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53,216 results · page 1767 of 2,218
Wanshan Wu, Aviral Kumar Tiwari, Giray Gözgör, Leping Huang
No abstract is available for this record.
Elie Bouri, Rangan Gupta, Xuan Vinh Vo
Are price discontinuities in cryptocurrencies jointly related to large swings in geopolitical risk? This is a relevant question to answer given recent news from the press that Bitcoinâs price jumps are driven by jumps in the level of geopolitical risk index. To answer this question, we examine first the jump incidence of daily returns for Bitcoin and other leading cryptocurrencies and then study the co-jumps between cryptocurrencies and the geopolitical risk index using logistic regressions. Our dataset is at the daily frequency and covers the period 30 April 2013 to 31 October 2019. The results show that the price behaviour of all cryptocurrencies under study is jumpy but only Bitcoin jumps are dependent on jumps in the geopolitical risk index. This revealed evidence of significant co-jumps for the case of Bitcoin only nicely complements previous studies arguing that Bitcoin is a hedge against geopolitical risk.
Yi Li, Andrew Urquhart, Pengfei Wang, Wei Zhang
No abstract is available for this record.
Larisa Yarovaya, Roman Matkovskyy, Akanksha Jalan
This paper analyses herding in cryptocurrency markets in the time of the COVID-19 pandemic. We employ a combination of quantitative methods to hourly prices of the four most traded cryptocurrency markets - USD, EUR, JPY and KRW - for the period from 1st January 2019 to 13th March 2020. While there are several strong theoretical reasons to observe the âblack swanâ effect on cryptocurrency herding, our results suggest that COVID-19 does not amplify herding in cryptocurrency markets. In all markets studied, herding remains contingent on up or down markets days, but does not get stronger during the COVID-19. These results are important for cryptocurrency investors and regulators to enhance their understanding of cryptocurrency markets and the financial effects of the COVID-19 pandemic.
Rocco Caferra, David Vidal-TomĂĄs
No abstract is available for this record.
Farida Sabry, Wadha Labda, Aiman Erbad, Qutaibah Malluhi
Decentralized cryptocurrencies have gained a lot of attention over the last decade. Bitcoin was introduced as the first cryptocurrency to allow direct online payments without relying on centralized financial entities. The use of Bitcoin has vastly grown as a financial asset rather than just a tool for online payments. A lot of cryptocurrencies have been created since 2011 with Bitcoin dominating the cryptocurrencies' market. With plenty of cryptocurrencies being used as financial assets and with millions of trades being executed through different exchange services, cryptocurrencies are susceptible to trading problems and challenges similar to those traditionally encountered in the financial domain. Price and trend prediction, volatility prediction, portfolio construction and fraud detection are some examples related to trading. In addition, there are other challenges that are specific to the domain of cryptocurrencies such as mining, cybersecurity, anonymity and privacy. In this paper, we survey the application of artificial intelligence techniques to address these challenges for cryptocurrencies with their vast amount of daily transactions, trades and news that are beyond human capabilities to analyze and learn from. This paper discusses the recent research work done in this emerging area and compares them in terms of used techniques and datasets. It also highlights possible research gaps and some potential areas for improvement.
Daniele Bianchi, Mykola Babiak
No abstract is available for this record.
Dodik Siswantoro, Rangga Handika, Aria Farah Mita
This research aims to evaluate the suitability of cryptocurrency as money from the Islamic perspective. Money, in the Islamic perspective, has specific characteristics and requirements, such as stability and is based on assets. Cryptocurrency may not fulfil this as it has queries as money from the Islamic perspective. The research method applied data of 23 cryptocurrency prices and related information. The result shows that cryptocurrency is hugely volatile and has limits to being called 'money,' as it is limited and used for speculation, which is prohibited in Islam. The research implies that Muslims would be reluctant to use cryptocurrency as money, as a currency of transaction. This reason raise an expectation that the cryptocurrency will not develop rapidly in Muslim countries.
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.
Brian D. Feinstein, Kevin Werbach
ABSTRACT The meteoric growth of global cryptocurrency markets presents novel challenges to regulators. Some policymakers and scholars warn that regulation will cause trading activity to cross borders into less-regulated jurisdictionsâor even smother a promising new financial asset class. Others believe regulatory actions will stimulate activity by providing clarity to market participants. Standing behind this disagreement is a debate about the desirability of either outcome. Some believe that governments should promote development of the cryptocurrency sector within their countries, while others view cryptocurrencies as conduits of illegality and fraud that should be restricted through strict regulation or even outright bans. Yet these debates have, to date, been conducted almost entirely without data concerning the effects of regulation on market activity. As a corrective, in this article we assembled original data on cryptocurrency regulations worldwide and used them to empirically examine movement in trading activity at a number of exchanges following key regulatory announcements. We found that a wide variety of models yielded almost entirely null results. From the creation of bespoke licensing regimes to targeted anti-money-laundering and anti-fraud enforcement actions, as well as many other categories of government activities, we found no systemic evidence that regulatory measures cause traders to flee, or enter into, the affected jurisdictions. These findings at last provide an empirical basis for regulatory decisions concerning cryptocurrency trading. Among other things, they call into question that capital flight or chilling effects should be a first-order concern.
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 Whitelawâs (2011) and Bali, Brown, Murray, and Tangâs (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, JiĆĂ Ć vec, Leqi Wang
No abstract is available for this record.
Leonardo Nizzoli, Serena Tardelli, Marco Avvenuti, Stefano Cresci · 6 authors
Cryptocurrencies represent one of the most attractive markets for financial speculation. As a consequence, they have attracted unprecedented attention on social media. Besides genuine discussions and legitimate investment initiatives, several deceptive activities have flourished. In this work, we chart the online cryptocurrency landscape across multiple platforms. To reach our goal, we collected a large dataset, composed of more than 50M messages published by almost 7M users on Twitter, Telegram and Discord, over three months. We performed bot detection on Twitter accounts sharing invite links to Telegram and Discord channels, and we discovered that more than 56% of them were bots or suspended accounts. Then, we applied topic modeling techniques to Telegram and Discord messages, unveiling two different deception schemes - âpump-and-dumpâ and âPonziâ - and identifying the channels involved in these frauds. Whereas on Discord we found a negligible level of deception, on Telegram we retrieved 296 channels involved in pump-and-dump and 432 involved in Ponzi schemes, accounting for a striking 20% of the total. Moreover, we observed that 93% of the invite links shared by Twitter bots point to Telegram pump-and-dump channels, shedding light on a little-known social bot activity. Charting the landscape of online cryptocurrency manipulation can inform actionable policies to fight such abuse.
Îikolaos Kyriazis, Stephanos Papadamou, Shaen Corbet
No abstract is available for this record.
Constantin Gurdgiev, Daniel OâLoughlin
No abstract is available for this record.
Lennart Ante, Ingo Fiedler, Elias Strehle
Stablecoins are digital currencies whose value is pegged to fiat currencies like the dollar or other assets. They were created as a more flexible alternative to fiat currencies for cryptocurrency exchanges and constitute an increasingly important aspect of cryptocurrency markets and alternative finance. We analyze the influence of stablecoin issuances on the returns of major cryptocurrencies across 565 issuance events of $1 million or more for seven different stablecoins on four different blockchains between April 2019 and March 2020. Our event study reveals cryptocurrency market downturns in the week before a stablecoin issuance and positive abnormal returns for major cryptocurrencies in the twenty-four hours before and after the issuance. Effect sizes differ across stablecoins. Counterintuitively, we find that issuance size does not significantly affect the abnormal returns. We conclude that stablecoin issuances contribute to price discovery and market efficiency of cryptocurrencies.
Shaen Corbet, Yang Hou, Yang Hu, Charles Larkin · 5 authors
Controlling for the polarity and subjectivity of social media data based on the development of the COVID-19 outbreak, we analyse the relationships between the largest cryptocurrencies and such time-varying realisation as to the scale of the economic shock centralised within the rapidly-escalating pandemic. We find evidence of significant growth in both returns and volumes traded, indicating that large cryptocurrencies acted as a store of value during this period of exceptional financial market stress. Further, cryptocurrency returns are found to be significantly influenced by negative sentiment relating to COVID-19. While not only providing diversification benefits for investors, results suggest that these digital assets acted as a safe-haven similar to that of precious metals during historiccrises.
Ender Demir, Mehmet HĂŒseyin Bilgin, Gökhan Karabulut, Aslı Cansın Doker
No abstract is available for this record.
Hanna HaĆaburda, 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.
John W. Goodell, Stéphane Goutte
Literature suggests assets become more correlated during economic downturns. The current COVID-19 crisis provides an unprecedented opportunity to investigate this considerably further. Further, whether cryptocur-rencies provide a diversification for equities is still an unsettled issue. Additionally , the question of whether cryptocurrency futures are safe havens has received very little attention. We employ several econometric procedures , including wavelet coherence, copula principal component, and neural network analyses to rigorously examine the role of COVID-19 on the paired co-movements of six cryptocurrencies, as well as bitcoin futures, with fourteen equity indices and the VIX. We find co-movements between cryptocurrencies and equity indices gradually increased as COVID-19 progressed. However, most of these co-movements are positively correlated, suggesting that cryptocurrencies do not provide a diversification benefit during downturns. Exceptions, however, are the co-movements of bitcoin futures and tether being negative with equities. Results are consistent with investment vehicles that attract either more informed or more speculative investors differentiating themselves as safe havens.
Marcin Wkatorek, Stanislaw Dro.zd.z, JarosĆaw KwapieĆ, 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.
Thomas Conlon, Shaen Corbet, Richard McGee
The COVID-19 pandemic provided the first widespread bear market conditions since the inception of cryptocurrencies. We test the widely mooted safe haven properties of Bitcoin, Ethereum and Tether from the perspective of international equity index investors. Bitcoin and Ethereum are not a safe haven for the majority of international equity markets examined, with their inclusion adding to portfolio downside risk. Only investors in the Chinese CSI 300 index realized modest downside risk benefits (contingent on very limited allocations to Bitcoin or Ethereum). As Tether successfully maintained its peg to the US dollar during the COVID-19 turmoil, it acted as a safe haven investment for all of the international indices examined. We caveat the latter findings with a warning that Tether's dollar peg has not always been maintained, with evidence of impaired downside risk hedging properties earlier in our sample.