Blockchain Papers

Κωνσταντίνος Γκίλλας, François Longin

No abstract is available for this record.

Zura Kakushadze, Ronald Russo

The authors discuss several uses of blockchain and, more generally, distributed ledger technologies outside of cryptocurrencies. They take a pragmatic view, focusing on three main areas: the role of coin economies for “data malls” (specialized data marketplaces), data provenance (a historical record of data and its origins), and “keyless payments,” which are payments that can be made without having to know other users’ cryptographic keys. They also discuss voting and other areas and give a sizable list of academic and nonacademic references. <b>TOPICS:</b>Currency, quantitative methods

Daniele Bianchi, Alexander Dickerson

We provide empirical evidence within the context of cryptocurrency markets that the returns from liquidity provision, proxied by the returns of a short-term reversal strategy, are primarily concentrated in trading pairs with lower levels of market activity. Empirically, we focus on a moderately large cross section of cryptocurrency pairs traded against the U.S. Dollar from March 1, 2017 to March 1, 2022 on multiple exchanges. Our findings suggest that expected returns from liquidity provision are amplified in smaller, more volatile, and less liquid cryptocurrency pairs, where fear of adverse selection might be higher. A panel regression analysis confirms that the interaction between lagged returns and trading volume contains significant predictive information for the dynamics of cryptocurrency returns. This is consistent with theories that highlight the roles of inventory risk and adverse selection for liquidity provision.

Gianna Figà‐Talamanca, Marco Patacca

No abstract is available for this record.

Kyoung Jin Choi, Alfred Lehar, Ryan M. Stauffer

No abstract is available for this record.

Takahiro Hattori, Ryo Ishida

No abstract is available for this record.

Felix Irresberger, Ruomei Yang

This paper studies the concentration of block production in selected Proof-of-Stake (PoS) blockchains and finds evidence consistent with participants entering and leaving the consensus process, thereby changing the concentration level, but not with disproportionate compounding of wealth for large stakes.

Thomas Heine Felix, Henk von Eije

Purpose The purpose of this paper is to analyze underpricing in initial coin offerings (ICO). It bridges the gap between findings in initial public offering (IPO) literature and empirical results from ICOs. Design/methodology/approach The sample set consists of 279 ICOs between April 2013 and January 2018. A regression analysis is performed with data from the ICOs. Findings The results show an average level of underpricing of ICOs of 123 percent in the USA and 97 percent in the other countries. The results for the US ICOs are significantly higher than for US IPOs on average and also higher than US IPOs at the beginning of the dot.com bubble. The authors also study the determinants of ICO underpricing. The authors use proxies based on asymmetric information from the IPO literature as well as ICO-related variables. First-day trading volume and a good sentiment on the ICO market go together with more ICO underpricing. Moreover, hot markets make first-day investors to benefit less. Finally, companies that use a large issue size or a pre-ICO (a sale of cryptocurrencies before the ICO) leave less money on the table. Research limitations/implications A first restriction is that the authors focus on ICOs and not on crowdfunding, though there are similarities in that both of them are novel ways to finance projects. A second restriction is that the authors had to decide on the definition of a listing day. Cryptocurrencies are traded on many exchanges, and if the exchange is tailored to the cryptocurrency itself, the data on, e.g., close prices are not necessarily to be trusted. The authors, therefore, decided to use close price data from coinmarketcap.com, which requires a listing on two exchanges. This choice implies that there may have been trades before the listing day itself. A third restriction arises from the relative newness of the ICO phenomenon. The authors gathered data on underpricing from coinmarketcap.com and combined that with project information from icobench.com. However, the data were not simply matched and they required manual adjustments based on several other sources. The authors hope that in due time data on ICOs will be as adequate as data on IPOs and that they become more readily available. It might help if regulators or the crypto community would institute publication requirements. Adherence to such requirements would also reduce the extent of fraud and of asymmetric information, so that solid issuers with good projects might benefit from less underpricing. Practical implications The research may help in reducing underpricing, as the authors find that issuers can reduce it by holding a pre-ICO and by considering larger issue sizes. If they do so, investors will get fewer opportunities to benefit from underpricing. Investors can, nevertheless, also profit from the knowledge generated in this paper. When market sentiment is positive and first-day trading volume is expected to be high, investing in ICOs is likely to give them higher first-day returns. Finally, the authors hope that this paper will serve as a basis for further research into the exciting and dynamic world of cryptocurrencies. Originality/value There is hardly any research on underpricing of ICOs. The paper is interesting for its table with a brief comparison of ICOs and IPOs. It also searches for variables from the asymmetric information theory behind IPOs to be applied in explaining ICOs. It shows high levels of ICO underpricing in comparison to IPOs. It also gives suggestions for issuers of (and investors in) ICOs.

Laura Alessandretti, Abeer ElBahrawy, Luca Maria Aiello, Andrea Baronchelli

Machine learning and AI-assisted trading have attracted growing interest for the past few years. Here, we use this approach to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. We analyse daily data for 1,681 cryptocurrencies for the period between Nov. 2015 and Apr. 2018. We show that simple trading strategies assisted by state-of-the-art machine learning algorithms outperform standard benchmarks. Our results show that non-trivial, but ultimately simple, algorithmic mechanisms can help anticipate the short-term evolution of the cryptocurrency market.

Stephen Chan, Jeffrey Chu, Yuanyuan Zhang, Saralees Nadarajah

In financial trading, cryptocurrencies like bitcoin use decentralization, traceability, and anonymity features to perform transactional activities. These digital currencies, using the emerging blockchain technologies, are forming the basis of the largest unregulated markets in the world. This creates various regulatory challenges, including the illicit purchase of drugs and weapons, money laundering, and funding terrorist activities. This chapter analyzes various legal and ethical implications, their effects, and various solutions to overcome the inherent issues that are currently faced by the policymakers and regulators. The authors present the result of an analysis of 30 recently published peer-reviewed scientific publications and suggest various mechanisms that can help in the detection and prevention of illegal activities that currently account for a substantial proportion of cryptocurrency trading. They suggest methods and applications that can also be used to identify the dark marketplaces in the future.

Ayesha Afzal, Aiman Asif

The evolution of money has accompanied the development of civilizations and technological innovations, leading to today’s cryptocurrencies. Cryptocurrencies have become a popular mode of payment globally because of their low cost, high-speed transferability and a decentralized tracking network that provides secure transactions and a high degree of anonymity. However, the decentralized system of cryptocurrencies has made global monetary systems more dynamic and therefore more prone to misuse as well as posing a threat to financial stability. Cryptocurrencies are also gaining popularity in Pakistan: its first cryptocurrency, named ‘Pakcoin’, was launched in 2015. The State Bank of Pakistan does not recognize any digital currency, and the Federal Board of Revenue and Federal Investigation Agency have taken legal action against local and internationally traded cryptocurrencies. This article reviews these risks and provides various regulatory solutions so that methods can be developed to improve the management of financial innovations and create a safer environment in which financial innovation can continue. Furthermore, developing countries such as Pakistan can take advantage of distributed ledger technology (used in cryptocurrencies) in applications including: microfinance to help the unbanked, in data identification systems and in land registries to help enforce property rights.

Κωνσταντίνος Γκίλλας, Stelios Bekiros, Costas Siriopoulos

In this paper, we study the contemporaneous tail dependence structure in a pairwise comparison of the ten largest cryptocurrencies, namely Bitcoin, Dash, Dogecoin, Ethereum, Litecoin, Monero, Namecoin, Novacoin, Peercoin, and Ripple. We apply multivariate extreme value theory and we estimate a bias-corrected extreme correlation coefficient. Our findings reveal clear patterns of significantly high bivariate dependency in the distribution tails of some of the most basic and widespread cryptocurrencies, primarily over various downside constraints. This means that extreme correlation is not related to cryptocurrency market volatility per se, but to the trend of the cryptocurrency market. Therefore, extreme correlation increases in bear markets, but not in bull markets for these pairs. Interestingly, there is also a significant number of pairs which exhibit a weak level of dependency in distribution tails.

Christian Masiak, Joern Block, Tobias Masiak, Matthias Neuenkirch · 5 authors

No abstract is available for this record.

Hoje Jo, Haehean Park, Hersh Shefrin

Abstract Baker and Wurgler identify high sentiment betas with small startup firms that have great growth potential. On the surface, cryptocurrencies share important features in common with high sentiment beta stocks. This paper investigates the degree to which, during the period July 18, 2010–February 26, 2018, the return to bitcoin displayed the characteristics of a high sentiment beta stock. Using a sentiment‐dependent factor model, the analysis indicates that in large measure, bitcoin returns resembled returns to high sentiment beta stocks. Additionally, we show that bitcoin's expected returns are low when sentiment measured by Volatility Index is high while expected returns are high when sentiment is low.

Cüneyt Gürcan Akçora, Matthew Dixon, Yulia R. Gel, Murat Kantarcıoğlu

A key challenge for Bitcoin cryptocurrency holders, such as startups using ICOs to raise funding, is managing their FX risk. Specifically, a misinformed decision to convert Bitcoin to fiat currency could, by itself, cost USD millions. In contrast to financial exchanges, Blockchain based crypto-currencies expose the entire transaction history to the public. By processing all transactions, we model the network with a high fidelity graph so that it is possible to characterize how the flow of information in the network evolves over time. We demonstrate how this data representation permits a new form of microstructure modeling - with the emphasis on the topological network structures to study the role of users, entities and their interactions in formation and dynamics of crypto-currency investment risk. In particular, we identify certain sub-graphs ('chainlets') that exhibit predictive influence on Bitcoin price and volatility, and characterize the types of chainlets that signify extreme losses.

Sinan Krueckeberg, Peter Scholz

Using tick-level bitcoin data from February 2013 through April 2018, we show substantial arbitrage spreads between global bitcoin markets. Spreads follow multiple consistent patterns. Minimum and maximum prices show significant clustering. Spreads increase during the early hours of a day (according to coordinated universal time), when new exchanges enter markets, and following bitcoin heists and hacks. The full year 2017 and the first quarter of 2018 each had exploitable net arbitrage profit opportunities of at least USD380 million that smart money failed to capture. Based on long-term analyses, we also found that bitcoin market inefficiency has increased over time.

Shaen Corbet, Paraskevi Katsiampa

Non-linearity is characterized by an asymmetric mean-reverting property, which has been found to be inherent in the short-term return dynamics of stocks. In this paper, we explore as to whether cryptocurrency returns, as represented by Bitcoin, exhibit similar asymmetric reverting patterns for minutely, hourly, daily and weekly returns between June 2010 and February 2018. We identify several differences in the behavior of Bitcoin price returns in the pre-and post-$1,000 sub-periods and evidence of asymmetric reverting patterns in the Bitcoin price returns under all the ANAR models employed, regardless of the data frequency considered. We also present evidence indicating stronger reverting behavior of negative price returns in terms of both reverting speed and magnitude compared to positive returns and evidence of positive serial correlation with prior positive price returns. Finally, we also investigated asymmetries in Bitcoin price return series’ persistence by employing higher order ANAR models, finding evidence of a higher persistence of positive returns than negative returns, a result that further supports the existence of asymmetric reverting behavior in the Bitcoin price returns.

Paolo Pagnottoni, Dirk G. Baur, Thomas Dimpfl

Trading of Bitcoin is spread about multiple venues where buying and selling is offered in various currencies. However, all markets trade one common good and by the law of one price, the different prices should not deviate in the long run. In this context we are interested in which platform is the most important one in terms of price discovery. To this end, we use a pairwise approach accounting for a potential impact of exchange rates. The contribution to price discovery is measured by Hasbrouck's and Gonzalo and Granger's information share. We then derive an ordering with respect to the importance of each market which reveals that the Chinese OKCoin platform is the leader in price discovery of Bitcoin, followed by BTC China.

Shaen Corbet, Charles Larkin, Brian M. Lucey, Larisa Yarovaya

Eastman Kodak is an American technology company that produces imaging products. In 2018, it announced its intentions to enter the crytpocurrency market, raising concerns that it could be taking advantage of a potential cryptocurrency bubble for short-term gains. We analyse the relationships between Kodak, crytocurrency and stock market index returns. We find evidence of a significant, sustained increase in both the share price and price volatility of Kodak after the KODAKCoin announcement, with an increased correlation between the price of Kodak shares and Bitcoin.

Pedro Bação, António Portugal Duarte, Hélder Sebastião, Srdjan Redžepagić

This paper investigates the information transmission between the most important cryptocurrencies -Bitcoin, Litecoin, Ripple, Ethereum and Bitcoin Cash. We use a VAR modelling approach, upon which the Geweke’s feedback measures and generalized impulse response functions are computed. This methodology allows us to fully characterize the direction, intensity and persistence of information flows between cryptocurrencies. At the availabledata granularity, most of information transmission is contemporaneous, that is, it occurs within a day. However, it seems that there are some lagged feedback effects, mainly from other cryptocurrencies to Bitcoin. The generalized impulse-response functions confirm that there is a strong contemporaneous correlation and that there is not much evidence of lagged effects. The exception appears to be related to the overreaction of Bitcoin returns to contemporaneous shocks

Leopoldo Catania, Stefano Grassi, Francesco Ravazzolo

Cryptocurrencies have recently gained a lot of interest from investors, central banks and governments worldwide. The lack of any form of political regulation and their market far from being “efficient”, require new forms of regulation in the near future. From an econometric viewpoint, the process underlying the evolution of the cryptocurrencies’ volatility has been found to exhibit at the same time differences and similarities with other financial time-series, e.g. foreign exchanges returns. This short note focuses on predicting the conditional volatility of the four most traded cryptocurrencies: Bitcoin, Ethereum, Litecoin and Ripple. We investigate the effect of accounting for long memory in the volatility process as well as its asymmetric reaction to past values of the series to predict: 1 day, 1 and 2 weeks volatility levels.

Nicola Borri, Kirill Shakhnov

Abstract At a given point in time, bitcoin prices are different on exchanges located in different countries, or against different currencies. While existing literature attributes the largest price differences to frictions, like market segmentation, trading platforms advertize how to execute trades based on this information. We provide a novel risk-based explanation of these price differences for a sample containing the most reputable exchanges and after accounting for all transaction costs and limitations to trade. Bitcoin prices for more expensive pairs are riskier because they depreciate more in bad times for cryptocurrency investors, when aggregate liquidity and investor sentiment are lower. (JEL G12, G14, G15, F31).

Emmanouil Platanakis, Andrew Urquhart

No abstract is available for this record.

Guglielmo Maria Caporale, Timur Zekokh

This paper aims to select the best model or set of models for modelling volatility of the four most popular cryptocurrencies, i.e. Bitcoin, Ethereum, Ripple and Litecoin. More than 1000 GARCH models are fitted to the log returns of the exchange rates of each of these cryptocurrencies to estimate a one-step ahead prediction of Value-at-Risk (VaR) and Expected Shortfall (ES) on a rolling window basis. The best model or superior set of models is then chosen by backtesting VaR and ES as well as using a Model Confidence Set (MCS) procedure for their loss functions. The results imply that using standard GARCH models may yield incorrect VaR and ES predictions, and hence result in ineffective risk-management, portfolio optimisation, pricing of derivative securities etc. These could be improved by using instead the model specifications allowing for asymmetries and regime switching suggested by our analysis, from which both investors and regulators can benefit.