show that cryptocurrencies were not a safe haven for the investor during the Covid 19 period, as they acted similarly to other financial assets.
Eray Gemi̇ci̇, Müslüm Polat
Purpose This study aims to examine the volatility spillovers between Bitcoin (BTC), Litecoin (LTC) and Ethereum (ETH) as they are related to structural breaks. Design/methodology/approach This study examines the daily period from August 7, 2015 to July 10, 2018 by conducting causality-in-mean and causality-in-variance tests among cryptocurrencies. Findings The findings showed that there was one-way causality-in-mean from BTC to LTC and ETH, but there was no causality-in-mean from LTC and ETH to BTC. On the other hand, considering the structural breaks included in the variance equations, the estimation results showed that there were short-term causality-in-variance from LTC to BTC and long-term causality-in-variance from BTC to LTC. Originality/value This study fills the gap by contributing in two ways. First, to the best of the authors’ knowledge, this is the first study that used the cross-correlation function (CCF) of causality to explore causality-in-variance among cryptocurrencies. Second, this study considers the structural breaks in variance in the return series.
Sercan Demiralay, Petros Golitsis
No abstract is available for this record.
Benjamin M. Blau, Todd G. Griffith, Ryan J. Whitby
No abstract is available for this record.
Ali Al-Ameer, Fouad M. AL‐Sunni
This paper discusses securities and cryptocurrency trading using artificial intelligence (AI) in the sense that it focuses on performing Exploratory Data Analysis (EDA) on selected technical indicators before proceeding to modelling, and then to develop more practical models by introducing new reward loss function that maximizes the returns during training phase. The results of EDA reveal that the complex patterns within the data can be better captured by discriminative classification models and this was endorsed by performing back-testing on two securities using Artificial Neural Network (ANN) and Random Forests (RF) as discriminative models against their counterpart Naïve Bayes as a generative model. To enhance the learning process, the new reward loss function is utilized to retrain the ANN with testing on AAPL, IBM, BRENT CRUDE and BTC using auto-trading strategy that serves as the intelligent unit, and the results indicate this loss superiorly outperforms the conventional cross-entropy used in predictive models. The overall results of this work suggest that there should be larger focus on EDA and more practical losses in the research of machine learning modelling for stock market prediction applications.
Natividad Blasco, Pilar Corredor
This paper studies the herding behaviour among different exchanges trading bitcoin. The analysis allows us to conclude that the size of the exchange is an influencing parameter. Since 2018, when the significant growth in the number of exchanges became a reality, smaller exchanges have shown strong herding behaviour, whereas large exchanges seem to respond to their own information and beliefs and lead the process of price definition. This result may originate some temporary profitable strategies in the process of evolution towards efficiency according to the Adaptive Markets Hypothesis.
Seyram Pearl Kumah, David Adjei Abbam, Ransford Armah, Evelyn Appiah-Kubi
The COVID-19 pandemic provides the first widespread bear market conditions since the inception of cryptocurrencies. We test the haven properties of cryptocurrencies for African stocks and commodity markets in a pandemic implementing the frequency domain spillover index. Data spans 11th August 2015 to 28th August 2020 at a daily frequency. Findings show weak interconnectedness across markets suggesting non-contagion risk and that cryptocurrency are safe havens for African stocks and commodity indices from the medium-term. We find the major transmitters of spillover effects across markets to be time-varying and heterogeneous. This study provides significant risk diversification benefits for policymakers and investors in the African financial markets.
Chuanzhen Wu
No abstract is available for this record.