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3,636 results ¡ page 98 of 152
Thanasis Zoumpekas, Elias N. Houstis, Manolis Vavalis
No abstract is available for this record.
Toan Luu Duc Huynh, Muhammad Ali Nasir, Xuan Vinh Vo, Thong Trung Nguyen
No abstract is available for this record.
Saba Qureshi, Muhammad Aftab, Elie Bouri, Tareq Saeed
No abstract is available for this record.
Mircea Constantin Čcheau, Simona Liliana Paramon CrÄciunescu, Iulia Brici, Monica Violeta Achim
Technological development brings about economic changes that affect most citizens, both in developed and undeveloped countries. The implementation of blockchain technologies that bring cryptocurrencies into the economy and everyday life also induce risks. Authorities are continuously concerned about ensuring balance, which is, among other things, a prudent attitude. Achieving this goal sometimes requires the development of standards and regulations applicable at the national or global level. This paper attempts to dive deeper into the worldwide operations, related to cryptocurrencies, as part of a general phenomenon, and also expose some of the intersections with cybercrime. Without impeding creativity, implementing suggested proposals must comply with the rules in effect and provide sufficient flexibility for adapting and integrating them. Different segments need to align or reposition, as alteration is only allowed in a positive way. Adopting cryptocurrency decisions should be unitary, based on standard policies.
Walter BazĂĄn-Palomino
No abstract is available for this record.
Zheng-Zheng Li, ChiâWei Su, Meng Qin, Muhammad Umar
This paper explores the interactions between the Bitcoin (BTC) prices in the US and Chinese markets, by employing the bootstrap rolling window causality test. The results reveal that BTC prices behave differently across markets, and also vary with time, which subjects to the theory of price discovery. In other words, the BTC price in one market could precede the other, and vice versa, based on the information advantage. Markets that are more flexible (US) respond sensitively to information, thus, in order to induce the price changes in Chinese markets. The improvements in the economic conditions of the emerging markets have exerted an influential role in global markets. Since the Chinese market possesses a considerable amount of trading volumes, the BTC price in the US can be assumed to chase the BTC price in China. The leadâlag relationship between these two markets also reflects the acknowledgement of the aversion towards the risks involved in accepting BTC as a currency. However, knowing which market reacts the most quickly to new information could prove to be beneficial to regulators who aim to implement a particular BTC price, and, as a result, prevent any arbitrary prices, and eventually stabilize the financial market.
Shaen Corbet, Brian M. Lucey
In this chapter, we investigate the literature on both broad- and narrow-based cryptocurrency research from a bibliometric and scientometric perspective. While Bitcoin, presented as the first every \ncryptocurrency by Nakamoto [2009], was established as the first piece of a decade-long expansion \nof academic literature based on the development of this new financial product and the associated \nbenefits and issues contained therein. We attempt to re-trace and provide a thorough explanation \nof the flow of research direction during this period across all disciplines. We provide clear evidence \nof a growing but fragmented research area. We conclude that there is a significant difference in \nhow researchers treat broad conceptual topics versus individual products. We finally provide a \nconcise overview of the current topics that have been central to recent research efforts, while attempting to provide oversight key areas that have presented evidence of particular deficiency. Such \nrecommendations will provide direction for future research synergy.
Mayank Puri, Aman Garg, Lekha Rani
Education on cryptocurrency is essential for individuals to make informed decisions regarding foreign investment in digital assets. In recent years there is an exponential increase in the price of cryptocurrency due to its easy trading especially in developing countries so the trend of financial institutions buying cryptocurrency into their portfolios has grown in the past decade which results in economic growth. The first completely digital assets that asset managers have included are cryptocurrencies. Traders have a unique opportunity to forecast price swings due to social mediaâs impact on cryptocurrency prices. Trading using Al and Machine Learning has drawn more attention in recent years. One could investigate the above hypothesis to determine if it is feasible to capitalize on the Bitcoin marketâs inefficiency for the purpose of generating unusually high profits. The advanced machine-learning techniques enable straightforward trading strategies to exceed conventional benchmarks. The findings demonstrate how basic computational processes might assist predict the near-term development of the bitcoin market. Further, there are prediction and comparison prices using SVM and Random Forest algorithms on the basics of efficiency while changing the number of days.
OlaOluwa S. Yaya, Xuan Vinh Vo, Ahamuefula E. Ogbonna, Adeolu O. Adewuyi
Abstract This paper empirically provides support for fractional cointegration of high and low cryptocurrency price series, using particularly, Bitcoin, Ethereum, Litecoin and Ripple; synchronized at different high time frequencies. The difference of high and low price gives the price range, and the rangeâbased estimator of volatility is more efficient than the returnâbased estimator of realized volatility. A more general fractional cointegration technique applied is the Fractional Cointegrating Vector Autoregressive framework. The results show that high and low cryptocurrency prices are actually cointegrated in both stationary and nonâstationary levels; that is, the range of highâlow price. It is therefore quite interesting to note that the fractional cointegration approach presents a lower measure of the persistence for the range compared to the fractional integration approach, and the results are insensitive to different time frequencies. The main finding in this work serves as an alternative volatility estimation method in cryptocurrency and other assets' price modelling and forecasting.
Jinghua Wang, Geoffrey Ngene
No abstract is available for this record.
Georgiy Kolesnik
A direct mechanism of impact on the utility functions of agents in social and economic systems is studied. This mechanism is widely used in various forms by public authorities, commercial and non-profit organizations for reaching the desired behavior of individuals. A game-theoretic model of a hierarchical system composed of agents and super-individuals who can modify their utility functions is considered. The properties of equilibria in this model are investigated and the systems of different structure are compared with each other in terms of efficiency. It is established that the centralized management of super-individuals in certain conditions may be less effective in terms of maximizing public welfare than the decentralized schemes. In particular, this property can explain the successful development of peer-to-peer markets and decentralized financing mechanisms of projects in various spheres of human activity. Also, the presence of vertical competition effects in the system is demonstrated, which reduce the efficiency of equilibria with increasing the number of super-individuals.
Îikolaos Kyriazis
This paper reviews the empirical literature on the highly popular phenomenon of herding behaviour in the markets of digital currencies. Furthermore, a comparison takes place with outcomes from earlier studies about traditional financial assets. Moreover, we empirically investigate herding behaviour of 240 cryptocurrencies during bull and bear markets. The present survey suggests that empirical findings about whether herding phenomena have made a significant appearance or not in cryptocurrency markets are split. The Cross-sectional absolute deviations (CSAD) and Cross-sectional standard deviations (CSSD) approaches for measuring herding tendencies are found to be the most popular. Different behaviour is detected in bull periods compared to bear markets. Nevertheless, evidence from primary studies indicates that herding is stronger during extreme situations rather than in normal conditions. However, our empirical estimations reveal that herding behaviour is evident only in bull markets. These findings cast light on and provide a roadmap for investment decisions with modern forms of liquidity.
Jinglan Zheng, Chun-Xiao Nie
This study examines the information flow between prices and transaction volumes in the cryptocurrency market, where transfer entropy is used for measurement. We selected four cryptocurrencies (Bitcoin, Ethereum, Litecoin and XRP) with large market values, and Bitcoin and BCH (Bitcoin Cash) for hard fork analysis; a hard fork is when a single cryptocurrency splits in two. By examining the real price data, we show that the long-term time series includes too much noise obscuring the local information flow; thus, a dynamic calculation is needed. The long-term and short-term sliding transfer entropy (TE) values and the corresponding [Formula: see text]-values, based on daily data, indicate that there is a dynamic information flow. The dominant direction of which is [Formula: see text]. In addition, the example based on minute Bitcoin data also shows a dynamic flow of information between price and transaction volume. The priceâvolume dynamics of multiple time scales helps to analyze the price mechanism in the cryptocurrency market.
Moch Doddy Ariefianto
We assess the qualification of Crypto Currency as a new emerging financial asset class using Bitcoin as a sample study. As a financial asset class, its value should be derived from business prospects, uncertainty, and opportunity cost of money (riskless rate). We model the asset value relationship in form of an error correction model in regard of possible nonstationary data properties. We use GSCI commodity index, S&P 500 Index, Economic Policy Uncertainty Index and Yield of 5-year US Treasury Bonds as proxies of explanatory variables. Our findings show that the notion of crypto currency as a financial asset might be spurious. Common stochastic trend is the source of apparent correlation between Bitcoin and the regressors. This lack of fundamental linkage opens a way to improve cryptocurrency business model for greater global acceptance.
Laura GarciaâJorcano, Sonia Benito Muela
No abstract is available for this record.
Juliana Zanelatto GaviĂŁo Mascarenhas, Artur Ziviani, Klaus Wehmuth, Alex Borges Vieira
Abstract Distributed blockchain-based consensus platforms have witnessed steady growth in recent years. In special, cryptocurrency is one of the main applications of the blockchain technology. Despite the recent interest in blockchain, we still lack in-depth analysis of systems that use such a technology. In fact, most of the existing works focus on Bitcoin. Moreover, blockchain-based cryptocurrency systems are highly dynamic. Their internal mechanisms and consensus algorithms evolve over time. Users also change their interests in a given platform, which in turn, reflect their behaviour. In this article, we model the Ethereum-based cryptocurrency transaction network, a more recent blockchain platform that is gaining a significant share in the cryptocurrency market. We model the transactions of Ethereum as a complex system, representing this complex system as a time-varying graph. Our model and the analysis we conduct rely on a 3-year dataset of Ethereum-based cryptocurrency transactions, comprising more than 38 million users (i.e. unique wallet addresses) and almost 300 million transactions. We analyse the evolution of users and transactions over time. Our study also highlights the centralization tendency of the transaction network on both user and time aspects. Finally, we also analyse the formation of communities and the evolution of connected components considering the dynamics of the Ethereum-based cryptocurrency transaction network.
Salim Lahmiri, Stelios Bekiros
The main purpose of our paper is to evaluate the impact of the COVID-19 pandemic on randomness in volatility series of world major markets and to examine its effect on their interconnections. The data set includes equity (Bitcoin and Standard and Poorâs 500), precious metals (Gold and Silver), and energy markets (West Texas Instruments, Brent, and Gas). The generalized autoregressive conditional heteroskedasticity model is applied to the return series. The wavelet packet Shannon entropy is calculated from the estimated volatility series to assess randomness. Hierarchical clustering is employed to examine interconnections between volatilities. We found that (i) randomness in volatility of the S&P500 and in the volatility of precious metals were the most affected by the COVID-19 pandemic, while (ii) randomness in energy markets was less affected by the pandemic than equity and precious metal markets. Additionally, (iii) we showed an apparent emergence of three volatility clusters: precious metals (Gold and Silver), energy (Brent and Gas), and Bitcoin and WTI, and (iv) the S&P500 volatility represents a unique cluster, while (v) the S&P500 market volatility was not connected to the volatility of Bitcoin, energy, and precious metal markets before the pandemic. Moreover, (vi) the S&P500 market volatility became connected to volatility in energy markets and volatility in Bitcoin during the pandemic, and (vii) the volatility in precious metals is less connected to volatility in energy markets and to volatility in Bitcoin market during the pandemic. It is concluded that (i) investors may diversify their portfolios across single constituents of clusters, (ii) investing in energy markets during the pandemic period is appealing because of lower randomness in their respective volatilities, and that (iii) constructing a diversified portfolio would not be challenging as clustering structures are fairly stable across periods.
Agam Shah, Yagnesh Chauhan, Bhaskar Chaudhury
No abstract is available for this record.
Andrei Shynkevich
No abstract is available for this record.
Chun Kwong Koo, Artur Semeyutin, Chi Keung Marco Lau, Jian Fu
We study the tailsâ behavior of four major Cryptocurrencies (Bitcoin, Litecoin, Ethereum and Ripple) by employing the Autoregressive Fr´echet model for conditional maxima. Using five-minute-high-frequency data, we report time-evolving tails as well as provide a straightforward measure of tails asymmetry for positive and negative intra-day returns. We find that only Bitcoin has a notable more massive tail for positive returns asymmetry while the remaining three Cryptocurrencies have a general tendency towards more massive negative intra-day tails. All considered Cryptocurrencies depict lighter tails as the market matures.
Venelina Nikolova, Juan Evangelista Trinidad Segovia, M. FernĂĄndezâMartĂnez, M.A. SĂĄnchez-Granero
One of the main characteristics of cryptocurrencies is the high volatility of their exchange rates. In a previous work, the authors found that a process with volatility clusters displays a volatility series with a high Hurst exponent. In this paper, we provide a novel methodology to calculate the probability of volatility clusters with a special emphasis on cryptocurrencies. With this aim, we calculate the Hurst exponent of a volatility series by means of the FD4 approach. An explicit criterion to computationally determine whether there exist volatility clusters of a fixed size is described. We found that the probabilities of volatility clusters of an index (S&P500) and a stock (Apple) showed a similar profile, whereas the probability of volatility clusters of a forex pair (Euro/USD) became quite lower. On the other hand, a similar profile appeared for Bitcoin/USD, Ethereum/USD, and Ripple/USD cryptocurrencies, with the probabilities of volatility clusters of all such cryptocurrencies being much greater than the ones of the three traditional assets. Our results suggest that the volatility in cryptocurrencies changes faster than in traditional assets, and much faster than in forex pairs.
Zhenghui Li, Yan Wang, Zhehao Huang
This paper examines the risk connectedness across the seven cryptocurrencies, Bitcoin, Ethereum, Ripple, Litecoin, Stellar, Monero and Dash, who admit large capitalizations in the cryptocurrency market. The data sample is from August 7, 2015 to February 15, 2020. We apply the CAViaR model to measure the return risks of the cryptocurrencies, showing their similar risk tendencies with volatility clusterings during the beginning of 2017 and the end of 2018. The net pairwise spillover index developed by Diebold and Yilmaz (2012) is use as the measure for the risk connectedness among the cryptocurrencies. We find that the risk spillover directions are highly correlative with the capitalizations of the cryptocurrencies. The cryptocurrencies with small capitalizations transmit risks to those with large cryptocurrencies. In the risk downward tendency, the risk spillover levels among the cryptocurrencies are stronger than that in the risk upward tendency, while the spillover directions keep the same in both risk tendencies, except the cryptocurrency Monero, which may be due to the trading volume difference from the others. We use the generalized forecast error variance decomposition for the spillover index and explore the risk connectedness across the cryptocurrencies in differen time frequencies, including the short term (0-4 days), medium term (4-30 days) and long term (30-300 days) frequency. The risk spillovers in the short term frequency can be neglected, which implies the delay effects of risk spillovers. The risk spillovers in medium term frequency are mostly stronger than that in long term frequency. The dynamic connectedness result shows the risk spillover mean in the long term frequency is larger than that in the medium term frequency. An inverse result holds for the risk spillover range. The risk spillover fluctuations in the long and medium term frequency admit the coincident comparison for spillover levels in these two frequencies. The findings in this paper provide suggestions for regulators controlling the market stability and investors generating investment strategies.
Emmanuel Joel Aikins Abakah, Luis A. GilâAlana, Godfrey Madigu, Fatima Romero-Rojo
No abstract is available for this record.
Silvia Bartolucci, Giuseppe Destefanis, Marco Ortu, Nicola Uras ¡ 6 authors
Abstract The network of developers in distributed ledgers and blockchains open source projects is essential to maintaining the platform: understanding the structure of their exchanges, analysing their activity and its quality (e.g. issues resolution times, politeness in comments) is important to determine how âhealthyâ and efficient a project is. The quality of a project affects the trust in the platform, and therefore the value of the digital tokens exchanged over it. In this paper, we investigate whether developersâ emotions can effectively provide insights that can improve the prediction of the price of tokens. We consider developersâ comments and activity for two major blockchain projects, namely Ethereum and Bitcoin, extracted from Github. We measure sentiment and emotions (joy, love, anger, etc.) of the developersâ comments over time, and test the corresponding time series (i.e. the affect time series ) for correlations and causality with the Bitcoin/Ethereum time series of prices. Our analysis shows the existence of a Granger-causality between the time series of developersâ emotions and Bitcoin/Ethereum price. Moreover, using an artificial recurrent neural network (LSTM), we can show that the Root Mean Square Error (RMSE)âassociated with the prediction of the prices of cryptocurrenciesâsignificantly decreases when including the affect time series.