Blockchain Papers

Leopoldo Catania, Stefano Grassi, Francesco Ravazzolo

No abstract is available for this record.

Andrii Bielinskyi, Vladimir Soloviev

This article demonstrates the possibility of constructing indicators of critical and crash phenomena in the volatile market of cryptocurrency. For this purpose, the methods of the theory of complex networks have been used. The possibility of constructing dynamic measures of network complexity behaving in a proper way during actual pre-crash periods has been shown. This fact is used to build predictors of crashes and critical events phenomena on the examples of all the patterns recorded in the time series of the key cryptocurrency Bitcoin, the effectiveness of the proposed indicators-precursors of these falls has been identified.

Mustafa Özyeşil

Purpose - This study conducts an analysis to reveal the interaction between Bitcoin and Exchange Rates to find out whether Bitcoin is becoming a substitution for the exchange rates.Methodology - To investigate the mutually interaction between the exchange rates and the Bitcoin, the interaction (relationship) between daily closing price of both exchange rates and Bitcoin was analyzed through the Var model. Thus, it was tried to show the sensitivity of the values of Bitcoin to the changes occured in the exchange rates.Findings - Based on Variance Decomposition analysis, BITCOIN and Euro can be considered as largely external variables and their prices are not significantly affected by USD. An interesting result in this study is that the USD exchange rate was found to be significantly sensitive to the Euro.Conclusion - Findings obtained from analysis show that Bitcoin and Excange Rates have not become an alternative tools for each other yet.

Pavel Ciaian, d’Artis Kancs, Miroslava Rajčániová

This is the first paper that estimates the price determinants of BitCoin in a Generalised Autoregressive Conditional Heteroscedasticity framework using high frequency data. Derived from a theoretical model, we estimate BitCoin transaction demand and speculative demand equations in a GARCH framework using hourly data for the period 2013-2018. In line with the theoretical model, our empirical results confirm that both the BitCoin transaction demand and speculative demand have a statistically significant impact on the BitCoin price formation. The BitCoin price responds negatively to the BitCoin velocity, whereas positive shocks to the BitCoin stock, interest rate and the size of the BitCoin economy exercise an upward pressure on the BitCoin price.

Олег Кудрявцев, Oleg Kudryavtsev, Кирилл Мозолев, Кирилл Мозолев · 8 authors

The article presents an econometric analysis of the effect of stock indicators, such as Comex Gold futures, Dow Jones Industrial Average index and NASDAQ Composite, on the Ethereum cryptocurrency dynamics in the 100-day period. As part of the study, an econometric model of the dynamics of e-currency was built. The survey results show that when the Comex gold futures price changes by 1% on average, the Ethereum price changes by 5.01% in the same direction, when the Dow Jones Industrial Average index changes by 1%, the Ethereum price is 10.897%, and when the NASDAQ Composite index changes, the Ethereum price will change in the opposite direction to 3.59%

Israa Alqassem, Iyad Rahwan, Davor Svetinović

Bitcoin is a cryptocurrency and a decentralized semi-anonymous peer-to-peer payment system in which the transactions are verified by network nodes and recorded in a public massively replicated ledger called the blockchain. Bitcoin is currently considered as one of the most disruptive technologies. Bitcoin represents a paradox of opposing forces. On one hand, it is fundamentally social, allowing people to transact in a peer-to-peer manner to create and exchange value. On the other hand, Bitcoin's core design philosophy and user base contain strong anti-social elements and constraints, emphasizing anonymity, privacy, and subversion of traditional centralized financial systems. We believe that the success of Bitcoin, and the financial ecosystem built around it, will likely rely on achieving an optimal balance between these social and anti-social forces. To elucidate the role of these forces, we analyze the evolution of the entire Bitcoin transaction graph from its inception, and quantify the evolution of its key structural properties. We observe that despite its different nature, the Bitcoin transaction graph exhibits many universal dynamics typical of social networks. However, we also find that Bitcoin deviates in important ways due to anonymity-seeking behavioral patterns of its users. As a result, the network exhibits a two-orders-of-magnitude larger diameter, sparse treelike communities, and an overwhelming majority of transitional or intermediate accounts with incoming and outgoing edges but zero cumulative balances. These results illuminate the evolutionary dynamics of the most popular cryptocurrency, and provide us with initial understanding of social networks rooted in and driven by anti-social constraints.

Lars Steinert, Christian Herff

Cryptocurrencies have recently received large media interest. Especially the great fluctuations in price have attracted such attention. Behavioral sciences and related scientific literature provide evidence that there is a close relationship between social media and price fluctuations of cryptocurrencies. This particularly applies to smaller currencies, which can be substantially influenced by references on Twitter. Although these so-called "altcoins" often have smaller trading volumes they sometimes attract large attention on social media. Here, we show that fluctuations in altcoins can be predicted from social media. In order to do this, we collected a dataset containing prices and the social media activity of 181 altcoins in the form of 426,520 tweets over a timeframe of 71 days. The containing public mood was then estimated using sentiment analysis. To predict altcoin returns, we carried out linear regression analyses based on 45 days of data. We showed that short-term returns can be predicted from activity and sentiments on Twitter.

Nikolaus Hautsch, Christoph Scheuch, Stefan Voigt

Abstract A blockchain replaces central counterparties with time-consuming consensus protocols to record the transfer of ownership. This settlement latency slows cross-exchange trading, exposing arbitrageurs to price risk. Off-chain settlement, instead, exposes arbitrageurs to costly default risk. We show with Bitcoin network and order book data that cross-exchange price differences coincide with periods of high settlement latency, asset flows chase arbitrage opportunities, and price differences across exchanges with low default risk are smaller. Blockchain-based trading thus faces a dilemma: Reliable consensus protocols require time-consuming settlement latency, leading to arbitrage limits. Circumventing such arbitrage costs is possible only by reinstalling trusted intermediation, which mitigates default risk.

choi seo yun, 신정순

No abstract is available for this record.

choi seo yun, 신정순

No abstract is available for this record.

Fatih Ceylan, Ramazan Eki̇nci̇, Osman Tüzün, Hakan Kahyaoğlu

Cryptocurrencies, especially Bitcoin, have been used very often recently. The need for analyzing the price movements of the cryptocurrencies, which are accepted as “a currency” and “a store of value”, has emerged. With the growth and global integration of these markets, whether there are speculative bubbles on the basis of significant changes in prices is important in terms of openness and security in respect of financial stability. In addition, speculative movements in the cryptocurrencies market raise the question of whether market participants act with herd mentality. For this reason, in the study, the presence of speculative bubbles in Bitcoin and Etherium is analyzed by using Philips et al. (2015) method and estimated when they were formed. While the presence of bubbles in the cryptocurrencies market and the existence of these bubbles as a duration of herd mentality, it is also known that there is no balancing speculation in this market. According to the findings, a large number of bubbles were found in the Bitcoin and Etherium cryptocurrencies. The emergence of large bubbles, especially between the years 2017-2018, has shown that these cryptocurrencies are prone to speculative movements.

Josh Kamps, Bennett Kleinberg

Pump-and-dump schemes are fraudulent price manipulations through the spread of misinformation and have been around in economic settings since at least the 1700s. With new technologies around cryptocurrency trading, the problem has intensified to a shorter time scale and broader scope. The scientific literature on cryptocurrency pump-and-dump schemes is scarce, and government regulation has not yet caught up, leaving cryptocurrencies particularly vulnerable to this type of market manipulation. This paper examines existing information on pump-and-dump schemes from classical economic literature, synthesises this with cryptocurrencies, and proposes criteria that can be used to define a cryptocurrency pump-and-dump. These pump-and-dump patterns exhibit anomalous behaviour; thus, techniques from anomaly detection research are utilised to locate points of anomalous trading activity in order to flag potential pump-and-dump activity. The findings suggest that there are some signals in the trading data that might help detect pump-and-dump schemes, and we demonstrate these in our detection system by examining several real-world cases. Moreover, we found that fraudulent activity clusters on specific cryptocurrency exchanges and coins. The approach, data, and findings of this paper might form a basis for further research into this emerging fraud problem and could ultimately inform crime prevention.

Jiahua Xu, Benjamin Livshits

While pump-and-dump schemes have attracted the attention of cryptocurrency observers and regulators alike, this paper represents the first detailed empirical query of pump-and-dump activities in cryptocurrency markets. We present a case study of a recent pump-and-dump event, investigate 412 pump-and-dump activities organized in Telegram channels from June 17, 2018 to February 26, 2019, and discover patterns in crypto-markets associated with pump-and-dump schemes. We then build a model that predicts the pump likelihood of all coins listed in a crypto-exchange prior to a pump. The model exhibits high precision as well as robustness, and can be used to create a simple, yet very effective trading strategy, which we empirically demonstrate can generate a return as high as 60% on small retail investments within a span of two and half months. The study provides a proof of concept for strategic crypto-trading and sheds light on the application of machine learning for crime detection.

Guglielmo Maria Caporale, Alex Plastun

This paper examines the day of the week effect in the cryptocurrency market using a variety of statistical techniques (average analysis, Student's t-test, ANOVA, the Kruskal–Wallis test, and regression analysis with dummy variables) as well as a trading simulation approach. Most crypto currencies (LiteCoin, Ripple, Dash) are found not to exhibit this anomaly. The only exception is BitCoin, for which returns on Mondays are significantly higher than those on the other days of the week. In this case the trading simulation analysis shows that there exist exploitable profit opportunities; however, most of these results are not significantly different from the random ones and therefore cannot be seen as conclusive evidence against market efficiency.

Boris Radovanov, Aleksandra Marcikić, Nebojša Gvozdenović

Because of increasing interest in cryptocurrency investments, there is a need to quantify their variation over time. Therefore, in this paper we try to answer a few important questions related to a time series of cryptocurrencies. According to our goals and due to market capitalization, here we discuss the daily market price data of four major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Ripple (XRP) and Litecoin (LTC). In the first phase, we characterize the daily returns of exchange rates versus the U.S. Dollar by assessing the main statistical properties of them. In many ways, the interpretation of these results could be a crucial point in the investment decision making process. In the following phase, we apply an autocorrelation function in order to find repeating patterns or a random walk of daily returns. Also, the lack of literature on the comparison of cryptocurrency price movements refers to the correlation analysis between the aforementioned data series. These findings are an appropriate base for portfolio management. Finally, the paper conducts an analysis of volatility using dynamic volatility models such as GARCH, GJR and EGARCH. The results confirm that volatility is persistent over time and the asymmetry of volatility is small for daily returns.

Pedro Chaim, Márcio Poletti Laurini

The narrative of a Bitcoin is a bubble is very common. We employ statistical techniques to empirically evaluate such claim. A branch of literature links the existence of a bubble in some financial asset’s price to strict local martingales — a finitely lived asset has a bubble if, and only if, it is a strict local martingale under the equivalent risk-neutral measure. A diffusion process is a strict local martingale if its volatility increases faster than linearly as its level grows. We apply a nonparametric method to estimate the volatility function of Bitcoin daily and high frequency prices, as well as of more traditional financial assets. We then estimate the stochastic volatility model of Andersen and Piterbarg (2007), whose parameter space has a specific subset under which the asset’s price is a strict local martingale. Results suggest the existence of a bubble in Bitcoin prices from early 2013 to mid 2014, but, interestingly, not in late 2017.

Dehua Shen, Andrew Urquhart, Pengfei Wang

No abstract is available for this record.

Yuen C Lo, Francesca Medda

Bitcoin is the world’s leading cryptocurrency, with a market capitalization briefly exceeding $300 billion. This hints at Bitcoin’s
\namorphous nature: is this a monetary or a corporate measure? Hard values become explicit in the processing of transactions and
\nthe digital mining of Bitcoins. Electricity is a primary input cost. Bitcoins earned are often used to circumvent local currency
\ncontrols and acquire US dollars. For the period August 2010 to February 2018, we examine the components of Bitcoin mining
\nrevenues, their statistical contribution to daily changes, and to its variance. We provide evidence that Bitcoin transaction processing
\nis capacity constrained.

Thanaset Chevapatrakul, Danilo V. Mascia

No abstract is available for this record.

Rubaiyat Islam, Yoshi Fujiwara, Shinya Kawata, Hiwon Yoon

In a closed economic system like blockchain, the total amount of generated cryptocurrency called bitcoin is conserved and the transaction patterns demonstrate an insight of money flow inside the blockchain. For the last 2 years, bitcoin market has grabbed an immense attention from the investors, technology entrepreneurs and currency enthusiasts. In this paper, we have come up with some findings in our investigation about the bitcoin time-series transaction patterns. We have graphically represented bitcoin’s weekly patterns as a real economic currency that has been minted, stored and exchanged inside the bitcoin blockchain network. We identified outliers’ activities with the help of descriptive statistical analysis. We also demonstrated transaction pattern behavioral change. The main implication of these findings is to understand some stylized facts of the time-series transaction of cryptocurrency-based fully digital financial system. Besides in our analysis, we have shown that the behavioral change of the transaction pattern is capable of explaining the system development events or major historical events that have a network impact.

Alexandre Sokic

This paper is deeply motivated by the need to explore the impressive Bitcoin price development by addressing Bitcoin as money in its essential attribute as a medium of exchange. We adopt a monetary economics viewpoint and resort to a representative agent modelling strategy within a money-in-the-utility function (MIUF) framework. First, we show that the impressive Bitcoin price development observed since its inception can be interpreted as a hyperdeflation when we focus on Bitcoin role as a medium of exchange. Second, we show that specific monetary features of Bitcoin, its asymptotical fixed nominal stock and divisibility down to eight decimal places, account for a strong possibility of speculative hyperdeflationary paths. It is shown that those paths are fully consistent with the medium of exchange monetary role of Bitcoin and the representative agent optimizing behavior.

Debojyoti Das, M. Kannadhasan

In this article, we attempt to delineate the relationship between bitcoin prices and global factors such as stock index, economic policy uncertainty, gold spot prices, implied volatility and crude oil prices in a time-frequency domain. We resort to wavelet-based analysis to capture the multiscale interactive behavior of bitcoin with global factors. We primarily show that bitcoin is insulated from global factors in the short run. However, the existence of a significant relationship of bitcoin with global factors cannot be denied in the medium to long run, which could be attributed to the endogenous and intertwined economic system. Among the global factors considered in the study, we find the impact of economic policy uncertainty and crude oil prices to be more prominent on bitcoin. Our study offers some interesting insights on multiscale sensitivity of bitcoin to global factors, which may be useful for investors for taking informed decisions

Nader Trabelsi

In the present paper, we investigate connectedness within cryptocurrency markets as well as across the Bitcoin index (hereafter, BPI) and widely traded asset classes such as traditional currencies, stock market indices and commodities, such as gold and Brent oil. A spill over index approach with the spectral representation of variance decomposition networks, is employed to measure connectedness. Results show no significant spillover effects between the nascent market of cryptocurrencies and other financial markets. We suggest that cryptocurrencies are real independent financial instruments that pose no danger to financial system stability. Concerning the connectedness within the cryptocurrency markets, we report a time–frequency–dynamics connectedness nature. Moreover, the decomposition of the total spill over index is mostly dominated by a short frequency component (2–4 days) leading to the conclusion that this nascent market is highly speculative at present. These findings provide insights for regulators and potential international investors.

Szabolcs Majoros, András Zempléni

In this paper we extend the known methodology for fitting stable\ndistributions to the multivariate case and apply the suggested method to the\nmodelling of daily cryptocurrency-return data. The investigated time period is\ncut into 10 non-overlapping sections, thus the changes can also be observed. We\napply bootstrap tests for checking the models and compare our approach to the\nmore traditional extreme-value and copula models.\n