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2,329 results · page 79 of 98
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.
Daniele Bianchi, Mykola Babiak
No abstract is available for this record.
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.
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.
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.
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.
Carol Alexander, Daniel F. Heck
No abstract is available for this record.
Shaen Corbet, Charles Larkin, Brian M. Lucey, Andrew Meegan · 5 authors
This paper examines the relationship between news coverage and Bitcoin returns. Previous studies have provided evidence to suggest that macroeconomic news affects stock returns, commodity prices and interest rates. We construct a sentiment index based on news stories that follow the announcements of four macroeconomic indicators: GDP, unemployment, Consumer Price Index (CPI) and durable goods. By controlling for a number of potential biases we determine as to whether each of the series' have a significant impact on Bitcoin returns. While an increase in positive news surrounding unemployment rates and durable goods would typically result in a corresponding increase in equity returns, we observe the opposite to be true in the case of Bitcoin. Increases in positive news after unemployment and durable goods announcements result in a decrease in Bitcoin returns. Conversely, an increase in the percentage of negative news surrounding these announcements is linked with an increase in Bitcoin returns. News relating to GDP and CPI are found not to have any statistically significant relationships with Bitcoin returns. Our results indicate that this developing cryptocurrency market is further maturing through interactions with macroeconomic news.
Harald Kinateder, Vassilios G. Papavassiliou
We use a GARCH dummy model to study the influence of calendar effects on daily conditional returns and volatility of Bitcoin during the period 2013â2019. The Halloween, day-of-the-week (DOW), and month-of-the-year (MOY) effects are analyzed. Our results reveal no evidence of a Halloween calendar anomaly. A classical DOW effect is not present in Bitcoin returns, however, we find significantly lower risk over the weekend whilst in the beginning of the week Bitcoin's volatility is more intense. Moreover, supporting evidence of a reverse January effect is detected. Our results also show that investorsâ risk drops substantially in September.
Jeffrey Chu, Stephen Chan, Yuanyuan Zhang
No abstract is available for this record.
Andrei Shynkevich
This study explores whether pricing inefficiency, or imperfect tracking in the market value of shares of a bitcoin fund of the fundâs net asset value (NAV), makes a significant impact on the fundâs market efficiency relative to the retail bitcoin market. Two bitcoin funds whose shares are traded at the exchanges which impose more stringent criteria for transparency compared to cryptocurrency exchanges are considered. The fund whose shares have been trading without significant premium or a discount relative to its NAV is found as weak-form efficient. The fund, whose shares have been trading at a significant premium over its NAV is found inefficient due to the presence of persistent and strong positive autocorrelation in its returns. Trading of shares in the inefficiently priced fund appears to be even more emotion-driven than the already volatile and emotional trading of bitcoin and exhibits a strong herding behaviour.
Roman Matkovskyy, Akanksha Jalan, Michael Dowling, Taoufik Bouraoui
No abstract is available for this record.
Bhubaneswar Mishra
No abstract is available for this record.
Taofik Hidajat
This paper aims to propose some behavioural biases of trading in Bitcoin. It is review literature in the areas of behavioural finance that address issues related to Bitcoin to underpin the conceptual model. A conceptual model for understanding the behavioural bias that affects investing in cryptocurrency is proposed. The biases are herding, optimism, overconfidence, confirmation bias, loss aversion, and gamblersâ fallacy. This paper ought to fill the research gap on cryptocurrency from the behavioral perspective. This paper implies that prices and Bitcoin transactions are more determined by psychological factors.
Klaus Grobys, Shaker Ahmed, Niranjan Sapkota
This paper studies simple moving average trading strategies employing daily price data on the eleven most-traded cryptocurrencies in the 2016â2018 period. Our results indicate a variable moving average strategy is successful when using the 20 days moving average trading strategy. Specifically, excluding Bitcoin the technical trading rule generates an excess return of 8.76% p.a. after controlling for the average market return. Our results suggest that cryptocurrency markets are inefficient.
Jan Jakub Szczygielski, Andreas Karathanasopoulos, Adam Zaremba
We perform the most comprehensive test of cryptocurrency return distributions to date. We fit 58 hypothetical distributions to 15 major cryptocurrencies to establish which of these best describes cryptocurrency returns. The answer is: âIt depends.â A sharp-peaked Cauchy distribution is the most likely distribution for the majority of return series. Specific distributions are definitively identified for only a handful of cryptocurrencies. The best fitting distributions are peaked and thick-tailed, with some possessing variable shape parameters. Our findings have implications for financial modelling and its applications, such as risk measurement and risk management.
Gaurang Bansal, Hasija Vikas, Vinay Chamola, Neeraj Kumar · 5 authors
Stock exchanges around the world are exploring the best possible solution that can improve trading efficiency, lower the risks and tighten secu- rity levels. The working and functioning of a stock exchange involves very hectic and cumbersome pro- cedures which are time consuming, cost inefficient and can be prone to numerous risks. Machine learning and Blockchain are most popular upcoming technologies. In this paper we present a novel secure and de- centralized intelligent stock market prediction model. We present a blockchain based solution for stock exchange model that uses machine learning accessible smart contracts. The machine learning model makes a prediction on the future of the stock market providing an intelligent solution for secure stock market.
Karlo ÄosiÄ, Anita Äeh Äasni
Abstract Cryptocurrencies are a sweltering topic in modern times of investment strategies. Since the cryptocurrency market is classified as an emerging market, in this paper a portfolio of emerging markets is compiled from the indices of four European Union (EU) countries and one cryptocurrency. The aim of this paper is to investigate how the incorporation of the Bitcoin cryptocurrency into the portfolio affects the performance of the portfolios of these countries. Moreover, by drawing an efficient frontier, the paper identifies where Bitcoin stands relative to other indices in the portfolio. The countries whose indices were used in the analysis are: Croatia, Hungary, Romania and Poland during the period from July 13, 2018 to June 07, 2019. The method used for an efficient frontier formation is Markowitzâs Modern Portfolio Theory (MPT). By applying this theory, the minimum variance portfolio at the efficient frontier was created for the portfolio with and without the cryptocurrency. The empirical analysis indicates that Bitcoin improves the effectiveness of the portfolio in emerging markets of the selected EU countries, where the expected risks of a portfolio that includes the cryptocurrency are smaller and with higher returns than those of portfolios without Bitcoin. From the Markowitzâs theory point of view, the results of the empirical analysis also indicate that Bitcoin is on the efficient frontier. Since all instruments on the efficient frontier according to the modern portfolio theory are efficient, it can be concluded that investments in such instruments depend on investorâs risk aversion.