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2,329 results · page 64 of 98
Larisa Yarovaya, Roman Matkovskyy, Akanksha Jalan
No abstract is available for this record.
Sofia Anyfantaki, Stelios Arvanitis, Nikolas Topaloglou
No abstract is available for this record.
Yao Xie, Sang Baum Kang, Jialin Zhao
We investigate whether stablecoins are safe havens for traditional cryptocurrencies with fresh evidence from the recent crisis period of the COVID-19 pandemic. Our results support the safe-haven properties of Tether for both before and during the pandemic. For Digix, a gold-backed stablecoin with relatively small market capitalization, we find a change in characteristics before and during the pandemic, but do not find statistically significant evidence for its safe-haven properties. Furthermore, we document that, when considering the economic benefits and costs of adding safe-haven assets into cryptocurrency portfolios, the one with Tether outperforms both a naked portfolio and the portfolio with a traditional safe-haven asset such as gold.
David Vidal-Tomás, Ana M. Ibáñez, José Emilio Farinós Viñas
We analyze the economic efficiency of the cryptocurrency market after the launch of Bitcoin futures by means of the Data Envelopment Analysis and Malmquist Indexes. Our results show that the introduction of Bitcoin futures did not affect the economic efficiency of the cryptocurrency market. However, we observe that Bitcoin obtained the highest risk-return trade-off due to its liquidity compared to the rest of cryptocurrencies. Therefore, our paper underlines the support of investors on Bitcoin to the detriment of the rest of cryptocurrencies.
Sashikanta Khuntia, J. K. Pattanayak
Purpose This study broadly attempts to explore adaptive or dynamics patterns of calendar effects existed in the cryptocurrency market as per the adaptive market hypothesis (AMH) framework. Another agendum of this study is to investigate the quantum of extra returns which may result from the presence of calendar effects. Design/methodology/approach The present study considers both parametric and non-parametric approaches to verify calendar effects empirically. Specifically, this study has implemented Generalised Autoregressive Conditional Heteroscedasticity (1, 1) and Kruskal–Wallis tests in the rolling window approach to reveal adaptive patterns of calendar effects. Additionally, the present study has used the implied trading strategy to evaluate the volume of excess returns resulted from calendar effects than buy-and-hold (BH) strategy. Findings The overall results of the current study exhibit that calendar effect in the cryptocurrency market is dynamic rather than static which indicates the calendar effect is a time-varying phenomenon. Moreover, this study also confirmed that ITS is not suitable to obtain extra returns despite the existence of calendar effects. Research limitations/implications The present study has covered some broad aspects of calendar anomalies in the cryptocurrency market, keeping aside certain other limitations which need to be addressed in the following dimensions. Future studies may aim at addressing issues like, Turn-of-the-Year effect, Halloween effect, weather effect, and Month-of-the-Year effects, and try to explore the reasons of presence of dynamic patterns of calendar effects. Practical implications The significant implication of this study is that it alerts investors about market return predictability due to calendar patterns or effects in different periods. It also suggests the period in which the ITS can perform better than the BH strategy. Originality/value It is the first study in the cryptocurrency literature which has adopted the AMH framework to verify adaptive calendar effects or anomalies. Furthermore, this study, instead of a mere examination of the presence of calendar effects, has evaluated the potential of calendar effects to produce extra returns through trading strategies.
Panos Fousekis, Vasilis Grigoriadis
Purpose This paper aims to identify and quantify directional predictability between returns and volume in major cryptocurrencies markets. Design/methodology/approach The empirical analysis relies on the cross-quantilogram approach that allows one to assess the temporal (lag-lead) association between two stationary time series at different parts of their joint distribution. The data are daily prices and trading volumes from four markets (Bitcoin, Ethereum, Ripple and Litecoin). Findings Extreme returns either positive or negative tend to lead high volume levels. Low levels of trading activity have in general no information content about future returns; high levels, however, tend to precede extreme positive returns. Originality/value This is the first work that uses the cross-quantilogram approach to assess the temporal association between returns and volume in cryptocurrencies markets. The findings provide new insights about the informational efficiency of these markets and the traders’ strategies.
Qiyu Wang, Terence Tai‐Leung Chong
No abstract is available for this record.
Stephanos Papadamou, Νikolaos Kyriazis, Panayiotis Tzeremes, Shaen Corbet
No abstract is available for this record.
Friedhelm Victor, Andrea Marie Weintraud
Dataset retrieved with an Ethereum client, and used by the code hosted here for this paper published in the Proceedings of the Web Conference 2021 (WWW ’21) Abstract: Cryptoassets such as cryptocurrencies and tokens are increasingly traded on decentralized exchanges. The advantage for users is that the funds are not in custody of a centralized external entity. However, these exchanges are prone to manipulative behavior. In this paper, we illustrate how wash trading activity can be identified on two of the first popular limit order book-based decentralized exchanges on the Ethereum blockchain, IDEX and EtherDelta. We identify a lower bound of accounts and trading structures that meet the legal definitions of wash trading, discovering that they are responsible for a wash trading volume in equivalent of 159 million U.S. Dollars. While self-trades and two-account structures are predominant, complex forms also occur. We quantify these activities, finding that on both exchanges, more than 30% of all traded tokens have been subject to wash trading activity. On EtherDelta, 10% of the tokens have almost exclusively been wash traded. All data is made available for future research. Our findings underpin the need for countermeasures that are applicable in decentralized systems.
Oliver Borgards, Robert Czudaj
No abstract is available for this record.
Prateek Sharma, Samit Paul
We examine stock price response around cryptocurrency‐related corporate name change announcements using an event study framework. We find that cryptocurrency‐related name changes generate larger gains in share price and volume than other corporate name changes. The valuation gains associated with cryptocurrency‐related name changes cannot be explained by standard asset pricing factors, firm and stock characteristics, industry specific shocks, or by the presence of outliers. These gains are higher when the announcements happen during periods of high sentiment for cryptocurrencies. There is evidence that shareholders with better access to private information about such name changes are able to front‐run other shareholders.
Iftikhar Ahmad, Muhammad Ovais Ahmad, Mohammed A. Alqarni, Abdulwahab Ali Almazroi · 5 authors
Cryptocurrencies such as Bitcoin (BTC) have seen a surge in value in the recent past and appeared as a useful investment opportunity for traders. However, their short term profitability using algorithmic trading strategies remains unanswered. In this work, we focus on the short term profitability of BTC against the euro and the yen for an eight-year period using seven trading algorithms over trading periods of length 15 and 30 days. We use the classical buy and hold (BH) as a benchmark strategy. Rather surprisingly, we found that on average, the yen is more profitable than BTC and the euro; however the answer also depends on the choice of algorithm. Reservation price algorithms result in 7.5% and 10% of average returns over 15 and 30 days respectively which is the highest for all the algorithms for the three assets. For BTC, all algorithms outperform the BH strategy. We also analyze the effect of transaction fee on the profitability of algorithms for BTC and observe that for trading period of length 15 no trading strategy is profitable for BTC. For trading period of length 30, only two strategies are profitable.
Piyachart Phiromswad, Pattanaporn Chatjuthamard, Sirimon Treepongkaruna, Sabin Srivannaboon
This paper empirically examines jumps and cojumps of both major and minor cryptocurrencies. Understanding the nature of their jumps and cojumps plays an important role in risk management, asset allocation and pricing of derivatives. We find that all cryptocurrencies display significant jumps. Furthermore, minor cryptocurrencies appear to have significantly higher jump intensity and jump size than major cryptocurrencies. Finally, we find that cojumps of the Thai stock market index and minor cryptocurrencies have a greater intensity than that of major cryptocurrencies.
Tetsuya Takaishi
This study investigates the volatility of daily Bitcoin returns and multifractal properties of the Bitcoin market by employing the rolling window method and examines relationships between the volatility asymmetry and market efficiency. Whilst we find an inverted asymmetry in the volatility of Bitcoin, its magnitude changes over time, and recently, it has become small. This asymmetric pattern of volatility also exists in higher frequency returns. Other measurements, such as kurtosis, skewness, average, serial correlation, and multifractal degree, also change over time. Thus, we argue that properties of the Bitcoin market are mostly time dependent. We examine efficiency-related measures: the Hurst exponent, multifractal degree, and kurtosis. We find that when these measures represent that the market is more efficient, the volatility asymmetry weakens. For the recent Bitcoin market, both efficiency-related measures and the volatility asymmetry prove that the market becomes more efficient.
Yixin Cao, Chuanwei Zou, Xianfeng Cheng
Flash Loan attack can grab millions of dollars from decentralized vaults in one single transaction, drawing increasing attention from the Decentralized Finance (DeFi) players. It has also demonstrated an exciting opportunity that a huge wealth could be created by composing DeFi's building blocks and exploring the arbitrage change. However, a fundamental framework to study the field of DeFi has not yet reached a consensus and there's a lack of standard tools or languages to help better describe, design and improve the running processes of the infant DeFi systems, which naturally makes it harder to understand the basic principles behind the complexity of Flash Loan attacks. In this paper, we are the first to propose Flashot, a prototype that is able to transparently illustrate the precise asset flows intertwined with smart contracts in a standardized diagram for each Flash Loan event. Some use cases are shown and specifically, based on Flashot, we study a typical Pump and Arbitrage case and present in-depth economic explanations to the attacker's behaviors. Finally, we conclude the development trends of Flash Loan attacks and discuss the great impact on DeFi ecosystem brought by Flash Loan. We envision a brand new quantitative financial industry powered by highly efficient automatic risk and profit detection systems based on the blockchain.
Panpan Zhu, Xing Zhang, You Wu, Hao Zheng · 5 authors
This paper adds to the growing literature of cryptocurrency and behavioral finance. Specifically, we investigate the relationships between the novel investor attention and financial characteristics of Bitcoin, i.e., return and realized volatility, which are the two most important characteristics of one certain asset. Our empirical results show supports in the behavior finance area and argue that investor attention is the granger cause to changes in Bitcoin market both in return and realized volatility. Moreover, we make in-depth investigations by exploring the linear and non-linear connections of investor attention on Bitcoin. The results indeed demonstrate that investor attention shows sophisticated impacts on return and realized volatility of Bitcoin. Furthermore, we conduct one basic and several long horizons out-of-sample forecasts to explore the predictive ability of investor attention. The results show that compared with the traditional historical average benchmark model in forecasting technologies, investor attention improves prediction accuracy in Bitcoin return. Finally, we build economic portfolios based on investor attention and argue that investor attention can further generate significant economic values. To sum up, investor attention is a non-negligible pricing factor for Bitcoin asset.
Xiang Gao, Weige Huang, Hua Wang
This paper studies how sentiment affect Bitcoin pricing by examining, at an hourly frequency, the linkage between sentiment of finance-related Twitter messages and return as well as the volatility of Bitcoin as a financial asset. On the one hand, there was calculated the return from minute-level Bitcoin exchange quotes and use of both rolling variance and high-minus-low price to proxy for Bitcoin volatility per each trading hour. On the other hand, the mood signals from tweets were extracted based on a list of positive, negative, and uncertain words according to the Loughran-McDonald finance-specific dictionary. These signals were translated by categorizing each tweet into one of three sentiments, namely, bullish, bearish, and null. Then the total number of tweets were adopted in each category over one hour and their differences as potential Bitcoin price predictors. The empirical results indicate that after controlling a list of lagged returns and volatilities, stronger bullish sentiment significantly foreshadows higher Bitcoin return and volatility over the time range of 24 hours. While bearish and neutral financial Twitter sentiments have no such consistent performance, the difference between bullish and bearish ratings can improve prediction consistency. Overall, this research results add to the growing Bitcoin literature by demonstrating that the Bitcoin pricing mechanism can be partially revealed by the momentum on sentiment in social media networks, justifying a sentimental appetite for cryptocurrency investment.
Kim Hung Pho, Sel Ly, Richard Lu, Thị Hồng Vân Hoàng · 5 authors
No abstract is available for this record.
Elie Bouri, David Gabauer, Rangan Gupta, Aviral Kumar Tiwari
No abstract is available for this record.
Muhammad Abubakr Naeem, Imen Mbarki, Syed Jawad Hussain Shahzad
No abstract is available for this record.
Mahsa Moosavi, Jeremy Clark
Financial regulators have long-standing concerns about fully decentralized exchanges that run 'on-chain' without any obvious regulatory hooks. The popularity of Uniswap, an automated market makers (AMM), made these concerns a reality. AMMs implement a lightweight dealer-based trading system, but they are unlike anything on Wall Street, require fees intrinsically, and are susceptible to front-running attacks. This leaves the following research questions we address in this paper: (1) are conventional (i.e., order books), secure (i.e., resistant to front-running and price manipulation) and fully decentralized exchanges feasible on a public blockchain like Ethereum, (2) what is the performance profile, and (3) how much do Layer 2 techniques (e.g., Arbitrum) increase performance? To answer these questions, we implement, benchmark, and experiment with an Ethereum-based call market exchange called Lissy. We confirm the functionality is too heavy for Ethereum today (you cannot expect to exceed a few hundred trade executions per block) but show it scales dramatically (99.88% gas cost reduction) on Arbitrum.
Timothy King, Dimitrios Koutmos
No abstract is available for this record.
Alexander Brauneis, Roland Mestel, Ryan Riordan, Erik Theissen
This paper investigates the efficacy of low-frequency transactions-based liquidity measures to describe actual (high-frequency) liquidity. We show that the Corwin and Schultz (2012) and Abdi and Ranaldo (2017) estimators outperform other measures in describing time-series variations, irrespective of the observation frequency, trading venue, high-frequency liquidity benchmark, and cryptocurrency. Both measures perform well during high and low return, volatility and volume periods. The Kyle and Obizhaeva (2016) estimator and the Amihud (2002) illiquidity ratio outperform when estimating liquidity levels. These two estimators also reliably identify liquidity differences between trading venues. Overall, the results suggest that there is not yet a universally bestmeasure but there are reasonably good low-frequency measures.
Abdulnasser Hatemi‐J, Mohamed Ali Hajji, Elie Bouri, Rangan Gupta
This paper investigates the potential portfolio diversification between Bitcoin, bonds, equities, and the US dollar. We make use of two approaches for constructing the portfolio. The first is the standard minimum variance approach, and the alternative is based on combining risk and return when the portfolio is constructed. The portfolio based on the minimum variance approach does not result in increasing the return per unit of risk compared to the corresponding value for the best single asset, in this case, Bitcoin. However, the portfolio based on the approach that combines risk and return in the optimization problem does show a return per unit risk higher than the corresponding value for any of the four assets. Thus, the portfolio diversification benefit with respect to these four assets, in terms of return per unit risk, exists only if the portfolio is constructed via the new approach.